System and method for selective multi-SSB configurations and beam shaping

Abstract

A network node (104) performs SSB beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a plurality of SSB beam-width shapes. A downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam is estimated as a composite cost function. The SSB beam sweeping and the estimation are performed until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed. A SSB beam coverage footprint is estimated as a percentage of a defined SSB beam shape. At least one SSB beam-width shape is selected from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold. At least one SSB power boosting parameter of the at least one selected SSB beam-width shape is modified.

Description

SYSTEM AND METHOD FOR SELECTIVE MULTI-SSB CONFIGURATIONS AND BEAM SHAPINGRESERVATION OF RIGHTS

[0001] 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

[0002] The present disclosure relates generally to the field of communication systems. More particularly, the present disclosure relates to systems and methods for selective multi-Synchronization Signal Block (SSB) configurations and beam shaping.DEFINITION

[0003] 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.

[0004] The expression “Synchronization Signal Block (SSB)” used hereinafter in the specification refers to synchronization / physical broadcast channel (PBCH) block, where the synchronization signal and the PBCH blocks are packed as a single block that always moves together.

[0005] The expression “SSB beam sweeping” used hereinafter in the specification refers to a transmission of multiple narrow beams carrying control information in sequence over an intended cell area.

[0006] The expression “SSB beam- width shape” used hereinafter in the specification refers to angular characteristics (i.e., width and direction) of the SSB beam in the network. The SSB beam- width shape describes how wide or narrow the SSB beam is and how the SSB beam radiates power across space when transmitting the SSB beams from a network node (e.g., base station) to the UE.

[0007] The expression “Coverage footprint” used hereinafter in the specification refers to a geographic area where a telecommunication network can reliably provide service. In particular, it represents a spatial boundary or an effective service area within which the telecommunication signal is strong and thus the signals can be transmitted and received with acceptable quality. The expression “SSB Beam coverage footprint” used hereinafter in the specification refers to a geographic or spatial area over which the SSB beams from the network node are received with sufficient signal quality by the UE.

[0008] The expression “Cost function” used hereinafter in the specification refers to a measure of error between model-predicted values and the actual values. The cost function is also referred to as a loss function.

[0009] The expression “Signal-to-Interference-plus-Noise Ratio (SINR)” used hereinafter in the specification refers to a measure of the quality of SSB beams, reflecting the strength of the desired SSB beams relative to both interference and noise. A higher SINR indicates better signal quality.

[0010] The expression “Random Access Channel (RACH) 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.

[0011] The expression “Cell throughput” used hereinafter in the specification refers to the amount of data transmitted by a cell”.

[0012] The expression “Signal to Noise and Interference Ratio (SS- SINR)” used hereinafter in the specification refers to the ratio of the strength of the transmitted signal compared to the background noise arising.

[0013] The expression “Power boosting” used hereinafter in the specification refers to an increase (boost) in the transmission power of downlink control channels against the power of the data channels.

[0014] The expression “SSB power boosting” used hereinafter in the specification refers to a process of increasing the transmit power of the SSBs in the network relative to other downlink channels or signals, to improve detection and coverage.

[0015] The expression “Capacity and coverage optimization (CCO)” used hereinafter in the specification refers to adjusting network parameters to maintain or improve radio signal coverage, ensuring a high-quality user experience while efficiently using network resources.

[0016] The expression “Reference Signal Received Power (RSRP)” used hereinafter in the specification refers to a measure of the signal strength.

[0017] The expression “Physical Random-Access Channel (PRACH)” used hereinafter in the specification refers to channel used by the UEs to request an uplink allocation from a base station.

[0018] The expression “Channel Quality Indicator (CQI)” 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.

[0019] The expression “Hybrid automatic repeat request (hybrid ARQ or HARQ)” used hereinafter in the specification refers to a technique used to improve data reliability and efficiency by combining error correction and retransmission of the data.

[0020] The expression “Block Error Rate (BLER)’ used hereinafter in the specification refers to a metric used to measure the data reliability. In particular, the BLER is a percentage of data blocks (transport blocks) that are received with errors and fail error correction, i.e., BLER = (Number of erroneous blocks / total number of received blocks)* 100%.

[0021] The expression “SSB beams” used hereinafter in the specification refers to broadcast-type beams. The SSB beams cover larger areas. SSB beams are used to provide 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.

[0022] The expression “SSB sweeping beams” refers to SSB beams sweeping over time (e.g., using Time Division Multiplexed (TDM) mapping) on different azimuth and elevation angles to cover a desired cell area.

[0023] The expression “Traffic beams” used hereinafter in the specification refers to focused radio signals used to transmit data between the base station and user devices. These beams are responsible for delivering user-specific traffic, such as internet data, video streams, voice calls, and other communication services. The traffic beams are optimized to provide high data rates, low latency, and reliable connectivity.

[0024] The expression “Coverage hole” used hereinafter in the specification refers to a region where there is little or no signal. In particular, received signal level of the serving cell or any other neighbor is below the minimum levels required to maintain the service under a minimum level of quality and robust radio performance.

[0025] The expression “UE traffic” used hereinafter in the specification refers to user data traffic generated by the UEs due to data and communication activities. The UE traffic is also referred to as a data volume.

[0026] The expression “UE trajectory” used hereinafter in the specification refers to a series of cells the UE will move through within a radio access network (RAN) node.

[0027] The expression “DL SINR” used hereinafter in the specification refers to a ratio of the power of the downlink signal to the sum of the interference power from other transmitters and the noise power at the UE.

[0028] The expression “UL SINR” used hereinafter in the specification refers to a ratio of the power of the uplink signal to the sum of the interference power at the network node (i.e., base station). The UL_SINR indicates the quality of a signal received by the network node (e.g., base station) from the UE in the uplink direction.

[0029] The expression “random access channel (RACH) process” used hereinafter in the specification refers to a process that allows the UE to initiate communication with the network for conditions such as when the UE connects to the network for a first time connection establishment, re-establishment, handover, and waking up from the idle state.

[0030] The expression “UL RACH performance” used hereinafter in the specification refers to the efficiency and reliability of the RACH procedure in the network. The UL RACH performance indicates how effectively the UE initiates a connection with the network by transmitting random access requests to the network node (i.e., base station).

[0031] The expression “DL UL SINR” used hereinafter in the specification refers to the DL_SINR and UL RACH performance.

[0032] These definitions are in addition to those expressed in the art.BACKGROUND

[0033] 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.

[0034] In cellular networks, next-generation Node B (gNB) densification and multi-layer deployment are leading to the downgrading of a signal-to-interference plus noise ratio (SINR). The multilayer deployment may refer to a strategy of deploying different types of network layers to optimize coverage, capacity, and performance. A multilayer deployment approach leverages multiple frequency bands and various types of base stations to create a robust and efficient network. The multilayers may include one or more macro layers, micro layers, small layers, and / or indoor layers. In a multilayer deployment, synchronization signal blocks (SSBs) play a critical role in ensuring efficient and effective network access across different layers.

[0035] Conventionally, synchronization signal block (SSB) beams have approximately 4-5 dB lower gain compared to traffic beams. This results in a coverage mismatch between idle mode and connected mode performance for user equipment (UE). For instance, SSB beam gains typically range from 13-16 dBi, whereas traffic beam gains may range from 18.5-21 dBi. Clearly, there is a 4-5 dB difference, leading to the coverage mismatch.

[0036] Currently, due to significant demand for bandwidth, massive Multiple Input, Multiple Output (MIMO) antenna arrays have been widely adopted. The massive MIMO antenna arrays provide the gNB with beamforming capabilities to form aradiation pattern for amplifying radiated power in certain directions. Conventional beamforming technique in the massive MIMO makes use of beamforming weights based on discrete Fourier transform (DFT) vectors. Using DFT for beamforming weights ensures narrow beams with possible array gain to compensate for the necessary coverage and outdoor-to-indoor penetration losses. The larger the antenna panel array at the gNB, the narrower its radiation pattern can become. This enables the formulation of focused traffic beams (e.g., user data beams or Physical Downlink Shared Channel (PDSCH) beams). The traffic beams are critical for high-throughput data transmission.

[0037] Further, the transmission of broader beam-width beams facilitates functions (e.g., synchronization, initial access or mobility signaling for control and broadcast channels). But for the large antenna arrays, creating a broad beam is not straightforward. To create the broad beam, a small sub-group of antenna dipole elements (sub-arrays) are transmitted with a broad beamwidth radiation pattern. This results in hugely underutilized power resources, which leads to significantly reduced coverage.

[0038] Another conventional beamforming technique in the massive MIMO beam-shaping is a tunable beam-shaping. This technique makes use of broader beamwidth to preserve the power. Amplitudes of beamforming weights of a group of subarrays is tuned (also known as amplitude tapering) while others use only phases of the beamforming weights (known as phase tapering) to broaden and shape the beam. The amplitude tapering may produce spatially broad beam patterns at a cost of reduced total transmit power, while phase tapering preserves full power utilization at a cost of spatial ripples in the beam shape leading to multiple side-lobes with power transmissions to unwanted directions. This causes inter-cell interference in dense network deployments.

[0039] Another conventional beamforming technique in the massive MIMO beam-shaping is performing a beam-sweep over a set of SSB beams to fully cover the desired cell area in a time-multiplexed manner. Instead of constantly transmitting asingle SSB broad beam, multiple SSBs are sequentially transmitted in a synchronization signal burst. The synchronization signal burst comprises a set of narrower beams spanning the angular (or the elevation) directions of the sector. By performing this SSB beam sweeping, the cell is covered by high antenna gains for the entire sector coverage area. The drawback of this SSB beam sweeping is that multi- SSB beam shapes with specific beam-width configurations are fixed across all the sites based on the antenna equipment capability. The fixed beam-width configurations lead to unnecessary transmissions to undesired directions, overlapping sector areas and inter-cell interference across neighboring dense sectors.

[0040] So, there is a need for an efficient approach to address the problems of the conventional beamforming techniques to improve the coverage and the corresponding SINR.OBJECTS

[0041] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0042] An object of the present disclosure is to provide a system and a method for selective multi-SSB configurations (i.e., selecting multi-SSB beam shapes with multi-SSB beam-width configurations and SSB beam power) and beam shaping.

[0043] Another object of the present disclosure is to dynamically perform SSB beams steering with the multi SSB beam shaping.

[0044] Yet another object of the present disclosure is to perform SSB beam boosting with the SSB beam shaping to improve signal to interference plus noise ratio (SINR) and keep coverage on a certain desired level.

[0045] Yet another object of the present disclosure is to determine coverage status in a network.

[0046] Yet another object of the present disclosure is to improve downlink (DL) coverage and uplink (UL) random access channel (RACH) performance.

[0047] Yet another object of the present disclosure is to improve DL synchronization signal-signal to interference plus noise ratio (SS-SINR).

[0048] Yet another object of the present disclosure is to reduce overlap areas and DL inter-cell interference in cells and sectors.

[0049] Yet another object of the present disclosure is to improve DL SINR and average cell throughput.

[0050] Yet another object of the present disclosure is to improve the telecom infrastructure and operational methods.

[0051] Other objects 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

[0052] In an exemplary embodiment, a method for determining a coverage status in a network is disclosed. The method comprises performing, by a network node, synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam-width shapes and, upon performing, estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR). The method comprises upon estimating the DL_SINR, estimating, by the network node, a SSB beam coverage footprint and selecting, by the network node, at least one SSB beam-width shape from the plurality of SSB beam-width shapes based on one or moreconditions. The method further comprises, upon selecting, modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beamwidth shape.

[0053] In some embodiments, performing the SSB beam sweeping comprises performing, by the network node, the SSB beam sweeping on a cell level foreach SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of the plurality of SSB beam-width shapes, where the first SSB beam-width shape is a default SSB beam-width shape or an operator-configurable SSB beam-width shape.

[0054] In some embodiments, the defined time cycle is 5 milliseconds.

[0055] In some embodiments, the estimation comprises, upon performing the SSB beam sweeping, estimating, by the network node, the DL_SINR for each SSB sweeping beam as a composite cost function.

[0056] In some embodiments, the method comprises: repeating, by the network node, the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed.

[0057] In some embodiments, the composite cost function comprises a plurality of parameters. The plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

[0058] In some embodiments, the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise at least one of a connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channelquality indicator (CQI) reports and level 1 (Ll)-SINR and LI -reference signal received power (RSRP) reporting.

[0059] In some embodiments, the connected UE measurement report comprises at least one of a LI -synchronization signal-RSRP (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a SS-SINR measurement, a CQI, cell level UE measurements and beam level UE measurements, where the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%, where the connected UE location information comprises at least one of latitude and longitude coordinates, a serving cell identifier (ID), and a radio link failure (RLF), where the idle mode UE random access performance parameters comprises at least one of a random access channel (RACH) success rate, number of RACH attempts to convert to a SINR from a graph of number of physical random access channel (PRACH) attempts vs the SINR, and where the number of UL PRACH counter attempts of a plurality of idle UEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs.

[0060] In some embodiments, the method comprises: estimating, by the network node, the SSB beam coverage footprint as a percentage of a defined SSB beam shape. The defined SSB beam shape is a default SSB beam shape.

[0061] In some embodiments, the one or more conditions comprises a first condition and a second condition, where the first condition comprises determining a maximum DL UL_SINR from the estimated DL_SINR for each SSB beam shape and the composite cost function, and where the second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold.

[0062] In some embodiments, the method comprises: selecting, by the network node, at least one SSB beam-width shape having the determined maximum DL UL_SINR, and the estimated SSB beam coverage footprint greater than the configurable threshold.

[0063] In some embodiments, the method comprises: estimating, by the network node, the DL_SINR based on the at least one selected SSB beam-width shape.

[0064] In some embodiments, the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications, where the cell coverage modifications comprise SSB beam power boosting, and where the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering.

[0065] In some embodiments, the method comprises: using, by the network node, the composite cost function to determine an SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network.

[0066] In some embodiments, the network node is one of an operations, administration, and maintenance (0AM), a base station, a central unit (CU) of the base station and a distributed unit (DU) of the base station.

[0067] In another exemplary embodiment, a method for determining a coverage status in a network is disclosed. The method comprises: performing, by a network node, synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of a plurality of SSB beam-width shapes, and estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam. The method comprises repeating, by the network node, the SSB beam sweeping and the estimation for each beam-width shape of the plurality ofSSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed, and upon repeating, estimating, by the network node, a SSB beam coverage footprint as a percentage of a defined SSB beam shape. The method further comprises selecting, by the network node, at least one SSB beamwidth shape from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold, and modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beam- width shape.

[0068] In yet another exemplary embodiment, a system for determining a coverage status in a network is disclosed. The system comprises an execution unit configured to perform synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam-width shapes. Upon performing, an estimation unit is configured to estimate a downlink signal to interference plus noise ratio (DL_SINR). Upon estimating the DL_SINR, the estimation unit is configured to estimate a SSB beam coverage footprint. A selection unit is configured to select at least one SSB beamwidth shape from the plurality of SSB beam-width shapes based on one or more conditions. Upon selecting, the execution unit is configured to modify at least one SSB power boosting parameter of the at least one selected SSB beam- width shape.

