Entrepreneurial Awareness Carrier Consolidation Management System

Abstract

The invention is a system used for interference-aware dynamic management of carrier aggregation resources in LTE-Forward and LTE-Forward Pro mobile networks, featuring: a multi-carrier metric aggregator (1) that collects comprehensive Layer 1 and Layer 3 metrics for each component carrier, an interference analysis engine (2) that performs analysis using deep signal processing techniques, a spectral efficiency calculator (3) that calculates theoretical capacity and estimates achievable data transfer rate, a user equipment capability analyzer (4) that decomposes the supported band combinations of the device, the maximum aggregated bandwidth, and the multiple-input multiple-output (MIMO) capability, a traffic demand forecaster (5) that predicts future traffic requirements by generating buffer status reports, quality-of-service class-based traffic classification, and application layer protocol analysis, and a historical interference database (6) that stores historical interference patterns in time series format.It includes a machine learning-based interference predictor (7) that predicts future interference levels using a multilayer long short-term memory neural network architecture, a carrier sorting algorithm (8) that implements a multi-criteria decision-making approach, a secondary cell activation decision engine (9) that implements state machine logic, a cross-carrier timer (10) that uses a modified proportional fair timing metric, a radio source control signaling controller (11) that generates 3GPP TS 36.331 compliant RRC link reconstruction messages, an inter-cell interference coordination interface (12) that provides binary coordination with neighboring enhanced nodes B via the X2 implementation protocol, and a performance monitor (13) that implements a closed-loop feedback system.

Description

1 TARIFF Entrepreneurial Awareness Carrier Consolidation Management System Technical Area 5 The invention enables carrier aggregation in LTE-Advanced and LTE-Advanced Pro mobile networks. (Carrier Aggregation - CA) resources' interference-aware dynamics It is related to cell management. Specifically, secondary cell (SCell) activation / deactivation. discontinuation decisions, component carrier (CC) selection, and timing. The policy criteria for inter-cell interaction, common channel interaction, and neighboring channel interaction are 10. It offers a system and method developed for optimization, taking these factors into consideration. State of the Art In current systems, secondary cell activation decisions are primarily based on user equipment. It is taken according to the ability and timing buffer status. Intercellular interference 15 levels, spectral efficiency per carrier, and common channel interference patterns are taken into consideration. is not being taken into account. Overall data transfer speed is being increased by enabling high-interference carriers. It is falling. Carrier selection is generally based on load balancing or frequency band preferences (in-band or between bands). (limited by carrier aggregation between neighboring cells). Real-time interference dynamics, neighboring cell 20 The effectiveness and time-dependent channel conditions are being ignored. When simultaneous scheduling is performed across multiple carriers, carrier-specific channel quality is considered. The indicator and the level of initiative are not coordinated. Resource allocation is insufficient. Spectral efficiency cannot be maximized. Aggressive carrier combining (5 carrier combining) significantly reduces user equipment battery consumption by 25%. It significantly increases performance gain. Optimization of the balance between performance gain and power cost. This is not done. High power costs that provide low data transfer speed improvement. Carriers are being activated. In the use of non-adjacent carrier aggregation within the band, shielding band optimization is crucial for preventing spoofing. Diffusion reduction and mixture degradation treatment are insufficient. Minimizing neighboring carrier interference 30 It cannot be done. In Time Division Duplex (TDD) carrier aggregation, different upstream / downstream connections are used. Its configurations include cross-subframe initiative, enhanced interference mitigation, and traffic. No adaptation coordination is being carried out. In conclusion, due to the negative aspects described above and the current solutions regarding issue 35 Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. 