Systems and methods for energy management in a network

Abstract

The present disclosure discloses a system (106) and a method (400) for energy management in a network (108). The method may include initialization by defining input and output parameters (baseline energy cost, optimized energy cost). The method may include performing real-time data collection, systematically gathering real-time metrics and calculating the baseline energy cost. The method may include conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns, and allowing for resource optimization and proactive adjustments. The method may include dynamically adjusting network parameters and calculating and optimizing real-time energy cost (EC), continuously monitoring the network's current EC based on dynamically adjusted parameters. Compare optimized EC with baseline EC, adjusting parameters as needed.

Description

SYSTEMS AND METHODS FOR ENERGY MANAGEMENT IN A NETWORKCROSS-REFERENCE TO RELATED DISCLOSURE

[0001] This application claims priority to the Indian Provisional Patent Application No. 202421059461, titled "SYSTEMS AND METHODS FOR ADAPTIVE SSB CONFIGURATION WITH INTEGRATED BEAM SHAPING, ENERGY CONSERVATION, AND OFF-LOADING", filed with the Indian Patent Office on August 06, 2024.RESERVATION OF RIGHTS

[0002] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.TECHNICAL FIELD

[0003] The present disclosure relates generally to the field of communication systems. More particularly, the present disclosure relates to systems and methods for energy management in a network.DEFINITION

[0004] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0005] The expression “Synchronization Signal (SS)” used hereinafter in the specification refers to a broadcast signal transmitted by a base station to enable user equipment (UE) to achieve time and frequency synchronization and identify the presence and identify of a cell for initial access and mobility procedures.

[0006] The expression “Synchronization Signal Block (SSB)” used hereinafter in the specification refers to synchronization / physical broadcast channel (PBCH) block as the synchronization signals and the PBCH channels are packed as a single block that always moves together.

[0007] The expression “SSB sweeping” used hereinafter in the specification refers to transmission of multiple narrow beams carrying control information in sequence over the intended cell area.

[0008] The expression “Signal to Interference plus Noise Ratio (SINR)” used hereinafter in the specification refers to a metric used in wireless communication to assess the quality of a received signal. It represents the ratio of the desired signal's power to the combined power of interference and noise.

[0009] The expression “Reference Signal Received Power (RSRP)” used hereinafter in the specification refers to a metric used to measure the signal strength of a specific reference signal transmitted by a cell tower. It represents the average power received from resource elements carrying cell-specific reference signals within a defined bandwidth.

[0010] The expression “Reference Signal Received Quality (RSRQ)” used hereinafter in the specification refers to a metric that measures the quality of the radio signal being received by a device (e.g., user equipment (UE)) from a network node (i.e., base station).

[0011] The expression “Coverage footprint” used hereinafter in the specification refers to an area needed to provide a desired coverage.

[0012] The expression “Cost function” used hereinafter in the specification refers to a measure of error between what the model predicts and the actual value. The cost function also refers to a loss function.

[0013] The expression “Energy Cost” used hereinafter in the specification refers to the total energy required to operate network components (e.g., base stations, core network, UE) for performing processes such as transmitting, receiving, processing, and maintaining connections across the network infrastructure and the UEs.

[0014] The expression “Baseline EC” used hereinafter in the specification refers to the minimum or reference level of energy usage required by the network or a specific network element (e.g., base station) to remain operational, regardless of traffic load.

[0015] The expression “Energy Saving” used hereinafter in the specification refers to a process of minimizing the electrical energy consumption of network infrastructure and UEs through dynamic resource management, hardware optimization, and intelligent control mechanisms.

[0016] The expression “Energy Cost (EC) prediction function” as used hereinafter in the specification refers to a mathematical or algorithmic model used to estimate the future energy consumption of network components (e.g., base stations, antennas, or the entire network) based on current and predicted operating conditions.

[0017] The expression “Random Access Channel (RACH)” as used hereinafter in the specification refers to a shared channel used in establishing an initial connection (Initial Access) between a user device and a network.

[0018] The expression “Cell throughput” as used hereinafter in the specification refers to the amount of data transmitted by a cell.

[0019] The expression “Signal to Noise and Interference Ratio (SS-SINR)” as used hereinafter in the specification refers to the ratio of the strength of the transmitted signal compared to the background noise arising.

[0020] The expression “Power boosting” as used hereinafter in the specification refers to increasing (boosting) the transmission power of downlink control channels against the power of the data channels.

[0021] The expression “Physical Random-Access Channel (PRACH)” as used hereinafter in the specification refers to the channel used by the UEs to request an uplink allocation from the base station.

[0022] The expression “Channel Quality Indicator (CQI)” as used hereinafter in the specification refers to a feedback mechanism used by the user equipment (UE) to inform the serving base station about the quality of the downlink channel.

[0023] The expression “Hybrid automatic repeat request (hybrid ARQ or HARQ)” as used hereinafter in the specification refers to a combination of high-rate forward error correction (FEC) and automatic repeat request (ARQ) error-control.

[0024] The expression “Block Error Rate (BLER)” as used hereinafter in the specification refers to a measurement type of quality. BLER = Number of erroneous blocks / total number of received blocks.

[0025] The expression “SSB beams” as used hereinafter in the specification refers to broadcasttype beams. The SSB beams cover larger areas. SSB beams are used to transmit broadcast or multicast services to multiple user devices simultaneously. These beams allow the base station to efficiently deliver content such as broadcasts, software updates, emergency alerts, and other mass communication services.

[0026] The expression “Traffic beams” as used hereinafter in the specification refers to transmitting data between the base station and user devices. These beams deliver user-specific traffic, such as internet data, video streams, voice calls, and other communication services. The traffic beams are optimized for high data rates, low latency, and reliable connectivity.

[0027] The expression “Coverage hole” as used hereinafter in the specification refers to a region where the received signal level of the serving cell and any other neighbor is below the levels required to maintain the service under a minimum level of quality and robust radio performance.

[0028] The expression “Transmission power” as used hereinafter in the specification refers to an output power level at which a transmitter emits radio frequency (RF) signals in the network, directly influencing the coverage area, signal quality, and interference levels.

[0029] The expression “Maximum Transmission Power” as used hereinafter in the specification refers to the highest power level at which a transmitter can operate to send signals.

[0030] The expression “Minimum Transmission Power” as used hereinafter in the specification refers to the lowest power level at which a transmitter can effectively communicate with its intended receiver.

[0031] The expression “Sleep Mode” used hereinafter in the specification refers to a power-saving state for network elements (e.g., base station) to reduce energy consumption while maintaining the ability to resume full activity when needed quickly.

[0032] The expression “Low-power mode” as used hereinafter in the specification refers to a state or operational mode of network elements or user equipment where power consumption is reduced by limiting or suspending certain functions, usually during periods of low traffic or inactivity, to save energy while maintaining essential connectivity.

[0033] The expression “Load Threshold” as used hereinafter in the specification refers to a predefined limit or boundary value that indicates the maximum acceptable or desired level of traffic, resource usage, or congestion in the network before specific actions are triggered.

[0034] The expression “Current load” as used hereinafter refers to the real-time measurement or estimate of the amount of traffic, resource usage, or user activity occurring in the network at a given moment or at time t.

[0035] The expression “Dynamic traffic predictions” as used hereinafter in the specification refers to estimations of upcoming network traffic loads and user behavior, generated by analyzing current and historical data, environmental factors, and user mobility, to optimize resource allocation and improve network performance.

[0036] The expression “Potential offloading action” as used hereinafter in the specification refers to a candidate network operation identified by network management or optimization algorithms that can transfer data traffic from one network segment to another, aiming to reduce congestion, balance load, and enhance energy efficiency.

[0037] The expression “Radio Frequency (RF) chain” as used hereinafter refers to a series of network components that process radio signals from the antenna to baseband (and vice versa).

[0038] The expression “Traffic Distribution” as used hereinafter in the specification refers to the spatial and temporal pattern of network traffic (e.g., data, voice) loads across different cells, frequency bands, and network nodes, reflecting how user demand varies and is managed within the network.

[0039] The expression “Proactive energy saving strategy” as used hereinafter in the specification refers to a planned and predictive approach that uses forecasting, traffic prediction, and intelligent control to adjust network operations (e.g., switching off base stations, scaling transmission power, or reallocating resources) in advance, aiming to minimize energy usage while maintaining service quality.

[0040] The expression “Power Consumption in Sleep Mode” as used hereinafter in the specification refers to an amount of electrical power consumed by a device in a low-power state (sleep mode) to conserve energy while still being able to wake up and resume normal operation quickly. It is typically much lower than the power consumed during active operation.

[0041] The expression “Traffic Load Threshold for Activating Sleep Mode” as used hereinafter in the specification refers to a specific level of network traffic or data load at which a device (such as a base station or access point) determines it is more efficient to enter sleep mode. This threshold helps optimize power consumption by allowing devices to save energy during periods of low activity.

[0042] The expression “Minimum SSB Beam Shaping Angle Width” as used hereinafter in the specification refers to the narrowest angle width for shaping the signal of a Synchronization Signal Block (SSB) beam in wireless communication. A narrower beam allows for more focused transmission, improving signal quality and reducing interference, but requires precise alignment with the intended receiver.

[0043] The expression “Maximum Capacity Threshold” as used hereinafter in the specification refers to the highest limit of data or signal capacity that a network or communication system can handle effectively without degradation in performance. Exceeding this threshold can lead to congestion, increased latency, or packet loss in the network.

[0044] The expression “UE Distribution” as used hereinafter refers to the spatial and / or statistical arrangement of the UEs across the network area. The UE distribution shows where and how many users are located within the network's coverage area at a given time.

[0045] The expression “Artificial Intelligence / Machine Learning (AI / ML) models” used hereinafter in the specification refers to a system used to analyze, learn from, and make decisions or predictions based on data collected from the network. The AI / ML models help the network operators automate, optimize, and predict various aspects of network performance, behavior, and user experience.

[0046] The expression “Capacity and Coverage Optimization (CCO)” used hereinafter in the specification refers to a process aimed at improving both the quality of service (QoS) and the network efficiency by maximizing coverage and capacity while minimizing interference and resource wastage.

[0047] The expression “Optimized CCO” as used hereinafter in the specification refers to the systematic adjustment and enhancement of network parameters to maximize coverage and capacity while minimizing interference, energy consumption, and user service degradation.

[0048] The expression “Beamforming Optimization” as used hereinafter refers to the process of adjusting and fine-tuning beamforming parameters (e.g., direction, power, phase, and shape) of the radio beams to maximize wireless communication performance while minimizing interference, energy usage, or other costs.

[0049] The expression “Beam Shaping” as used hereinafter refers to a process of adjusting the beam pattern’s shape (e.g., width, side lobes, elevation) to optimize signal coverage or limit interference. The beam shaping may widen or narrow the beam to maximize signal strength and avoid blocked areas.

[0050] The expression “Beam Steering” as used hereinafter refers to a process of dynamically pointing the beam in the direction of the user (or reflectors) based on location and movement.

[0051] The expression “Beam control” as used hereinafter refers to a process of managing when and how to switch beams, including beam selection, prioritization, and deactivation.

[0052] The expression “Beam management” as used hereinafter refers to a process of beam sweeping for scanning different beam directions to find the best signal path, beam measurement, beam reporting, beam refinement, and beam switching.

[0053] The expression “Coverage and capacity demand” as used hereinafter in specification refers to the level of network performance required to ensure that users within a specific geographic area have reliable signal access (coverage) and sufficient resources to support voice, video, and data services at acceptable quality levels (capacity).

[0054] The expression “Traffic Offloading” as used hereinafter in the specification refers to a process of redirection of user traffic from a congested network cell (or base station) to another network cell that can handle the traffic more efficiently.

[0055] The expression “Energy Cost (EC) Prediction” as used hereinafter refers to a process of estimating the future energy consumption or expenditure required to operate network components (e.g., base stations) under varying traffic loads, time periods, and environmental conditions.

[0056] The expression “Integrated EC prediction and traffic offloading” as used hereinafter refers to an approach that simultaneously forecasts energy costs and manages the redistribution of network traffic to optimize resource usage, performance, or cost-efficiency.

[0057] The expression “Predefined Margin” as used hereinafter refers to a fixed buffer, tolerance or threshold value set in advance to account for uncertainty, variability, or safety in performance to ensure that the performance stays within acceptable limits even in worse -than-expected conditions.

[0058] The expression “High EC footprint” as used hereinafter refers to a situation or system component in the network that consumes a large amount of energy, leading to a significant energy cost (EC) in terms of power usage or operational expenses.

[0059] The expression “Channel Quality” as used hereinafter refers to the condition or performance level of the communication channel between a transmitter (e.g., a base station) and a receiver (e.g., user equipment), measured in terms of signal strength, interference, noise, and data throughput.

[0060] The expression “Delta EC” as used hereinafter refers to the difference in energy cost between two base stations, network nodes, or time instances, used to inform traffic balancing, offloading, or resource allocation decisions.

[0061] These definitions are in addition to those expressed in the art.BACKGROUND

[0062] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0063] In modem telecommunications, high-density networks comprising a large number of small and macro cells play a critical role in delivering high-speed, low-latency connections. These dense deployments are designed to enhance the capacity and performance of telecommunications systems. However, they also introduce significant challenges, particularly regarding energy consumption. The close spacing and large number of base stations and small cells lead to increased energy usage, as these components require continuous transmission and sophisticated coordination of radio resources.

[0064] Advanced technologies such as massive MIMO (Multiple Input Multiple Output) and millimeter-wave (mmWave) communications further contribute to the network's energy demands. Massive MIMO uses a large number of antennas at the base station to simultaneously serve multiple users, requiring considerable power for beamforming and signal processing. Similarly, mmWave technology, which operates in higher frequency bands for faster data transmission, demands high power to maintain adequate signal strength and coverage. These technologies become especially energy -intensive in scenarios requiring high data rates and extensive coverage.

[0065] While network densification offers clear benefits such as improved capacity and reduced latency, it also has notable drawbacks. Chief among these is the rise in operational energy costs and the increased potential for environmental impact. Energy consumption and management are becoming critical concerns as the telecommunications industry evolves toward more complex and dense architecture. This trend highlights the urgent need for robust, scalable energy management strategies to mitigate environmental impacts without compromising network performance.

[0066] A central challenge in densely deployed networks is the substantial energy required to ensure reliable coverage, high capacity, and uninterrupted connectivity. Efficient management of this energy consumption is vital to maintaining operational performance and long-term sustainability.

[0067] A comprehensive strategy that incorporates energy management and network optimization is essential to address these challenges. A core component of this strategy is an energy cost (EC) prediction function, which enables accurate energy consumption forecasting. This predictive capability supports adaptive, real-time energy-saving measures, helping to ensure that network densification does not result in unsustainable power usage. By integrating the EC prediction function, operators can enhance energy efficiency while maintaining high levels of network performance.

[0068] Furthermore, in conventional Radio Access Network (RAN) architectures, two critical functions (e.g., beam parameter tuning and load balancing) are often executed simultaneously. Beam tuning involves adjusting the direction, shape, or power of radio beams using techniques such as beamforming to optimize signal quality and provide focused coverage. In parallel, load balancing aims to distribute user traffic across multiple cells or nodes to avoid congestion and utilize network resources efficiently. Although both functions are essential for performance optimization, their joint execution introduces significant coordination challenges.

[0069] A key challenge stems from the interdependence between beam configuration and traffic load distribution. Adjusting a beam to better serve a specific user group can shift the traffic load, influencing which users are connected to which cells. Conversely, load balancing actions (e.g., offloading user equipment (UE) to a different node) affect how beams must be oriented to maintain adequate coverage. These interrelated actions create parameter entanglement, often leading to conflicting outcomes. For example, enhancing beam direction might unintentionally overload a neighboring cell, while offloading may weaken beam alignment and degrade coverage. As a result, the simultaneous optimization of these two functions increases control complexity and can hinder the achievement of stable, energy -efficient network operation.

[0070] Therefore, an efficient approach to energy management is needed to balance the goal of maintaining high-quality service with minimizing environmental impact.OBJECTS

[0071] Some of the objectives of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0072] An objective of the present disclosure is to provide a system and a method for energy management in a network.

[0073] Yet another objective of the present disclosure is to improve downlink (DL) coverage and SINR with energy constraints.

[0074] Yet another objective of the present disclosure is to integrate EC prediction with CCO actions.

[0075] Yet another objective of the present disclosure is to dynamically adjust transmission power to meet coverage and capacity needs without wasting energy.

[0076] Yet another objective of the present disclosure is to enable adaptive adjustments in coverage (by controlling transmission power and antenna tilts) and capacity (by adjusting resource allocation and scheduling).

[0077] Yet another objective of the present disclosure is to implement an EC prediction algorithm, comprising CCO parameters, with artificial intelligence / machine learning assisted optimization actions.

[0078] Yet another objective of the present disclosure is to integrate the EC prediction with the CCO action and potential offloading.

[0079] Yet another objective of the present disclosure is to perform dynamic load balancing and traffic distribution.

[0080] Yet another objective of the present disclosure is to include an enhanced traffic distribution offloading action in the EC prediction to improve energy efficiency.

[0081] Yet another objective of the present disclosure is to include the CCO and enhanced traffic distribution offloading action in the EC prediction to improve energy efficiency.

[0082] Yet another objective of the present disclosure is to perform the EC prediction with a proactive energy-saving strategy.

[0083] Another objective of the present disclosure is to perform a traffic and load forecasting analysis, a coverage and capacity demand analysis and a neighbor cell assessment.

[0084] Yet another objective of the present disclosure is to tolerate the EC prediction with respect to the potential offloading action with potential CCO beam optimization action capabilities.

[0085] Yet another objective of the present disclosure is to maintain energy efficiency through the CCO and advanced beamforming.

[0086] Yet another objective of the present disclosure is to integrate the EC prediction with dynamic traffic predictions.

[0087] Yet another objective of the present disclosure is to provide a system and a method for integrated energy cost (EC) prediction and traffic offloading in the network.

[0088] Yet another objective of the present disclosure is to predict energy cost (EC) for energy savings based on potential neighboring cell offloading.

[0089] Yet another objective of the present disclosure is to perform EC feedback transmission between a source base station and a target base station.

[0090] Yet another objective of the present disclosure is to transmit user equipment (UE)-specific delta EC and EC impact metrics from a source network node (gNB) to a neighbor network node (e.g., gNB).

[0091] Yet another objective of the present disclosure is to receive forecasted incremental EC by the source network node from the neighbor network node.

[0092] Yet another objective of the present disclosure is to determine whether offloading is acceptable based on the forecasted incremental EC.

[0093] Yet another objective of the present disclosure is to establish a bilateral EC negotiation protocol to enable energy -efficient UE migration decisions between the network nodes (i.e., source network node and neighbor network node).

[0094] Other objectives and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.SUMMARY

[0095] In an exemplary embodiment, a method for energy management in a network is disclosed. The method comprises receiving, by a receiving unit, a plurality of first parameters and collecting, by a collection unit, one or more real-time metrics from a serving cell and one or more neighboring cells. The method comprises calculating, by a processing unit, a baseline EC based on power consumption at time t and performing, by the processing unit, one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics. The method comprises executing, by the processing unit, at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism. The methodcomprises upon executing, calculating, by the processing unit, an optimized EC based on the plurality of first parameters and the at least one second parameter. The method comprises comparing, by the processing unit, the calculated optimized EC with the baseline EC and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting, by the processing unit, one or more parameters associated with the one or more actions based on the collected real-time data.