[0069] In yet another exemplary embodiment, a system for determining a coverage status in a network is described. The system comprises an execution unit configured to perform synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of a plurality of SSB beam-width shapes. An estimation unit is configured to estimate a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam. The execution unit is configured to repeat the SSB beam sweeping and the estimation for each beam- width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSBbeam- width shapes are completed. The estimation unit is configured to estimate a SSB beam coverage footprint as a percentage of a defined SSB beam shape. A selection unit is configured to select at least one SSB beam-width shape from the plurality of SSB beam- width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold. The execution unit is configured to modify at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

[0070] 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 determining a coverage status in a network is disclosed. The method comprises performing, by a network node, synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam-width shapes and, upon performing, estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR). The method comprises upon estimating the DL_SINR, estimating, by the network node, a SSB beam coverage footprint and selecting, by the network node, at least one SSB beam-width shape from the plurality of SSB beam-width shapes based on one or more conditions. The method further comprises, upon selecting, modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

[0071] 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 determining a coverage status in a network is disclosed. The method comprises: performing, by a network node, synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of a plurality of SSBbeam-width shapes, and estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam. The method comprises repeating, by the network node, the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed, and upon repeating, estimating, by the network node, a SSB beam coverage footprint as a percentage of a defined SSB beam shape. The method further comprises selecting, by the network node, at least one SSB beam-width shape from the plurality of SSB beamwidth shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold, and modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0072] 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.

[0073] FIG. 1A illustrates an exemplary network architecture for performing selective multi-Synchronization Signal Block (SSB) configurations and beam shaping, in accordance with an embodiment of the present disclosure.

[0074] FIG. IB illustrates an exemplary block diagram describing a system / digital unit for performing the selective multi-SSB configurations and beam shaping, in accordance with an embodiment of the present disclosure.

[0075] FIG. 2A illustrates an exemplary schematic diagram describing SSB to physical downlink shared channel (PDSCH) beam mismatch, in accordance with an embodiment of the present disclosure.

[0076] FIG. 2B illustrates another exemplary schematic diagram describing SSB beam shaping, in accordance with an embodiment of the present disclosure.

[0077] FIG. 3A illustrates an exemplary functional framework for artificial intelligence / machine learning (AI / ML) based coverage and capacity optimization (CCO), in accordance with an embodiment of the present disclosure.

[0078] FIG. 3B illustrates an exemplary AI / ML operational workflow for AI / ML based CCO, in accordance with an embodiment of the present disclosure.

[0079] FIG. 4 illustrates an exemplary flow diagram for performing selective multi-SSB configurations and beam shaping in accordance with an embodiment of the present disclosure.

[0080] FIG. 5A illustrates an exemplary flow diagram of a method for determining coverage status in a network, in accordance with an embodiment of the present disclosure.

[0081] FIG. 5B illustrates another exemplary flow diagram for a method for determining the coverage status in the network, in accordance with an embodiment of the present disclosure.

[0082] FIG. 6 illustrates an exemplary block diagram of a computer system in which or with which embodiments of the present disclosure may be implemented.

[0083] The foregoing shall be more apparent from the following more detailed description of the disclosure.LIST OF REFERENCE NUMERALS100A Network Architecture102 User Equipment104 Network Node104-1 Base Station 1104-2 Base Station 2106 Network108 System / Digital Unit100B Block diagram112 Processor114 Memory116 Interface118 Execution Unit120 Estimation Unit122 Selection Unit128 Database130 Processing Engine200A Schematic Diagram202 Synchronization Signal Block (SSB) Beams204 Physical Downlink Shared Channel (PDSCH) Traffic Beams200B Schematic Diagram212-1 Cell-1 212-2 Cell-2214 Coverage hole216-1 SSB-1216-2 SSB-2300A Framework for Artificial Intelligence / Machine Learning (AI / ML) 302 Data Collection304 Model Training306 Management308 Inference310 Model Storage 300B AI / ML Operational Workflow312 Training Phase314 Deployment Phase316 Inference Phase322 ML Training324 ML Testing326 AI / ML Deployment328 AI / ML Inference400 Method Flow Diagram500A Method Flow Diagram500B Method Flow Diagram600 Computer System610 External Storage Device620 Bus630 Main Memory640 Read-Only Memory650 Mass Storage Device660 Communication Ports670 ProcessorDETAILED DESCRIPTION

[0084] 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 theproblems 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.

[0085] 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 an exemplary 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.

[0086] 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.

[0087] 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, asubprogram, 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.

[0088] 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.

[0089] 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.

[0090] 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 of one 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.

[0091] 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.

[0092] Massive MIMO antenna arrays have been widely adopted due to the high demand for bandwidth, offering beamforming capabilities to focus radiated power in specific directions. Conventional beamforming techniques use discrete Fourier transform (DFT) vectors for narrow beams that help with coverage and penetration losses. While broader beam-width beams are useful for functions like synchronization and access signaling, they can lead to underutilized power resources and reduced coverage in large antenna arrays. Tunable beam-shaping techniques balance beamwidth by adjusting beamforming weights, but amplitude tapering sacrifices power for broader beams, while phase tapering preserves power at the cost of unwanted side-lobes. Another approach, SSB beam sweeping, transmits multiple narrower beams sequentially to cover a sector, but fixed beam- width configurations lead to unnecessary transmissions in undesired directions, causing overlapping sectors and inter-cell interference in dense networks.

[0093] There is a need for systems and methods to provide an efficient approach to address the problems of the conventional beamforming techniques to improve the coverage and the corresponding signal-to-interference plus noise ratio (SINR).

[0094] 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 performing selective multi-SSB configurations and beam shaping. The beam widths of the multi-SSB beams are dynamically shaped along with the SSB beam boosting (e.g., SSB power boosting). This dynamic shaping of SSB beams and the SSB beam boosting leads to improvement in the SINR while keeping the corresponding network coverage on a certain desired level. A reference signal received power (RSRP) and the SINR are also improved, thereby improving the user experience.

[0095] In an embodiment, a plurality of SSB beam shapes with a plurality of beam- width configurations and a SSB beam power (for example, SSB power boosting) are dynamically selected and adjusted by an artificial algorithm (Al) / a machine learning (ML) algorithm per sector. An appropriate beam shape and the SSB power for each SSB beam of the plurality of SSB beams per sector are dynamically selected from the available plurality of beam-width beams. The intra-cell and inter-cell beam overlapping is minimized. The sector coverage is preserved. The SINR for different inter-site distances and geography is maximized. Hence, the multi-SSB beam-width beams improve downlink (DL) coverage and throughput. The DL performance is examined based on the SSB power boosting and the SSB beam-width shaping for different SSB beam gains on the beam-sweeping procedure.

[0096] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings FIGs. 1A-6.

[0097] FIG. 1A illustrates an exemplary network architecture (100 A) for performing selective multi-Synchronization Signal Block (SSB) configurations and beam shaping, in accordance with an embodiment of the present disclosure.

[0098] Referring to FIG. 1 A, the network architecture (100A) comprises a user equipment (UE) (102), a network node (104) and a system / digital unit (108).

[0099] In an embodiment, the user equipment (102) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system / device. 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 (102) 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 cloudcomputing system or any other device that is network-connected.

[0100] 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, aportable 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, electro-mechanical, 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. A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “user equipment” may be used interchangeably throughout the disclosure.

[0101] In an embodiment, the network node (104) may be a network infrastructure that provides wireless access to one or more terminals associated therewith. The network node (104) may have coverage defined as a predetermined geographic area, which is calculated based on the distance over which a signal may be effectively transmitted. In an aspect, the network node (104) may be a base station. The network node (104) may include, but not be limited to, a wireless access point, an evolved NodeB (eNodeB), a 5G node or next generation NodeB (gNB), a wireless point, a transmission / reception point (TRP), and the like.

[0102] In another aspect, the network node (104) may be an operations, administration and maintenance (0AM). The 0AM may be a network node or network element responsible for processes, activities and tools used to ensure the reliablefunctioning of the network. The OAM performs activities (e.g., monitoring performance, troubleshooting faults, and implementing configurations).

[0103] In an embodiment, the system / digital unit (108) may be part of the network node (104). The digital unit (108) 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 / digital unit (108) may perform various operations using software algorithms (e.g., AL / ML algorithms). A person of ordinary skill in the art will appreciate that the terms “system” and “digital unit” may be used interchangeably throughout the disclosure.

[0104] In an embodiment, the network (106) 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 (106) 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.

[0105] As shown in FIG. 1A, a plurality of SSB beams is present between the network node (104) and the UE (102). The UE (102) uses a physical random-access channel (PRACH) to send a connection request to the network node (104) using the network (106). The network node (104) then sends an acknowledgment to the connection request. Once the connection is established between the network node (104) and the UE (102), the network node (104) receives a plurality of signals andmeasurements (e.g., Hybrid Automatic Repeat Request (HARQ), Channel Quality Indicator (CQI), etc.) from the UE (102).

[0106] The network node (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 network node (104) facilitates 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.

[0107] The system / digital unit (108) is configured to perform selective multi- SSB configurations and beam shaping, as explained in detail with reference to FIGs. IB-6. Further, the system / digital unit (108) is configured to determine the coverage status in the network (106) as explained in detail with reference to FIGs. IB to 6.

[0108] In an aspect, the multi-SSB configuration refers to a setup for selection and modifications of different SSB beam shapes with different beam-width configurations and SSB power boosting. In an aspect, the SSB beam shaping refers to a process of adjusting the shape, width, direction, and power of the SSB beams to optimize coverage and signal quality in the network (106). In an aspect, the coverage status refers to an evaluation or indication of whether the SSB beams adequately cover a given geographical or service area in the network (106).

[0109] The system / digital unit (108) is configured to use software algorithms (e.g., AI / ME algorithms) to select and adjust a plurality of SSB beam shapes with a plurality of beam- width configurations and SSB beam power for a desired area / sector. The digital unit (108) is configured to select an appropriate beam shape, an appropriate SSB power for each SSB beam of the plurality of SSB beams using the AI / ME algorithms for desired coverage and SINR. The plurality of SSB beams is part of a beam-sweeping set per sector. In this way, the plurality of SSB beam shapes having the plurality of SSB beam-width configurations with SSB beam power are analyzed toperform the selection. The selection of the appropriate beam shape and the appropriate SSB power helps to minimize the intra-cell and inter-cell beam overlapping. Also, maintaining sector coverage while optimizing SINR across varying inter-site distances and geographical conditions. The selective multi-SSB configurations and beam shaping improve the DL coverage and throughput. In an aspect, antenna beams radiation pattern in azimuth (0) and vertical (cp), is estimated along with the side lobes. Further, a projection of the radiation pattern to ground level is performed with simple geometry considering the tilting angle and the SSB beam tilting characteristics.

[0110] FIG. IB illustrates an exemplary block diagram (100B) describing the system / digital unit (108) for performing selective multi-SSB configurations and beam shaping, in accordance with an embodiment of the present disclosure.

[0111] The system / digital unit (108) comprises a processor (112), a memory (114), an interface (116), a database (128) and a processing engine (130).

[0112] In an aspect, the digital unit (108) 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 the memory (114) of the digital unit (108). 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 (108). 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.

[0113] In an embodiment, the digital unit (108) 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 RO devices, storage devices, and the like. The interface(s) (116) may facilitate communication of the digital unit (108). The interface(s) (116) may also provide a communication pathway for one or more components of the digital unit (108). Examples of such components include, but are not limited to, processing unit / engine(s) (120) and the database (128). The interface ( 116) is configured to provide a communication pathway for one or more components of the digital unit (108).

[0114] 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 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 digital unit (108) 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 butaccessible to the digital unit (108) and the processing resource. In other examples, the processing engine(s) (130) may be implemented by an electronic circuitry.

[0115] In an embodiment, the processing engine (130) may be an artificial intelligence (Al) / a machine learning (ML) engine. The AI / ML engine may automate processes of performing selective multi-SSB configurations and beam shaping and determining coverage status by employing one or more Al models (used interchangeably with the term AI / ML models), as explained in detail in FIGs 3A-3B. 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 the plurality of SSB beams with the SSB boosting (e.g., SSB power boosting). The AI / ML models may provide feedback on the results of monitoring to the digital unit (108) 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.

[0116] In an embodiment, the database (128) 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 digital unit (108).

[0117] In an aspect, the system / digital unit (108) may be a part of the network node (104). In another aspect, the system / digital unit (108) may be communicatively coupled with the network node (104).

[0118] The processing engine (130) comprises an execution unit (118), an estimation unit (120) and a selection unit (122).

[0119] The system (108) is configured to perform selective multi-SSB configurations and beam shaping. Further, the system (108) is configured to determine the coverage status in the network (106). In an aspect, the system (108) performs selective multi-SSB configurations by selecting and adjusting multiple SSB beam shapes with different beam-width configurations and SSB power boosting. Based on the performed selective multi-SSB configurations and the beam shaping, the system (108) determines the coverage status (i.e., selecting a proper beam shape and SSB power per individual SSB beam of the beam-sweeping set), under the constraint of desired coverage and SINR. The performed selective multi-SSB configurations 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. Further, the DL coverage and throughput improve.

[0120] The execution unit (118) is configured to perform synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam-width shapes. In an aspect, the SSB beam sweeping refers to a process used to establish an initial connection between the network node (e.g., base station or the network node (104)) and the UE (e.g., the UE (102)) by transmitting the SSB beams in different directions. The network node (104) sends a sequence of SSB beams, each with a slightly different beam direction, to cover the surrounding area and help the UE (104) find the best possible link.

[0121] In an aspect, the SSB beam sweeping is performed by selecting each SSB beam-width shape from the plurality of SSB beam-width shapes. The selection of each SSB beam- width shape from the plurality of SSB beam-width shapes is performed based on a selection type of a plurality of selection types. The plurality of selection types comprises a sequential selection, a user-defined selection, a dynamic selection, an adaptive selection, or a random selection, etc. In the sequential selection, a fixed or predetermined sequence is used for the selection of each SSB beam-width shape of theplurality of SSB beam- width shapes. In the user-defined selection, a user (i.e., a network operator) defines a sequence for the selection of each SSB beam-width shape. The user-defined selection further comprises the user defining a first SSB beam-width shape, and then the remaining SSB beam-width shapes are selected randomly. The dynamic selection comprises the selection of SSB beam-width shapes based on realtime conditions, such as UE location, mobility type, area type, etc. The adaptive selection comprises the selection of SSB beam-width shapes based on factors such as mobility, signal quality, interference conditions, and environmental variables. The random selection comprises a random selection of a SSB beam-width shape from the plurality of SSB beam- width shapes.

[0122] In an aspect, the execution unit (118) is configured to perform SSB beam sweeping by selecting one selection type from the plurality of selection types for the selection of each SSB beam-width shape of the plurality of SSB beam-width shapes. In particular, the SSB beam sweeping is performed for all SSB beam-width shapes by selecting each SSB beam-width shape from the plurality of SSB beam-width shapes, until all SSB beam-width shapes are selected.

[0123] In an operative embodiment, the execution unit (118) is configured to perform the SSB beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for each SSB beam-width shape of the plurality of SSB beam-width shapes. In an aspect, SSB sweeping beams are SSB beams sweeping over time (e.g., using time division multiplexing (TDM) mapping) on different azimuth and elevation angles to cover the cell area. In an aspect, the defined time cycle refers to a specific, repeating interval of time during which certain operations or processes (for example, in an operative aspect, SSB beam sweeping) occur in a synchronized manner. In an aspect, the time division multiplexing (TDM) is a technique in which multiple SSB sweeping beams share a single communication channel by transmitting in designated time slots.