2 Purpose of the Invention The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main purpose of the invention is to enable carrier aggregation in LTE-Forward and LTE-Forward Pro mobile networks. The goal is to ensure the entrepreneurial and dynamic management of resources. Another aim of the invention is to enable secondary cell activation / deactivation decisions to be made by the components. carrier selection and timing policy inter-cell initiative, joint channel initiative and The aim is to ensure optimization by taking into account neighboring channel intervention criteria. Another aim of the invention is to provide real-time interference measurements, spectral efficiency metrics, 10 By analyzing user equipment capabilities and traffic demands, we can determine the most suitable option for each user. The goal is to define the carrier aggregation configuration; interference reduction and spectral optimization across the network. The goal is to optimize efficiency; real-time signal-to-noise-interference for each carrier. The frequency ratio (SINR) is calculated by ranking the carriers to determine the highest interference-thermal ratio. The goal is to prioritize carriers that provide spectral efficiency; traffic demand and 15 Continuous monitoring of interference conditions, low data transfer speed / high interference carriers This ensures that it is disabled beforehand; via the X2 interface with neighboring cells. ensuring coordination of initiatives, almost without inter-cellular initiative coordination. Subframes, cell edge interference reduction using reduced power subframes The aim is to ensure its realization; learning from past entrepreneurial patterns, long-term and short-term 20 Predicting future interference levels with memory networks, using predictive carrier transitions. The goal is to minimize service disruption; multi-carrier initiative awareness. proportional fair timing implementation, channel quality indicator per carrier normalization aims to ensure initiative-weighted resource allocation; data transfer Perform a power cost analysis with speed gain, with a minimum data transfer speed improvement threshold of 25. The goal is to disable the underlying carriers; dynamic time-division Enhanced interference mitigation and traffic adaptation coordination in duplex configurations. ensuring cross-subframe interference detection and mitigation, flexible The goal is to increase capacity through uplink / downlink ratio optimization. The structural and characteristic features and all the advantages of the invention are given below in Figure 30. Thanks to the detailed explanation written with references to the diagram, it becomes clearer. It will be understood. Figures that will help understand the invention. Figure 1 shows the general architecture of the system that is the subject of the invention. 35 Explanation of Part References 3 1. Multi-carrier measurement collector 2. Business analysis engine 3. Spectral Efficiency Calculator 4. User equipment capability analyzer 5. Traffic Demand Forecaster 5 6. Historical startup database 7. Machine learning-based venture predictor 8. Carrier sorting algorithm 9. Secondary cell activation decision engine 10. Cross carrier timer 10 11. Radio source control signal controller 12. Intercellular interaction coordination interface 13. Performance audience Detailed Description of the Invention 15 In this detailed description, the preferred configurations of the system that is the subject of the invention are listed only. This will contribute to a better understanding of the subject and will not have any limiting effects. The invention relates to carrier aggregation resources in LTE-Forward and LTE-Forward Pro mobile networks. A multi-layered AI-powered optimization for initiative-aware dynamic management. It offers a framework. 20 The system is a multi-layer system that collects comprehensive Layer 1 and Layer 3 measurements for each component carrier. Carrier measurement collector (1), an initiative that performs analysis with deep signal processing techniques. analysis engine (2), which calculates theoretical capacity and estimates the achievable data transfer speed Spectral efficiency calculator (3) supports band combinations of the device, maximum User 25 separates combined bandwidth and multiple input multiple output (MIMO) capability. equipment capability analyzer (4), buffer status reports, service quality class based traffic future traffic by classifying and performing application layer protocol analysis Traffic demand forecaster (5) which estimates the requirements, past in time series format Historical startup database (6) storing startup patterns, multilayer long short term Machine 30 predicts future levels of interference using memory neural network architecture. learning-based venture predictor (7), applying a multi-criteria decision-making approach Carrier sorting algorithm (8), secondary cell implementing state machine logic activation decision engine (9), cross using modified proportional fair timing criterion Carrier timer (10), 3GPP TS 36.331 compliant RRC link reconfiguration Radio source control signal controller (11) that produces