[0096] In some embodiments, the energy management is performed by integrating EC prediction and traffic offloading in the network.

[0097] In some embodiments, the plurality of first parameters comprise at least two or more of maximum transmission power, minimum transmission power, power consumption in a sleep mode, a traffic load threshold for activating a sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold, neighbor cell data, historical data for training the one or more ML models, a predicted energy saving and a forecasted incremental EC at a target base station.

[0098] In some embodiments, the real-time metrics from the serving cell comprises at least one of a synchronization signal (SS)- signal to interference plus noise ratio (SINR), a SS-reference signal received power (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a number of RRC connected user equipments (UEs), a physical resource block (PRB) usage, a UE geo-location, an angular density, a beam activity and power consumption per sector. The real-time metrics from the one or more neighboring cells comprise at least one of a traffic load at the time t, a predicted load at the time t, coverage overlap information, an estimated offload impact of one or more local EC models and a target base station admission control.

[0099] In some embodiments, the one or more analysis types comprise a traffic and load forecasting analysis, a coverage demand analysis and a neighbor cell assessment.

[0100] In some embodiments, the plurality of second parameters comprises at least one of one or more predicted traffic patterns, one or more predicted load distributions, a predicted coverage demand, one or more predicted UE mobility patterns, a predicted UE density, a predicted load at the time t and a projected load capacity of the one or more neighboring cells for potential off-loading.

[0101] In some embodiments, the integrated EC prediction and traffic offloading mechanism comprises determining, by a determining unit, one or more UEs having at least one of a high EC footprint and poor channel quality and calculating, by the processing unit, a delta EC for each determined UE. The integrated EC prediction and traffic offloading mechanism comprises performing, by the processing unit, an EC feedback transmission between a source base station and a target base station. The EC feedback transmission comprises transmitting, by a transmitting unit, an identifier (ID) corresponding to each determined UE, the calculated delta EC for each determined UE, a traffic profile, quality of service (QoS), and an estimated offload to the target base station and receiving, by the receiving unit, a calculated EC for at least one UE accepted by the target base station and a flag from the target base station. The integratedEC prediction and traffic offloading mechanism comprises determining, by the determining unit, whether the calculated delta EC is greater than the received calculated EC and upon determining that the calculated delta EC is greater than the received calculated EC, triggering, by the processing unit, offloading of the at least one accepted UE to the target base station. Upon determining that the calculated delta EC is not greater than the received calculated EC, rejecting, by the processing unit, offloading of the at least one accepted UE to the target base station.

[0102] In some embodiments, the method comprises upon offloading the at least one accepted UE to the target base station, determining, by the determining unit, whether the load at the time t is less than the traffic load threshold for activating the sleep mode and based on the determination, executing, by the processing unit, transition of the serving base station to the sleep mode.

[0103] In some embodiments, the adjustment of the one or more parameters associated with the one or more actions comprises updating the one or more ML models, redefining the integrated EC prediction and traffic offloading mechanism, and adjusting one or more offloading thresholds and one or more beamforming parameters.

[0104] In some embodiments, the method comprises performing, by the processing unit, at least one of a transmission power control and one or more beamforming parameter adjustments based on the one or more performed analysis types.

[0105] In another exemplary embodiment, a system for energy management in a network is disclosed. The system comprises a receiving unit configured to receive a plurality of first parameters and a collection unit configured to collect one or more real-time metrics from a serving cell and one or more neighboring cells. A processing unit is configured to calculate a baseline EC based on power consumption at time t and perform one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics. The processing unit is configured to execute at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism and upon executing, calculate an optimized EC based on the plurality of first parameters and the at least one second parameter. The processing unit is configured to compare the calculated optimized EC with the baseline EC and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjust one or more parameters associated with the one or more actions based on the collected real-time data.

[0106] In yet another exemplary embodiment, a computer program product comprising a non- transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method for energy management in a network is disclosed. The method comprises receiving, by a receiving unit, a plurality of first parameters and collecting, by a collection unit, one or more real-time metrics from a serving cell and one or more neighboring cells. The method comprises calculating, by a processing unit, a baseline EC based on powerconsumption at time t and performing, by the processing unit, one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics. The method comprises executing, by the processing unit, at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism. The method comprises upon executing, calculating, by the processing unit, an optimized EC based on the plurality of first parameters and the at least one second parameter. The method comprises comparing, by the processing unit, the calculated optimized EC with the baseline EC and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting, by the processing unit, one or more parameters associated with the one or more actions based on the collected real-time data.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0107] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.

[0108] FIG. 1A illustrates an exemplary network architecture describing a system for energy management in a network, in accordance with an embodiment of the present disclosure.

[0109] FIG. IB illustrates an exemplary block diagram of the system for energy management in the network, in accordance with an embodiment of the present disclosure.

[0110] FIG. 2A illustrates an exemplary schematic diagram describing synchronization signal block (SSB) to Physical Downlink Shared Channel (PDSCH) beam mismatch, in accordance with an embodiment of the present disclosure.

[0111] FIG. 2B illustrates an exemplary schematic diagram describing SSB beam shaping, in accordance with an embodiment of the present disclosure.

[0112] FIG. 3 illustrates another exemplary flow diagram of a method for integrated EC prediction and the traffic offloading in the network, in accordance with an embodiment of the present disclosure.

[0113] FIG. 4 illustrates another exemplary flow diagram of a method for energy management in the network, in accordance with an embodiment of the present disclosure.

[0114] FIG. 5 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present disclosure may be implemented.

[0115] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100A Network Architecture102 User Equipment104 Base Station106 System108 Network100B Block diagram112 Processor114 Memory116 Interface118 Receiving Unit120 Processing Unit122 Determining Unit124 Database126 Transmitting Unit128 Collection Unit130 Processing Engine200A Schematic Diagram202 Synchronization Signal Block (SSB) Beams204 Physical Downlink Shared Channel (PDSCH) Traffic Beams200B Schematic Diagram212-1 Cell-1212-2 Cell-2214 Coverage hole216-1 Synchronization Signal Block 1 (SSB-1)216-2 Synchronization Signal Block 2 (SSB-2)300 Flow Diagram400 Flow Diagram500 Computer System510 External Storage Device520 Bus530 Main Memory540 Read-Only Memory550 Mass Storage Device560 Communication Ports570 ProcessorDETAILED DESCRIPTION

[0116] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like reference numerals refer to the same parts throughout the different drawings.

[0117] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing anexemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0118] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0119] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0120] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive like the term “comprising” as an open transition word without precluding any additional or other elements.

[0121] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0122] The terminology used herein is to describe particular embodiments only and is not intended to be limiting the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition ofone or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any combinations of one or more of the associated listed items. It should be noted that the terms “mobile device”, “user equipment”, “user device”, “communication device”, “device” and similar terms are used interchangeably for the purpose of describing the invention. These terms are not intended to limit the scope of the invention or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The invention is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the invention as defined herein.

[0123] While considerable emphasis has been placed herein on the components and component parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.

[0124] In modem telecommunications, high-density networks comprising a large number of small and macro cells play a critical role in delivering high-speed, low-latency connections. These dense deployments are designed to enhance the capacity and performance of telecommunications systems. However, they also introduce significant challenges, particularly regarding energy consumption. The close spacing and large number of base stations and small cells lead to increased energy usage, as these components require continuous transmission and sophisticated coordination of radio resources.

[0125] Advanced technologies such as massive MIMO (Multiple Input Multiple Output) and millimeter-wave (mmWave) communications further contribute to the network's energy demands. Massive MIMO uses a large number of antennas at the base station to simultaneously serve multiple users, requiring considerable power for beamforming and signal processing. Similarly, mmWave technology, which operates in higher frequency bands for faster data transmission, demands high power to maintain adequate signal strength and coverage. These technologies become especially energy -intensive in scenarios requiring high data rates and extensive coverage.

[0126] While network densification offers clear benefits such as improved capacity and reduced latency, it also has notable drawbacks. Chief among these is the rise in operational energy costs and the increased potential for environmental impact. Energy consumption and management are becoming critical concerns as the telecommunications industry evolves toward more complex and dense architectures. This trend highlights the urgent need for robust, scalable energy management strategies to mitigate environmental impacts without compromising network performance.

[0127] A central challenge in densely deployed networks is the substantial energy required to ensure reliable coverage, high capacity, and uninterrupted connectivity. Efficient management of this energy consumption is vital to maintaining operational performance and long-term sustainability.

[0128] A comprehensive strategy that incorporates energy management and network optimization is essential to address these challenges. A core component of this strategy is an energy cost (EC) prediction function, which enables accurate energy consumption forecasting. This predictive capability supports adaptive, real-time energy-saving measures, helping to ensure that network densification does not result in unsustainable power usage. By integrating the EC prediction function, operators can enhance energy efficiency while maintaining high levels of network performance.

[0129] Furthermore, in conventional Radio Access Network (RAN) architectures, two critical functions (e.g., beam parameter tuning and load balancing) are often executed simultaneously. Beam tuning involves adjusting the direction, shape, or power of radio beams using techniques such as beamforming to optimize signal quality and provide focused coverage. In parallel, load balancing aims to distribute user traffic across multiple cells or nodes to avoid congestion and utilize network resources efficiently. Although both functions are essential for performance optimization, their joint execution introduces significant coordination challenges.

[0130] A key challenge stems from the interdependence between beam configuration and traffic load distribution. Adjusting a beam to serve a specific user group better can shift the traffic load, influencing which users are connected to which cells. Conversely, load balancing actions (e.g., offloading user equipment (UE) to a different node) affect how beams must be oriented to maintain adequate coverage. These interrelated actions create parameter entanglement, often leading to conflicting outcomes. For example, enhancing beam direction might unintentionally overload a neighboring cell, while offloading may weaken beam alignment and degrade coverage. As a result, the simultaneous optimization of these two functions increases control complexity and can hinder the achievement of stable, energy -efficient network operation.

[0131] There is, therefore, a need for systems and methods to provide an efficient approach to address the problems of conventional energy management techniques to balance high-quality service delivery with minimizing environmental impact.

[0132] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing a system and a method for energy management in the network.

[0133] The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing a system and a method for adaptive SSB configurations integrated beam shaping, energy conservation, and offloading. The beam widths of the SSB beams are dynamically shaped along with the SSB beam boosting (e.g., SSB power boosting). This dynamic shaping of SSB beamsand the SSB beam boosting leads to improvement in the SINR and keeping the corresponding coverage on a certain desired level.

[0134] In another energy-saving technique, the present disclosure may use Synchronization SignalBlock (SSB) beam shaping and boosting. SSB beam shaping allows for the adjustment of beam patterns to focus on specific areas, improving signal strength and coverage while efficiently managing radio resources. By enhancing the precision of signal targeting, this technique helps reduce energy waste and improve overall network efficiency. The integration of the energy cost prediction function with the SSB beam shaping offers a data-driven approach to forecasting energy usage. This function analyzes real-time and historical network parameters, including traffic load, user density, and environmental conditions, to predict energy consumption patterns. With accurate energy forecasts, network operators can make informed decisions about operational modes, such as dynamically adjusting transmission power, optimizing beamforming configurations, and selectively activating or deactivating small cells based on current demand. For instance, during low-traffic periods, the network can reduce power levels or put certain cells into sleep mode to conserve energy.

[0135] The present disclosure may employ mutually exclusive CCO action and offloading actions into an energy cost (EC) prediction function for a base station (e.g., gNB) in the network (e.g., 5G RAN) in a more flexible way. In the evolving landscape of 5G networks, optimizing resource and energy efficiency is a key priority. Two central strategies play a vital role in achieving this: offloading and Coverage and Capacity Optimization (CCO). While these techniques have traditionally been applied together, emerging research highlights the potential advantages of treating them as independent, complementary actions to unlock greater flexibility and efficiency.

[0136] Offloading focuses on redistributing network traffic from one cell to another, helping to balance load and reduce congestion, particularly in high-demand areas. When guided by energy-aware considerations, it can also shift traffic to more energy-efficient cells, thereby reducing overall power consumption. In contrast, CCO enhances network performance by fine-tuning parameters such as antenna tilt, transmission power, and beamforming techniques to improve both coverage and capacity.

[0137] Typically, these approaches are closely intertwined with high traffic in a given area may trigger both offloading and CCO to maintain stability and performance. For example, CCO might expand coverage using beam steering, while offloading helps lighten the load on congested cells. However, during an Energy Cost (EC) prediction process, it may be more energy-efficient to prioritize a targeted CCO adjustment (e.g., refined SSB beam-shaping, steering, or high-resolution beam prediction) over initiating an offloading action. This shift allows for improved energy savings without compromising network performance

[0138] By exploring these strategies as distinct yet coordinated tools, operators may better adapt to dynamic network conditions, reduce energy consumption, and pave the way for more sustainable 5G deployment.

[0139] To enhance energy efficiency through CCO and advanced beamforming, advanced beamforming techniques (e.g., high-resolution beam prediction) offer a powerful means to significantly improve energy efficiency in 5G networks. By precisely directing beams toward user equipment (UEs), the network enhances signal quality while minimizing energy waste. This level of precision enables a single cell to manage its traffic load more effectively, often eliminating the need for offloading by staying within its energy budget.

[0140] Coverage and Capacity Optimization (CCO) actions, including SSB and traffic beamshaping or steering, enable the network to dynamically adapt coverage areas and focus energy where it's needed most in high-demand zones. Through careful tuning of beam parameters, the network can achieve optimal performance and energy savings, reducing the dependency on traffic offloading and maintaining consistent service quality.

[0141] Viewing offloading and CCO as independent yet complementary strategies bring added flexibility to network management. Decoupling these actions reduces operational complexity, as operators can optimize each approach independently without managing intricate interdependencies. This simplifies decision-making, promotes faster responses to network conditions, and enables more targeted energysaving interventions.

[0142] Mutually exclusive CCO and offloading actions enhance the accuracy of energy cost (EC) predictions. Since CCO actions can be proactively designed to optimize energy use through beam control. This provides a more stable foundation for EC forecasting. In contrast, offloading, often reactive in nature, introduces unpredictability that complicates such assessments. By treating CCO and offloading as distinct processes, the network achieves more consistent and reliable estimates of energy consumption. For example, a predicted offloading scenario could be re-evaluated in favor of a more energy-efficient beam adjustment (e.g., SSB beam-shaping, steering, or high-resolution beamforming) within the CCO framework.

[0143] The present disclosure empowers 5G networks to balance performance and sustainability more effectively, paving the way for smarter, greener network management.

[0144] The primary goal is to enhance energy savings by optimizing network performance and resource usage. The algorithm follows specific rules: offloading conditions are mutually exclusive to CCO actions, offloading should include CCO considerations, and CCO actions should be prioritized to manage the load before considering offloading.

[0145] The present disclosure may combine energy cost prediction with offloading strategies in one effective approach. Offloading uses advanced analytics to predict energy demands based on traffic patterns, user mobility, environmental conditions, and network configurations. This enables network operators to optimize network elements, adjust transmission power, and refine beamforming techniques. For example, during periods of low demand, users can be shifted from high-power macro cells to moreenergy -efficient small cells. Conversely, during peak usage times, users can be directed to high-capacity cells to ensure service quality while managing energy consumption effectively.

[0146] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0147] FIG. 1A illustrates an exemplary network architecture (100A) for energy management in a network (108), in accordance with an embodiment of the present disclosure.

[0148] Referring to FIG. 1A, the network architecture (100 A) comprises a user equipment (UE) (102), a base station (104) and a system (106). In an aspect, the system (106) may be employed in or with a digital unit. The system (106) may also be referred to as the digital unit.

[0149] In an embodiment, the user equipment (102) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such an embodiment, the user equipment (102) may include, but is not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, smart television (TV), computers, smart security system, smart home system, other devices for monitoring or interacting with or for the users and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the user equipment (102) may include, but is not limited to, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloud-computing system or any other device that is network- connected.

[0150] In an embodiment, the user equipment (102) may include, but is not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like. In an embodiment, the user equipment (102) may include, but is not limited to, any electrical, electronic, electromechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general -purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the user equipment (102) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user or an entity such as touch pad, touch enabled screen, electronic pen, and the like. The user may be an administrator. A person of ordinary skill in the art will appreciate that the user equipment (102) may not be restricted to the mentioned devices and various other devices may be used. Aperson of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment may be used interchangeably throughout the disclosure.

[0151] In an embodiment, the base station (104) may be a network infrastructure that provides wireless access to one or more terminals associated therewith. The base station (104) may have coverage defined by a predetermined geographic area based on the distance over which a signal may be transmitted. The base station (104) may include, but not be limited to, wireless access point, evolved NodeB (eNodeB), 5G node or next generation NodeB (gNB), wireless point, transmission / reception point (TRP), and the like.

[0152] In an embodiment, the system (106) (i.e., digital unit) may be part of the base station (104). A person of ordinary skill in the art will appreciate that the terms “system” and “digital unit” may be used interchangeably throughout the disclosure. The digital unit may be a baseband unit. In an embodiment, the digital unit may include, but not be limited to, transceivers, baseband unit (BBU), remote radio unit (RRU), radio network control units, and one or more processors associated thereto. The system (106) / digital unit may perform various operations using software algorithms (e.g., AL / ML algorithms).

[0153] In an embodiment, the network (108) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth. The network (108) may also include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.

[0154] As shown in FIG. 1A, a plurality of SSB beams between the base station (104) and the user equipment (UE) (102). The UE (102) uses a physical random-access channel (PRACH) to send a connection request to the base station ( 104). The base station ( 104) sends an acknowledgment to the connection request. After establishing connection between the base station (104) and the UE (102), the base station (104) receives of a plurality of signals and measurements (e.g., Hybrid Automatic Repeat Request (HARQ), Channel Quality Indicator (CQI), etc.) from the UE (102).

[0155] The base station (104) in wireless communication is a critical infrastructure component that serves as the central point of communication for mobile devices within a specific area. The base station (104) facilitates the transmission and reception of signals to and from the UE 102, such as smartphones, tablets, and loT devices, enabling them to connect to the wider network.

[0156] The system (106) is configured for energy management in the network (108) (as explained in detail in FIG. IB). The system (106) is configured to use software algorithms (e.g., AI / ML algorithms) to select and adjust input parameters. For example, the input parameters may include maximum transmission power, minimum transmission power, power consumption in sleep mode, a traffic loadthreshold for activating sleep mode, a minimum SSB beam shaping angle width, and a maximum capacity threshold. The system (106) is configured to select the maximum transmission power and calculate a baseline EC. In an aspect, for performing integrated EC prediction and the traffic offloading, the system (106) is configured to perform traffic offloading using an integrated EC prediction and traffic offloading mechanism. At least one neighboring cell (or base station) and one or more UEs to be offloaded to the neighboring cell are selected based on the integrated EC prediction and traffic offloading mechanism. The selected UEs are handover to the selected neighboring cell. In this way, the offloading is performed based on EC predictions in the source base station and the target base station. Further, an optimized EC is calculated based on the input parameters and the predicted traffic load. The optimized EC is compared with the baseline EC to enable EC predication recalibration when one action renders the other suboptimal.