[0124] The execution unit (118) is configured to initiate SSB beam sweeping for a first SSB beam-width shape of the plurality of SSB beam-width shapes. The first SSB beam-width shape can be a default SSB beam-width shape, an operator- configurable SSB beam-width shape, or a network operator defined SSB beam-width shape. The SSB beam sweeping is performed on the cell level based on the first SSB beam-width shape for each SSB sweeping beam of the plurality of SSB sweeping beams in the defined time cycle. The network node (104) performs SSB beam sweeping on the cell level by transmitting the plurality of SSB sweeping beams to cover the area of the cell of the network node (104). The network node (104) performs the SSB beam sweeping in the defined time cycle. In an exemplary embodiment, without limiting the scope of the invention, the defined time cycle can be of 5 milliseconds (ms). So, in that case, the network node (104) may perform the SSB beam sweeping for 5ms for the first SSB beam-width shape using each SSB sweeping beam of the plurality of SSB sweeping beams.

[0125] Upon performing the SSB beam sweeping, the estimation unit (120) is configured to estimate a downlink signal to interference plus noise ratio (DL_SINR) for the first SSB beam-width shape. The estimation unit is configured to estimate the DL_SINR for each SSB sweeping beam in conjunction with a cost function. In an aspect, the DL_SINR is a radio link quality metric used to evaluate the quality of signals received at the UE (104) from the base station. The DL_SINR is the ratio of the power of the downlink signal to the sum of the interference power from other transmitters and the noise power at the UE. In an aspect, the cost function refers to a mathematical expression that assigns a cost or score to each SSB beam based on measurable criteria such as signal quality, interference, beam width, energy consumption, or coverage efficiency. The cost function helps determine the optimal beam direction by quantifying the performance of different beam options, thereby guiding the capacity and coverage optimization (CCO) process to select the SSB beam that maximizes overall signal quality and network efficiency. The CCO process refersto a network optimization process for improving the coverage, capacity, and quality of service (QoS) of the network (106).

[0126] After performing the SSB beam sweeping and estimating the DL_SINR and the cost function for the first SSB beam-width shape, the selection unit (122) is configured to select a next SSB beam-width shape from the plurality of SSB beamwidth shapes. Once the next SSB beam- width shape is selected, the execution unit (118) is configured to perform SSB beam sweeping on the cell level in the defined time cycle using the next SSB beam- width shape, and the estimation unit (120) is configured to estimate the DL_SINR in conjunction with the cost function for each SSB sweeping beam of the plurality of SSB sweeping beams for the next selected SSB beam-width shape.

[0127] The execution unit (118) is configured to repeat the SSB beam sweeping and the estimation of the DL_SINR for each beam-width shape of the plurality of SSB beam- width shapes and the cost function until the SSB beam sweeping and the estimation for all SSB beam-width shapes are completed. For each SSB beam-width shape, the SSB beam sweeping is performed on the cell level for each SSB sweeping beam of the plurality of SSB sweeping beams in the defined time cycle. Upon performing the SSB beam sweeping for each SSB sweeping beam of each SSB beamwidth shape, the estimation unit (120) estimates the DL_SINR for each SSB sweeping beam of each SSB beam-width shape and the cost function. For example, for 5 SSB beam- width shapes, a 1stSSB beam- width shape from the 5 SSB beam- width shapes is selected. The 1stSSB beam- width shape may be a default SSB beam- width shape or an operator-configurable SSB beam-width shape. In an aspect, the default SSB beamwidth shape refers to a predefined beam-width shape used by the network node when no advanced beam shaping or customization is applied. In an aspect, the operator- configurable SSB beam-width shape refers to a beam-width shape manually definedby a network operator based on coverage requirements and performance goals in the network.

[0128] The SSB beam sweeping is then performed with the 1stSSB beamwidth shape. After performing the SSB beam sweeping, the DL_SINR and the cost function are estimated for the 1stSSB beam width shape. After the 1stSSB beam- width shape, a 2ndSSB width shape is selected. The SSB beam sweeping and estimation of the DL_SINR and the cost function are performed for the 2ndSSB beam-width shape. Similarly, a 3rd, a 4thand a 5thSSB beam-width shape of 5 SSB beam-width shapes are selected. The SSB beam sweeping and estimation of the DL_SINR and the cost function are performed for the 3rd, 4thand 5thSSB beam width shapes. In this way, the SSB beam sweeping and the estimation are performed on all 5 SSB beam-width shapes.

[0129] In one aspect, the estimation unit (120) estimates the DL_SINR jointly with the cost function for each SSB sweeping beam of the plurality of SSB sweeping beams of each SSB beam-width shape of the plurality of SSB beam-width shapes i.e., the estimation unit (120) estimates the DL_SINR for each SSB sweeping beam by considering parameters simultaneously. In another aspect, the estimation unit (120) estimates the DL_SINR for each SSB sweeping beam of the plurality of SSB sweeping beams of each SSB beam-width shape of the plurality of SSB beam-width shapes as a composite cost function i.e., the cost function is computed collectively using the DL SINR per SSB beam together in conjunction with a plurality of parameters (e.g., Uplink Physical Random Access Channel (UL PRACH), uplink channel quality indicator (UL CQI), Hybrid Automatic Repeat request (HARQ) key performance indicators (KPIs), level 1 (LI) SINR (Ll-SINR) and reference signal received power (RSRP) reporting, making the cost function as the composite cost function.

[0130] In an aspect, the cost function comprises a plurality of parameters. The plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

[0131] In an embodiment, the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise, but is not limited to, at least one of connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channel quality indicator (CQI) reports and level 1 (Ll)-SINR and Ll- reference signal received power (RSRP) reporting.

[0132] The connected UE measurement report comprises at least one of a Ll- synchronization signal-RSRP (SS-RSRP), a SS-reference signal received quality (SS- RSRQ), a SS-SINR measurement, CQI, cell level UE measurements and beam level UE measurements. In an aspect, the Ll-SS-RSRP refers to a measurement of the power received from the 5G NR Search Signal (SS) reference signals at Layer 1, which is a lower layer of a protocol stack. In an aspect, the SS-RSRQ refers to a power level of the synchronization signal, which is used for initial cell access and channel estimation. In an aspect, the SS-SINR refers to a measurement of the quality of the signal received by the UE from the SSB beams used for initial cell search and other purposes. In an aspect, the CQI refers to a metric used to indicate the downlink channel quality as perceived by the UE, such as the UE (104).

[0133] In an aspect, the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%. In an aspect, the HARQ refers to a mechanism used to ensure reliable data transmission. The HARQ combines both error detection and error correction. The NACK occurs when the receiver fails to decode the received data correctly. The receiver sends the NACK to a transmitter to request a retransmission of the data. In an aspect, the BLER is a performance metric used to indicate the reliability of the transmission. The BLER > 10%, indicates that a large amount of data is failing to decode or requiringretransmission. The NACKs sent by the receiver are an indication of failed transmissions, and retransmissions may occur to attempt to recover the lost data.

[0134] In an aspect, the connected UE location information comprises at least one of latitude and longitude coordinates, a serving cell identifier (ID), and a radio link failure (RLF). The Cell ID refers to a unique identifier assigned to each cell to distinguish between different cells within the network, such as the network (106). The latitude and longitude coordinates are used to locate points on Earth's surface. The RLF refers to a loss of radio link between the UE, such as the UE (102) and the network node (e.g., gNB or node (104)). The RLF signifies a degradation of the radio link quality to the point where communication is unreliable, leading to potential disruptions in data transmission.

[0135] The idle mode UE random access performance comprises at least one of a random-access channel (RACH) success rate, a number of RACH attempts to convert to a SINR from a graph of a number of physical random-access channel (PRACH) attempts vs the SINR. The RACH success rate refers to a measure of percentage of successful attempts by the UE to connect to the network node using the RACH. In an aspect, the RACH attempts refer to the number of times the UE has attempted to access the network through the RACH. The PRACH attempts refer to the UE’s attempt to access the network using the PRACH.

[0136] In an aspect, the number of UL PRACH counter attempts of a plurality of idle UEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs. In an aspect, the idle UEs refer to the UEs that are powered on but are not actively engaged in data transmission or voice communication. The idle UEs are in a low-power, non-connected state where dedicated radio resources are not assigned to the idle UEs. Further, the number of UL PRACH counter attempts is derived from a graph showing PRACH attempts versus the SINR of idle UEs. The number of ULPRACH counter attempts represents how many times idle UEs attempted to connect to the network using PRACH, correlated with the SINR observed at the time of access.

[0137] Further, the plurality of parameters comprises inputs from a local node. The inputs from the local node comprise at least one of a measured and / or predicted radio resource status, a predicted coverage status, measured or predicated UE traffic. In an aspect, the local node refers to a current node that a device (e.g., UE (102)) is directly connected to or interacting with at a given time. The local node serves as the main point of access, control, or communication for the connected device (i.e., UE (102)).

[0138] In an aspect, the measured radio resource status refers to real-time or periodically collected data that indicates current usage, availability, and quality of radio resources in the network. These measurements are typically gathered by the network nodes (e.g., gNBs) and user equipment (UEs). In an aspect, the predicted radio resource status refers to the forecasted or estimated condition of the radio resources in the network, based on historical data, or machine learning algorithms. The predicted radio resource status anticipates future availability, usage, and quality of the radio resources to help the network proactively manage coverage, capacity and performance.

[0139] In an aspect, the predicted coverage status refers to the forecast or estimation of network coverage quality and availability in a specific area, based on analysis of existing data, propagation models, and environmental factors. The predicated coverage status helps the network operators anticipate where the signal will be strong, weak, or unavailable before actual deployment or changes occur.

[0140] In an aspect, the UE traffic refers to data transmitted and received by the UE (102). In an aspect, the measured UE traffic refers to the actual, observed data transmission activity generated or received by the UE within the network. The measured UE traffic includes the amount, type, and pattern of data sent and receivedover time. The Predicted UE traffic" refers to an estimate or forecast of the data transmission or communication demand that the UE will generate or require over the network during a given period of time.

[0141] Further, the plurality of parameters comprises inputs from a neighbouring node. The inputs from the neighbouring node comprise at least one of the measured and / or predicted radio resource status, the predicted coverage status, the measured or predicated UE traffic, and a measured or predicated UE trajectory. In an aspect, the neighbouring node refers to any node that is adjacent or near the local node, capable of providing similar services or acting as a backup or handover for the connected device (i.e., UE (102)).

[0142] The predicated coverage status comprises at least one of SSB beamwidth adjustments and SSB azimuth / elevation steering corrections. In an aspect, the UE trajectory refers to a path of movement for the UE within the network. In an aspect, the measured UE trajectory refers to the recorded path or movement of the UE over time within the network. In an aspect, the predicted UE trajectory refers to a forecasted path or future movement of the UE based on current or historical data corresponding to movement patterns of the UE.

[0143] In an operative aspect, the cost function is estimated for each SSB sweeping beam in terms of UL RACH performance based on the plurality of parameters. The UL RACH performance is a measure of how well the network supports UE access attempts on the uplink. The UL RACH performance is evaluated based on a success rate, a failure rate, and an access delay.

[0144] Further, based on the SSB beam sweepings, the estimation unit (120) is configured to estimate an SSB beam coverage footprint. In an aspect, the coverage footprint refers to an area over which SSB sweeping beams are detected and decoded by the UE (102). The coverage footprint provides data such as how SSB beamsweeping supports initial cell search and synchronization, and how factors such as beamforming and signal strength influence coverage. The SSB sweeping beams are transmitted by the network node (e.g., base station) (104) to assist the UE (102) in locating and synchronizing with the strongest available cell associated with the network node. Within the coverage footprint, the UE (102) is able to detect and decode the SSB, enabling the UE (102) to initiate cell search and initial access procedures.

[0145] In an aspect, the coverage footprint is estimated based on SSB beam shape, transmit power, antenna gain, receiver sensitivity, and path loss.

[0146] In an operative aspect, the coverage footprint is estimated as a percentage of the SSB beam shape. The SSB beam coverage footprint refers to an effective geographical area covered by the SSB beam compared to theoretical / default radiation pattern. The defined SSB beam shape is a default SSB beam shape. In an aspect, the default SSB beam shape refers to a predefined or pre-configured radiation pattern of the SSB beam. The default SSB beam shape is provided by the network operator.

[0147] The percentage of the SSB beam shape quantifies how much the current beam's coverage deviates from the ideal shape of the SSB beam shape. The coverage footprint is calculated as the percentage of the SSB beam shape using geometric overlap. A formula for the coverage footprint is given as coverage % = Actual coverage area (from UE reports) / default Beam Area x 100.

[0148] In another aspect, the coverage footprint is estimated based on the transmit power. The transmit power is the power level at which the network node (e.g., base station) transmits the signal. The transmit power is measured in dBm (decibels relative to 1 milliwatt) or Watt. The higher the transmit power, the larger the coverage footprint because the signal can travel farther before losing its strength. Further, higher transmit power leads to better signal penetration and reduces the effect of path loss. Ifthe transmit power is increased, the coverage footprint increases proportionally, as the signal can reach a greater distance before it decays to below the sensitivity threshold of the receiver (e.g., UE).

[0149] In another aspect, the coverage footprint is estimated based on the antenna gain. The antenna gain is a measure of the efficiency of the antenna in directing energy in a specific direction. Higher antenna gain translates to greater directivity, i.e., the antenna focuses its signal energy more tightly in one direction. The antenna gain is usually measured in dB. The higher antenna gain provides a more focused beam, resulting in a smaller, more concentrated coverage area, while the lower antenna gain provides a broader, more dispersed beam, leading to a larger coverage area.

[0150] In another aspect, the coverage footprint is estimated based on the receiver sensitivity, which is the minimum signal strength that the receiver (e.g., UE) detects and demodulates the signal effectively. The coverage footprint is generally expressed in dBm. The larger the receiver sensitivity, the larger the coverage footprint. If the receiver sensitivity is very low, the UE receives signals from farther distances, increasing the coverage footprint.

[0151] In another aspect, the coverage footprint is estimated based on the path loss. The path loss refers to the loss of signal strength as the radio wave travels from the base station to the receiver (e.g., UE). The higher the path loss, the smaller the coverage footprint. The lower the path loss, the larger the coverage footprint.

[0152] Further, the selection unit (122) is configured to select at least one SSB beam-width shape from the plurality of SSB beam-width shapes based on one or more conditions. In an operative aspect, the one or more conditions comprise a first condition and a second condition. The first condition comprises determining a maximum DL UL_SINR based on the estimated DL_SINR for each SSB sweeping beam and the composite cost function. The DL UL_SINR is referred to as the DL_SINR and the ULRACH performance. The second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold.

[0153] To determine the maximum DL UL_SINR, the execution unit (118) is configured to store, in a DL_SINR list, each estimated DL_SINR of each SSB sweeping beam of the plurality of SSB sweeping beams of each SSB beam- width shape of the plurality of SSB beam- width shapes. The selection unit (122) is configured to select a maximum DL_SINR from the DL_SINR list. To determine the maximum DL_SINR, each estimated DL_SINR is compared with a DL_SINR threshold to identify one or more estimated DL_SINRs that are greater than the DL_SINR threshold. A maximum DL_SINR is selected from the one or more identified DL_SINRs.

[0154] Further, the execution unit ( 118) is configured to store the cost function in terms of UL RACH performance for each SSB sweeping beam of the plurality of SSB sweeping beams. The execution unit (118) is configured to store, in a UL RACH performance list, each estimated cost function in terms of the UL RACH performance for each SSB sweeping beam of the plurality of SSB sweeping beams of each SSB beam- width shape of the plurality of SSB beam- width shapes. The selection unit (122) is configured to select a maximum cost function in terms of UL RACH performance from the UL RACH performance list. Thereafter, the selection unit (122) is configured to use the selected maximum DL_SINR and the maximum UL RACH performance to determine the maximum DL UL_SINR. Further, the selection unit (122) is configured to determine the at least one SSB beam- width shape having the determined maximum DL UL_SINR.