messages, X2 application 35 Intercellular protocol enabling bilateral coordination with neighboring enhanced node Bs 4 Initiative coordination interface (12), closed-loop feedback system implementing performance Includes viewers (13). Multiple carrier measurement collector (1), periodic and event-triggered measurement for each component carrier. It collects reports. Broadband CQI (one value for all carriers), subband CQI (each source block). (for the group), it separates RSRP / RSRQ / RSSI values. Measurement reporting period: 200ms to 5 Configurable between 10240ms. RRC Measurement Report according to 3GPP TS 36.331. It processes the messages. The interference analysis engine (2) calculates the SINR per carrier from the collected measurements: SINR_k = RSRP_k / (RSSI_k - RSRP_k + Thermal_Noise). Separates interference components: intercellular. interference (from neighboring cells), common channel interference (same frequency), neighboring channel interference (adjacent 10 (Leakage from carriers). Interference with Fast Fourier Transform (FFT) based spectral analysis. It identifies the sources. It classifies the interference-thermal ratio (IoT) as: Low (< 3 dB), Medium (3-10 dB), High (> 10 dB). The spectral efficiency calculator (3) applies the Shannon-Hartley theorem: SE_k = BG_k × log₂(1 + SINR_k) where BG_k is the bandwidth of carrier k (in MHz). Theoretical spectral 15 Compares productivity to actual MCS: Productivity_Rate = Actual_Data_Rate / Expected SINR using the Theoretical_Capacity.LTE MCS table (3GPP TS 36.213) It maps the MCS index and data rate. Normalized spectral efficiency score for each carrier. It produces (between 0-100). User equipment capability analyzer (4) parses UE capability information element: supported 20 CA combinations (in-band contiguous, in-band non-contiguous, inter-band), maximum Number of carriers (from 1CC to 5CC), total number of layers, supported bandwidth. Classes (A: up to 100MHz, B: 20MHz, C: etc.). Category controls capabilities: Category Category 6 (300 Mbps DL), Category 12 (600 Mbps DL), Category 16 (1 Gbps DL). UE's power class. (23dBm or 26dBm) is determined. 25 Traffic Demand Forecaster (5) analyzes Buffer Status Reports (BSR): short BSR (single Logical channel group), long BSR (four LCG). Traffic priority according to QCI values. It classifies: QCI 1-4 (guaranteed bit rate), QCI 5-9 (unguaranteed bit rate). Application layer It identifies the service type through protocol analysis (HTTP / HTTPS, RTP / RTCP, SIP). Autoregressive integrated circuit. It predicts future traffic demand over the next 60 seconds using the Moving Average (ARIMA) model. 30 Instantaneous traffic load thresholds are defined: Low (< 5 Mbps), Medium (5-20 Mbps), High (> 20 Mbps). The historical venture database (6) stores timestamped venture records: (time_stamp, Time series data (UE_ID, cell_ID, carrier_ID, SINR, IoT, intervention_type, location_information). It uses a database structure (InfluxDB or TimescaleDB). Data retention policy: for the last 90 days. Raw data, hourly aggregations for 90-365 days, daily aggregations for older than 1 year. 35 Spatial indexing optimizes geographic querying. For data size management. Compression algorithms (gzip, LZ4) are applied. Machine learning-based interference predictor (7) trains LSTM neural network architecture: input The layer takes 50 attributes (historical SINR values, time information, spatial coordinates, cell (payload, neighboring cell information). 3 hidden LSTM layers (128, 64, 32 units) temporal dependencies 5 It learns. The output layer generates a prediction of the future 60-second interference level (each carrier). (for). Adam uses an optimization algorithm (learning rate: 0.001). Mini-batch training. (Batch size: 256). The model retrains every 24 hours. Average absolute percentage. It measures the accuracy of prediction using the MAPE (Material Percentage of Prediction) error, target: MAPE < 20%. The carrier ranking algorithm (8) calculates multidimensional scores for each user and each carrier. 10 Four main criteria: (1) Spectral efficiency score (SE_score = 0-100, according to Shannon capacity) (2) Initiative score (Initiative_score = 100 - IoT_normalized), (3) Coverage score (Cap_score = RSRP_normalized), (4) Load balancing score (Load_score = 100 - (PRB_usage_percentage). Weighted total: Total_Score_k = 0.4×SE_score + 0.3×Entry_score + 0.2×Cap_points + 0.1×Load_points. Sorts carriers in descending order according to Total_Points. First N 15 It selects carriers (N = UE, maximum 5) as candidate carriers. The secondary cell activation decision engine (9) implements state machine-based decision logic. IDLE status: only the primary cell is active, traffic demand and metrics are being monitored. EVALUATION status: candidate carriers are evaluated IF (Traffic > 10 Mbps AND The condition SINR_SCell > 5 dB and SE_gain > 15% is checked. Activation status: 20 The RRC reconfiguration message is prepared, and a 100ms hysteresis timer is started. ACTIVE status: secondary cell in use, performance continuously monitored. DISABLE Status: IF (Traffic < 5 Mbps OR SINR < 0 dB OR SE_gain < 10%) then 500ms Deactivation is done via timer. Energy efficiency check: power consumption increase / data. Prevent activation if the speed gain ratio is > threshold. 