[0157] FIG. IB illustrates an exemplary block diagram (100B) of the system for energy management in the network (108), in accordance with an embodiment of the present disclosure.

[0158] The system (106) comprises a processor (112), a memory (114), an interface (116), a database (124) and a processing engine (130). The processing engine (130) comprises a receiving unit (118), a processing unit (120), a determining unit (122), a transmitting unit (126), and a collection unit (128).

[0159] In an aspect, the system (106) may include one or more processor(s) (112). The one or more processor(s) (112) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. The processor (112) is configured to fetch and execute computer-readable instructions stored in a memory (114) of the system (106). Among other capabilities, one or more processor(s) (112) may be configured to fetch and execute computer-readable instructions stored in the memory (114) of the system (106). The memory (114) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer- readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (114) may comprise any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like. The memory (114) is configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, fetched and executed to create or share data packets over a network service.

[0160] In an embodiment, the system (106) may include an interface(s) (116). The interface(s) (116) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (116) may facilitate communication of the system (106). The interface(s) (116) may also provide a communication pathway for one or more components of the system (106). Examples of such components include, but are not limited to, processing unit / engine(s) (120, 130) and a database (124). The interface (116) is configured to provide a communication pathway for one or more components of the system (106).

[0161] The processing engine(s) (130) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (130). In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (130) may be processor-executable instructions stored on a non-transitory machine- readable storage medium, and the hardware for the processing engine(s) (130) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (130). In such examples, the system (106) may comprise the machine- readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (106) and the processing resource. In other examples, the processing engine(s) (130) may be implemented by an electronic circuitry.

[0162] In an embodiment, the processing engine (130) may be an artificial intelligence (Al) / a machine learning (ML) engine. The AI / ML engine may automate a process of performing adaptive SSB configuration with integrated beam shaping, energy conservation, and offloading by employing one or more Al models (used interchangeably with the term AI / ML models). In some embodiments, the AI / ML engine may include one or more pre -trained AI / ML models that may be configured to perform dynamic shaping of plurality of SSB beams with the SSB beam boosting (e.g., SSB power boosting). The AI / ML models may provide feedback on the results of monitoring results in the system (106) for closed-loop actions and for continuous optimization and configuration of one or more parameters. In some embodiments, the one or more parameters may include, but are not limited to, network latency jitter, SINR, RSRP, uplink random access channel (UL RACH) counter, CQI reports, HARQ Key Performance Indicators (KPIs), Block Error Rate (BLER), etc.

[0163] In an embodiment, the database (124) may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor(s) (112) or the processing engine(s) (130) or the system / digital unit (106).

[0164] The present disclosure allows for the dynamic adaptation of network resources based on realtime conditions, thereby reducing unnecessary energy usage. Further, the present disclosure enhances the granularity of energy management, enabling more precise control over which network elements are active at any given time. Also, the present disclosure supports the implementation of network strategies, such as turning off redundant cells during low-demand periods or optimizing the energy efficiency of active cells. The CCO considerations with the potential offloading of traffic to neighboring cells into the EC prediction function for a single network node (e.g., gNodeB, gNB) in the network (e.g., 5G network) offer energysaving enhancements. The CCO involves optimizing the network's coverage and capacity, directly impacting energy consumption. By distributing the traffic load more evenly across gNBs, the network prevents situations where some gNBs are overburdened while others are underutilized. Underutilized gNBs are placed into low -power modes or even turned off, reducing overall energy consumption. Efficient loadbalancing ensures that no single gNB is pushed to operate at full capacity unnecessarily, thus conserving energy across the network.

[0165] The components of the processing engine (130) may be configured to perform energy management in the network (108). The system (106) or the processing engine (130) is further configured to integrate energy cost (EC) prediction and traffic offloading in the network (108).

[0166] The receiving unit (118) is configured to receive a plurality of first parameters. The plurality of first parameters comprise at least two or more of maximum transmission power, minimum transmission power, power consumption in a sleep mode, a traffic load threshold for activating the sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold, neighbor cell data, historical data for training the one or more ML models, a predicted energy saving and a forecasted incremental EC at a target base station.

[0167] In an aspect, the maximum transmission power refers to the highest power level allowed or configured to transmit signals. The minimum transmission power refers to the lowest power level at which signals can be transmitted without affecting the communication. The power consumption in the sleep level refers to the amount of power used while in a low -power or standby state, where most of the functions are suspended, but remain partially active to allow rapid reactivation. The traffic load threshold for activating the sleep mode refers to a predefined minimum level of network traffic below which the network node (e.g., base station) may switch to a low -power or sleep mode to save energy without compromising service quality. The minimum SSB beam shaping angle width refers to the narrowest angular width of the SSB beam formed for transmitting SSB, which enables the UE to detect and synchronize with the network. The neighbor cell data refers to information about neighboring cells used to assist in handover decisions, signal quality measurements, and network optimization. The network cell data comprises, but is not limited to, cell identifiers, frequency, physical cell identifiers, tracking area code, signal threshold, offset parameters, technology types, etc. In an aspect, the historical traffic data refers to recorded information about network traffic patterns over past time periods. The historical traffic data includes metrics related to how users accessed the network, how much data was transferred, and how resources were utilized. The historical traffic data comprises, but is not limited to, user traffic volume, call attempts, throughput, network load, congestion level, handover trends, usage trends, etc. In an aspect, the predicted energy saving by offloading UE refers to the estimated reduction in energy consumption that may be achieved when the UE offloads its traffic to a neighboring network node. In an aspect, the forecasted incremental energy cost (EC) at the target base station refers to the predicted additional energy expenditure that a neighboring base station is expected to incur if it accepts additional traffic or the UE as a result of traffic offloading from the source base station.

[0168] The collection unit (128) is configured to collect one or more real-time metrics from a serving cell and one or more neighboring cells. The real-time metrics from the serving cell comprises at least one of a synchronization signal (SS)- signal to interference plus noise ratio (SINR), a SS-reference signal received power (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a number of RRCconnected UEs, a physical resource block (PRB) usage, a UE geo-location, an angular density, a beam activity and power consumption per sector. In an aspect, the SS-SINR refers to a metric used to assess the quality of the radio channel for the synchronization Signals. The SS-SINR is a measurement of the ratio of the received signal power from the Secondary Synchronization Signal (SSS) to the combined power of interference and noise, also measured on the SSS resource elements within the same frequency bandwidth. The SS-RSRQ refers to a metric that indicates the quality of the received signal using the SSs and channel state information (CSI). The SS-RSRQ is used for cell selection, handover decisions, and overall signal quality assessment. The SS-RSRP refers to a metric that indicates the average received power of the SSS. The SS-RSRP focuses on the power of the synchronization signals used for cell detection and initial access in the network. In an aspect, the number of RRC connected UEs refers to total number of user devices (UEs) that are currently in the RRC_CONNECTED state with the network node.

[0169] The PRB refers to the smallest unit of radio resources allocated in the frequency -time grid in the network. The PRB usage refers to a number of PRBs used by the base station in a given time period. The UE geo-location refers to the geographical position (e.g., latitude and longitude) of the UE determined using various positioning technologies.

[0170] The angular density refers to the distribution or concentration of elements (e.g., users, beams, or signals) per unit angle within a specified field of view or angular space.

[0171] The beam activity refers to an operational status and utilization of a directional transmission or reception beam indicating whether the beam is currently active, idle, or engaged in data or control signaling with the UE.

[0172] The real-time metrics from the one or more neighboring cells comprise at least one of a traffic load at the time t, a predicted load at the time t, coverage overlap information, an estimated offload impact of one or more local EC models and a target base station admission control.

[0173] The traffic load at the time t refers to the amount of data or signaling activity being carried by any network element (e.g., cell, sector, or base station) at the time t. The traffic load reflects the utilization level of network resources (e.g., bandwidth, time slots, or radio channels). The traffic load comprises, but is not limited to, data traffic, signaling traffic and voice traffic.

[0174] The predicted load at time t refers to the forecasted amount of traffic or resource demand (e.g., data throughput, number of users, or CPU utilization) expected to occur at a specific future time, based on historical data, real-time metrics, or predictive models.

[0175] The coverage overlap information refers to data or metrics that describe the geographic areas where the coverage of two or more base stations or cells intersect, meaning multiple network nodes simultaneously serve or detect the UE in those regions.

[0176] The estimated offload impact of one or more local EC models refers to the predicted effect (e.g., increase or decrease in energy consumption) that a traffic or task offloading action would have on the energy cost of the target neighbor node, based on one or more locally deployed energy cost prediction models.

[0177] The target base station admission control refers to the process by which the base station receiving a potential offloading request or handover evaluates whether it has sufficient resources (e.g., radio capacity, energy, or backhaul bandwidth) to accept additional user equipment (UE) or traffic from the source base station.

[0178] The processing unit (120) is configured to calculate a baseline EC. The baseline EC refers to the minimum or fixed amount of energy consumed by a system or device (e.g., base station) even when the system or device is idle or operating at minimal load. The baseline EC represents the constant overhead energy usage required to keep the system (e.g., base station) powered on and ready to operate, regardless of the traffic or usage level.

[0179] The baseline EC is calculated based on power consumption at time t as given in Equation 1 C base St=o ^current^f) ' 1t Equation 1Where, PCUrrent( is the power consumption between a time interval from the time t to time T, and At is a time interval between measurements. In an aspect, the time t represents current time.

[0180] Equation 1 describes total power consumed by the system (e.g., base station) over time period t=0 to t=T. For example, let’s consider At = 1 hour, T = 3 hours and power usage at each hour Pcurrent(O) =500 W, Pcurrent(O) =700 W, Pcurrent(O) =850 W and Pcurrent(O) =600 W. So, Etotai = (500+700+800+600) = 2600Wh =2.6 kWh.

[0181] The processing unit (120) is configured to perform one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models based on the plurality of first parameters and the one or more collected real-time metrics. The one or more analysis types comprise a traffic and load forecasting analysis, a coverage demand analysis and a neighbor cell assessment.

[0182] In an aspect, the traffic and load forecasting analysis refers to an analysis to predict traffic patterns and load distributions to proactively manage resources, optimize performance, and improve energy efficiency. The AI / ML models are used to predict the traffic patterns and load distributions based on historical data and real-time data. The traffic pattern refers to recurring or observable variations in the volume, type, and distribution of network traffic over time and space. The traffic pattern reflects how users generate data and voice traffic on the network, including when and where the demand increases or decreases. The traffic patterns include temporal variations (e.g., rush hours, off-peak), user mobility (e.g.,vehicles, pedestrians), event-based spikes (e.g., concerts, sport games), weather, app usage (e.g., gaming, video, browsing, voice over internet protocol (VoIP)) and device type (e.g., smartphone, internet of things (loT), wearables). For example, in a business district, traffic is low at night, peaks between 9 AM-5 PM, and drops after work hours. In residential areas, traffic peaks in the evening as users stream video or browse the internet.

[0183] In an aspect, the traffic load distribution refers to the amount of network resource usage (e.g., bandwidth, time, or frequency) by the users or applications in a specific cell or base station over a given time period. For example, to predict the downlink traffic load (in Mbps) for Cell i 01 from 6 PM to 12 AM for Friday, the AI / ML model uses historical data (e.g., traffic every hour for the last 30 days) to forecast the traffic load from 6 PM to 12 AM for Friday, the predicted traffic load = 18:00 = 120 Mbps, 19:00 = 145 Mbps, , 20:00 = 160 Mbps, , 21:00 = 140 Mbps, 22:00 = 100 Mbps, and 23:00 =75 Mbps.

[0184] In an aspect, the coverage demand analysis refers to an analysis to identify where, when, and how much wireless coverage is needed based on user behavior, traffic, and network conditions. The coverage demand represents the geographical areas and time windows where users require cellular connectivity, current signal strength or capacity may be insufficient or expansion, optimization, or reconfiguration is needed. For example, the coverage demand analysis to identify areas that currently lack sufficient coverage but are likely to have high demand in the next 24 hours. The coverage demand analysis identifies cell ID 101 of city mall area having predicted high coverage demand and current coverage quality = poor (RSRP < -110 dBm) and cell ID 310 of stadium having predicted very high coverage demand due to upcoming match and current coverage quality = low.

[0185] In an aspect, the neighbor cell assessment is used to evaluate the neighboring cell's current and projected load capacity. The neighbor cell assessment is the process of evaluating and managing the list and qualify of neighboring cells around the serving cell in the network. The neighbor cell assessment involves measuring the signal quality and other parameters of the neighboring cells to optimize handover decisions, reduce dropped calls, and improve overall network performance. In an aspect, the cell current load capacity refers to the real-time ability of the cell (base station) to handle active user traffic based on available resources, bandwidth and network configuration. In an aspect, the projected load capacity refers to the estimated or predicted future traffic load that the cell or a group of cells in the network will need to handle based on forecasted user activity, traffic trends, and historical data.

[0186] Upon performing the analysis types, the processing unit (120) is configured to execute at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters. The execution of the at least one action (i.e., traffic offloading) is perform using an integrated EC prediction and traffic offloading mechanism. The integrated EC prediction and traffic offloading mechanism is used to perform the traffic offloading based on delta EC. The delta EC is calculated using one or more local EC models to estimate offloading impact.

[0187] In an embodiment, the determining unit (122) is configured to determine offloading condition based on an offloading criteria. The offloading criteria includes activation or triggering of the offloading, conditions, for example, when the current load is greater than the load threshold (i.e., Lcurrent> Ltfl) or predicted high-demand periods. Upon determining one condition (i.e., Lcurrent> Ltflor predicted high-demand periods), one or more neighboring cells (also referred to as target cells) are selected based on current and predicted load, coverage overlap, and capacity. The target cell is selected using the following Equation 2:Target Cell = ar grain (LJcurrent+ Lpred) Equation 2 j

[0188] Equation 2 describes j = index of neighboring cell in the network, L]current= Current load on the cell j and Lpred= Predicted load on the cell j. Select the neighboring cells having the minimum sum of the current load and the predicted load. For example, for cell (j=l), Lcurrent= 0.7, LPred= 0.2 and total load = 0.9. For cell (j=2), Lcurrent= 0.3, LPred= 0.6 and total load = 0.9. For cell (j=3), Lcurrent= 0.3, LPred= 0.2 and total load = 0.5. For cell (j=4), Lcurrent= 0.5, LPred= 0.4 and total load = 0.9. So, cell 3 is selected as total Lcurrentand LPred= 0.5 less than out of 4 neighboring cells.

[0189] To perform the offloading to the selected target cell, the integrated EC prediction and traffic offloading mechanism is applied. The integrated EC prediction and traffic offloading mechanism comprises the determining unit (122) configured to determine one or more UEs having at least one of a high EC footprint and poor channel quality. To perform the traffic offloading, the determining unit (122) is configured to perform, using local EC models, determination of candidate UEs (also referred to as target UEs) from the plurality of UEs having high EC footprint or poor channel quality. In an aspect, the local EC models refer to energy consumption models that estimate power or energy usage at the level of individual network elements or subsystems, rather than for the entire system as a whole. In an operative aspect, the local EC models may be used at the base station (e.g., source base station, target base station).

[0190] In an aspect, the UEs having high EC footprint refers to UEs that consumes a large amount of energy which leads to significant EC. The UEs having poor channel quality indicates the UEs experiencing weak or unstable signals, leading to poor performance. For example, in the base station 1A, the UE1 is watching a 4K livestream from multiple angles. UE2 is uploading hundreds of high-resolution photos to the cloud. UE1 and UE2 consume a lot of bandwidth and power, which leads to high EC footprint for UE1 and UE2. Further, in example, UE11 is at the edge of the coverage area, with signal constantly dropping. UE12 is behind a thick wall where radio signals are weak. The UE11 and UE 12 have poor channel quality. In this way, the local EC models intelligently select which UEs to offload by checking who is draining the most network resources or experiencing bad connections. These selected UEs are then directed to neighboring network node (e.g., base station, cells, etc.) to balance the load and improve performance.

[0191] The processing unit (120) is configured to calculate a delta EC for each determined UE. In an aspect, the processing unit (120) is configured to calculate the delta EC (AECiocai ) for each determined UE (i.e., candidate UEs have a high EC footprint or poor channel quality). In an aspect, the delta EC is a metric used to determine how much more (or less) energy is consumed when the determined UEs remains connected to its current base station, compared to the neighboring base station. The delta EC quantifies the difference in EC for each determined UE if the determined UE stays connected to the current base station (local) vs gets offloaded to neighboring base station (e.g., a neighboring gNodeB, small cell). In an aspect, the delta EC is also referred to as delta local EC. The delta local EC may be calculated at the source base station using the local EC models. A person of ordinary skill in the art will appreciate that the terms “delta EC” and “delta local EC” may be used interchangeably throughout the disclosure.

[0192] The processing unit (120) is configured to perform an EC feedback transmission between the source base station and the target base station. In an aspect, the EC feedback transmission between base stations refers to the exchange of information about the EC metrics between two or more base stations in the network. An EC feedback exchange protocol is employed to perform the EC feedback transmission between the source base station (gNB) and the target base station (gNB). In an aspect, the EC feedback exchange protocol is a communication mechanism that allows distributed system components (e.g., antennas, base stations, user equipments, or network nodes) to report, share, and coordinate their local energy consumption data with other entities in the network. In an operative aspect, the EC feedback exchange protocol is used between the source base station and the target base station to exchange EC information.

[0193] The EC feedback transmission comprises the transmitting unit (126) configured to transmit one or more parameters corresponding to the determined UEs, for example, an identifier (ID) corresponding to each determined UE, the calculated delta EC for each determined UE, a traffic profile, quality of service (QoS), and an estimated offload, to the target base station. In an aspect, the traffic profile of the UE describes the pattern and characteristics of the communication the UE engages in over time. This helps the network understand how, when, and how much traffic the UE transmits and receives for optimizing network resource allocation, handovers, and offloading. In an aspect, the estimated offload for target base station refers to the amount of traffic (or UEs) that a specific neighboring (also referred to as target) base station is expected to receive from serving base station due to traffic offloading or handover.

[0194] The receiving unit (118) is configured to receive a calculated EC for at least one UE accepted by the target base station and a flag from the target base station. In an aspect, upon receiving the parameters from the source base station, the target base station determines the number of UEs to be selected for offloading by calculating EC for the corresponding UE at the target base station (i.e., ECremote). The target base station sets flag to each UEs whose data received from the source base station. The flag is set as accepted / rejected based on local thresholds of the target base station. The flag indicates whether the UE is accepted or rejected. Upon determining the UEs to be accepted, the target base station sends messagescomprising the calculated EC of the UEs accepted by the target base station and the flag (i.e., accepted or rejected).