[0155] In an embodiment, the second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold. In particular, the estimation unit (120) is configured to determine whether the estimated SSB beam coverage footprint is greater than the configurable threshold. In anembodiment, the configurable threshold is provided by the network operator. The configurable threshold defines the acceptable or desired coverage area. For example, the network operator sets the configurable threshold for the coverage footprint as up to 100 meters in radius. The estimated coverage footprint is 130 meters. As in this case, the estimated coverage footprint (i.e., 130 meters) exceeds the configurable threshold (i.e., 100 meters), the second condition is considered as satisfied.

[0156] In an operative aspect, the selection unit (122) is configured to select at least one SSB beam- width shape from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than the configurable threshold. Upon determining that the estimated coverage footprint is greater than the configurable threshold, the selection unit (122) is configured to select the at least one SSB beam-width shape having the selected maximum DL_SINR. Further, upon selecting the at least one SSB beam- width shape, the execution unit (118) is configured to modify at least one SSB power boosting parameter of the at least one selected SSB beam-width shape. In an aspect, the power boosting is a process to adjust the transmission power of the SSB sweeping beams to ensure that the SSB sweeping beams are received with sufficient quality by the UE. The power boosting is essential when different SSB beamwidths are used during beam sweeping to ensure consistent coverage and signal strength. The power boosting parameter defines how much additional power is applied to the SSB sweeping beam transmission. The power boosting parameter is configured at the network node (e.g., base station). Power boosting helps to compensate for beam characteristics (e.g., width and directionality). In an aspect, the at least one power boosting parameter comprises, but is not limited to, a physical resource block (PRB). In an aspect, a resource block refers to a fundamental unit of resource allocation used in the network. Types of resource block comprise, but are not limited to, the PRB, a virtual resource block (VRB), a common resource block, a dedicated resource block, etc. In an aspect, the PRB is the smallest unit of resources that can be allocated in the time -frequency domain. In an aspect, the VRB is a is alogical abstraction of the PRB. The VRBs are mapped to PRBs during the scheduling process. In an aspect, the common resource block is a block used for control / broadcast information. In an aspect, the dedicated resource block is a block assigned to individual UE.

[0157] In an operative aspect, the power boosting allocates more power to the SSB beam physical resource blocks (PRBs) than the rest of the resource blocks used for physical downlink shared channel (PDSCH).

[0158] The estimation unit (120) is further configured to estimate the DL_SINR based on the at least one selected SSB beam-width shape. The DL_SINR is calculated based on the selected SSB beam-width shape to check any possibility of improving the SINR. The estimated DL_SINR for the selected SSB beam-width shape is compared with the DL_SINR threshold. Upon detecting that the estimated DL_SINR for the at least one selected SSB beam-width shape is greater than the DL_SINR threshold, determination of the coverage status is again performed up to a point when the estimated DL_SINR is less than the DL_SINR threshold.

[0159] In an aspect, after selecting the SSB beam-width shape with maximum DL_SINR and the coverage footprint being greater than the configurable threshold, the network node (104) adjusts the SSB power. The network node (104) may receive SINR input from the UE (102). The network node (104) may estimate the SSB beam-width shape and adjust the SSB power. In this way, the network node (104) may adjust the SSB power to an incremental level.

[0160] In an embodiment, the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications. The cell coverage modifications comprise SSB beam power boosting. The SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering. The cell coverage modifications perform adjustments to cell-level powerparameters. The SSB coverage modifications perform adjustments to beam-specific SSB configurations.

[0161] In an embodiment, the coverage status includes cell coverage modification and SSB coverage modification. The cell coverage modification includes SSB beam power boosting for the cell coverage state. The cell coverage state is used to solve the predicted CCO issue of a certain predicted cell. The SSB coverage modification includes SSB beam width coverage state, which is used to solve the predicted CCO issue of a certain predicted SSB.

[0162] In an aspect, cell-wide adjustments that affect all beams and UEs in the cell are performed, in the cell coverage modifications. The cell coverage modifications are managed by an operations, administration and management (0AM) or SelfOrganizing Networks (SON). Key parameters adjusted in the cell coverage modifications are shown below in Table 1.Table 1

[0163] The cell coverage modifications are used for macro-level coverage issues (e.g., entire cell has poor RSRP), energy-saving modes (reducing power during low traffic) and interference coordination (lowering power near cell edge).

[0164] In SSB coverage modifications, beam-specific adjustments are performed to optimize individual SSB beams (used for initial access). The SSB coverage modifications are managed by AI / ML -based CCO or beam management algorithms (P1 / P2 / P3). Key parameters that are adjusted in the SSB coverage modifications are shown below in Table 2:Table 2

[0165] The SSB coverage modifications are used in cases such as mmWave deployments (highly directional beams), UE clustering (e.g., stadiums, highways) and dynamic null-steering (avoiding interference).

[0166] The key differences between the cell coverage modifications and SSB coverage modifications are shown below in Table 3:Table 3

[0167] In one example, in an urban macro-cell, the cell coverage modification is used to reduce cell power at night to save energy. The SSB coverage modification is used to steer SSB beams toward high-rise buildings during the daytime.

[0168] In another example, in mmWave small cells, the cell coverage modification is used for beamforming. In the SSB coverage modification, narrow SSB beams are used for high-capacity hotspots and SSB power is boosted in Non-Line-of- Sight (NLOS) areas.

[0169] In an aspect, the estimation unit ( 118) is configured to use the composite cost function to determine an SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network (106). The composite cost function evaluates the beam configurations (i.e., SSB beam width and steering azimuth / elevation angle) to ensure that no coverage holes are present, to maximize signal-to-interference ratio, balance UE load distribution (i.e., data traffic) across the SSB beams, and minimize inter-beam interference.

[0170] Although FIG. IB shows the exemplary block diagram (100B) of the system / digital unit (108), in other embodiments, the system / digital unit (108) 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 / digital unit (108) may perform functions described as being performed by one or more other components of the system / digital unit (108).

[0171] 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.

[0172] As shown in FIG. 2A, the network node (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 network node(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). As both types of beams are present between the network node (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).

[0173] FIG. 2B illustrates an exemplary schematic diagram (200B) describing a SSB beam shaping process, in accordance with an embodiment of the present disclosure.

[0174] 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 transmitted from 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).

[0175] 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.

[0176] 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).

[0177] 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, orthe 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.

[0178] FIG. 3A illustrates an exemplary functional framework (300A) for an artificial intelligence / machine learning (AI / ML) based coverage and capacity optimization (CCO), in accordance with an embodiment of the present disclosure.

[0179] In an embodiment, a Self-Organizing Network (SON) used in communication network (i.e., network (106)) refers to a system that automatically configures, optimizes, and manages the network without human intervention, using intelligent algorithms and data analytics. The SON comprises a Coverage and Capacity Optimization (CCO) function to autonomously manage and optimize network performance. The CCO function is used to identify, address, and mitigate issues related to coverage and capacity within random access networks (RANs), ensuring robust and reliable communication services. This enhances the efficiency and performance of the RANs. The CCO function enables self-healing and self-optimization capabilities within the network (106) by leveraging advanced algorithms and real-time data analysis. Thus, reducing the need for manual intervention and enhancing overall network performance.

[0180] The network node (e.g., NG-RAN node) (104) determines CCO issues based on UE radio measurements, radio link failure (RLF), mobility performance, radio connection establishment failure (i.e., RACH accessibility) and performance (e.g., throughput, packet loss) of the attached UEs, such as the UE (102). Upon detecting the CCO issue, the network node (e.g., NG-RAN node) performs dynamic cell-level and beam-level coverage configuration adjustments and changes. Adjustments and changes of the cell-level and beam-level coverage configuration include SSB and traffic beampower parameter adjustments, beam- width adjustments, beam steering adjustments and potential dynamic beam switching actions.

[0181] In an embodiment, the network node (104) employs an artificial intelligence (Al) and machine learning (ML) framework for CCO. The network node (e.g., NG-RAN node) executes the AI / ML CCO training and the corresponding inference autonomously in a local node without informing neighbor cells in a cluster about the changes. The neighbor nodes operate autonomously, running the AI / ML- based CCO. The network node cluster dynamically auto-adjusts CCO parameters during predefined optimization periods, without any further signaling over an Xn interface. In the split gNB architecture, the CCO function resides in the gNB-CU. The split architecture of the gNB in the network (106) involves dividing the gNB into two main entities: a centralized unit (CU) and one or more distributed units (DU). This split allows for flexibility in deployment and scalability, enabling different functions to be implemented in separate physical locations.

[0182] The network node (e.g., NG-RAN node) (104) performs CCO configuration changes to alleviate the detected CCO issue by using parameters such as beam management information, Power control parameters (i.e., SSB beam boosting, uplink channels), UE measurement reports, etc.

[0183] When any CCO issue is detected, each network node (e.g., NG-RAN node) (104) autonomously adjusts the CCO configurations dynamically without the Xn signaling from other network nodes. Further, when a CCO issue is detected on cell performance, all other neighbor nodes of the cluster autonomously adjust CCO configurations dynamically without the Xn signaling to the neighbor nodes.

[0184] In the CCO configurations, the network node (e.g., NG-RAN node) (104) dynamically optimizes network performance based on the cell-level and UE- level parameters from the serving node. The cell-level parameters are network-widemetrics that help optimize coverage, capacity, and interference across a plurality of cells.

[0185] In an aspect, the cell-level parameters comprise signal strength and quality metrics, load and utilization metrics, mobility and handover metrics and beam management (for mmWave). The signal strength and quality metrics comprise a reference signal received power (RSRP), a reference signal received quality (RSRQ), and a signal-to-interference -plus-noise ratio (SINR). The RSRP measures downlink signal strength (dBm) and is used to adjust cell transmit power or antenna tilt for better coverage. The RSRQ indicates signal quality (interference + strength) and helps in detecting coverage holes or overlapping cells. The SINR determines interference levels between neighboring cells and is used for inter-cell interference coordination (ICIC). The load and utilization metrics comprise physical resource block (PRB) utilization, and central processing unit (CPU) and memory utilization (in central unit (CU) / distributed unit (DU). The PRB utilization is a percentage of used vs. available radio resources and helps in load balancing between cells. The CPU and memory utilization indicate processing load on the network node and trigger scaling (e.g., adding more cells in control and user plane separation (CUPS) architecture). The mobility and handover metrics comprise handover success rate (HOSR) and radio link failures (RLF) per cell. The HOSR tracks successful vs. failed handovers and is used to optimize handover thresholds in A3 / A5 events. The A3 event is used in intrafrequency handover where the neighboring cell's signal strength or quality (RSRP or RSRQ) becomes better than the serving cell. The A5 event is used in inter-frequency handover where the serving cell's signal degrades below a threshold, and the neighboring cell's signal strength becomes better than another threshold. Further, the radio link failures (RLF) per cell help to detect coverage gaps or mobility issues. The beam management comprises beamforming configuration and beam failure recovery (BFR) stats. The beamforming configuration adjusts beam width, direction, and power for optimal coverage. The BFR stats track how often UEs lose beam alignment.

[0186] The UE-level parameters are per-user measurements that help optimize individual connections and mobility. The UE-level parameters comprise UE measurements and feedback, mobility and location data, quality of service (QoS) and latency requirements, and connection stability metrics. The UE measurements and feedback comprise a channel quality indicator (CQI), a precoding matrix indicator / rank indicator (PMI / RI) and a hybrid automatic repeat request (HARQ) acknowledgment (ACK) / negative acknowledgment (NACK). The CQI reports downlink channel quality (used for adaptive modulation and coding). The PMI / RI helps with MIMO optimization (e.g., selecting the best antenna configuration). The HARQ ACK7NACK indicates if transmissions succeeded / failed (used for link adaptation). The mobility and location data comprise UE speed and trajectory, global positioning system (GPS) / observed time difference of arrival (OTDOA) positioning, QoS and latency requirements and connection stability metrics. The UE speed and trajectory help predict handovers (e.g., fast-moving UEs may need earlier handovers). The GPS / OTDOA positioning is used for location-based beamforming or cell selection. The QoS and latency requirements comprise a QoS identifier (QI), which determines priority (e.g., Ultra-Reliable Low Latency Communications (URLLC) vs. Enhanced Mobile Broadband (eMBB) traffic). The UE-reported latency / throughput triggers resource reallocation if the QoS targets are not met. The connection stability metrics comprise radio link failure (RLF) reports, and indicates why a UE lost connection (e.g., too weak signal, interference). The handover failure reasons help adjust time-to-trigger (TTT) or hysteresis values.

[0187] In particular, the AI / ML models enhance network performance by channel state information (CSI) feedback enhancement, beam management, and positioning accuracy improvements.

[0188] The functional framework (300A) for the AI / ML model for new radio (NR) air interface is illustrated in FIG. 3A. The framework comprises a generalfunctional architecture describing both model-identifier (ID) based life cycle management (LCM) and functionality-based LCM.

[0189] As illustrated in FIG. 3A, the framework (300A) comprises one or more function blocks. The one or more functional blocks comprise a data collection block (302), a model training block (304), a management block (306), an inference block (308), and a model storage block (310).

[0190] The data collection block (302) is a function block that provides input data for the model training, management, and inference functions. In particular, the data collection block (302) provides training data, monitoring data, and inference data. The training data is provided as an input to the model training block (304). The monitoring data is provided as an input to the management block (306) responsible for the management of the AI / ML model or AI / ML functionalities. The inference data is provided as an input to the inference block (308).

[0191] The model training block (304) is a functional block that performs AI / ML model training, validation, and testing. The model training block (304) generates model performance metrics that can be used as part of the model testing procedure. The model training block (304) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the training data delivered by the data collection block (302). The model training block (304) outputs a trained / updated model, which is then stored in the model storage block (310). In particular, the model training block (304) is configured to deliver trained, validated, and tested AI / ML models to the model storage or to deliver an updated version of a model to the model storage.

[0192] The management block (306) is a functional block that oversees the operation (e.g., selection / (de)activation / switching / fallback) and monitoring (e.g., performance) of the AI / ML models or AI / ML functionalities. The management block(306) is responsible for making decisions to ensure the proper inference operation based on data received from the data collection block (302) and the inference block (308). As seen in FIG. 3A, the management block (306) has two inputs and three outputs. The two inputs comprise the monitoring data and an inference output. The three outputs comprise a management instruction, a model transfer / delivery request and a performance feedback / retraining request. The management instruction is provided to the inference block (308). The management instruction is information needed as input to manage the inference. The information includes selection / (de)activation / switching of AI / ML models or AI / ML -based functionalities, fallback to non-AI / ML operation (i.e., not relying on inference process), etc. The model transfer / delivery request is used to request model(s) from the model storage. The performance feedback / retraining request provides information needed as input for the model training, e.g., for model (re)training or updating purposes.

[0193] The inference block (308) is a functional block that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the data collection block (302) (i.e., inference data) as an input. The inference block (308) is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on inference data delivered by the data collection block (302). The output of the inference block (308) is an inference output. The inference output is the data used by the management block (306) to monitor the performance of AI / ML models or AI / ML functionalities.

[0194] The model storage block (310) is a functional block responsible for storing trained / updated models that can be used to perform the inference function. The model storage block (310) is used as a reference point when applicable for protocol terminations, model transfer / delivery, and related processes. The model storage block (310) has two inputs (i.e., trained / updated model and model transfer / delivery requestand one output (i.e., model transfer / delivery). The model transfer / delivery is used to deliver an AI / ML model to the inference function.