[0195] The determining unit (122) is configured to determine whether the calculated delta EC is greater than the received calculated EC. In an aspect, upon receiving the messages from the target base station, the source base station compares the calculate delta local EC with the received remote EC for each determined UE to determine whether the calculated delta local EC is greater than the received remote EC i.e., EC10cai> ECremote

[0196] Upon determining that the calculated delta local EC is greater than the received remote EC, the processing unit (120) is configured to perform offloading of the at least one accepted UE to the target base station. In an aspect, when the source base station determines AECiocai > ECremote, then UE is selected for offloading to the target base station. Upon selecting the UEs based on AECiocaij > ECremotej, the source base station triggers the handover and updates scheduling / beam alignment. Handover of UEs to the selected target cell (or neighboring cells) is performed to balance the network load and reduce energy consumption.

[0197] Further, upon determining that the calculated delta EC is not greater than the received calculated EC (i.e., AECiocai < ECremote), the processing unit (120) is configured to reject offloading of the at least one accepted UE to the target base station.

[0198] Upon offloading the at least one accepted UE to the target base station, a new load of the source base station is calculated by using the following Equation 3 ^new ^current ~ ^offloaded Equation 3

[0199] Equation 3 describes the new load (Lnew) is calculated based on the current load (L current ) and the offloaded load (,Lofftoaded).

[0200] Further, the determining unit (122) is configured to determine whether the load at the time t of the source base station is less than the traffic load threshold for activating the sleep mode. In an aspect, the determining unit (122) is configured to determine whether the current load (Lcurrent) of the source base station is less than the traffic load threshold (Ltfl) (i.e., Lcurrent< Lth), the processing unit (120) is configured to execute a transition of the source base station to the sleep mode. The power of the base station is the same as the power of the base station in the sleep mode (i.e., PCUrrent( = Psieep).

[0201] The processing unit (120) is configured to perform at least one of a transmission power control and one or more beamforming parameter adjustments based on the one or more performed analysis types.

[0202] In an aspect, the processing unit (120) is configured to perform the transmission power control. The processing unit (120) is configured to calculate an optimal transmission power as a min -max optimization function. The optimal transmission power is given in Equation 4 Equation 4

[0203] Equation 4 describes the optimal transmission power calculated based on a predicted traffic load and the coverage requirements. The Lpred(t) is the predicted traffic load at the time t. Equation 4 describes that the optimal transmission power at given time t is based on the predicted load while enforcing minimum and maximum power constraints.

[0204] In an aspect, the processing unit (120) is configured to perform the beamforming parameter adjustments (i.e., beamforming angles and widths), beamforming angles and widths are adjusted based on the user density and demand. The beamforming optimization is given by Equation 5 below: Equation 5Where, UEpred(t~) is the predicted UE distribution. Equation 5 describes that the optimal beamwidth is calculated based on the minimum SSB beam shaping angle width and the predicted UE distribution. Equation 5 describes the optimal beamwidth at time t based on the predicted UE distribution, while ensuring the minimum SSB beam shaping angle width.

[0205] The processing unit (120) is configured to calculate an optimized EC based on the plurality of first parameters and the plurality of second parameters. In an aspect, the optimized EC is calculated as minimax optimized function. In an aspect, the minimax optimization function is very useful in our analysis to find a solution that minimizes the maximum value of a function, often within a given set of constraints. The minimax optimization function is used in scenarios where there is a conflict or a worst -case scenario to consider. A value for one variable that minimizes the maximum value of another variable is determined, and the second variable is chosen to maximize the function. In an operative aspect, the minimax optimization function is used when the base station (i.e., gNB) wants to minimize its maximum possible power consumption.

[0206] In an aspect, the optimized EC is calculated based on the maximum transmission power, the minimum transmission power and the predicted load at the time t. The optimized EC is calculated as given in Equation 6 Equation 6

[0207] Equation 6 describes that the optimized EC is calculated based on the maximum transmission power, the minimum transmission power and the predicted load at time t. The optimized EC is calculated based on predicted load, while ensuring the minimum and maximum power constraints.

[0208] The determining unit (122) is configured to compare the optimized EC and the baseline EC. In an aspect, the optimized EC is compared with the baseline EC to validate that optimization reduces energy consumption. The baseline EC serves as a reference point or benchmark that reflects current operations.

[0209] Upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, the processing unit (120) is configured to one or more parameters associated with the one or more actions based on the collected real-time data. In an aspect, the predefined margin refers to as a specified threshold or minimum allowable difference that quantifies the level of improvement required for the optimization to be considered significant or effective. For example, the baseline EC (before optimization) = 100 units, the predefined margin (the minimum energy saving required) = 10 units. For the optimization to be considered effective, the optimized EC < 90 units. When the optimized EC = 94 units, energy savings (i.e., 100 - 94) = 6 units. 6 units saved is less than the predefined margin of 10 units. The optimized EC is not less than the baseline EC by the predefined margin, so the optimization is not accepted. Further, the optimized EC = 88 unit, the energy savings (i.e., 100 - 88) = 12 units. 12 units saved exceed the predefined margin of 10 units. The optimization is accepted, because it achieves energy savings.

[0210] The adjustment of the one or more parameters associated with the one or more actions comprises updating the one or more ML models, redefining the integrated EC prediction and traffic offloading mechanism, and adjusting one or more offloading thresholds and one or more beamforming parameters. In an aspect, at the continuous learning and feedback loop step, various steps such as performance evaluation, model refinement, and optimization refinement are performed. In the performance evaluation, the network performance of optimizations is evaluated in terms of energy savings and network quality. In the model refinement, the AI / ML models are updated with new data for improved prediction accuracy. In the optimization refinement, CCO strategies and parameters are refined based on performance evaluations and feedback.

[0211] Although FIG. IB shows the exemplary block diagram (100B) of the system (106), in other embodiments, the system (106) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. IB. Additionally, or alternatively, one or more components of the system (106) may perform functions described as being performed by one or more other components of the system (106).

[0212] As advanced features continue to enhance network performance, there is growing attention on optimizing their energy consumption. The energy demands of 5G infrastructure present an opportunity to innovate in ways that reduce operational costs and minimize environmental impact. Incorporating anenergy cost prediction function alongside traditional Cell Coverage Optimization (CCO) techniques offers a proactive approach to achieving more sustainable and cost-efficient network operations.

[0213] According to another implementation, the processing unit (120) may be configured to calculate baseline energy cost (denoted by ECbase) and optimized energy cost (denoted by ECopL) based on one or more input network parameters. In examples, the input network parameters may include maximum transmission power (denoted by Pmax). minimum transmission power (denoted by Pmin), power consumption in sleep mode (denoted by Psieep), traffic load threshold for activating sleep mode (denoted by Ltfl), minimum SSB beam shaping angle width (denoted by Bmin), maximum capacity threshold (denoted by Cmax). and historical data for training artificial intelligence (Al) and / or machine learning (ML) models. Additionally, the processing unit (120) may be configured to collect real-time data including information about real-time metrics, channel prediction, and neighboring cells data and capacity.

[0214] In examples, the processing unit (120) may collect information about real-time metrics including network performance metrics and traffic metrics. In examples, the network performance metrics may include Synchronization Signal - Signal to Interference plus Noise Ratio (SS-SINR) metrics, Synchronization Signal - Reference Signal Received Power (SS-RSRP) metrics, and Synchronization Signal - Reference Signal Received Quality (SS-RSRQ) metrics. Further, in examples, the traffic metrics include information about current traffic load (number of Radio Resource Control (RRC) connected UEs) and UE distribution.

[0215] According to an implementation, the processing unit (120) may also collect information about channel prediction based on physical obstructions. The processing unit (120) may collect information about channel modeling predictions. In examples, the processing unit (120) may use Al and / or ML models to predict channel conditions based on physical obstructions. Further, the processing unit (120) may collect information about neighboring cells’ load and capacity. The processing unit (120) may be configured to calculate the baseline energy cost and the optimized energy cost based on the collected real-time data.

[0216] In an implementation, the processing unit (120) may calculate the baseline energy cost based on current power consumption without Coverage and Capacity Optimization (CCO) optimizations. For example, the processing unit (120) may calculate the baseline energy cost using Equation provided below.where, ECbaserepresents baseline energy cost and PCUrrent(t) represents power consumption at time t during the time interval of measurements and At.

[0217] According to an implementation, the processing unit (120) may use Al and / or ML-based prediction for traffic and load forecasting, coverage demand analysis, and neighboring cell assessment. In an implementation, the processing unit (120) may use Al and / or ML algorithms to predict future trafficpatterns and load distribution based on historical and real-time data. The processing unit (120) may also predict coverage demand and user mobility patterns.

[0218] According to an implementation, the processing unit (120) may perform dynamic adjustment of the input network parameters. In examples, the dynamic adjustment of the network parameters includes adaptive transmission power control. In an implementation, the processing unit (120) may perform adaptive transmission power control to calculate optimal transmission power. In an implementation, the processing unit (120) may calculate the optimal transmission power based on predicted traffic load and coverage requirements. In examples, the processing unit (120) may calculate the optimal transmission power using Equation provided below.where Poptrepresents optimal transmission power, Pminrepresents minimum transmission power, Pmaxrepresents maximum transmission power, and Lpred(t) represents the predicted traffic load at time t.

[0219] According to an implementation, the processing unit (120) may perform beamforming optimization. In an implementation, the processing unit (120) may be configured to adjust beamforming angles and widths based on user density and demand. The processing unit (120) may calculate an optimized beam using Equation provided below.where Bopt (t) represents optimized beam, Bminrepresents minimum SSB beam shaping angle width, and UEpred(t) represents the predicted UE distribution.

[0220] In an implementation, the processing unit (120) may activate sleep mode for gNB. In examples, the processing unit (120) may activate the sleep mode based on criteria for sleep mode. In examples, if current load (Lcurrent) is significantly reduced post offloading, the processing unit (120) may transition the gNB to sleep mode. In example, the sleep mode for the gNB is represented using Equation provided below.^current (—^sleep

[0221] In an implementation, the processing unit (120) may perform real-time energy cost calculation and optimization. In examples, the processing unit (120) may calculate the optimized energy cost using the Equation provided below.where, ECoptrepresents optimized energy cost (EC).

[0222] In an aspect, the processing unit (120) may be further configured to compare the calculated Optimized EC with the baseline EC. Based on the comparison, the present disclosure may adjust optimization parameters if ECoptis not sufficiently lower than ECbase.

[0223] In an aspect, the processing unit (120) may be configured to employ a learning and feedback loop. In an aspect, the processing unit (120) may opt for a performance evaluation, a model refinement, and an optimization refinement for continuous learning and a feedback loop. In the performance evaluation, the present disclosure may evaluate the performance of optimizations in terms of energy savings and network quality. In the model refinement, the present disclosure may update AI / ML models with new data for improved prediction accuracy. In the optimization refinement, the present disclosure may refine CCO strategies and parameters based on performance evaluations and feedback.

[0224] Hence, there is an increasing concern about energy consumption associated with these advanced features. The high energy demands of 5G infrastructure can lead to significant operational costs and environmental impacts. Therefore, integrating an energy cost prediction function alongside traditional CCO techniques becomes crucial.

[0225] The present disclosure discloses a set of different SSB beam shapes with different beamwidth configurations and SSB beam power (SSB power boosting), to be dynamically selected and adjusted by the AI / ML optimization algorithm per sector, considering an algorithm for energy savings based on an Energy Cost (EC) function. Further, the present disclosure involves predicting energy consumption based on real-time network data and adjusting network operations accordingly to minimize energy use without compromising service quality. By incorporating this predictive capability, network operators can balance maintaining optimal performance and reducing energy expenditures.

[0226] The present disclosure employs an algorithm to dynamically select, among the available beamwidth beams, the proper beam shape, the most suitable SSB power per individual SSB beam of the beam-sweeping set per sector, under the constraint of desired energy savings, coverage and SINR. The goal is to minimize the intra-cell and inter-cell beam overlapping, preserve the sector coverage and maximize the expected SINR for different inter-site distances and geography. Hence, ultimately, the multi-SSB beamwidth beams will improve the DL coverage and throughput.

[0227] In one aspect, the system (106) may be configured to integrate SSB beam shaping, SSB beam boosting, and energy cost prediction, enabling a more sustainable and efficient 5G network operation. This integrated approach facilitates dynamic adjustments to beamforming strategies, which can be tailored to optimize coverage and capacity and align energy-saving goals.

[0228] Incorporating an energy cost prediction function into the Coverage and Capacity Optimization (CCO) framework represents a significant advancement towards sustainable networkmanagement. One of the primary aspects of CCO is the dynamic adjustment of transmission power to meet coverage and capacity needs without wasting energy. This not only promotes responsible use of energy resources but also supports global sustainability goals. As 5G networks continue to grow and evolve, adopting such proactive energy management strategies will play a vital role in harmonizing technological progress with environmental responsibility and cost-effectiveness.

[0229] FIG. 2A illustrates an exemplary schematic diagram (200A) describing SSB to Physical Downlink Shared Channel (PDSCH) beam mismatch, in accordance with an embodiment of the present disclosure.

[0230] As shown in FIG. 2A, the base station (104) transmits a plurality of SSB beams (202-1, 202-2, 202-3, 202-4) towards the UE (102). A person of ordinary skill in the art will understand that one or more SSB beams (202-1, 202-2, 202-3, 202-4) may collectively be referred to as the SSB beams (202). A plurality of PDSCH traffic beams (204-1, 204-2, 204-3, 204-4) are also transmitted between the base station (104) and the UE (102). A person of ordinary skill in the art will understand that one or more PDSCH traffic beams (204-1, 204-2, 204-3, 204-4) may collectively be referred to as the PDSCH traffic beams (204).

[0231] The SSB beam is the beam through which the base station transmits synchronization signals that the UE uses to detect the cell and measure channel quality. Meanwhile, the PDSCH beam is the beam used to send the actual user data to the UE. As both types of beams are present between the base station (104) and the UE (102), the SSB to PDSCH beam mismatch occurs due to gaps between the shapes of the SSB beams (202) and the PDSCH traffic beams (204). When the SSB and PDSCH beams differ a situation (also known as SSB to PDSCH beam mismatch), the channel quality measurements based on the SSB beam do not accurately reflect the quality of the PDSCH beam. This discrepancy leads to challenges in communication performance because the UE’s decisions and network scheduling are based on potentially misleading signal quality information.

[0232] This beam mismatch negatively impacts energy efficiency, coverage, and capacity optimization (CCO). The UE wastes power attempting to decode weaker PDSCH beams than expected, while the base station may increase transmission power or use less efficient beamforming, raising overall energy consumption. False channel quality reports cause coverage holes, poorer cell-edge performance, and suboptimal handover or expansion decisions. Mismatched beams result in inefficient scheduling, reduced spectral efficiency, increased retransmissions, and higher latency. Additionally, managing and mitigating these mismatches introduces extra signalling overhead, further reducing the usable capacity of the network.

[0233] FIG. 2B illustrates an exemplary schematic diagram (200B) describing SSB beam shaping, in accordance with an embodiment of the present disclosure.

[0234] As shown in FIG. 2B, a cell 1 (212-1) and a cell 2 (212-2) are present between a network node-1 (104-1) and a network node-2 (104-2). An SSB 1 (216-1) and an SSB 2 (216-2) are transmittedfrom the network node-2 (104-2) to the cell 2 (212-2). As seen in FIG. 2B, the shape of the SSB 1 (216-1) does not cover the upper edge of the cell 2 (212-2), which leads to a coverage hole (214) in the cell 2 (212- 2).

[0235] To solve the problem of the coverage hole (214) in cell 2 (212-2), the beam shaping is performed on the SSB 1 (216-1). After performing the SSB 1 beam shaping, the shape of the SSB beam covers the coverage hole formed in the cell 2 (212-2). By SSB 1 beam shaping, the problem of the coverage hole (214) is solved. The shape, direction, and power of the SSB beams are adjusted through beam shaping to fill areas of the coverage hole. By shaping the SSB beams (i.e., making the beams wider, steering towards underserved areas, or increasing the power in specific directions), the base station may improve signal strength in those coverage holes. This targeted adjustment helps ensure that UEs receive strong enough synchronization signals to access and maintain connection with the network. Ultimately, the SSB beam shaping reduces coverage gaps, improves initial cell detection, and enhances overall user experience by providing more consistent and reliable coverage across the cell.

[0236] In an embodiment, a switch in the coverage area of the SSB 1 (216-1) may be taken without any effect on a coverage area of the SSB 2 (216-2). In order to obtain a coordination of coverage between the cell 1 (212-1) and the cell 2 (212-2), an indication of switching in the coverage area of the SSB 1 (216- 1) and the SSB 2 (216-2) is provided to the cell 1 (212-1) and cell 2 (212-2) over an interface (e.g., Xn interface).

[0237] In an aspect, on changing the shape of the SSB beam in one sector, the coverage footprint is estimated for that sector. On detecting any error or anomaly, or the coverage footprint is found to be less than the configurable threshold (i.e., higher threshold), the shape of the SSB beam is again changed in that sector. In this way, the SSB beam shaping is a synchronizing process that is being performed throughout the sector.

[0238] The present disclosure employs a combined algorithmic approach, based on a functional integration of several system functional integrities like SSB beam shaping, SSB beam steering, Energy Cost (EC) function and offloading techniques, which will enhance the system performance and preserve the green energy network retainability.

[0239] The Energy Cost prediction function plays a critical role in managing the energy efficiency of dense networks. The SSB multi-beam management allows for the simultaneous control and optimization of multiple beams within a network. The Offloading, or handover, actions are critical in maintaining optimal load distribution across the network. In dense deployments, efficient handover mechanisms ensure that users are seamlessly transitioned between cells, minimizing congestion and optimizing resource utilization. The Energy Cost prediction function can inform these handover decisions by predicting the energy costs associated with different handover scenarios. Finally, the SSB beam shaping involves adjusting the beam's shape to focus signal energy on specific directions, enhancing coverage and signal quality in targeted areas. Azimuth and elevation angle steering further refine this by adjusting the direction of the beams in three-dimensional space. These capabilities are particularly important in dense networks, where precise control over beam direction can significantly reduce interference and improve user experience.

[0240] The integration of these technologies and functions offers a comprehensive approach to energy savings and system performance optimization. The predictive capabilities of the energy cost prediction function, combined with the dynamic control offered by SSB multi -beam management, beam shaping, and steering, allow for real-time adjustments that balance energy consumption with network performance. Offloading mechanisms complement these efforts by efficiently distributing user load, reducing the strain on any single cell and optimizing overall network efficiency.

[0241] The present disclosure is related mainly to gNB CU / DU and / or 0AM functional blocks. In an embodiment, the present disclosure addresses SSB (Synchronization Signal Block) multi-beam management in dense 5G and upcoming 6G networks. This approach integrates several key components: energy cost prediction, offloading (handover) actions, beam shaping, and beam steering (azimuth / elevation).

[0242] In an aspect, the following steps outline the system's process for implementing SSB multibeam management while prioritizing energy savings and optimal network performance:

[0243] 1. Initialization

[0244] Input Parameters:Network topology and cell layoutUser equipment (UE) distribution and mobility patternsCurrent network traffic loadEnergy consumption models for network elementsBeamforming capabilities (beam width, angles)Quality of Service (QoS) requirements

[0245] Initialization of System States:Initial energy consumption stateBeam configuration and current orientationsActive UEs and their associated beams / cellsPredicted traffic demand and UE mobility patterns

[0246] 2. Energy Cost Prediction

[0247] Data Collection:Monitor real-time data on traffic load, user activity, and environmental conditions.Collect historical data to build predictive models.Prediction Model Application:Utilize machine learning or statistical models to forecast energy consumption based on traffic patterns, user density, and predicted load.Estimate the energy cost for different beam configurations and network states.Decision Making:Calculate the potential energy savings for various configurations.Identify configurations that balance energy efficiency with QoS requirements.

[0248] 3. Offloading (Handover) Actions

[0249] Trigger Detection:Identify conditions that necessitate handovers, such as deteriorating signal qualify, congestion, or energy-saving potential.

[0250] Handover Decision:Determine the optimal target cell / beam for each UE based on:Signal strengthPredicted load and energy costsQoS requirements and user preferences

[0251] Execution:Execute the handover, ensuring minimal disruption and maintaining connectivity.Update system states to reflect the new UE associations and energy metrics.

[0252] 4. SSB Beam Shaping

[0253] Optimization Goals:Maximize signal coverage and qualify.Minimize interference with adjacent beams / cells.Align with energy cost predictions.

[0254] Beam Pattern Adjustment:Calculate the optimal beam shape (e.g., width, power distribution) for each sector or cell based on the current UE distribution and predicted traffic.

[0255] Implementation:Dynamically adjust the antenna array settings to achieve the desired beam shape.Monitor the impact on network performance and energy consumption.

[0256] 5. SSB Beam Azimuth / Elevation Angle Steering

[0257] Target Determination:Identify the optimal azimuth and elevation angles for each beam to maximize coverage and signal strength for active UEs.Consider physical obstacles, user densify, and mobility patterns.

[0258] Angle Calculation:Calculate the desired angles based on the spatial distribution of UEs and the network's energysaving objectives.

[0259] Adjustment Execution:Adjust the beamforming hardware / software to steer the beams to the calculated angles.Continuously monitor and adapt to changes in the environment and user distribution.

[0260] 6. Feedback Loop and Monitoring

[0261] Continuous Monitoring:Real-time monitoring of network performance metrics (e.g., signal quality, user throughput, energy consumption).Regular updates of predictive models with new data.

[0262] Feedback and Adjustment:Use performance data to refine energy cost predictions, beam configurations, and handover strategies.Implement automatic adjustments based on monitored data to maintain optimal performance and energy efficiency.

[0263] 7. Optimization and Learning

[0264] Machine Learning Integration:Implement reinforcement learning or other Al techniques to continually optimize the system based on observed outcomes.

[0265] Adaptive Learning:Allow the system to learn from past configurations and outcomes, improving prediction accuracy and decision-making over time.

[0266] 8. Conclusion and Future Adjustments

[0267] Evaluation:Periodically evaluate the performance of the multi -beam management system.Compare energy savings, QoS metrics, and user satisfaction against targets.

[0268] Future Planning:Plan for future adjustments based on evolving technology, user needs, and environmental factors.

[0269] As mobile networks grow more complex with diverse traffic patterns and evolving user behaviors, traditional optimization methods become inadequate in addressing the dynamic and real-time requirements of modem networks.

[0270] AI / ML offers a robust, data-driven approach to predict and manage these conditions effectively. The disclosure describes the processes for training, validating, and deploying AI / ML models, ensuring that they meet the high standards necessary for effective CCO.

[0271] The integration of AI / ML technologies in CCO is a crucial step toward developing selforganizing networks (SON). SON can autonomously manage and optimize network parameters, such as load balancing, interference management, and the dynamic configuration of network resources. This autonomy allows networks to adapt to changing conditions and demands, thereby enhancing overall efficiency and user experience. The use of AI / ML in network management not only addresses current challenges but also sets the stage for future innovations in telecommunications.

[0272] SSB beamwidth refers to the angular width of the beam used to transmit synchronization signals. Proper management of SSB beamwidth is essential for ensuring optimal coverage and capacity. Narrower beams can provide higher gain and better coverage in specific directions, while wider beams can cover larger areas with lower gain. SSB beam steering refers to the elevation angle and / or the azimuth angle. The trade-off between azimuth beamwidth, azimuth / elevation beam steering and coverage area is crucial for network performance.

[0273] Efficient energy consumption can also be achieved by other means such as reduction of load, coverage modification, or other RAN configuration adjustments. The optimal energy saving decision depends on many factors, including the load situation at different RAN nodes, RAN nodes' capabilities, KPI / QoS requirements, number of active UEs, UE mobility, cell utilization, etc.

[0274] Potential improvements with SSB Beamwidth Management are:

[0275] Enhanced Coverage - By dynamically adjusting the SSB beamwidth based on real-time network conditions, AI / ML algorithms can ensure that coverage is maximized in areas with high user density or poor signal quality. This adaptive beamwidth management allows for more efficient utilization of network resources, reducing coverage gaps and enhancing overall user experience.

[0276] Improved Capacity - AI / ML algorithms can analyze traffic patterns and predict areas where capacity enhancements are needed. By narrowing the SSB beamwidth in these areas, the network can provide higher signal strength and better data rates to users, effectively increasing network capacity. This is particularly beneficial in urban environments with high user density.

[0277] Interference Management -Interference is a significant challenge in dense network deployments. By intelligently managing the SSB beamwidth, AI / ML algorithms can minimize interference between neighboring cells. Narrower beams can be used to reduce overlap and interference, while wider beams can be employed in less congested areas to maintain coverage without causing excessive interference.

[0278] Energy Efficiency - Adjusting SSB beamwidth can also contribute to energy efficiency. By focusing energy only where it is needed, the network can reduce unnecessary power consumption. AI / ML algorithms can optimize beamwidth settings to balance coverage and capacity needs with energy efficiency goals.

[0279] The management of SSB (Synchronization Signal Block) beamwidth, along with azimuth and elevation beam steering, offers various potential improvements for mobile network performance. These improvements leverage AI / ML algorithms to dynamically adapt network parameters based on real-time conditions, thus enhancing coverage, capacity, interference management, and energy efficiency. Here are potential improvements focusing on both beamwidth and beam azimuth / elevation steering:

[0280] Potential improvements with SSB Beamwidth Management and beam steering are:

[0281] Enhanced CoverageDynamic Beamwidth Adjustment: By adjusting the beamwidth dynamically, networks can maximize coverage in areas with high user density or poor signal quality. For instance, wider beamwidths can cover larger areas, which is useful in sparsely populated regions.Adaptive Beam Steering: Azimuth and elevation steering can be used to direct beams more precisely towards areas with higher demand, ensuring better signal penetration and reducing dead zones. This is especially beneficial in complex terrains or urban environments with high-rise buildings.

[0282] Improved CapacityT argeted Narrow Beams: Narrowing the beamwidth in areas with high traffic demand can increase signal strength and data rates, thereby boosting network capacity. This is crucial in urban hotspots or events with dense user concentrations.Optimal Beam Steering Angles: By optimizing the azimuth and elevation angles, the network can focus beams more effectively on clusters of users, enhancing data throughput and reducing signal interference with other users.

[0283] Interference ManagementInterference Mitigation Through Beamwidth Control: In dense network deployments, narrower beams can be utilized to reduce overlap and interference between adjacent cells. This minimizes cross-cell interference and improves the quality of service.Directional Steering to Avoid Interference: By steering beams away from interference -prone areas or towards underutilized spectrum segments, networks can further mitigate interference issues. This is especially useful in areas with dense infrastructure or high-frequency reuse.

[0284] Energy EfficiencyEnergy Conservation with Focused Beamwidth: By narrowing the beamwidth and targeting specific areas, the network can reduce power wastage, focusing energy only where it is needed. This leads to a more efficient use of power resources.Steering for Optimal Power Usage: Adjusting the azimuth and elevation angles can also contribute to energy efficiency. For example, in less congested areas, beams can be steered to broader coverage with lower power settings, while in high -demand areas, more precise, high-power beams can be used.Training AI / ML models for CCO involves using vast amounts of network data, including user mobility patterns, signal strength measurements, traffic loads, and environmental factors. This data is used to train algorithms to recognize patterns and predict network performance under varying conditions.

[0285] The training process includes several stages:

[0286] Data Collection and Preprocessing: Collecting data from various network elements and preprocessing it to ensure quality and relevance.

[0287] Feature Selection: Identifying key features that influence network coverage and capacity.

[0288] Model Training: Using supervised or unsupervised learning techniques to train the model. Techniques such as neural networks, decision trees, or support vector machines can be used depending on the complexity of the task.

[0289] Validation and Testing: Validating the model with a separate dataset to ensure accuracy and generalizability. This step includes testing the model under different scenarios to evaluate its performance.

[0290] Typically, once trained, the AI / ML model is deployed in the network to perform real-time inference. The model continuously analyzes incoming data to make predictions and recommendations for optimizing coverage and capacity.

[0291] Key aspects of the inference process include:

[0292] Real-Time Data Processing: Continuously collecting and processing data from network elements to feed into the model.

[0293] Predictive Analysis: Using the trained model to predict potential coverage gaps, congestion hotspots, and other network issues.

[0294] Decision Making: Making real-time decisions on parameter adjustments, such as antenna tilt, power levels, and handover thresholds, to optimize network performance.

[0295] Feedback Loop: Implementing a feedback mechanism to update the model based on the effectiveness of the optimization actions taken.

[0296] The disclosure focuses on enhancing performance through AI / ML models, including potential use cases such as channel state information (CSI) feedback enhancement, beam management, and positioning accuracy improvements.

[0297] The disclosure focus outlines the management capabilities required for AI / ML across different phases: training, validation, testing, deployment, and inference. This includes aspects such as ML entity loading, inference control, and performance evaluation.

[0298] The disclosure utilizes a diverse set of SSB beam shapes with varying beamwidth configurations and incorporates SSB power boosting. These parameters are dynamically selected and adjusted by an AI / ML optimization algorithm for each sector. The AI / ML algorithm will dynamically choose the appropriate beam shape and optimal SSB power level for each individual SSB beam within the beam-sweeping set per sector, considering both azimuth and elevation angles. This selection process aims to optimize coverage and Signal-to-Interference-plus-Noise Ratio (SINR) while meeting network performance constraints.

[0299] Specifically, the algorithm will assess real-time network conditions to determine the most suitable beamwidth and steering angles. By adjusting the azimuth and elevation angles, the beams can be precisely directed to target-specific user clusters or areas with high traffic demand. This ensures thatcoverage is maximized, and signal quality is maintained, even in challenging environments such as urban canyons or high-density event venues.

[0300] Additionally, the algorithm will control the power boosting for each S SB beam, fine-tuning the power levels to balance between achieving sufficient coverage and minimizing interference. This dynamic adjustment helps to ensure efficient energy usage and enhances overall network performance. The proposed approach enables a flexible and adaptive network configuration, capable of responding to varying user densities and traffic patterns, thereby optimizing both capacity and user experience.

[0301] In an embodiment, an EC prediction may be combined with CCO actions. One of the primary aspects of CCO is the dynamic adjustment of transmission power to meet coverage and capacity needs without wasting energy. In a static system, gNBs might operate at a fixed power level, potentially over-providing coverage and capacity, especially during periods of low traffic.

[0302] By integrating CCO, the transmission power of a base station (e.g., gNB) may be adjusted in real-time based on actual demand and network conditions. This means during off-peak hours or in less populated areas, the transmission power can be reduced, thus conserving energy. Conversely, power can be increased only, when necessary, such as during high traffic loads or to support edge users. This targeted use of energy prevents wastage and optimizes power consumption.

[0303] In an embodiment, the EC prediction may be combined with CCO actions including dynamic adjustments on transmitted power. CCO strategies often include the implementation of sleep modes or low-power states for gNBs during periods of low activity. This involves selectively powering down certain components of the gNB, such as antennas or transmitters, while maintaining minimal functionality for essential operations.

[0304] The ability to switch gNBs or their components into low -power modes during low traffic periods significantly reduces energy consumption. The integration of CCO in the EC prediction function allows the network to dynamically determine when to activate these modes based on real-time data, such as traffic volume and user distribution. The overall reduction in active operational time for gNBs directly translates to lower energy usage.

[0305] In an embodiment, the EC prediction may be combined with CCO actions, including Low- Power mode. Beam management, including dynamic SSB / Traffic beam shaping or beam steering, is another critical technology in 5G that allows gNBs to direct their signals specifically towards user equipment (UE) rather than broadcasting them indiscriminately. CCO includes optimizing beamforming parameters to ensure that coverage and capacity are provided where needed, without excess.

[0306] Optimized beamforming reduces the power needed to cover a given area, especially when fewer UEs are present or in scenarios where precise targeting can minimize interference and energy loss. By managing UE connections more efficiently (e.g., connecting them to the optimal gNB or beam), thenetwork can reduce the power required to maintain quality of service. This precise control and targeting reduce the overall energy consumption of the gNBs.

[0307] In an embodiment, the EC prediction may be combined with CCO actions, including beamforming parameter optimization actions.

[0308] CCO includes advanced traffic prediction capabilities, leveraging AI / ML algorithms to forecast user demand and network conditions. This foresight allows the network to prepare and allocate resources efficiently.

[0309] By predicting traffic patterns, the network can proactively adjust gNB settings in anticipation of changes in demand. For example, during predicted low-traffic periods, gNBs can be preemptively transitioned into low-power states, and transmission power can be lowered. Conversely, during expected peak times, resources can be efficiently ramped up without overshooting, thus preventing unnecessary energy expenditure.

[0310] In an embodiment, the EC prediction may be combined with CCO actions including dynamic traffic predictions.

[0311] CCO considerations are integrated into an EC prediction function for a single gNB in 5G RAN. By leveraging AI / ML-based predictions, dynamic adjustments, and continuous optimization, the algorithm aims to minimize energy consumption while maintaining or improving network performance. The adaptive nature of the algorithm ensures that it can respond effectively to real-time network conditions and evolving traffic demands, thereby achieving enhanced energy savings.

[0312] The present disclosure introduces an energy-efficient strategy for 5G and beyond RAN systems, wherein Coverage and Capacity Optimization (CCO) actions and traffic offloading are treated as mutually exclusive yet cooperative mechanisms within a unified Energy Cost (EC) prediction and control framework. Advanced CCO techniques (e.g., high-resolution beamforming, SSB / traffic beam-shaping, and beam steering) significantly reduce energy consumption by dynamically targeting high-demand areas with optimized directional coverage. These enhancements may eliminate the need for traffic offloading altogether, enabling the serving network node (e.g., a base station such as a gNB) to deliver energy -efficient service without exceeding power budgets.

[0313] By decoupling CCO actions from offloading procedures, the network gains interpretability and reduces the complexity of energy optimization. In conventional architecture, the joint tuning of beam parameters and load balancing introduces parameter interdependencies and decision-making conflicts. The proposed approach enables a simpler, modular control logic, where CCO actions are prioritized, and offloading is considered only when CCO is insufficient or ineffective, serving as a fallback mechanism.

[0314] Furthermore, EC prediction at each NG-RAN node becomes more accurate and actionable when beamforming improvements are evaluated independently of traffic migration. Since offloading introduces external load variability and dynamic resource shifts between nodes, it inherently complicates EC forecasting. A CCO-first strategy offers a stable and consistent foundation for predicting energy savings particularly in scenarios where localized beam control adapts to UE traffic behavior (e.g., bursty or intermittent patterns), enabling sleep-mode activations or sub-array deactivations.

[0315] The present disclosure also presents an advanced EC-aware coordination mechanism, in which a serving network node (e.g., gNB) intending to offload UEs shares a load profile with a candidate target node. The target node responds with a predicted EC delta based on its ability to absorb the load efficiently via its CCO capabilities. If the combined EC is reduced, the offloading proceeds; otherwise, beam adjustments are applied at the source node. This inter-node coordination ensures that offloading only occurs when the resulting energy profile across all involved nodes is optimal.

[0316] By treating CCO and offloading as mutually exclusive steps within a single algorithm, the system achieves greater decision clarity, reduced control complexity, and interpretable EC evaluations. The present disclosure supports a distributed, energy-aware RAN control plane, aligned with sustainability objectives and scalable automation in next-generation networks.

[0317] FIG. 3 illustrates an exemplary flow diagram (300) of a method for integrated EC prediction and the traffic offloading in the network (108), in accordance with an embodiment of the present disclosure.

[0318] In an embodiment, the EC prediction may be combined with CCO actions, including potential offloading action to neighboring cells.

[0319] Combining Coverage and Capacity Optimization (CCO) with potential neighboring cell offloading (mobility management) into a single Energy Cost (EC) prediction function may significantly enhance energy savings in 5G Radio Access Networks (RAN) for several reasons. This integrated approach allows for more holistic and dynamic management of network resources, leading to more efficient energy usage while maintaining the desired service quality.

[0320] The combined CCO and offloading functionality includes dynamic load balancing and traffic distribution.

[0321] Traffic loads may vary significantly across different cells and at times of the day. Some cells may become congested, while others remain underutilized. By incorporating neighboring cell offloading into the EC function, the network dynamically distributes traffic from congested cells to underutilized neighboring cells. This redistribution helps prevent excessive power usage in heavily loaded cells and allows for potential power reductions or sleep mode activation in less utilized cells, thus optimizing overall energy consumption.

[0322] In an embodiment, the EC prediction may include an enhanced traffic distribution offloading action to improve energy efficiency.

[0323] Coverage and capacity requirements fluctuate based on user density and demand patterns. For instance, during special events or peak hours, higher capacity and coverage might be needed, whereas off-peak hours might allow for reductions. Moreover, mobility management, including handovers and load balancing, is crucial in a mobile network to maintain service quality. However, inefficient handling leads to unnecessary power consumption, especially if users frequently move between cells with differing loads

[0324] In adaptive coverage and capacity management, the combined consideration of CCO and mobility in the EC function enables adaptive adjustments in both coverage (by controlling transmission power and antenna tilts) and capacity (by adjusting resource allocation and scheduling). This means that the network minimizes energy usage by reducing unnecessary coverage areas and reallocating resources more efficiently based on real-time demand.

[0325] In an embodiment, the EC prediction may include both CCO and enhanced traffic distribution offloading action to improve energy efficiency.

[0326] As, traditional networks often react to traffic changes after they occur. However, predictive analytics and proactive measures may significantly enhance energy efficiency. In proactive energy saving strategies, the EC prediction function, when informed by CCO and mobility data, forecasts potential traffic patterns and energy consumption trends. This predictive capability enables the network to pre-emptively adjust parameters, such as reducing power in areas with anticipated low demand or preparing cells for higher traffic. This proactive approach helps in smoothing out energy consumption peaks and valleys, resulting in more consistent and lower overall energy usage.

[0327] In an embodiment, the EC prediction may be derived with a mutual exclusive consideration of a potential offloading action in coordination with CCO beam improvements. Thus, EC function may enhance the energy saving actions between NG-RAN nodes in order to be interpretable or actionable by the receiving node.

[0328] The intricate interplay between offloading and CCO beam improvements significantly enhances energy efficiency in NG-RAN networks. By strategically offloading UEs with high EC profiles and optimizing beam configurations, network operators achieve substantial energy savings. The decoupling of high energy demand nodes from those capable of efficient energy management creates a more balanced and efficient network. This approach not only reduces overall energy costs but also improves the sustainability and reliability of the network, aligning with the broader goals of modem telecommunications to support growing data demands while minimizing environmental impact.