[0195] FIG. 3B illustrates an exemplary representation (300B) depicting AI / ML operational workflow for AI / ML based CCO, in accordance with an embodiment of the present disclosure.

[0196] The AI / ML operational workflow represents operational steps in the lifecycle of an AI / ML model or an entity outlining the management capabilities required for AI / ML across multiple phases. The multiple phases comprise training, validation, testing, deployment, and inference. The phases are used for ML entity loading, inference control, and performance evaluation.

[0197] As illustrated in FIG. 3B, the AI / ML operational workflow comprises three main phases. The main phases comprise a training phase (312), a deployment phase (314) and an inference phase (316).

[0198] The training phase (312) comprises ML training (322) and ML testing (324). The ML training (322) is an initial step of the operational workflow. The ML training (322) provides training data to generate the (new or updated) ML entity that is used for inference. The training data is data used to teach the AI / ML model how to make predictions or decisions. The training data comprises input data and corresponding target labels or outcomes, which the model uses to learn patterns and relationships. The ML training (322) also includes the validation of the generated ML entity to evaluate the performance variance of the ML entity when performing on the training data and validation data. If the validation result does not meet expectations (e.g., the variance is not acceptable), the ML entity needs to be re-trained. The ML testing (324) is used to perform testing of the validated ML entity with testing data to evaluate the performance of the trained ML entity for selection for inference. When the performance of the trained ML entity meets the expectations on both training data andvalidation data, the ML entity is finally tested to evaluate the performance on testing data. If the testing result meets the expectation, the ML entity may be counted as a candidate for use towards the intended use case or task. Otherwise, the ML entity may need to be further (re)trained. In one aspect, the ML entity needs to be verified in a special case of testing to check whether it works in the AI / ML inference function or the target node. In another aspect, the verification step is skipped in cases where the input and output data, data types and formats have been unchanged from the last ML entity. In the training phase, one or more AI / ML models are trained based on historical data to select a suitable AI / ML model. The selected AI / ML model uses the training model to optimize the performance of the selected AI / ML model.

[0199] The deployment phase (314) comprises an AL / ML deployment (326). The AL / ML deployment (326) is used for deployment of the trained and tested ML entity to the target inference function, which uses a subject ML entity for inference (i.e., integrating the trained model into a real-world application or service). The deployment phase (314) is omitted in cases when the training function and inference function are in the same entity. The output of the training phase (312) is provided to the deployment phase (314).

[0200] The inference phase (316) comprises an AI / ML inference (328). The AI / ML inference (328) is used to perform inference using the ML entity by the inference block (308). The AI / ML inference (328) uses the deployed AI / ML model to make real-time predictions. Outputs of the ML testing (324) of the training phase (312) and the output of the deployment phase (314) are provided to the inference phase (316). The output of the inference phase (316) is fed back to the ML training (322) of the training phase (312) to train the AI / ML model based on the predictions made during inference, which are then evaluated, and any discrepancies or errors are used to update the training data. The AI / ML model is retrained to improve the performance by using the updated training data, which leads to an improved AI / ML model that can makemore accurate predictions. The output of the ML testing is provided to the AI / ML inference to validate the AI / ML model's performance and ensure that the Al / ML model functions as expected in real-world scenarios. This process helps identify potential issues, optimize the model, and verify reliability and consistency.

[0201] In an aspect, AI / ML operational workflow is executed using one or more learning methods, i.e., AI / ML models (e.g., supervised learning method and reinforcement learning method). In the supervised learning methods, the inference phase starts after the training phase ends. In the reinforcement learning method, the inference phase starts while the training phase is still in progress.

[0202] In an aspect, the AI / ML model training (322) is located in an operations, administration, and maintenance (0AM) and the AI / ML model inference (328) is located in the network node (e.g., NG-RAN node (gNB-CU) or the node (104)).

[0203] In another aspect, the AI / ML model training and AI / ML model inference are both located in the network node (e.g., NG-RAN node (gNB-CU)).

[0204] For AI / ML based CCO, for AI / ML model training (322) or AI / ML model inference (328), the inference input is collected from UE or neighbour network node (e.g., NG-RAN node in case of non-split architecture) or neighbour network node (e.g., gNB-CU / gNB-DU in case of split architecture).

[0205] In an aspect, the inference inputs from the UE, such as the UE 102 comprise UE measurement report, UE location information, random access performance and radio link failure (RLF) report. The UE measurement report comprises a synchronization signal reference signal received power (SS-RSRP) measurement, a synchronization signal reference signal received quality (SS-RSRQ) measurement, a synchronization signal signal-to-interference and noise ratio (SS- SINR) measurement, and cell-level and beam-level UE measurements. The UE location information comprises coordinates and a serving cell identifier (ID).

[0206] Inputs from the local node comprise measured and / or predicted (i.e., AI / ML) radio resource status, predicted coverage status, and measured / predicted UE traffic (e.g., data volume). The predicted coverage status includes both SSB beamwidth adjustments and SSB beam azimuth / elevation steering corrections.

[0207] Inputs from the neighboring node comprise measured and / or predicted (i.e., AI / ML) radio resource status, predicted coverage status, measured / predicted UE traffic (e.g., data volume) and measured / predicted UE trajectory. The predicted coverage status includes both SSB beam-width adjustments and SSB beam azimuth / elevation steering corrections.

[0208] The SON AI / ML entity provides an input to the network nodes (e.g., NG-RAN nodes). The SON AI / ML is located at the 0AM and / or the network node (e.g., NG-RAN nodes). The training data from the network nodes (e.g., NG-RAN nodes) is provided to the AI / ML entity either at the 0AM or at the network nodes (e.g., NG-RAN nodes), depending on the deployment scenario. The trained models are used at the network node (e.g., NG-RAN node) for inference before evaluating the optimal CCO configurations, taking into consideration the real-time measurement and other information elements.

[0209] In an aspect, for the SON-CCO, the AI / ML model training takes place either at the 0AM or at the network node (e.g., gNB-CU). The model inference takes place either at the network node (i.e., gNB-CU or gNB-DU).

[0210] In another aspect, for the SON CCO use case, the AI / ML model training takes place solely at the network node (e.g., gNB-CU). The model inference takes place either at the network node (e.g., gNB-CU or gNB-DU).

[0211] In CCO corrective actions, a set of SSB beam shapes with varying beam-width configurations and SSB power levels (via SSB power boosting) is dynamically selected and adjusted by an AI / ML optimization algorithm on a per-sectorbasis. The optimization algorithm dynamically chooses the appropriate beam shape from the available beam-width options, along with the optimal steering azimuth and elevation angles, and suitable SSB power for each individual SSB beam in the beamsweeping set for the sector. These selections are made under the constraints of achieving the desired coverage and maintaining the target SINR. Coverage and corresponding SINR are adjusted by dynamically shaping SSB beam-widths of the multi-SSB beams along with SSB beam boosting. This improves the SINR and the corresponding coverage on a certain desired level.

[0212] In an aspect, SSB power boosting is performed in two ways, for example, per-sector power boosting and per-cell power boosting.

[0213] In the per-sector power boosting, SSB power is adjusted for all beams within a sector uniformly. For example, in rural macro cells, the per-sector power boosting is performed when the entire sector requires coverage enhancement. Further, the per-sector power boosting is performed when interference coordination is managed at the sector level (e.g., via open radio access network (0-RAN) related application. The per-sector power boosting is less granular than per-cell boosting. The per-sector power boosting requires neighboring sectors to be coordinated to avoid inter-sector interference.

[0214] In per-cell power boosting, SSB power is adjusted individually per SSB beam within a sector. The per-cell power boosting is used for beam-specific optimization (e.g., boosting a beam facing a high-demand hotspot) and mmWave deployments where beamforming is highly directional. The per-cell power boosting provides more precise SINR and coverage control and enables dynamic null-steering to reduce interference.

[0215] The SSB power boosting algorithm (e.g., AI / ML SSB power boosting model) is implemented for power boosting of the SSB beams. The SSB power boostingalgorithm has one or more inputs. The one or more inputs comprise cell / sector-level key performance indicators (KPIs) (e.g., RSRP, SINR, RLF reports, etc.), UE-level measurements (e.g., CQI, beam-specific RSRP, etc.) and current SSB beam configurations (e.g., width, power, azimuth / elevation, etc.).

[0216] The SSB power boosting algorithm is used to maximize coverage and SINR while minimizing interference & power waste. The SSB power boosting algorithm has regulatory max power limits and neighbor cell interference thresholds.

[0217] The SSB power boosting algorithm comprises the following steps:

[0218] At step 1, the power boosting algorithm performs data collection and preprocessing. In data collection, real-time measurements are collected from operations, administration and maintenance (0AM), UE reports, and gNB logs. The 0AM provides data such as cell load and historical RLF data. The UE reports comprise per-beam RSRP, SINR, and HARQ feedback. The gNB logs comprise beam failure events and handover success rates. In the preprocessing, the collected real-time measurements are normalized to generate normalized data for the AI / ML model input.

[0219] At step 2, the power boosting algorithm identifies optimization targets. The optimization targets comprise coverage holes (e.g., low RSRP areas), interference zones (e.g., low SINR despite good RSRP), and high-demand zones (UE clustering in certain beams).

[0220] At step 3, the power boosting algorithm performs AI / ML-based beam selection and power adjustment. To perform AI / ML-based beam selection and power adjustment, the power boosting algorithm selects one or more AI / ML model options. The one or more AI / ML model options comprise a reinforcement learning (RL) model and a supervised learning (SL) model. The RL model learns optimal power / width adjustments via trial and error. The SL uses historical data to predict the best configurations. The power boosting algorithm selects one AI / ML model from one ormore AI / ML model options based on the decision parameters. The decision parameters comprise beam width, SSB power boosting and beam steering. For the beam width, wider beams are selected for coverage, and narrower ones for capacity. The SSB power boosting incrementally increases power (e.g., +ldB steps) for weak coverage beams and decreases power for beams causing interference. The beam steering adjusts azimuth / elevation to focus on high-demand areas.

[0221] At step 4, the power boosting algorithm performs validation and a feedback loop. In validation and the feedback loop, new SSB configuration is applied in a controlled manner (e.g., using A / B testing), post-optimization KPIs are monitored, and reinforcement learning updates are checked. The monitoring of the postoptimization KPIs comprises SINR improvement in target zones and checking interference impact on neighboring cells. A reward function for the reinforcement learning update, such as SINR gains and interference penalty.

[0222] At step 5, the power boosting algorithm performs dynamic reconfiguration. The dynamic reconfiguration is continuously adjusted based on time- of-day patterns and event-driven scenarios. The time-of-day patterns include, for example, boosting beams near offices during work hours. The event-driven scenarios include, for example, stadium congestion.

[0223] In an embodiment, the AI / ML based CCO, different SSB beam-width configurations and SSB beam steering in azimuth / elevation are dynamically and autonomously selected to optimize coverage and SINR for the SON functionality and for the existing SSB beam sweeping functionality on the serving node.

[0224] In the SSB beam steering, beamforming (especially in mmWave & Massive MIMO) is used. The SSB beams are transmitted in a beam-sweeping manner for initial access. Two types of beam steering (i.e., static and dynamic) are considered. The static beam steering may lead to coverage holes (if the UE moves) and suboptimalSINR (if the interference changes). The dynamic steering may allow tracking UE clusters, avoiding interference, and adapting to traffic patterns.

[0225] The beam steering comprises antenna array adjustments, phase shifters adjust beam direction (azimuth / elevation), and digital / analog beamforming controls beam shape.

[0226] In an embodiment, open-radio access network (O-RAN) and service management and orchestration (SMO) integration optimize beam angles using the AI / ML model. Further, a non-real-time RAN intelligent controller (RIC) application applies fast adjustments to the beam angles.

[0227] In an example, an AI / ML algorithm is employed for dynamic SSB beam steering. The AI / ML algorithm is used to maximize coverage and SINR while minimizing interference, dynamically adjust azimuth angle (0° to 360°) and elevation angle (-90° to +90°) and beam width (wide vs. narrow).

[0228] The AI / ML algorithm of dynamic SSB beam steering performs steps such as data collection, AI / ML model selection, beam steering decision, validation and feedback loop, and continuous optimization loop.

[0229] At step 1, data collection collects inputs for the AI / ML model. The inputs comprise UE Measurements (e.g., RSRP, SINR, CQI, beam failure reports), gNB data (e.g., current beam angles, interference maps) and traffic patterns (e.g., UE density, mobility trends).

[0230] At step 2, the AI / ML model selection is performed. The AI / ML model may comprise a reinforcement learning (RL) model for dynamic adaptation and a supervised learning (SL) model. In the RL AI / ML model, model states (e.g., current beam angles, UE distribution, SINR) are determined. An action, for example, adjusting azimuth / elevation (±A0). A reward (i.e., objective of RL AI / ML model), for example,SINR_improvement - interference_cost. Further, in the supervised learning (SL) AI / ML model, the AI / ML model is trained on historical optimal beam positions.

[0231] At step 3, the beam steering decision is taken based on one or more following conditions:

[0232] At condition 1 , if a high UE density is found in sector X, the steering of beams is performed towards X (azimuth adjustment) and a narrow beam width is selected for higher SINR.

[0233] At condition 2, if interference is detected from the neighbor cell Y, null steering (avoid Y’s direction) is performed.

[0234] At condition 3, if vertical coverage is needed (e.g., High-Rise Buildings), the elevation angle is increased.

[0235] At step 4, validation and feedback steps are performed. The validation is performed by applying new beam angles in a test window and monitoring KPIs. Upon detecting the success of monitoring KPIs, a configuration of the AI / ML model is retained. Upon detecting that monitoring of KPIs has failed, the RL model is reverted and penalized.

[0236] At step 5, a continuous optimization loop is employed for periodic updates (e.g., every 1-5 mins). The continuous optimization loop is adapted to time-of- day changes (e.g., office vs. residential traffic) and at any special events (e.g., stadiums, emergencies).

[0237] In an embodiment, the autonomous SON AI / ML algorithm optimizes the network node’s CCO performance with the minimum signaling overhead and overload in the Xn interface. Pl beam management procedures are used to adjust the SSB beams, minimize the intra-cell and inter-cell beam overlapping, preserve thesector coverage and maximize the expected SINR for different inter-site distances and geography. The autonomous SON AI / ML algorithm updates the parameter settings periodically based on a predefined periodicity. Hence, the multi-SSB beam-width beams improve the DL coverage and throughput without affecting the UE capability.

[0238] In an aspect, the Pl beam management algorithm is used for initial beam selection. The P 1 beam management algorithm is used in CCO for wide beam scanning (for coverage) and narrow beam refinement (for capacity).

[0239] For SON AI / ML based CCO, the Pl beam management algorithm is an autonomous algorithm selection per node, without the need of any Xn specific signaling, periodically updated to dynamically adjust to changing radio environment conditions.

[0240] Each network node (e.g., NG-RAN node) autonomously selects and updates a CCO algorithm (e.g., Pl beam management) without relying on Xn signaling, adapting dynamically to changing radio conditions. The radio conditions comprise sudden UE traffic surge (e.g., stadium event), new interference source (e.g., neighboring cell activation), UE mobility patterns (e.g., highway vs. pedestrian), weather impact (e.g., rain attenuation in mmWave), obstruction changes (e.g., new building blocking line of sight (LOS)) and cell failure (e.g., neighboring gNB outage).