[0329] Consequently, a predicted high EC might be a consequence of the NG-RAN node serving several UEs with low, bursty, but frequent traffic that prevents it from enabling energy saving features dueto offloading but it might be assisted by the energy saving actions due to CCO beam improvements. Hence, the neighboring NG-RAN node may be able to serve the new, offloaded UEs with a marginal increment in its EC; at the same time, the offloading might enable the source NG-RAN node to reduce its own EC significantly and to optimize its energy saving by proper CCO optimization actions.

[0330] In an embodiment, the EC prediction on one node may be tolerated with respect to a potential offloading action between neighbor NG-RAN nodes with potential CCO beam optimization action capabilities. The source node of the offloading action may signal to the target node a description of the “additional load” for the target node. The target node sends a prediction of the EC at the target node to the source node, assuming the offloading action was successfully executed.

[0331] CCO actions are integrated with offloading actions into an Energy Cost (EC) prediction function for the base station in the network (5G RAN) in a more flexible mutually exclusive way. The primary goal is to enhance energy savings by optimizing network performance and resource usage. The algorithm follows specific rules: offloading conditions are mutually exclusive to CCO actions, offloading may include CCO considerations, and CCO actions may be prioritized to manage the load before considering offloading.

[0332] At step (302), the method (300) may be initialized. During this initialization process, a set of input parameters and output parameters may be defined. For example, the input parameters may include a maximum transmission power, a minimum transmission power, a power consumption in sleep mode, a traffic load threshold for activating sleep mode, a minimum SSB beam shaping angle width, a maximum capacity threshold, the neighbor cell data (capabilities, current load, and potential for handling offloaded traffic), historical data for training AI / ML models, predicted energy saving and forecasted incremental EC. In another example, the output parameters may include a baseline energy cost and an optimized energy cost.

[0333] At step (304), the method (300) may involve performing real-time data collection, which includes the systematic gathering of Real-Time Metrics. In this context, the real-time metrics may encompass various network performance indicators, such as the Synchronization Signal Signal -to- Interference-plus-Noise Ratio (SS-SINR), the Synchronization Signal Reference Signal Received Power (SS-RSRP), and the Synchronization Signal Reference Signal Received Quality (SS-RSRQ). Additionally, the real-time metrics may include the number of Radio Resource Control (RRC) connected user equipment (UE), angular density, beam activity and power consumption per sector. Furthermore, the real-time metrics may include current load, predicted load, coverage overlap information, local EC models for estimating offload impact, and the target base station admission control to accept offload requests.

[0334] At step (306), the method (300) includes calculating a baseline energy cost. In an example, the baseline energy cost refers to the standard or reference level of energy consumption associated with the operation of a system or network under typical conditions. In an example, the baseline energy cost may be calculated using the following Equation:based on current power consumption without CCO optimizations, where PCUrrent( is the power consumption at time t during the time interval of measurements and At.

[0335] At step (308), the method (300) may involve AI / ML-based prediction, which encompasses three key areas: traffic and load forecasting, coverage and capacity demand analysis and neighbor cell assessment. For example, in traffic and load forecasting, one or more artificial intelligence / machine learning (AI / ML) models may be employed to analyze historical and real-time data in order to predict future traffic patterns and load distribution across the network. This predictive capability of the AI / ML models enables the system to anticipate changes in user demand, helping to optimize resource allocation and enhance network performance. In an aspect, the AI / ML models may be able to perform coverage demand analysis. The AI / ML models focus on predicting coverage and capacity demand, user mobility patterns, and user distribution (density) in coverage demand analysis. The AI / ML models may perform neighboring cell assessment. For example, AI / ML models may evaluate neighboring cells' current and projected load capacity for potential offloading. By leveraging the AI / ML models, the system forecasts areas of high user concentration and anticipated movement, allowing for proactive adjustments in network configuration and resource management to ensure adequate coverage and seamless connectivity.

[0336] At step (310), the method (300) may involve dynamic adjustment of network parameters. In an aspect, for dynamic adjustment of network parameters, the present disclosure may employ adaptive transmission power control and beamforming optimization, traffic offloading to neighboring cells and sleep mode activation. The adaptive transmission power control enables the network to adjust transmission power levels in real time based on the predicted traffic patterns, load distribution, and coverage demand identified in previous steps. By optimizing transmission power dynamically, the network enhances signal quality for users while minimizing energy consumption and reducing interference with neighboring cells. This adaptive approach ensures that power is allocated efficiently, allowing for improved overall network performance and user experience, especially in varying conditions such as fluctuating traffic loads and user mobility patterns. In the adaptive transmission power control, the present disclosure may calculate an optimal transmission power using the following Equation:

[0337] The optimal transmission power may be calculated based on predicted traffic load and coverage requirements, where Lpred(t) is the predicted traffic load at time t.

[0338] In the beamforming optimization, the present disclosure may adjust beamforming angles and widths based on user density and demand using the following Equation.In an aspect, the UEpredt) is the predicted UE distribution.

[0339] In traffic offloading to neighboring cells, offloading criteria are applied to trigger offloading of one or more UEs from a source cell (or base station) to one or more neighboring cells (or base stations). The offloading criteria are checked to determine whether offloading action is required. The offloading criteria comprise Lcurrent> Ltflor predicted high-demand periods. The traffic offloading to neighboring cells may be performed based on offloading criteria. In an implementation, traffic offloading may be triggered when the current traffic load is greater than the traffic load threshold for activating sleep mode (when Lcurrent> Ltfl) or during predicted high-demand periods.

[0340] In an implementation, target cells may be selected from amongst the neighboring cells. For example, neighboring cells may be selected based on their current and predicted load, coverage overlap, and capacity. Target cells may be determined based on the following Equation.Target Cell = argmin (L]current+ L]pred) j

[0341] In an operative aspect, the EC predication and traffic offloading is used for energy management. Upon performing the selection of the target cell for offloading, candidate UE selection is performed to identify UEs with high EC footprint or poor channel qualify and AECiocai j is computed for each candidate UEj.

[0342] An EC feedback exchange protocol is employed between the source base station (gNB) and the target base station (gNB). So, a bilateral EC negotiation protocol is established to enable energyefficient UE migration decisions between the network nodes (i.e., source base station and neighbor base station)

[0343] The source base station sends UEj identifier (ID), AEC10caiy, traffic profile, qualify of service (QoS) needs, estimated offload benefit to the target base station.

[0344] The target base station sends ECremoteif UE j is accepted by the target base station, accept / reject flag based on local thresholds.

[0345] The offloading is performed based on a decision logic. The decision logic is used to approve UEj offloading to neighbor gNB, if AEC10cai > ECremote.

[0346] Upon detecting AECiocaij > ECremote, handover is triggered, and scheduling / beam assignment is updated. If AEC10caij < ECremotej, offloading is rejected.

[0347] In an implementation, offloading execution may be performed to handover UEs to selected neighboring cells to balance load and reduce energy consumption. In an implementation, the offloading may be performed using the following Equation.^new ^current ^off loaded

[0348] In the sleep mode activation, the present disclosure may be configured to determine If ^current < ^th and activate a transition of gNB (network node) to sleep mode. In the sleep mode, the power may be defined asP current^) P sleep

[0349] At step (312), the method (300) may involve real-time energy cost (EC) calculation and optimization. In this step, the present disclosure continuously monitors and calculates the current EC of the network based on the dynamically adjusted parameters and operational states. In an aspect, the present disclosure may calculate optimized EC using the following Equation.

[0350] In an aspect, the present disclosure may be further configured to compare the calculated Optimized EC with the baseline EC. Based on the comparison, the present disclosure may adjust offloading thresholds, power levels and beamforming optimization parameters if ECoptis not sufficiently lower than E Cbase.

[0351] At step (314), the method (300) may involve continuous learning and feedback loop. In an aspect, the present disclosure may opt for a performance evaluation, a model refinement, and an optimization refinement for continuous learning and a feedback loop. In the performance evaluation, the present disclosure may evaluate the performance of optimizations in terms of energy savings and network quality. In the model refinement, the present disclosure may update AI / ML models with new data for improved prediction accuracy. In the optimization refinement, the present disclosure may refine CCO strategies, offloading strategies, and parameters based on performance evaluations and feedback.

[0352] The present disclosure proposes a combination of SSB beam shaping, SSB beam boosting, and energy cost prediction with potential offloading actions, thus enabling more sustainable and efficient 5 G network operation. This integrated approach facilitates dynamic adjustments to beamforming strategies, optimizing both coverage and capacity while aligning with energy-saving and processing load goals. By combining CCO with potential neighboring cell offloading (mobility management) into a single Energy Cost (EC) prediction function, this approach significantly enhances energy savings in 5G Radio Access Networks (RAN) for several reasons. This integrated approach allows for more holistic and dynamic management of network resources, leading to efficient energy usage while maintaining the desired servicequality. The incorporation of an energy cost prediction function into the CCO framework represents a significant advancement toward sustainable network management. Additionally, this approach ensures the efficient use of energy resources and aligns with global efforts to reduce carbon footprints and improve traffic load distribution. As 5G networks continue to expand and evolve, such proactive energy management strategies with offloading actions are essential in balancing technological advancements with environmental and economic considerations.

[0353] The present disclosure allows for the dynamic adaptation of network resources based on real-time conditions, thereby reducing unnecessary energy usage. Further, the present disclosure enhances the granularity of energy management, enabling more precise control over which network elements are active at any given time. Also, the present disclosure supports the implementation of network strategies, such as turning off redundant cells during low-demand periods or optimizing the energy efficiency of active cells. For example, integrating CCO considerations with the potential offloading of traffic to neighboring cells into the EC prediction function for a single gNodeB (gNB) in a 5G network offers energy-saving enhancements. The integration of CCO involves optimizing the network's coverage and capacity, directly impacting energy consumption. By distributing the traffic load more evenly across gNBs, the network prevents situations where some gNBs are overburdened while others are underutilized. Underutilized gNBs are placed into low -power modes or even turned off, reducing overall energy consumption. Efficient load balancing ensures that no single gNB is pushed to operate at full capacity unnecessarily, thus conserving energy across the network.

[0354] The combined use of EC prediction and intelligent offloading is essential in balancing high- performance demands with sustainable energy consumption. In conclusion, the amalgamation of the EC prediction function with offloading strategies represents a critical advancement in managing dense 5G and future 6G networks. This approach enables significant energy savings and supports the efficient utilization of network resources, ensuring that the benefits of dense deployments do not come at the expense of excessive energy consumption. By leveraging predictive analytics and adaptive handover mechanisms, this integrated strategy helps achieve a sustainable balance between network performance and energy efficiency, which is crucial for the future of telecommunications infrastructure

[0355] The present disclosure involves an algorithm that dynamically selects the appropriate beam shape among the available beamwidth beams and the most suitable SSB power per individual SSB beam of the beam-sweeping set per sector. This selection process operates under the constraints of desired energy savings and potential offloading actions, aiming to sustain or improve coverage and Signal-to-Interference- plus-Noise Ratio (SINR). This method ensures that network performance is maintained or enhanced while achieving energy efficiency goals, making it a pivotal development in modem network management.

[0356] In a communication network, Massive MIMO (Multiple Input Multiple Output) antenna arrays enable a network node, such as a gNodeB (gNB), to enhance beamforming capabilities by dynamically forming a radiation pattern that amplifies radiated power in specific directions. This focused transmission optimizes signal strength at user equipment, thereby reducing interference and improvingoverall communication quality. By effectively directing energy rather than broadcasting uniformly, the network maximizes the utilization of available spectrum, which is particularly crucial in 5G mid-band Time Division Duplex (TDD) deployments, where efficient spectrum management is necessary to accommodate both uplink and downlink traffic.

[0357] There are, however, situations where the use of broader beamwidth beams is advantageous for various system functions, including synchronization, initial access, and mobility signaling. These functions are particularly relevant for control and broadcast channels, which are designed to disseminate information to multiple users simultaneously. The broader beamwidth beams facilitate a larger coverage area, ensuring that signals reach a wider range of devices, thereby enhancing the reliability of synchronization processes and simplifying initial access for users connecting to the network. Additionally, since control and broadcast channels typically handle information with a broadcast nature and a low bitrate per user, the broader beams allow for effective communication without the need for high precision in directionality, resulting in improved network efficiency and user experience, especially in scenarios with high user densify or mobility.

[0358] For large antenna arrays, creating a broad beam is not straightforward. There are several different ways, like transmitting a small sub-group of antenna dipole elements (sub-arrays) with a broad beamwidth radiation pattern or using a tunable beam-shaping methodology to broaden the beamwidth and preserve the power. An alternative and efficient tunable beam-shaping methodology constitutes a common approach used for beam management procedures in 5G New Radio (NR), relying on performing a beamsweep over a set of SSB beams to cover the desired cell area in a time -multiplexed manner fully. Hence, instead of constantly transmitting a single SSB broad beam, multiple SSBs are sequentially transmitted in what is known as a “synchronization signal burst” containing a set of narrower beams spanning the sector's angular (and perhaps the elevation) directions. However, this method is prone to a major disadvantage that multi-SSB beam shapes with specific beamwidth configurations are fixed across all the sites based on the antenna equipment capability. This leads to unnecessary transmissions to undesired directions, overlapping sector areas and inter-cell interference across neighbor dense sectors with high energy consumption and lower energy efficiency.

[0359] In an embodiment, for AI / ML based CCO, for the existing SSB beam sweeping functionality on serving node, different SSB beams with specific SSB beam-width configurations along with SSB beam steering azimuth and elevation angles are dynamically selected to optimize coverage and SINR.

[0360] The goal is to minimize the intra-cell and inter-cell beam overlapping, preserve the sector coverage and maximize the expected SINR for different inter-site distance and geography. Hence, ultimately the multi-SSB beam-width beams will improve the DL coverage and throughput without affecting the UE capability.

[0361] In an embodiment, for AI / ML based CCO, the proposed algorithm refers to Pl beam management algorithm, hence the user device is unaffected and follows the specific 3GPP and GSMA.

[0362] The proposed algorithm, to support AI / ML in NG-RAN, gets inputs only from connected UEs in the serving cell, building a cost function to predict the SSB coverage per individual SSB beam width out of the existing SSB beam sweeping beams.

[0363] In an embodiment, for AI / ML based CCO, the inference input may be collected from UE or local node or neighbour node, e.g. including:

[0364] Inputs from connected UEs as follows:- connected UE measurement report (e.g., LI SS-RSRP, SS-RSRQ, SS-SINR measurement, CQI, etc), including cell level and beam level UE measurements- connected UE performance (HARQ NACK KPI retransmissions (BLER>10%)),- connected UE location information (e.g., coordinates, serving cell ID)- idle mode UE’s Random Access performance (RACH success rate, number of RACH attempts) to convert to SINR from existing graph of number of PRACH attempts vs SINR).- connected UE RLF report

[0365] The future coverage status may be Cell Coverage Modification(s) (i.e., power parameters, SSB boosting etc.) or SSB Coverage Modification(s) (i.e., SSB beam width modifications).

[0366] In an embodiment, for AI / ML based CCO, the future coverage status may include:Cell Coverage Modification including SSB beam power boosting, for the Cell Coverage State which is used to solve the predicted CCO issue of the certain predicted cell;SSB Coverage Modification including SSB beam width and SSB beam steering Coverage State which is used to solve the predicted CCO issue of the certain predicted SSB.

[0367] DL performance is investigated based on SSB power boosting and SSB beam-width shaping for different SSB beam gains on the beam-sweeping procedure, building a proper cost function.

[0368] Goal is to improve (increase) DL SINR and average DL cell throughput

[0369] In an embodiment, for AI / ML based CCO, the future coverage status may include the following NG-RAN gNB or gNB-CU / gNB-DU algorithm steps:

[0370] Step 1: Initial SSB Beam Sweeping

[0371] Start SSB beam sweeping at the cell level with the default (operator-configurable) SSB beam-width shape. Perform sweeping for all SSB beams within a 5ms cycle.

[0372] Step 2: SINR Estimation and Data Collection

[0373] Estimate the Downlink (DL) SINR per SSB sweeping beam using a combined cost function. This includes:Number of Uplink (UL) PRACH counter attempts (build a graph of PRACH attempts vs. SINR) for idle UEs.Connected UEs' UL Channel Quality Indicator (CQI) reports.HARQ KPI retransmissions (Block Error Rate > 10%).LI -SINR and LI -Reference Signal Received Power (RSRP) reporting.

[0374] Step 3: Beam Steering and Beam-width Shape Testing

[0375] For each SSB beam-width shape, dynamically adjust the SSB beam's azimuth and elevation angles. Repeat Step 1 and Step 2 for each configuration, ensuring all combinations of beam -width shapes and steering angles are exhaustively tested.

[0376] Step 4: Coverage Footprint Estimation

[0377] For each tested configuration, estimate the SSB beam coverage footprint as a percentage of the default SSB beam shape.

[0378] Step 5: Selection of Optimal Beam Configuration

[0379] Identify the configuration with the argmax DL UL SINR performance that also meets a predefined coverage footprint threshold (configurable). This configuration includes the optimal combination of beam -width shape, azimuth / elevation angles, and SSB power level.

[0380] Step 6: Fine-tuning with SSB Power Boosting

[0381] For the selected optimal beam-width shape and steering angles, adjust the SSB power boosting parameters to further enhance SINR. Re-estimate the DL SINR based on the refined settings.

[0382] Step 7: Final Configuration Application

[0383] Apply the optimal beam configuration (beam-width shape, steering angles, and power boosting) to the network for active use, ensuring maximum coverage and optimal signal quality.

[0384] Step 8: Continuous Monitoring and Adaptation

[0385] Continuously monitor the network's performance metrics, such as SINR, coverage, and user experience indicators. Use AI / ML algorithms to dynamically adjust the configurations in response to changing network conditions and traffic patterns.

[0386] In an embodiment, an energy cost prediction should be combined with CCO actions.

[0387] One of the primary aspects of CCO is the dynamic adjustment of transmission power to meet coverage and capacity needs without wasting energy. In a static system, gNBs might operate at a fixed power level, potentially over-providing coverage and capacity, especially during periods of low traffic.

[0388] By integrating CCO, the transmission power of a gNB can be adjusted in real-time based on actual demand and network conditions. This means during off-peak hours or in less populated areas, the transmission power can be reduced, thus conserving energy. Conversely, power can be increased only, when necessary, such as during high traffic loads or to support edge users. This targeted use of energy prevents wastage and optimizes power consumption.

[0389] In an embodiment, an energy cost prediction should be combined with CCO actions including dynamic adjustments on transmitted power.

[0390] CCO strategies often include implementing sleep modes or low-power states for gNBs during periods of low activity. This involves selectively powering down certain components of the gNB, such as antennas or transmitters, while maintaining minimal functionality for essential operations.

[0391] The ability to switch gNBs or their components into low -power modes during low traffic periods significantly reduces energy consumption. The integration of CCO in the EC prediction function allows the network to dynamically determine when to activate these modes based on real-time data, such as traffic volume and user distribution. The overall reduction in active operational time for gNBs directly translates to lower energy usage.