[0241] Autonomous algorithm selection is performed without Xn Signaling. Xn interface is used for inter-gNB communication (e.g., handovers, load balancing). But for CCO, the Xn signaling is avoided as frequent Xn signaling causes latency (delays in optimization), overhead (excessive inter-node messaging) and scalability issues (in dense deployments).

[0242] The autonomous algorithm selection is employed at each network node independently. At each network node, local KPIs (e.g., RSRP, SINR, RLF, beam failures) are monitored, and the best algorithm (e.g., Pl beam management forcoverage optimization) is selected. Self-optimizing steps (e.g., adjusting beams, power, and tilt without neighbor coordination) are employed. Re-evaluation is performed in a timely manner or based on triggers (e.g., every 15 mins or based on triggers).

[0243] In autonomous adjustments, if the SINR drops, the network node (e.g., gNB) switches to wider beams. If UE density increases, the network node (e.g., gNB) uses narrower beams.

[0244] An AI / ML technique is implemented by using an AI / ML model for Pl beam management, for example, reinforcement learning (RL) model. The states of the AI / ML model comprise the current beam config, UE distribution, and interference. The AI / ML model applies action to select Pl algorithm parameters (e.g., beam width, power). AI / ML model reward is coverage improvement - interference cost. The Xn signalling is not needed as decisions are fully local (no neighbor cell input required).

[0245] In an embodiment, the autonomous SON AI / ML algorithm gets inputs only from connected UEs in the serving cell and the serving cell itself for AI / ML optimization functions in NG-RAN. A cost function is built to predict the best SSB beam- width and steering angles per optimization period. This CCO optimization is not forwarded to the neighboring cells, thus minimizing the Xn signaling load.

[0246] In an aspect, the cost function evaluates beam configurations and selects the best beam configuration based on the coverage (e.g., RSRP) to ensure no coverage holes, the SINR (e.g., quality) to maximize signal-to-interference ratio, UE load distribution to balance traffic across beams, and beam overlap penalty to minimize inter-beam interference. The cost function is mathematically represented as:Cost=a-Coverage_Penalty+p-SINR_Penalty+yLoad_Imbalance+5-Be am_0 verlapCost=a • Coverage_Penalty+P • S I N R Penal ty+y • Load lm bal ance+5 -B earn Overlap

[0247] Where, a, , y, 5 = Weighting factors (tuned via AI / ML). The lower cost leads to better beam configuration.

[0248] The A I / ML algorithm performs the following steps for building the cost function.

[0249] At step 1, data collection (e.g., local inputs only) is performed. The data is collected from the serving cell and UEs connected in the serving cell. The data from the serving cell comprises current SSB beam configurations (e.g., width, power, azimuth / elevation angles), PRB utilization, cell load, and interference levels. The data collected from the UEs connected in the serving cell comprises RSRP / RSRQ per beam, SINR, CQI, beam failure reports (RLF), and UE location (if available via GPS or OTDOA).

[0250] At step 2, optimization objectives are defined as shown below in Table 4.Table 4

[0251] At step 3, AI / ML-based optimization is performed. The input comprises the current beam config + UE measurements. The output comprises new beam width, angles, and power.

[0252] At step 4, for AI / ML-based optimization, one or more AI / ML models are implemented. The one or more AI / ML models comprise a reinforcement learning (RL) model and a genetic algorithm (GA). The RL AI / ML model has an action space to adjust the beam width (±5°), steering angle (±10°), and the power (±3dB). The reward for RL AI / ML model R= -CostR=-Cost (minimize cost maximize reward). The genetic algorithm (GA) model evolves beam configurations over generations and selects the best-performing "genes" (beam parameters).

[0253] At step 5, validation and the feedback loop are implemented. For validation, apply a new beam configuration for a short optimization period (e.g., 5-15 mins) and monitor KPIs. For the feedback loop, if the cost decreases, the new beam configuration is kept. If the cost increases, the Al model reverts and adjusts.

[0254] At step 6, periodic re-optimization is performed based on one or more trigger conditions. The one or more trigger conditions comprise time-based (e.g., every 30 mins) and event-based (e.g., SINR drops by 3dB).

[0255] In an embodiment, for an AI / ML-based CCO, the inference input is collected from the UE and the local node. The inference input comprises inputs from the connected UEs. The inputs from the connected UEs comprise connected UE measurement report, connected UE performance, connected UE location information,idle mode UE’s random-access performance, and connected UE radio link failure (RLF) report. The connected UE measurement report comprises LI SS-RSRP, SS- RSRQ, SS-SINR measurement, channel quality indicator (CQI), and cell level and beam level UE measurements. The connected UE performance comprises hybrid automatic repeat request (HARQ) negative acknowledgement (NACK) key performance indicator (KPI) retransmissions (BLER>10%). The connected UE location information comprises latitude and longitude coordinates and serving cell ID. The idle mode UE’s random-access performance (e.g., RACH success rate, number of RACH attempts) to convert to SINR from existing graph of number of PRACH attempts vs SINR.

[0256] In an embodiment, the coverage status includes cell coverage modification(s) (i.e., power parameters, SSB boosting, etc.) or SSB coverage modification(s) (i.e., SSB beam width modifications and beam steering). The cell coverage modifications perform adjustments to cell-level power parameters. The SSB coverage modifications perform adjustments to beam-specific SSB configurations.

[0257] For the AI / ML based CCO, the coverage status includes cell coverage modification and SSB coverage modification. The cell coverage modification includes SSB beam power boosting for the cell coverage state. The cell coverage state is used to solve the predicted CCO issue of a certain predicted cell. The SSB coverage modification includes SSB beam width coverage state, which is used to solve the predicted CCO issue of a certain predicted SSB.

[0258] In SSB coverage modifications, beam-specific adjustments are performed to optimize individual SSB beams (used for initial access). The SSB coverage modifications are managed by AI / ML -based CCO or beam management algorithms (P1 / P2 / P3).

[0259] In an embodiment, downlink (DL) performance is analyzed based on AI / ML CCO, for SON SSB power boosting, SSB beam width shaping and steering for different SSB beam gains on the Pl beam-sweeping procedure. Based on the analysis, a cost function is built. The DL performance is analyzed through parameters such as throughput, interference, and SINR / CQI reporting.

[0260] For the APML-based SON CCO, the coverage status includes the algorithm (e.g., AI / ML model for coverage status) having steps as follows:

[0261] At step 1, SSB beam sweeping starts on the cell level with a default SSB beam-width shape for all SSB sweeping beams in a 5-ms cycle. The default SSB beamwidth shape is operator configurable.

[0262] At step 2, the DL SINR is estimated per SSB sweeping beam in jointly a cost function. The cost function is determined based on the number of UL PRACH counter attempts (obtained from a graph of PRACH attempts vs SINR) of idle UEs, connected UEs' UL CQI reports, HARQ KPI retransmissions (BLER>10%), and Ll- SINR and Ll-RSRP reporting.

[0263] At step 3, each SSB beam-width shape is selected in a configurable way and steps 1 and step 2 are repeated until all SSB beam-width shapes are exhaustively tested and completed.

[0264] At step 4, SSB beam coverage footprint is estimated as a percentage (%) of the default SSB beam shape.

[0265] In an embodiment, the SSB beam coverage footprint is estimated as a percentage (%) of the default SSB beam shape. The SSB beam coverage footprint refers to the effective geographical area covered by the beam compared to its theoretical / default radiation pattern. The percentage of the default SSB beam shape quantifies how much the current beam's coverage deviates from its ideal shape. Stepsof estimation of the SSB beam coverage footprint are as follows: at step 1, a default beam shape is defined. For example, the default beam shape is defined by using the antenna manufacturer’s default radiation pattern (azimuth / elevation) or defined SSB beam characteristics. The key parameters of the default beam shape comprise beamwidth (e.g., 65° azimuth x 15° elevation for a default macro-cell beam) and front- to-back ratio, sidelobe levels. At step 2, actual coverage is measured using one or more data sources. The data sources comprise UE measurements and network node (e.g., gNB) measurements. The UE measurements comprise RSRP / RSRQ reports from UEs per beam and a map of UE locations (via GPS, OTDOA, or cell fingerprinting). The network node (e.g., gNB) measurements comprise SINR distribution per beam and beam failure events (RLF reports). At step 3, the coverage footprint is calculated using geometric overlap. The formula for the coverage % = Actual Coverage Area (from UE reports) / Default Beam Area x 100.

[0266] At step 5, the beam is selected with the argmax DL_SINR performance and the coverage footprint being greater than a configurable threshold corresponding to the DL_SINR and a configurable threshold corresponding to the coverage footprint, from step 3.

[0267] At step 6, the SSB power boosting parameters are adjusted for the selected SSB beam-width shape to improve SINR. The DL SINR is again estimated based on the selected beam-width shapes.

[0268] FIG. 4 illustrates an exemplary flow diagram (400) for performing selective multi-SSB configurations and beam shaping, in accordance with an embodiment of the present disclosure.

[0269] At step (402), a default SSB beam-width shape is selected from a plurality of SSB beam-width shapes. In an embodiment, the selection of the default SSB beam-width shape is operator configurable.

[0270] At step (404), SSB beam sweeping is performed for the selected default SSB beam-width shape on the cell level. The SSB beam sweeping is performed on the cell level to cover the entire area of the cell.

[0271] At step (406), a plurality of parameters is measured. The plurality of parameters comprises uplink (UL) random access channel (RACH) counter, UL hybrid automatic repeat request (HARQ) negative acknowledgment (NACK), UL channel quality indicator (CQI) reports, and Ll-SINR and Ll-RSRP reporting.

[0272] At step (408), a cost function per SSB beam is calculated out of the beam sweeping procedure based on the plurality of measured parameters. A coverage footprint of the SSB beam is calculated. In an aspect, the DL SINR per SSB sweeping beam is calculated in conjunction with the cost function using a combination of plurality of measured parameters. The plurality of parameters further comprises number of UL PRACH counter attempts of idle UEs, UL CQI reports of connected UEs, HARQ KPI retransmissions (e.g., BLER>10%), Ll-SINR and Ll-RSRP reporting. The number of UL PRACH counter attempts of idle UEs may be obtained from a graph of the PRACH attempts versus the SINR. The SSB beam coverage footprint is calculated as a percentage of a default SSB beam shape. The default SSB beam shape is defined by the network operator.

[0273] At step (410), the selected SSB beam-width shape is checked to determine whether the selected SSB beam-width shape is the last SSB beam-width shape. Checking the last SSB beam-width shape is performed to ensure that all SSB beam-width shapes are exhaustively selected.

[0274] At step (412), on detecting that the selected SSB beam-width shape is the last SSB beam-width shape, the SSB beam-width shape having maximum UL- SINR performance and the coverage footprint greater than a configurable threshold is selected from the plurality of SSB beam-width shapes based on the condition.

[0275] At step (414), for the selected SSB beam-width shape, the SSB power boosting parameters are adjusted. The SSB power boosting parameters are adjusted to improve the DL SINR. The DL SINR is calculated based on the selected SSB beamwidth shape.

[0276] At step (416), on detecting that the selected SSB beam-width shape is not the last SSB beam-width shape, a next SSB beam-width shape is selected from the plurality of the SSB beam- width shapes. The steps (404)-(410) are again performed for the SSB beam sweeping.

[0277] FIG. 5A illustrates an exemplary flow diagram for a method (500A) determining a coverage status in the network (106), in accordance with an embodiment of the present disclosure.

[0278] As illustrated in FIG. 5A, the determination of the coverage status is performed by the network node (104). The network node (104) is one of a base station, a central unit (CU) of the base station, a distributed unit (DU) of the base station and an operations, administration, and maintenance (0AM).

[0279] At step (502), the method (500A) includes performing, by the network node (104), synchronization signal block (SSB) beam sweeping for each SSB beamwidth shape of a plurality of SSB beam-width shapes. The network node (104) is configured to perform the SSB beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for each SSB beamwidth shape of the plurality of SSB beam-width shapes.

[0280] To perform SSB beam shaping for each SSB beam-width shape, the network node selects a first SSB beam-width shape from the plurality of SSB beamwidth shapes. The first SSB beam-width shape is a default SSB beam-width shape or an operator-configurable SSB beam-width shape. After performing the beam sweeping of the first SSB beam-width shape, the network node (104) selects a next SSB beam-width shape from the plurality of SSB beam-width shapes. In this way, the network node (104) selects each SSB beam- width shape until all the SSB beam- width shapes are selected.

[0281] Further, in an embodiment, the network node (104) is configured to perform the SSB beam sweeping on the cell level for each SSB sweeping beam of the plurality of SSB sweeping beams for the defined time cycle. The defined time cycle is 5 milliseconds. In an aspect, the defined time cycle is configurable based on the requirements (e.g., High-mobility users (e.g., cars), static environments, dense urban areas). The SSB beam sweeping is performed for each SSB sweeping beam of the plurality of SSB sweeping beams of each SSB beam-width shape of the plurality of SSB beam-width shapes.

[0282] At step (504), upon performing the SSB beam sweeping, the method (500A) includes estimating, by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR). In an operative aspect, the network node (104) estimates the DL_SINR for each SSB sweeping beam of the plurality of SSB sweeping beams as a composite cost function i.e., the cost function is computed collectively using the DL SINR per SSB beam together in conjunction with parameters such as UL PRACH, UL CQI, HARQ KPI LI -SINR and RSRP reporting to make the cost function the composite cost function. Further, the composite cost function is estimated in terms of UL RACH performance.

[0283] The network node (104) repeats the SSB beam sweeping (i.e., step 502) and the estimation of the DL_SINR and the composite cost function (i.e., step 504) for each beam-width shape of the plurality of SSB beam-width shapes, until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed.

[0284] At step 506, the method (500A) includes estimating, by the network node (104), a SSB beam coverage footprint based on the SSB beam sweepings. In anaspect, the network node (104) estimates the SSB beam coverage footprint as a percentage of a defined SSB beam shape. The coverage footprint refers to an effective geographical area covered by the defined SSB beam shape. The coverage footprint is estimated by a formula, for example, Coverage % = Actual Coverage Area (from UE reports) / Default Beam Area x 100.

[0285] At step 508, the method (500A) includes selecting, by the network node (104), at least one SSB beam- width shape from the plurality of SSB beam-width shapes based on one or more conditions. The one or more conditions comprise a first condition and a second condition. The first condition comprises determining a maximum DL UL_SINR from the estimated DL_SINR for each SSB beam shape and the composite cost function. A maximum DL_SINR is determined from each estimated DL_SINR of each SSB sweeping beam. A maximum composite cost function in terms of the UL RACH performance is determined from each estimated composite cost function in terms of the UL RACH performance. The maximum DL UL_SINR is determined from the determined maximum DL_SINR and the maximum composite cost function in terms of UL RACH performance.

[0286] The second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold. The estimated SSB beam coverage footprint is compared with the configurable threshold corresponding to the SSB beam coverage footprint. Upon determining that the estimated SSB beam coverage footprint is greater than the configurable threshold, the network node performs selection of the at least one SSB beam-width shape. In an exemplary embodiment, upon determining that the estimated SSB beam coverage footprint is not greater than the configurable threshold, the steps (i.e., Step 502-step 508) corresponding to the determination of the coverage status are performed. The SSB beam coverage footprint is determined by selecting another SSB beam shape.

[0287] The network node (104) selects at least one SSB beam-width shape upon satisfying the first condition (i.e., SSB beam-width shape having maximum DL UL_SINR) and the second condition (i.e., the estimated SSB beam coverage footprint is greater than the configurable threshold).

[0288] In an operative aspect, the network node (104) selects the at least one SSB beam-width shape having the determined maximum DL UL_SINR and the estimated SSB beam coverage footprint greater than the configurable threshold. Upon determining that the estimated SSB beam coverage footprint greater than the configurable threshold, the network node (104) selects the at least one SSB beam-width shape having the determined maximum DL UL_SINR.

[0289] At step 510, upon selecting the at least one SSB beam- width shape, the method (500A) includes modifying, by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam-width shape. In an aspect, the at least one power boosting parameter comprises, but is not limited to, a physical resource block (PRB). The power boosting allocates more power to the SSB beam physical resource blocks (PRBs) than the rest of the resource blocks used for physical downlink shared channel (PDSCH). The power boosting parameter of the at least one selected SSB beam-width shape is adjusted to maximize the coverage and SINR while minimizing interference and power waste.

[0290] Thereafter, the method (500A) includes estimating, by the network node(104), the DL_SINR based on the at least one selected SSB beam-width shape. In one aspect, the estimated DL_SINR of at least one selected SSB beam-width shape is compared with a DL_SINR threshold. If the estimated DL_SINR of at least one selected SSB beam-width shape is greater than the DL_SINR threshold, determination of the coverage status is again performed until the estimated DL_SINR of at least one selected SSB beam-width shape is less than the DL_SINR threshold. Further, the estimated DL_SINR based on the at least one selected SSB beam-width shape is usedto determine throughput, interference, and SINR / CQI reporting. The DL_SINR is further improved based on the determined throughput, interference and SINR / CQI reporting.

[0291] Further, the method (500A) includes using, by the network node (104), the composite cost function to determine an SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network. The composite cost function evaluates the beam configurations (i.e., SSB beam width and steering azimuth / elevation angle) to ensure that no coverage holes are present, maximizes signal-to-interference ratio, balances UE load distribution (i.e., data traffic) across the SSB beams, and minimizes inter-beam interference.

[0292] FIG. 5B illustrates another exemplary flow diagram for a method (500B) determining the coverage status in the network (104), in accordance with an embodiment of the present disclosure.

[0293] As illustrated in FIG. 5B, the determination of the coverage status is performed by the network node (104). The network node (104) is one of a base station, a central unit (CU) of the base station, a distributed unit (DU) of the base station and an operations, administration, and maintenance (0AM).

[0294] At step 522, the method (500B) includes performing, by the network node (104), synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of a plurality of SSB beam-width shapes.

[0295] The network node (104) performs SSB beam sweeping for each SSB beam-width shape of the plurality of SSB beam-width shapes. The SSB beam sweeping is initiated for the first SSB beam-width shape from the plurality of SSB beam-width shapes. The first SSB beam-width shape is a default beam-width shape or a preconfigured beam-width shape by the network operator. For the first SSB beam-width shape, the network node performs SSB beam sweeping on the cell level for each SSB sweeping beam from the plurality of SSB sweeping beams in the defined time cycle. The network node (104) performs SSB beam sweeping on the cell level by transmitting SSB sweeping beams to cover the area of the cell of the network node (104). The network node (104) performs the SSB beam sweeping in the defined time cycle. The defined time cycle, for example, may be 5ms cycle, i.e., the network node (104) performs the SSB beam sweeping for 5ms for the first SSB beam-width shape using each SSB sweeping beam of the plurality SSB sweeping beams.

[0296] At step (524), the method (500B) includes estimating, by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam. In an aspect, the DL_SINR is estimated for each SSB sweeping beam as a composite cost function.

[0297] In an aspect, the composite cost function comprises a plurality of parameters. The plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell. The inputs received from the at least one of the plurality of UEs connected in the serving cell comprise at least one of a reference signal received power (RSRP), reference signal received quality (RSRQ), a SINR, a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK), a channel quality indicator (CQI), latitude and longitude coordinates, a serving cell identifier (ID), and a radio link failure (RLF). Further, the plurality of the parameters comprises inputs from the local node and the neighbouring node. The inputs from the local node and the neighboring node comprise at least one of radio resource status, coverage status, UE traffic and UE trajectory. The coverage status comprises at least one of SSB beam-width adjustments and SSB azimuth / elevation steering corrections.

[0298] In an embodiment, the DL_SINR is estimated for each SSB sweeping beam. The estimated DL_SINR is used in conjunction with the parameters of the costfunction to compute the composite cost function. Further, in another embodiment, the DL_SINR for each SSB sweeping beam and the cost function are simultaneously estimated (i.e., composite cost function). The composite cost function is further estimated in terms of UL RACH performance.

[0299] At step (526), the method (500B) includes repeating, by the network node (104), the SSB beam sweeping and the estimation for each beam- width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all the SSB beam-width shapes are completed. In an aspect, the network node (104) repeats the SSB beam sweeping and the estimation of the DL_SINR and the cost function for all SSB beam-width shapes of the plurality of SSB beam-width shapes by selecting each SSB beam-width shape sequentially (i.e., selecting SSB beamwidth shape one after another). For example, for 5 SSB beam-width shapes, the network node (104) performs SSB beam sweeping and simultaneously estimates the DL_SINRs and the cost functions for the plurality of SSB sweeping beams for 5 SSB beam-width shapes.

[0300] At step 528, the method (500B) includes estimating, by the network node (104), a SSB beam coverage footprint as a percentage of a defined SSB beam shape based on the SSB beam sweepings. In an aspect, after performing the SSB beam sweeping and the estimation for all SSB beam-width shapes, the network node (104) estimates the coverage footprint. The network node (104) estimates the SSB beam coverage footprint as the percentage of the defined SSB beam shape. In an embodiment, the defined SSB beam shape is provided by the network operator.

[0301] In an aspect, the SSB beam shape refers to the pattern of the signal’s coverage area, which can be wide (large footprint) or narrow (small footprint), depending on the configuration. The SSB beam shape helps in determining how the SSB sweeping beams are transmitted and the area over which the beams are detected by the UE. A narrow beam provides a smaller coverage area, and a wider beamprovides coverage over a larger area. The network operator defines the beam shapes based on one or more factors. The one or more factors include a type of area (e.g., urban, rural, indoor, outdoor), cell size (e.g., macro, small cell, etc.), traffic demand and coverage requirements, frequency band being used (low-band, mid-band, mmWave).

[0302] The SSB beam coverage footprint refers to effective geographical area covered by a SSB beam compared to the theoretical / default radiation pattern of the SSB beam. The percentage of the defined SSB beam shape quantifies how much the current beam's coverage deviates from an ideal shape of the SSB beam. The coverage footprint is computed by the formula coverage % = Actual Coverage Area (from UE reports) / Default Beam Area x 100.

[0303] At step (530), the method (500B) includes selecting, by the network node (104), at least one SSB beam- width shape from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated SSB beam coverage footprint greater than a configurable threshold. The maximum DL UL_SINR is determined from the estimated DL_SINRs for the plurality of SSB sweeping beams of each SSB beam-width shape of the plurality of SSB beam-width shapes and the composite cost function. The higher the DL UL_SINR, the better the signal quality for the UE. Further, a wider beam- width shape (i.e., shape covering a larger coverage area), lowers the SINR. A narrow beam-width shape (i.e., a shape that focuses on a small coverage area) results in higher SINR.

[0304] The network node (104) determines whether the estimated SSB beam coverage footprint is greater than the configurable threshold. The configurable threshold is provided by the network operator. The configurable threshold defines the acceptable or desired coverage area. For example, if the network operator sets the configurable threshold for the coverage footprint as up to 100 meters in radius and theestimated coverage footprint is 130 meters. Then, in this case, the estimated coverage footprint (i.e., 130 meters) exceeds the configurable threshold (i.e., 100 meters).

[0305] Upon determining that the estimated SSB beam coverage footprint is greater than the configurable threshold, the network node selects at least one SSB beam-width shape from the plurality of SSB beam-width shapes having the maximum DL UL_SINR.

[0306] At step 532, the method (500B) includes modifying, by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam-width shape. Upon performing the selection of the at least one SSB beam-width shape based on the maximum DL UL_SINR and determination of the estimated SSB beam coverage footprint greater than the configurable threshold, the network node (104) adjusts the at least one SSB power boosting parameter of the at least one selected SSB beam-width shape. In an aspect, the power boosting is a process to adjust the transmission power of the SSB sweeping beams to ensure that the SSB sweeping beams are received with sufficient quality by the UE. The power boosting is important when different SSB beam width shapes are used during beam sweeping. The power boosting parameter defines how much additional power is applied to the SSB sweeping beam transmission. The power boosting parameter is configured at the network node (e.g., base station). The power boosting helps to compensate for beam characteristics (e.g., width and directionality). In an aspect, the at least one power boosting parameter comprises, but is not limited to, a physical resource block (PRB). The power boosting allocates more power to the SSB beam physical resource blocks (PRBs) than the rest of the resource blocks used for physical downlink shared channel (PDSCH).

[0307] The method (500B) further includes estimating, by the network node, the DL_SINR based on the at least one selected SSB beam- width shape. The DL_SINR is estimated based on the at least one selected SSB beam-width shape to determine whether the estimated DL_SINR is greater than the DL_SINR threshold.

[0308] In an embodiment, upon determining the estimated DL_SINR for the at least one selected SSB beam- width shape is less than the DL_SINR threshold, determination of the coverage status is successful (i.e., the network exhibits optimal CCO performance and high SINR). Further, upon determining the estimated DL_SINR for the at least one selected SSB beam-width shape is greater than the DL_SINR threshold, the determination of the coverage status is unsuccessful. Upon determining the coverage status is unsuccessful, determination of the coverage status is iteratively performed until the estimated DL_SINR for the at least one selected SSB beam-width shape is less than the DL_SINR threshold).

[0309] The method (500B) further includes using, by the network node, the cost function to determine a SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network. For managing the CCO, the SSB beam width helps to optimize coverage holes or overshooting areas. The wider beam width ensures coverage in sparse areas, and the narrow beam width avoids excessive overlap. Further, the narrow SSB beam width improves the SINR by focusing energy on the UE and minimizes leakage into neighboring cells, reducing interference. For example, in a dense urban area, SSB beams with narrow widths are used to increase SINR, reducing interference and improving capacity. In a suburban or rural area, SSB beams with wider widths provide better coverage, supporting CCO by reducing the number of required cells.

[0310] Further, performing steering of the azimuth and / or elevation angle for managing the CCO and the SINR in the network. The azimuth angle is a horizontal angle of beam direction, measured from a reference direction (typically North), ranging from 0° to 360°. The elevation angle is a vertical angle of the beam, usually relative to the horizon (0°), allowing the beam to be directed upward or downward. The steering adjusts the azimuth and elevation angles for directing the beams to fill coverage holes, reduce overshooting, or better serve high-demand areas. The steeringazimuth / elevation angle focuses the SSB sweeping beams toward the UE, enhancing signal strength and reducing interference. For example, if the UE is located at the cell edge, the beam can be steered (azimuth) precisely toward the UE and slightly down- tilted (elevation) to maximize the SINR. The steering of the azimuth angle helps to avoid directing the SSB sweeping beams into high-interference zones (e.g., areas with overlapping cells or sectors). In another example, in suburban areas with sparse UEs, a wide SSB beam width with low tilt (near 0°) ensures broad coverage. In urban areas, down-tilting the elevation angle (e.g., -6°) can help reduce overshooting and interference to neighboring cells.

[0311] In an aspect, the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications. The cell coverage modifications refer to the changes and adjustments made to the coverage area of the cell for maintaining optimal coverage, capacity, and performance as per network requirements. The cell coverage modifications optimize the network’s performance, ensure that coverage meets the demands of users while minimizing interference and improving capacity. In an aspect, the cell coverage modifications comprise modifying parameters (e.g., beamforming, transmit power, antenna tilt, and small cell deployment). The coverage area of the cell is dynamically changed to meet specific environmental and network needs. The SSB beam coverage modifications refer to changes made to the characteristics of the SSB beams to transmit the SSB beams in the desired direction. The SSB beam coverage modifications ensure reliable UE detection, efficient initial access, and optimized coverage and interference control. In an aspect, the SSB beam coverage modifications comprise beam shape modifications, beam steering, power control, number of beams, etc.

[0312] In an operative aspect, the cell coverage modifications comprise SSB beam power boosting. The SSB beam power boosting SSB beam power boosting enhances coverage and initial access for the UE. The SSB beam power boostingincreases the power of the SSB beams to improve signal strength, causing UEs to detect and synchronize with the network.

[0313] In an operative aspect, the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering. In an aspect, the SSB beam coverage modifications modify the beam width of the SSB beams to refine the way signals are directed to the UE, leading to improved signal-to-noise ratio (SINR) and network efficiency. The SSB beam width modifications dynamically modify beam patterns for specific environments and user distributions, enhancing coverage at cell edges and mitigating coverage holes. In an aspect, the beam steering dynamically directs SSB beams toward the UE instead of broadcasting in all directions. The beam steering improves signal strength, coverage, capacity, and energy efficiency.

[0314] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented.

[0315] As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. The processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (660) 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) (660) 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.

[0316] The main memory (630) may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640)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 (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and / or instructions. Exemplary mass storage device (650) 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.

[0317] The bus (620) communicatively couples the processor (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) 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 (670) to the computer system.

[0318] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick, and a cursor control device, may also be coupled to the bus (620) 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) (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.

[0319] The exemplary computer system (600) 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 determining a coverage status in a networkis disclosed. The method comprises performing, by a network node, synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam-width shapes and upon performing, estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR). The method comprises estimating, by the network node, a SSB beam coverage footprint and selecting, by the network node, at least one SSB beam-width shape from the plurality of SSB beamwidth shapes based on one or more conditions. The method further comprises upon estimating, modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

[0320] The exemplary computer system (600) 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 determining a coverage status in a network is disclosed. The method comprises performing, by a network node, synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of a plurality of SSB beam-width shapes and estimating, by the network node, a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam. The method comprises repeating, by the network node, the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed and estimating, by the network node, a SSB beam coverage footprint as a percentage of a defined SSB beam shape based on the SSB beam sweepings. The method further comprises selecting at least one SSB beam-width shape from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold and modifying, by the network node, at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

[0321] The present disclosure provides technical advancements related to coverage management (i.e., coverage and capacity optimization (CCO)). The advancement addresses the limitations of existing solutions by selecting and adjusting different SSB beam shapes with different beam-width configurations and SSB beam power (SSB power boosting). The beam shape and suitable SSB power are selected for each SSB beam of the beam-sweeping set per sector. Intra-cell and inter-cell beam overlapping is minimized, which further preserves the sector coverage and maximizes SINR for different inter-site distances and geography. The downlink coverage and throughput are also improved for the multi-SSB beam-width beams.

[0322] 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.ADVANTAGES OF THE PRESENT DISCLOURE

[0323] The present disclosure provides a system and a method for selective multi-SSB configurations and beam shaping, and determining coverage status in a network.

[0324] The present disclosure supports dynamic SSB beams steering with the multi SSB beam shaping.

[0325] The present disclosure supports the SSB beam boosting with the SSB beam shaping.

[0326] The present disclosure helps to minimize the intra-cell and inter-cell beam overlapping based on the selection of the appropriate beam shape and the appropriate SSB power.

[0327] The present disclosure improves downlink (DL) coverage and uplink (UL) random access channel (RACH) performance.

[0328] The present disclosure improves DL synchronization signal-signal to interference plus noise ratio (SS-SINR).

[0329] The present disclosure reduces overlap areas and DL inter-cell interference.