[0392] In an embodiment, an Energy Cost prediction should be combined with CCO actions, including Low-Power mode.

[0393] Beam management including dynamic SSB / Traffic beam shaping or beam steering is another critical technology in 5G that allows gNBs to direct their signals specifically towards user equipment (UE) rather than broadcasting them indiscriminately. CCO includes optimizing beamforming parameters to ensure that coverage and capacity are provided where needed, without excess.

[0394] Optimized beamforming reduces the power needed to cover a given area, especially when fewer UEs are present or in scenarios where precise targeting can minimize interference and energy loss.By managing UE connections more efficiently (e.g., connecting them to the optimal gNB or beam), the network can reduce the power required for maintaining quality of service. This precise control and targeting reduce the overall energy consumption of the gNBs.

[0395] In an embodiment, an Energy Cost prediction should be combined with CCO actions including beamforming parameter optimization actions.

[0396] CCO includes advanced traffic prediction capabilities, leveraging AI / ML algorithms to forecast user demand and network conditions. This foresight allows the network to prepare and allocate resources efficiently.

[0397] By predicting traffic patterns, the network can proactively adjust gNB settings in anticipation of changes in demand. For example, during predicted low-traffic periods, gNBs can be preemptively transitioned into low-power states, and transmission power can be lowered. Conversely, during expected peak times, resources can be efficiently ramped up without overshooting, thus preventing unnecessary energy expenditure.

[0398] In an embodiment, an Energy Cost prediction should be combined with CCO actions including dynamic traffic predictions.

[0399] An algorithm which outlines a systematic approach to integrating CCO considerations into an EC prediction function for a single gNB in 5G RAN. By leveraging AI / ML-based predictions, dynamic adjustments, and continuous optimization, the algorithm aims to minimize energy consumption while maintaining or improving network performance. The adaptive nature of the algorithm ensures that it can respond effectively to real-time network conditions and evolving traffic demands, thereby achieving enhanced energy savings.

[0400] In an embodiment, an Energy Cost prediction should be combined with CCO actions including potential offloading action to neighbor cells.

[0401] Hence, combining Coverage and Capacity Optimization (CCO) with potential neighbor cell offloading (mobility management) into a single Energy Cost (EC) prediction function can significantly enhance energy savings in 5G Radio Access Networks (RAN) for several reasons. This integrated approach allows for more holistic and dynamic management of network resources, leading to more efficient energy usage while maintaining the desired service quality.

[0402] The following are some key reasons and the corresponding proposals for this combined CCO and offloading functionality:

[0403] Dynamic Load Balancing and Traffic Distribution:

[0404] In a 5G network, traffic loads may vary significantly across different cells and times of the day. Some cells may become congested, while others remain underutilized.

[0405] By incorporating neighbor cell offloading into the EC function, the network can dynamically distribute traffic from congested cells to underutilized neighboring cells. This redistribution helps prevent excessive power usage in heavily loaded cells and allows for potential power reductions or sleep mode activation in less utilized cells, thus optimizing overall energy consumption.

[0406] In an embodiment, an Energy Cost prediction should include an enhanced traffic distribution offloading action to improve energy efficiency.

[0407] In Adaptive Coverage and Capacity Management, Coverage and capacity requirements fluctuate based on user density and demand patterns. For instance, during special events or peak hours, higher capacity and coverage might be needed, whereas off-peak hours might allow for reductions. Moreover, mobility management, including handovers and load balancing, is crucial in a mobile network to maintain service quality. However, inefficient handling may lead to unnecessary power consumption, especially if users frequently move between cells with differing loads.

[0408] The combined consideration of CCO and mobility in the EC function enables adaptive adjustments in both coverage (by controlling transmission power and antenna tilts) and capacity (by adjusting resource allocation and scheduling). This means that the network may minimize energy usage by reducing unnecessary coverage areas and reallocating resources more efficiently based on real-time demand.

[0409] In an embodiment, an Energy Cost prediction should include both CCO and enhanced traffic distribution offloading action to improve energy efficiency.

[0410] Proactive Energy Saving Strategies:

[0411] Traditional networks often react to traffic changes after they occur. However, predictive analytics and proactive measures can significantly enhance energy efficiency.

[0412] The EC prediction function, when informed by CCO and mobility data, can forecast potential traffic patterns and energy consumption trends. This predictive capability enables the network to pre-emptively adjust parameters, such as reducing power in areas with anticipated low demand or preparing cells for higher traffic. This proactive approach helps in smoothing out energy consumption peaks and valleys, resulting in more consistent and lower overall energy usage.

[0413] An Energy Cost prediction with proactive energy saving strategy would enhance energy efficiency.

[0414] A proper algorithm effectively integrates CCO with the potential offloading of traffic to neighboring cells into an EC prediction framework for a single gNB. By dynamically adjusting transmission power, optimizing beamforming, leveraging neighboring cells' capacity, and employing sleep modes, the algorithm significantly reduces energy consumption. The continuous learning and feedback loop ensures that the system adapts and optimizes performance, achieving the ultimate goal of enhanced energy savings.

[0415] During the initialization process, a set of input parameters and output parameters may be defined. For example, the input parameters may include a maximum transmission power, a minimum transmission power, a power consumption in sleep mode, a traffic load threshold for activating sleep mode, a minimum SSB beam shaping angle width, a maximum capacity threshold, neighbor cells data (capabilities, current load, and potential for handling offloaded traffic), historical data for training AI / ML models. In another example, the output parameters may include a baseline energy cost and an optimized energy cost.

[0416] The method may involve performing real-time data collection, which includes the systematic gathering of Real-Time Metrics. In this context, the real-time metrics may encompass various network performance indicators, such as the Synchronization Signal Signal-to-Interference-plus-Noise Ratio (SS-SINR), the Synchronization Signal Reference Signal Received Power (SS-RSRP), and the Synchronization Signal Reference Signal Received Quality (SS-RSRQ). Additionally, the real-time metrics may include various traffic metrics having details of the current traffic load, the number of Radio Resource Control (RRC) connected user equipment (UE), and the distribution of UEs across the network. Furthermore, the real-time metrics may include neighboring cells' load and capacity, channel prediction metrics having channel modeling predictions that account for physical obstructions, allowing for enhanced accuracy in forecasting channel conditions and improving overall network performance.

[0417] A baseline energy cost is calculated. In an example, the baseline energy cost refers to the standard or reference level of energy consumption associated with the operation of a system or network under typical conditions. In an example, the baseline energy cost may be calculated using the following Equation:based on current power consumption without CCO optimizations, where PCUrrent(t) is the power consumption at time t during the time interval of measurements and At.

[0418] The method may involve AI / ML-based prediction, encompassing two key areas: traffic and load forecasting, and coverage demand analysis. For example, in the traffic and load forecasting, one or more Machine learning (AI / ML) models may be employed to analyze historical and real-time data in order to predict future traffic patterns and load distribution across the network. This predictive capability of the AI / ML models enables the system to anticipate changes in user demand, helping to optimize resourceallocation and enhance network performance. In an aspect, the AI / ML models may be able to perform coverage demand analysis. The AI / ML models focus on predicting coverage demand, user mobility patterns, and user distribution (density) in coverage demand analysis. The AI / ML models may perform neighboring cell assessment. For example, AI / ML models may evaluate neighboring cells' current and projected load capacity for potential offloading. By leveraging the AI / ML models, the system can forecast areas of high user concentration and anticipated movement, allowing for proactive adjustments in network configuration and resource management to ensure adequate coverage and seamless connectivity.

[0419] The method may involve dynamic adjustment of network parameters. In an aspect, for dynamic adjustment of network parameters, the present disclosure may employ adaptive transmission power control, beamforming optimization, and sleep mode activation. The adaptive transmission power control enables the network to adjust transmission power levels in real time based on the predicted traffic patterns, load distribution, and coverage demand identified in previous steps. By optimizing transmission power dynamically, the network can enhance signal quality for users while minimizing energy consumption and reducing interference with neighboring cells. This adaptive approach ensures that power is allocated efficiently, allowing for improved overall network performance and user experience, especially in varying conditions such as fluctuating traffic loads and user mobility patterns. In the adaptive transmission power control, the present disclosure may calculate an optimal transmission power using following Equation:

[0420] The optimal transmission power may be calculated based on predicted traffic load and coverage requirements, where Lpred(t) is the predicted traffic load at time t.

[0421] In the beamforming optimization, the present disclosure may adjust beamforming angles and widths based on user density and demand using the following Equation.In an aspect, the UEpredt) is the predicted UE distribution.

[0422] In the sleep mode activation, the present disclosure may be configured to determine If ^current < ^th and activate a transition of gNB (network node) to sleep mode. In the sleep mode, the power may be defined as^current (—^sleep

[0423] According to an implementation, traffic offloading to neighboring cells may be performed based on offloading criteria. In an implementation, traffic offloading may be triggered when current trafficload is greater than the traffic load threshold for activating sleep mode (when Lcurrent> Lth) or during predicted high-demand periods.

[0424] In an implementation, target cells may be selected from amongst the neighboring cells. For example, neighboring cells may be selected based on their current and predicted load, coverage overlap, and capacity. Target cells may be determined based on the following Equation.Target Cell = argmin (L]current+ L]pred) j

[0425] In an implementation, offloading execution may be performed to handover UEs to selected neighboring cells to balance load and reduce energy consumption. In an implementation, the offloading may be performed using the Equation below.^new ^current ^offloaded

[0426] The method may involve real-time energy cost (EC) calculation and optimization. In this step, the present disclosure continuously monitors and calculates the current EC of the network based on the dynamically adjusted parameters and operational states. In an aspect, the present disclosure may calculate optimized EC using the following Equation.

[0427] In an aspect, the present disclosure may be further configured to compare the calculated Optimized EC with the baseline EC. Based on the comparison, the present disclosure may adjust offloading thresholds, power levels and beamforming optimization parameters if ECoptis not sufficiently lower than ECfrase-

[0428] The method may involve continuous learning and feedback loop. In an aspect, the present disclosure may opt for a performance evaluation, a model refinement, and an optimization refinement for continuous learning and a feedback loop. In the performance evaluation, the present disclosure may evaluate the performance of optimizations in terms of energy savings and network quality. In the model refinement, the present disclosure may update AI / ML models with new data for improved prediction accuracy. In the optimization refinement, the present disclosure may refine CCO strategies, offloading strategies, and parameters based on performance evaluations and feedback.

[0429] In an implementation, a proper algorithm effectively integrates CCO with the potential offloading of traffic to neighboring cells into an EC prediction framework for a single gNB. By dynamicallyadjusting transmission power, optimizing beamforming, leveraging neighboring cells' capacity, and employing sleep modes, the algorithm significantly reduces energy consumption. The continuous learning and feedback loop ensures that the system adapts and optimizes performance, achieving the ultimate goal of enhanced energy savings in 5G RAN.

[0430] In an embodiment, an Energy Cost prediction should be derived with a mutual exclusive consideration of a potential offloading action in coordination with CCO beam improvements. Thus, a holistic approach of EC function would enhance the energy saving actions between NG-RAN nodes in order to be interpretable or actionable by the receiving node.

[0431] The intricate interplay between offloading and CCO beam improvements can significantly enhance energy efficiency in NG-RAN networks. Network operators can achieve substantial energy savings by strategically offloading UEs with high EC profiles and optimizing beam configurations. The decoupling high-energy demand nodes from those capable of efficient energy management creates a more balanced and efficient network. This approach not only reduces overall energy costs but also improves the sustainability and reliability of the network, aligning with the broader goals of modem telecommunications to support growing data demands while minimizing environmental impact.

[0432] Consequently, a predicted high EC might result from the NG-RAN node serving several UEs with low, bursty, but frequent traffic that prevents it from enabling energy saving features due to offloading, but might be assisted by the energy saving actions due to CCO beam improvements. Hence, the neighboring NG-RAN node may be able to serve the new, offloaded UEs with a marginal increment in its EC; at the same time, the offloading might enable the source NG-RAN node to reduce its own EC significantly and to optimize its energy saving by proper CCO optimization actions.

[0433] In an embodiment, an Energy Cost prediction on one node should be tolerated with respect to a potential offloading action between neighbor NG-RAN nodes with potential CCO beam optimization action capabilities.

[0434] Under this paradigm, the source node of the offloading action should signal to the target node a description of the “additional load” for the target node; it would then receive from the target node a prediction of the Energy Cost at the target node, assuming the offloading action was successfully executed.

[0435] Follows a proposed algorithm to integrate Coverage and Capacity Optimization (CCO) with offloading actions into an Energy Cost (EC) prediction function for a gNB in a 5G RAN in a more flexible mutually exclusive way. The primary goal is to enhance energy savings by optimizing network performance and resource usage. The algorithm follows specific rules: offloading conditions are mutually exclusive to CCO actions, offloading should include CCO considerations, and CCO actions should be prioritized to manage the load before considering offloading.

[0436] 1. InitializationInput Parameters: o Pmax'. Maximum transmission power. o Pmin. Minimum transmission power. o Psieep:Power consumption in sleep mode. o Lth_cco '- Traffic CCO load activation threshold. o ^th_offioad'- Traffic offload (mobility) activation threshold o Bmin. Minimum SSB beam shaping angle width. o Neighbor Cells Data: current load and capacity info. o Historical traffic profile data: data to understand traffic patterns based on AI / ML models.• Output Parameters: o ECbaseBaseline Energy Cost. o ECoptOptimized Energy Cost.

[0437] 2. Real-Time Data Collection• Collect Real-Time Metrics: o Network performance metrics: SS-SINR, SS-RSRP, SS-RSRQ. o Traffic metrics: Current traffic load (RRC Connected UEs), user equipment (UE) distribution. o Channel prediction: Channel modeling predictions based on physical obstructions. o Neighboring cells' load and capacity• Baseline Energy Cost Calculation o Calculate:based on current power consumption without CCO optimizations, where PCUrrent (0 is the power consumption at time t during the time interval of measurements and At.

[0438] 3. AI / ML-Based Prediction• Traffic and Load Forecasting: o Use machine learning models to predict future traffic patterns and load distribution based on historical and real-time data.• Coverage and Capacity Demand Analysis: o Predict coverage and capacity demand and user mobility patterns and user distribution (density).• Neighbor Cell Assessment: o Evaluate neighboring cells' current and projected load capacity for potential offloading.

[0439] 4. Dynamic Adjustment of Network ParametersA. Check current load against thresholds• Low Load Conditions: o if Lcurrent< Lth ccono additional action is required, with potential sleep mode if low traffic conditions persist• Intermediate Load Conditions: o if Lth_cco< Lcurrent< Lth Offioad additional action is required with CCO activation.B. CCO actions consideration• Power control optimization: o Calculate optimal transmission power as:based on predicted traffic load and coverage requirements, where Lpred(t) is the predicted traffic load at time t.• Beamforming adjustments: o Adjust beamforming angles and widths based on user density and demand to focus coverage and minimize interference:where UEpred(t) is the predicted UE distribution.• Evaluate impact on EC function:o Calculate the potential reduction in EC due to CCO:• Check sufficiency of CCO actions: o Assess if CCO actions sufficiently reduce load and EC without needing offloadingC. Traffic offloading Consideration to neighbor cells• Offloading Criteria: o Consider potential offloading when Lcurrent> Ltflor during predicted high-demand periods.D. Traffic offloading Actions to neighbor cells• Selection of Target cells: o Select neighboring cells based on their current and predicted load, coverage overlap, and capacity:Target Cell = argmin (L]current+ L]pred) j• Offloading execution: o Handover UEs to selected neighboring cells to balance load and reduce energy consumption:^new ^current ^offloaded• Combined CCO and offloading EC calculation: o Calculate the total EC with offloading and additional CCO:• Criteria for Sleep Mode: o If Lcurrentis significantly reduced post offloading, consider transitioning gNB to sleep mode:Pcurrent (—Psleep

[0440] 5. Real-Time Energy Cost Calculation and Optimization• Comparison and Adjustment: o Continuously compare ECopt, ECbaseto assess energy savings o Adjust offloading thresholds, power levels and beamforming optimization parameters ifECoptis not sufficiently lower than ECbase.• Performance Evaluation: o Evaluate the performance of optimizations in terms of energy savings and network quality.• Model Refinement: o Update AI / ML models with new data for improved prediction accuracy.• Optimization Refinement: o Refine CCO and offloading strategies and parameters based on performance evaluations and feedback.

[0441] Furthermore, in an embodiment, the potential load may be expanded to contain more parameters and metrics / variables than the number of UEs. The EC load considers multiple factors as follows:1. Traffic Characteristics: Differentiating between traffic types and their respective bandwidth, latency, and QoS requirements. Hence, other factors to contribute to the EC function might be the PRB utilization, UL / DL data volume, etc.2. Radio and Environmental Conditions: Accounting for variations in signal quality (UE RSRP, SINR) and the additional resources (i.e., RSRP / SINR vs. PRB) needed for UEs in challenging conditions.3. Temporal Variations: Recognizing patterns over time, such as peak hours or event -driven traffic spikes. This could include RRM measurements.4. Behavioral Insights: Understanding user mobility patterns and their impact on resource usage based on QoS characteristics.

[0442] The metrics may be selected by considering the impact on the energy consumption the UE may generate, such as UE capabilities and types of services used.

[0443] In an embodiment, incorporating a wide range of detailed metrics and factors may enhance the analysis, though it may increase processing requirements and complexity. By thoughtfully selecting the EC and relevant metrics, performance is optimized while maintaining manageable processing loads and allowing for future enhancements.

[0444] FIG. 4 illustrates an exemplary flow diagram (400) of a method for energy management in the network (108), in accordance with an embodiment of the present disclosure.

[0445] The method (400) includes integrated energy cost (EC) prediction and traffic offloading in the network (108).

[0446] At step (402), the method (400) includes receiving, by the receiving unit (118), a plurality of first parameters. The plurality of first parameters comprises, but is not limited to, maximum transmission power, minimum transmission power, power consumption in a sleep mode, a traffic load threshold for activating a sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold, neighbor cell data, historical data for training the one or more ML models, a predicted energy saving and a forecasted incremental EC at a target base station. The plurality of firstparameters is provided by a network operator. The network operator provides the plurality of first parameters based on network requirements, for example, usage traffic, and performance metrics.

[0447] At step (404), the method (400) includes collecting, by the collection unit (128), one or more real-time metrics from a serving cell and one or more neighboring cells in the network. The one or more real-time metrics comprise the real-time metrics from the serving cell comprises at least one of a synchronization signal (SS)- signal to interference plus noise ratio (SINR), a SS-reference signal received power (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a number of RRC connected user equipments (UEs), a physical resource block (PRB) usage, a UE geo-location, an angular density, a beam activity and power consumption per sector. The real-time metrics from the one or more neighboring cells comprise at least one of a traffic load at the time t, a predicted load at the time t, coverage overlap information, an estimated offload impact of one or more local EC models and a target base station admission control.

[0448] At step (406), the method (400) includes calculating, by the processing unit (120), a baseline EC based on power consumption at time t. The baseline EC is calculated asP base ~ ^t=0P>current(J-') ’ 21t-

[0449] The Pcurrent (0 is the power consumption between a time interval from the time t to time T, and 21 t is a time interval between measurements. In an aspect, the baseline EC provides the expected or reference energy consumption and cost under normal or standard operating conditions, without any interventions, changes, or optimizations. The baseline EC serves as a benchmark to compare actual or optimized energy usage against. This enables evaluation of energy savings, efficiency measures, or cost impacts of changes in operations, equipment, or behavior.

[0450] At step (408), the method (400) includes performing, by the processing unit (120), one or more analysis types, to determine a plurality of second parameters, using one or more machine learning (ML) models based on the plurality of first parameters and the collected real-time data. The one or more analysis types comprise a traffic and load forecasting analysis, a coverage and capacity demand analysis and a neighbor cell assessment. The plurality of second parameters comprises at least one of one or more predicated traffic patterns, one or more predicted load distributions, a predicted coverage and capacity demand, one or more predicted user mobility patterns and user distributions, a current load capacity and a projected load capacity of one or more neighboring cells for potential offloading. In an aspect, the traffic and load forecasting analysis is performed to predict traffic patterns (e.g., traffic behavior, geographical traffic pattern, seasonal traffic pattern, event-based traffic, app-based) and load distributions (e.g., uniform, hotspot, temporal, directional, user-type). In an aspect, the coverage and capacity demand analysis is performed to predict coverage and capacity demand (e.g., coverage demand such as indoor, outdoor, rural, urban, cell -edge and capacity demand such as static, dynamic / peak, spatial, temporal, service -specific), user mobility patterns (e.g., static, pedestrian, vehicular, high-speed, random) and user distributions (e.g., uniform, clustered, temporal, directional, hierarchical). The neighboring cell’s current load capacitycomprises physical resource block utilization, connected UEs, throughput, memory usage. The neighboring cell’s projected load capacity based on historical trends, temporal pattern, handover pattern, event -based, etc.

[0451] At step (410), the method (400) includes executing, by the processing unit (120), at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism. The integrated EC prediction and traffic offloading mechanism comprises determining, by the determining unit (122), one or more UEs having at least one of a high EC footprint (i.e., UEs consuming a large amount of energy) and poor channel quality (i.e., UE experiencing weak or unstable signals). The processing unit (120) calculates a load EC for each determined UE. An EC feedback transmission is performed between the source base station and the target base station. In the EC feedback transmission, the source base station transmits an identifier (ID) corresponding to each determined UE, the calculated delta EC for each determined UE, a traffic profile, quality of service (QoS), and an estimated offload to the target base station. A calculated EC for at least one UE accepted by the target base station and a flag (indicating whether the UE is accepted or rejected) are transmitted from the target base station to the source base station.

[0452] The determining unit (122) is configured to determine whether the calculated delta EC is greater than the received calculated EC (i.e., AECiocai j > ECremote). Upon determining that the calculated delta EC is greater than the received calculated EC (i.e., AECiocai > ECremotej), triggering, by the processing unit (120), offloading of the at least one accepted UE to the target base station. Upon determining that the calculated delta EC is not greater than the received calculated EC (i.e., AECiocai < ECremote,j rejecting, by the processing unit (120), offloading of the at least one accepted UE to the target base station.

[0453] Upon offloading the at least one accepted UE to the target base station, the determining unit determines whether the load at the time t is less than the traffic load threshold for activating the sleep mode (i.e., Lcurrent< Ltfl). Based on the determination (i.e., Lcurrent< Ltfl), the processing unit executes transition of the serving base station to sleep mode.

[0454] In an aspect, the method comprises the processing unit (120) configured to perform transmission power control and one or more beamforming parameter adjustments based on the one or more performed analysis types.

[0455] At step (412), the method (400) includes upon executing, calculating, by the processing unit (120), an optimized EC based on the plurality of first parameters and the at least one second parameter. In an operative aspect, the optimized EC is calculated based on the maximum transmission power, the minimum transmission power and the predicted load at the time t. The optimized EC provides energy consumption value obtained after applying optimization techniques to minimize power usage while maintaining desired network performance.

[0456] At step (414), the method (400) includes comparing, by the determining unit (122), the optimized EC and the baseline EC. The optimized energy cost (EC) is compared with the baseline EC to validate that optimization reduces energy consumption.

[0457] At step (416), the method (400) includes upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting, by the processing unit (120), one or more parameters associated with the one or more actions based on the collected real-time data. In an aspect, the optimized EC (ECopt) is compared with the baseline EC (ECbase) to determine whether the optimized EC is not less than the baseline EC by the predefined margin which is a set threshold or minimum allowable difference that quantifies the level of improvement required for the optimization to be considered significant or effective. The optimized EC is not less than the baseline EC by the predefined margin, so the optimization is not accepted. When the optimized EC is less than the baseline EC by the predefined margin, the optimization is accepted. This achieves energy savings. The adjustment of the one or more parameters associated with the one or more actions comprises updating the one or more ML models, redefining the integrated EC prediction and traffic offloading mechanism, and adjusting one or more offloading thresholds and one or more beamforming parameters.

[0458] Further, the continuous learning and feedback loop is carried out to perform processes, for example, performance assessment, model updating, and optimization tuning. During the performance assessment, the effectiveness of the optimizations is measured with respect to energy efficiency and overall network quality. In the model updating phase, AI / ML models are retrained or adjusted using fresh data to enhance the prediction capabilities. Finally, in the optimization tuning step, the CCO strategies and parameters are adjusted and improved based on the results of performance assessments and received feedback.

[0459] In an embodiment, a method for integrated energy cost (EC) prediction and traffic offloading in the network. The method comprises initialization by defining input parameters and output parameters. The input parameters comprise, but are not limited to, maximum transmission power, minimum transmission power, power consumption in a sleep mode, traffic load threshold for activating the sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold and historical data for training artificial intelligence / machine learning (AI / ML). The output parameters comprise, but are not limited to, baseline energy cost and optimized energy cost. The method includes performing real-time data collection, systematically gathering real-time metrics. The real-time metrics comprise, but are not limited to, network performance SSB metrics, traffic metrics and channel predictions. The method comprises calculating the baseline energy cost based on power consumption at time t (i.e., current time t). The method comprises conducting AI / ML-based prediction for traffic and load forecasting and coverage demand analysis, using historical and real-time data to predict future patterns and traffic load and allowing for traffic offloading and resource optimization. The method comprises executing traffic offloading using an integrated EC prediction and traffic offloading mechanism. The number of UEs to be offloaded, from the source base station to the target base station, are selected using the integrated EC prediction and traffic offloading mechanism. The selected UEs are offloaded to the target base station.Further, after performing the offloading, the method comprises calculating of the optimized EC based on the input parameters and the predicted traffic load. Real-time energy cost (EC) is optimized by continuously monitoring the network's current EC based on dynamically adjusted parameters. The optimized EC is compared with the baseline EC to adjust network parameters as needed. Further, the network performance of optimization is continuously evaluated in terms of energy savings and network quality. The AI / ML models are updated with new data to improve prediction accuracy. CCO strategies and parameters are refined based on performance evaluation and feedback.

[0460] FIG. 5 illustrates an exemplary computer system (500) in which or with which embodiments of the present disclosure may be implemented.

[0461] As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), a main memory (530), a read-only memory (540), a mass storage device (550), communication port(s) (560), and a processor (570). A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. The processor (570) may include various modules associated with embodiments of the present disclosure. The communication port(s) (560) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (560) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system connects.

[0462] The main memory (530) may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (540) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (570). The mass storage device (550) may be any current or fiiture mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage device (550) includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks.

[0463] The bus (520) communicatively couples the processor (570) with the other memory, storage, and communication blocks. The bus (520) may be, e.g., a Peripheral Component Interconnect (PCIj / PCI Extended (PCLX) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (570) to the computer system.

[0464] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (520) to support direct operator interaction with the computer system. Other operator and administrative interfaces can be provided through network connections connected through the communication port(s) (560). Components described above are meantonly to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.

[0465] The exemplary computer system (500) is configured to execute a computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method for energy management in a network is disclosed. The method comprises receiving, by a receiving unit, a plurality of first parameters and collecting, by a collection unit, one or more real-time metrics from a serving cell and one or more neighboring cells. The method comprises calculating, by a processing unit, a baseline EC based on power consumption at time t and performing, by the processing unit, one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics. The method comprises executing, by the processing unit, at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism. The method comprises upon executing, calculating, by the processing unit, an optimized EC based on the plurality of first parameters and the at least one second parameter. The method comprises comparing, by the processing unit, the calculated optimized EC with the baseline EC and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting, by the processing unit, one or more parameters associated with the one or more actions based on the collected real-time data.

[0466] The present disclosure provides technical advancements related to energy management in the network. Advancement addresses the limitations of existing solutions by facilitating the provision of a signaling mechanism in which the source node of an offloading action transmits a description of the additional load to the target node. The source node then receives a prediction of the energy cost (EC) at the target node, assuming the offloading action is successfully executed. Inter-node coordination helps to ensure that offloading only occurs when the resulting energy profile across all involved nodes is optimal

[0467] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.ADVANCEMENTS OF THE PRESENT DISCLOSURE

[0468] The present disclosure provides a system and a method for energy management in the network.

[0469] The present disclosure improves downlink (DL) coverage and SINR with energy constraints.

[0470] The integration of these technologies and functions offers a comprehensive approach to energy savings and system performance optimization.

[0471] The present disclosure is related mainly to gNB CU / DU and / or 0AM functional blocks. In an embodiment, the present disclosure addresses SSB (Synchronization Signal Block) multi-beam management in dense 5G and upcoming 6G networks.

[0472] The present disclosure provides a system and a method for integrated energy cost (EC) prediction and traffic offloading in a network.

[0473] The present disclosure provides a signaling mechanism in which the source node of an offloading action transmits a description of the additional load to the target node. The source node then receives a prediction of the energy cost (EC) at the target node, assuming the offloading action is successfully executed.

[0474] The present disclosure predicts energy cost (EC) for energy savings with a potential neighboring cell offloading.

[0475] The present disclosure provides inter-node coordination to ensure that offloading only occurs when the resulting energy profile across all involved nodes is optimal.

[0476] The present disclosure prioritizes beamforming and power control before mobility decisions, avoiding simultaneous complexity.

[0477] The present disclosure improves network coverage and capacity with energy -saving and network load processing.

Claims

CLAIMSWe claim:

1. A method (400) for energy management in a network (108), the method (400) comprising: receiving (402), by a receiving unit (118), a plurality of first parameters; collecting (404), by a collection unit (128), one or more real-time metrics from a serving cell and one or more neighbor cells; calculating (406), by a processing unit (120), a baseline energy cost (EC) based on power consumption at time t; performing (408), by the processing unit (120), one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics; executing (410), by the processing unit (120), at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism; upon executing, calculating (412), by the processing unit (120), an optimized EC based on the plurality of first parameters and the at least one second parameter; comparing (414), by the processing unit (120), the calculated optimized EC with the baseline EC; and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting (416), by the processing unit (120), one or more parameters associated with the one or more actions based on the collected real-time data.

2. The method (400) as claimed in claim 1, wherein the energy management is performed by integrating EC prediction and traffic offloading in the network (108).

3. The method (400) as claimed in claim 1, wherein the plurality of first parameters comprise at least two or more of maximum transmission power, minimum transmission power, power consumption in a sleep mode, a traffic load threshold for activating a sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold, neighbor cell data, historical data for training the one or more ML models, a predicted energy saving and a forecasted incremental EC at a target base station.

4. The method (400) as claimed in claim 1, wherein the real-time metrics from the serving cell comprises at least one of a synchronization signal (SS)- signal to interference plus noise ratio(SINR), a SS-reference signal received power (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a number of RRC connected user equipments (UEs), a physical resource block (PRB) usage, a UE geo-location, an angular density, a beam activity and power consumption per sector, and wherein the real-time metrics from the one or more neighbor cells comprise at least one of a traffic load at the time t, a predicted load at the time t, coverage overlap information, an estimated offload impact of one or more local EC models and a target base station admission control.

5. The method (400) as claimed in claim 1, wherein the one or more analysis types comprise a traffic and load forecasting analysis, a coverage demand analysis and a neighbor cell assessment.

6. The method (400) as claimed in claim 1, wherein the plurality of second parameters comprises at least one of one or more predicted traffic patterns, one or more predicted load distributions, a predicted coverage demand, one or more predicted UE mobility patterns, a predicted UE density, a predicted load at the time t and a projected load capacity of the one or more neighbor cells for potential off-loading.

7. The method (400) as claimed in claim 1, wherein the integrated EC prediction and traffic offloading mechanism comprising: determining, by a determining unit (122), one or more UEs having at least one of a high EC footprint and poor channel quality; calculating, by the processing unit (120), a delta EC for each determined UE; performing, by the processing unit (120), an EC feedback transmission between a source base station and a target base station, wherein the EC feedback transmission comprising: transmitting, by a transmitting unit (126), an identifier (ID) corresponding to each determined UE, the calculated delta EC for each determined UE, a traffic profile, quality of service (QoS), and an estimated offload to the target base station; and receiving, by the receiving unit (118), a calculated EC for at least one UE accepted by the target base station and a flag from the target base station; determining, by the determining unit (122), whether the calculated delta EC is greater than the received calculated EC; and upon determining that the calculated delta EC is greater than the received calculated EC, triggering, by the processing unit (120), offloading of the at least one accepted UE to the target base station, wherein upon determining that the calculated delta EC is not greater than the received calculated EC, rejecting, by the processing unit (120), offloading of the at least one accepted UE to the target base station.

8. The method (400) as claimed in claim 7, comprising: upon offloading the at least one accepted UE to the target base station, determining, by the determining unit (122), whether the load at the time t is less than the traffic load threshold for activating the sleep mode; and based on the determination, executing, by the processing unit (120), transition of the serving base station to the sleep mode.

9. The method (400) as claimed in claim 1, wherein the adjustment of the one or more parameters associated with the one or more actions comprises updating the one or more ML models, redefining the integrated EC prediction and traffic offloading mechanism, and adjusting one or more offloading thresholds and one or more beamforming parameters.

10. The method (400) as claimed in claim 1, comprising: performing, by the processing unit (120), at least one of a transmission power control and one or more beamforming parameter adjustments based on the one or more performed analysis types.

11. A system (106) for energy management in a network (108), the system (106) comprising: a receiving unit (118) configured to receive a plurality of first parameters; a collection unit (128) configured to collect one or more real-time metrics from a serving cell and one or more neighbor cells; a processing unit (120) configured to: calculate a baseline EC based on power consumption at time t; perform one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics; execute at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism; upon executing, calculate an optimized EC based on the plurality of first parameters and the at least one second parameter; compare the calculated optimized EC with the baseline EC; andupon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjust one or more parameters associated with the one or more actions based on the collected real-time data.

12. The system (106) as claimed in claim 11, wherein the energy management is performed by integrating EC prediction and traffic offloading in the network (108).

13. The system (106) as claimed in claim 11, wherein the plurality of first parameters comprise at least two or more of maximum transmission power, minimum transmission power, power consumption in a sleep mode, a traffic load threshold for activating a sleep mode, minimum synchronization signal block (SSB) beam shaping angle width, maximum capacity threshold, neighbor cell data, historical data for training the one or more ML models, a predicted energy saving and a forecasted incremental EC at a target base station.

14. The system (106) as claimed in claim 11, wherein the real-time metrics from the serving cell comprises at least one of a synchronization signal (SS)- signal to interference plus noise ratio (SINR), a SS-reference signal received power (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a number of RRC connected user equipments (UEs), a physical resource block (PRB) usage, a UE geo-location, an angular density, a beam activity and power consumption per sector, and wherein the real-time metrics from the one or more neighbor cells comprise at least one of a traffic load at the time t, a predicted load at the time t, coverage overlap information, an estimated offload impact of one or more local EC models and a target base station admission control.

15. The system (106) as claimed in claim 11, wherein the one or more analysis types comprise a traffic and load forecasting analysis, a coverage demand analysis and a neighbor cell assessment.

16. The system (106) as claimed in claim 11, wherein the plurality of second parameters comprises at least one of one or more predicted traffic patterns, one or more predicted load distributions, a predicted coverage demand, one or more predicted UE mobility patterns, a predicted UE density, a predicted load at the time t and a projected load capacity of the one or more neighbor cells for potential off-loading.

17. The system (106) as claimed in claim 11, wherein the integrated EC prediction and traffic offloading mechanism comprising: a determining unit (122) configured to determine one or more UEs having at least one of a high EC footprint and poor channel quality; the processing unit (120) configured to: calculate a delta EC for each determined UE;perform an EC feedback transmission between a source base station and a target base station, wherein the EC feedback transmission comprising: a transmitting unit (126) configured to transmit an identifier (ID) corresponding to each determined UE, the calculated delta EC for each determined UE, a traffic profile, quality of service (QoS), and an estimated offload to the target base station; and the receiving unit (118) configured to receive a calculated EC for at least one UE accepted by the target base station and a flag from the target base station; the determining unit (122) configured to determine whether the calculated delta EC is greater than the received calculated EC; and upon determining that the calculated delta EC is greater than the received calculated EC, the processing unit (120) configured to perform offloading of the at least one accepted UE to the target base station, wherein upon determining that the calculated delta EC is not greater than the received calculated EC, the processing unit (120) is configured to reject offloading of the at least one accepted UE to the target base station.

18. The system (106) as claimed in claim 17, comprising: upon offloading the at least one accepted UE to the target base station, the determining unit (122) configured to determine whether the load at the time t is less than the traffic load threshold for activating the sleep mode; and based on the determination, the processing unit (120) configured to execute transition of the serving base station to the sleep mode.

19. The system (106) as claimed in claim 11, wherein the adjustment of the one or more parameters associated with the one or more actions comprises updating the one or more ML models, redefining the integrated EC prediction and traffic offloading mechanism, and adjusting one or more offloading thresholds and one or more beamforming parameters.

20. The system (106) as claimed in claim 11, comprising: the processing unit (120) configured to perform at least one of a transmission power control and one or more beamforming parameter adjustments based on the one or more performed analysis types.

21. A computer program product comprising a non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to execute a method (400) for energy management in a network ( 108), the method (400) comprising: receiving (402), by a receiving unit (118), a plurality of first parameters;collecting (404), by a collection unit (128), one or more real-time metrics from a serving cell and one or more neighbor cells; calculating (406), by a processing unit (120), a baseline energy cost (EC) based on power consumption at time t; performing (408), by the processing unit (120), one or more analysis types to predict a plurality of second parameters, using one or more machine learning (ML) models, based on the plurality of first parameters and the one or more collected real-time metrics; executing (410), by the processing unit (120), at least one action of one or more actions based on the plurality of first parameters and the plurality of second parameters using an integrated EC prediction and traffic offloading mechanism; upon executing, calculating (412), by the processing unit (120), an optimized EC based on the plurality of first parameters and the at least one second parameter; comparing (414), by the processing unit (120), the calculated optimized EC with the baseline EC; and upon determining that the calculated optimized EC is not less than the baseline EC by a predefined margin, adjusting (416), by the processing unit (120), one or more parameters associated with the one or more actions based on the collected real-time data.

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