[0330] The present disclosure improves the coverage and the correspondingSINR by dynamically shaping the beam- widths of the multi-SSB beams along with the SSB beam boosting. The SINR is improved by keeping the corresponding coverage on a certain desired level.

[0331] The present disclosure improves the telecom network capabilities, in 5G and beyond technologies.

Claims

CLAIMS1. A method (500 A) for determining a coverage status in a network (106), the method (500A) comprising: performing (502), by a network node (104), synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam- width shapes; upon performing, estimating (504), by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR); upon estimating the DL_SINR, estimating (506), by the network node (104), a SSB beam coverage footprint; selecting (508), by the network node (104), at least one SSB beamwidth shape from the plurality of SSB beam-width shapes based on one or more conditions; and upon selecting, modifying (510), by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

2. The method (500A) as claimed in claim 1, wherein the performing of SSB beam sweeping comprises: performing, by the network node (104), the SSB beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of the plurality of SSB beam-width shapes, wherein the first SSB beam-width shape is a default SSB beam-width shape or an operator-configurable SSB beam-width shape.

3. The method (500A) as claimed in claim 2, wherein the defined time cycle is 5 milliseconds.

4. The method (500A) as claimed in claim 2, wherein the estimation comprises: upon performing the SSB beam sweeping, estimating, by the network node (104), the DL_SINR for each SSB sweeping beam as a composite cost function.

5. The method (500 A) as claimed in claim 4, comprising: repeating, by the network node (104), the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed.

6. The method (500A) as claimed in claim 4, wherein the composite cost function comprises a plurality of parameters, wherein the plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

7. The method (500A) as claimed in claim 6, wherein the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise at least one of: connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channel quality indicator (CQI) reports and level 1 (Ll)-SINR and Ll-reference signal received power (RSRP) reporting.

8. The method (500A) as claimed in claim 7, wherein the connected UE measurement report comprises at least one of a LI -synchronization signal -RSRP (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a SS- SINR measurement, CQI, cell level UE measurements and beam level UE measurements, wherein the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%, wherein the connected UE location information comprises at least one of latitude and longitude coordinates and a serving cell identifier (ID), and a radio link failure (RLF), wherein the idle mode UE random access performance comprises at least one of a random access channel (RACH) success rate, number of RACH attempts to convert to a SINR from a graph of number of physical random access channel (PRACH) attempts vs the SINR, and wherein the number of UL PRACH counter attempts of a plurality of idle UEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs.

9. The method (500A) as claimed in claim 1, comprising: estimating, by the network node (104), the SSB beam coverage footprint as a percentage of a defined SSB beam shape, wherein the defined SSB beam shape is a default SSB beam shape.

10. The method (500 A) as claimed in claim 1 , wherein the one or more conditions comprises a first condition and a second condition, wherein the first condition comprises determining a maximum DL UL_SINR from the estimated DL_SINR for each SSB beam shape and the composite cost function, and wherein the second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold.

11. The method (500A) as claimed in claim 11, comprising:selecting, by the network node (104), at least one SSB beam-width shape having the determined maximum DL UL_SINR and the estimated SSB beam coverage footprint greater than the configurable threshold.

12. The method (500A) as claimed in claim 11, comprising: estimating, by the network node (104), the DL_SINR based on the at least one selected SSB beam-width shape.

13. The method (500 A) as claimed in claim 1, wherein the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications, wherein the cell coverage modifications comprise SSB beam power boosting, and wherein the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering.

14. The method (500A) as claimed in claim 4, comprising: using, by the network node (104), the composite cost function to determine a SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network (106).

15. The method (500A) as claimed in claim 1, wherein the network node (104) is one of an operations, administration, and maintenance (0AM), a base station, a central unit (CU) of the base station and a distributed unit (DU) of the base station.

16. A method (500B) for determining a coverage status in a network (106), the method (500B) comprising:performing (522), by a network node (104), synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beamwidth shape of a plurality of SSB beam-width shapes; estimating (524), by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam; repeating (526), by the network node (104), the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beamwidth shapes until the SSB beam sweeping and the estimation of all SSB beam- width shapes are completed; upon repeating, estimating (528), by the network node (104), a SSB beam coverage footprint as a percentage of a defined SSB beam shape; selecting (530), by the network node (104), at least one SSB beamwidth shape from the plurality of SSB beam- width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold; and modifying (532), by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam- width shape.

17. The method (500B) as claimed in claim 16, wherein the first SSB beam-width shape is a default SSB beam-width shape or an operator-configurable SSB beam- width shape, and wherein the defined SSB beam shape is a default SSB beam shape.

18. The method (500B) as claimed in claim 16, comprising: estimating, by the network node (104), the DL_SINR for each SSB sweeping beam as a composite cost function.

19. The method (500B) as claimed in claim 18, wherein the composite cost function comprises a plurality of parameters, wherein the plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

20. The method (500B) as claimed in claim 19, wherein the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise at least one of: connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channel quality indicator (CQI) reports and level 1 (Ll)-SINR and Ll-reference signal received power (RSRP) reporting.

21. The method (500B) as claimed in claim 20, wherein the connected UE measurement report comprises at least one of a LI -synchronization signal - RSRP (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a SS- SINR measurement, CQI, cell level UE measurements and beam level UE measurements, wherein the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%, wherein the connected UE location information comprises at least one of latitude and longitude coordinates and a serving cell identifier (ID), and a radio link failure (RLF), wherein the idle mode UE random access performance comprises at least one of a random access channel (RACH) success rate, number of RACH attempts to convert to a SINR from a graph of number of physical random access channel (PRACH) attempts vs the SINR, and wherein the number of UL PRACH counter attempts of a plurality of idleUEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs.

22. The method (500B) as claimed in claim 16, wherein the defined time cycle is 5 milliseconds.

23. The method (500B) as claimed in claim 18, wherein the maximum DL UL_SINR is determined from each estimated DL_SINR for each SSB sweeping beam and the composite cost function.

24. The method (500B) as claimed in claim 16, comprising: estimating, by the network node (104), the DL_SINR based on at least one selected SSB beam-width shape.

25. The method (500B) as claimed in claim 16, wherein the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications, wherein the cell coverage modifications comprise SSB beam power boosting, and wherein the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering.

26. The method (500B) as claimed in claim 18, comprising: using, by the network node (104), the composite cost function to determine a SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network (106).

27. The method (500B) as claimed in claim 17, wherein the network node (104) is one of an operations, administration, and maintenance (0AM), a base station, a central unit (CU) of the base station and a distributed unit (DU) of the base station.

28. A system (108) for determining a coverage status in a network (106), the system (108) comprising: an execution unit (118) configured to perform synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam- width shapes; upon performing, an estimation unit (120) is configured to estimate a downlink signal to interference plus noise ratio (DL_SINR); upon estimating the DL_SINR, the estimation unit (120) configured to estimate a SSB beam coverage footprint; a selection unit (122) configured to select at least one SSB beam-width shape from the plurality of SSB beam-width shapes based on one or more conditions; and upon selecting, the execution unit ( 118) is configured to modify at least one SSB power boosting parameter of the at least one selected SSB beamwidth shape.

29. The system (108) as claimed in claim 28, wherein the performing of SSB beam sweeping comprises: the execution unit (118) configured to perform the SSB beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beam-width shape of the plurality of SSB beam-width shapes, wherein the first SSB beam-width shape is adefault SSB beam-width shape or an operator-configurable SSB beam-width shape.

30. The system (108) as claimed in claim 29, wherein the defined time cycle is 5 milliseconds.

31. The system (108) as claimed in claim 28, wherein the estimation comprises: upon performing the SSB beam sweeping, the estimation unit (120) is configured to estimate the DL_SINR for each SSB sweeping beam as a composite cost function.

32. The system (108) as claimed in claim 31 , wherein the execution unit ( 118) is configured to repeat the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beam-width shapes until the SSB beam sweeping and the estimation of all SSB beam-width shapes are completed.

33. The system (108) as claimed in claim 31 , wherein the composite cost function comprises a plurality of parameters, wherein the plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

34. The system (108) as claimed in claim 33, wherein the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channel quality indicator (CQI) reportsand level 1 (Ll)-SINR and Ll-reference signal received power (RSRP) reporting.

35. The system (108) as claimed in claim 34, wherein the connected UE measurement report comprises at least one of a LI -synchronization signal - RSRP (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a SS- SINR measurement, CQI, cell level UE measurements and beam level UE measurements, wherein the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%, wherein the connected UE location information comprises at least one of latitude and longitude coordinates and a serving cell identifier (ID), and a radio link failure (RLF), wherein the idle mode UE random access performance comprises at least one of a random access channel (RACH) success rate, number of RACH attempts to convert to a SINR from a graph of number of physical random access channel (PRACH) attempts vs the SINR, and wherein the number of UL PRACH counter attempts of a plurality of idle UEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs.

36. The system (108) as claimed in claim 28, wherein the estimation unit (120) is configured to estimate the SSB beam coverage footprint as a percentage of a defined SSB beam shape, and wherein the defined SSB beam shape is a default SSB beam shape.

37. The system (108) as claimed in claim 28, wherein the one or more conditions comprises a first condition and a second condition, wherein the first condition comprises determining a maximum DL UL_SINR from the estimatedDL_SINR for each SSB beam shape and the composite cost function, and wherein the second condition comprises determining whether the estimated SSB beam coverage footprint is greater than a configurable threshold.

38. The system (108) as claimed in claim 37, wherein the selection unit (122) is configured to select the at least one SSB beam-width shape having the determined maximum DL UL_SINR and the estimated SSB beam coverage footprint greater than the configurable threshold.

39. The system (108) as claimed in claim 38, wherein the estimation unit (120) is configured to estimate the DL_SINR based on the at least one selected SSB beam-width shape.

40. The system (108) as claimed in claim 28, wherein the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications, wherein the cell coverage modifications comprise SSB beam power boosting, and wherein the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering.

41. The system (108) as claimed in claim 31, wherein the estimation unit (120) is configured to use the composite cost function to determine a SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network (106).

42. The system (108) as claimed in claim 28, wherein the network node (104) is one of an operations, administration, and maintenance (0AM), a base station,a central unit (CU) of the base station and a distributed unit (DU) of the base station.

43. A system (108) for determining a coverage status in a network (106), the system (108) comprising: an execution unit (118) configured to perform synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beamwidth shape of a plurality of SSB beam-width shapes; an estimation unit (120) configured to estimate a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam; the execution unit (118) configured to repeat the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beamwidth shapes until the SSB beam sweeping and the estimation of all SSB beam- width shapes are completed; upon repeating, the estimation unit (120) configured to estimate a SSB beam coverage footprint as a percentage of a defined SSB beam shape; a selection unit (122) configured to select at least one SSB beam-width shape from the plurality of SSB beam-width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold; and the execution unit (118) configured to modify at least one SSB power boosting parameter of the at least one selected SSB beam- width shape.

44. The system (108) as claimed in claim 45, wherein the first SSB beam-width shape is a default SSB beam-width shape or an operator-configurable SSB beam- width shape, and wherein the defined SSB beam shape is a default SSB beam shape.

45. The system (108) as claimed in claim 43, wherein the estimation unit (120) is configured to estimate the DL_SINR for each SSB sweeping beam as a composite cost function.

46. The system (108) as claimed in claim 45, wherein the composite cost function comprises a plurality of parameters, wherein the plurality of parameters comprises inputs received from at least one of a plurality of user equipments (UEs) connected in a serving cell.

47. The system (108) as claimed in claim 46, wherein the inputs received from the at least one of the plurality of UEs connected in the serving cell comprise at least one of: connected UE measurement report, connected UE performance, connected UE location information, idle mode UE random access performance, connected UE radio link failure (RLF) report, number of UL PRACH counter attempts, connected UE uplink (UL) channel quality indicator (CQI) reports and level 1 (Ll)-SINR and Ll-reference signal received power (RSRP) reporting.

48. The system (108) as claimed in claim 47, wherein the connected UE measurement report comprises at least one of a LI -synchronization signal - RSRP (SS-RSRP), a SS-reference signal received quality (SS-RSRQ), a SS- SINR measurement, CQI, cell level UE measurements and beam level UE measurements, wherein the connected UE performance comprises a hybrid automatic repeat request (HARQ) negative acknowledgment (NACK) key performance indicator (KPI) retransmission as block error rate (BLER) > 10%, wherein the connected UE location information comprises at least oneof latitude and longitude coordinates and a serving cell identifier (ID), and a radio link failure (RLF), wherein the idle mode UE random access performance comprises at least one of a random access channel (RACH) success rate, number of RACH attempts to convert to a SINR from a graph of number of physical random access channel (PRACH) attempts vs the SINR, and wherein the number of UL PRACH counter attempts of a plurality of idle UEs is obtained from a graph of the PRACH attempts vs SINR of the plurality of idle UEs.

49. The system (108) as claimed in claim 43, wherein the defined time cycle is 5 milliseconds.

50. The system (108) as claimed in claim 43, wherein the maximum DL UL_SINR is determined from each estimated DL_SINR for each SSB sweeping beam and the composite cost function.

51. The system (108) as claimed in claim 43, wherein the estimation unit (120) is configured to estimate the DL_SINR based on the at least one selected SSB beam-width shape.

52. The system (108) as claimed in claim 43, wherein the coverage status comprises at least one of cell coverage modifications or SSB beam coverage modifications, wherein the cell coverage modifications comprise SSB beam power boosting, and wherein the SSB beam coverage modifications comprise at least one of SSB beam width modifications and beam steering.

53. The system (108) as claimed in claim 45, wherein the estimation unit (120) is configured to use the composite cost function to determine a SSB beam width and a steering azimuth / elevation angle for managing coverage and capacity optimization (CCO) and SINR in the network (106).

54. The system (108) as claimed in claim 45, wherein the network node (104) is one of an operations, administration, and maintenance (0AM), a base station, a central unit (CU) of the base station and a distributed unit (DU) of the base station.

55. 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 (500A) for determining a coverage status in a network (106), the method (500 A) comprising: performing (502), by a network node (104), synchronization signal block (SSB) beam sweeping for each SSB beam-width shape of a plurality of SSB beam- width shapes; upon performing, estimating (504), by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR); upon estimating the DL_SINR, estimating (506), by the network node (104), a SSB beam coverage footprint; selecting (508), by the network node (104), at least one SSB beamwidth shape from the plurality of SSB beam-width shapes based on one or more conditions; and upon selecting, modifying (510), by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam-width shape.

56. 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 (500B) for determining a coverage status in a network (106), the method (500B) comprising: performing (522), by a network node (104), synchronization signal block (SSB) beam sweeping on a cell level for each SSB sweeping beam of a plurality of SSB sweeping beams in a defined time cycle for a first SSB beamwidth shape of a plurality of SSB beam-width shapes; estimating (524), by the network node (104), a downlink signal to interference plus noise ratio (DL_SINR) for each SSB sweeping beam; repeating (526), by the network node (104), the SSB beam sweeping and the estimation for each beam-width shape of the plurality of SSB beamwidth shapes until the SSB beam sweeping and the estimation of all SSB beam- width shapes are completed; upon repeating, estimating (528), by the network node (104), a SSB beam coverage footprint as a percentage of a defined SSB beam shape; selecting (530), by the network node (104), at least one SSB beamwidth shape from the plurality of SSB beam- width shapes having a maximum DL UL_SINR and the estimated beam coverage footprint greater than a configurable threshold; and modifying (532), by the network node (104), at least one SSB power boosting parameter of the at least one selected SSB beam- width shape.

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