25 Cross carrier timer (10), modified proportional fair for each user i and each carrier k. Calculates the criterion: PF_i,k = (R_i,k / R̄_i) × (1 - α × Interference_k) where R_i,k is the carrier of user i. k is the instantaneous data rate, R̄_i is the average data rate of user i, and α is the interference penalty coefficient (typical value: 0.3), Interference_k normalized interference level (between 0-1). In each timing period (1ms subframe), selects the user with the highest PF_i,k value for each carrier. Physical 30 Allocates resource blocks (PRB): MCS selection based on the user's CQI, adaptive. Modulation and encoding. Multi-carrier coordination: total user data rate. It maximizes while minimizing carrier-to-carrier interference. Radio source control signal controller (11), customized RRC for each UE It generates a Connection Reconfiguration message. It populates the SCellToAddModList information element: 35 sCellIndex (1-7), cellIdentification (physical cell identifier, carrier frequency), 6 radioResourceConfigCommonSCell (uplink / downlink bandwidth, custom subframe) configuration for ZBB), radioResourceConfigDedicatedSCell (physical configuration, MAC (configuration). ASN.1 encodes the message using PER (Packaged Encoding Rules). S1-AP: The downlink NAS transport message sends a confirmation message from the eNodeB to the UE. (RRC Connection Reconfiguration Complete) waits; if it times out, try again. It will send the item (maximum 3 attempts). Intercellular interference coordination interface (12), neighbor via X2 protocol It exchanges information with eNodeBs. It sends a LOAD INDICATION message: up / down connection. PRB usage indicator (0-100 for each PRB), hardware load indicator. (Low / Medium / High / Overloaded), combined available capacity group. RESOURCE 10 Coordination of initiatives with the STATUS UPDATE message: High Initiative Indicator (HII) bit map (which PRBs have high interference), Overload Indicator (OI) bit map, Proposed Nearly Empty Subframe (ABS) pattern. ENB CONFIGURATION UPDATE The message discusses the ABS configuration: which subframes are ABS. The level of power reduction to be indicated (40-bit bitmap, each bit representing a subframe). 15 Additional signaling for Coordinated Multipoint (CoMP) scenarios: coordinated timing / coordinate Beamforming (CS / CB), Dynamic Point Selection (DPS), Joint Transmission (JT). The performance tracker (13) calculates key performance indicators every 5 minutes: (a) Carrier aggregation utilization rate = (number_of_UEs_using_CA / total_RRC_connected_UEs) × 100, (b) Average data rate per carrier = total_data_volume_k / total_active_time_k 20 (for each carrier k), (c) Average interference level = Σ(SINR_k) / number_of_active_carriers, (d) Activation / deactivation frequency = (activation_count + deactivation_count) / total_UE / time_period, (e) Ping-pong rate = number_of_unnecessary_activations / total_activations × 100 (unnecessary: ​​deactivated in 2 minutes). Target KPI It monitors its thresholds, and when a deviation is detected (for example, if the average data rate drops by more than 10%), it triggers 25 It triggers automatic corrective actions: sets algorithm parameters (sort weights, thresholds) (values), retrains the machine learning model. The performance tracker (13) also runs an A / B test framework: 20% of UEs are the control group. (traditional static carrier selection), 80% experimental group (interference-aware adaptive system). Independent KPIs are collected for both groups: average data rate, cell edge data rate (5% 30%). percentage), battery consumption (mAh / hour), activation frequency, user satisfaction score. Mann- The Whitney U test is used to check for statistical significance (p-value < 0.05 significance threshold). The results are displayed on a visualization dashboard: time series graphs, box plot, scatter plot. graphs. Decision support system: provides the system administrator with an optimization strategy across the network. He recommends spreading it. 35 7 The feedback loop mechanism activates when KPI values ​​do not meet target thresholds. learning-based interference predictor (7) and carrier sorting algorithm (8) parameters Automatic settings. Performs hyperparameter optimization with gradient descent algorithm: LSTM It adjusts the network's learning rate (between 0.0001 and 0.01) and optimizes the number of hidden layer units. It recalibrates the ranking algorithm weights: top 5 with Bayesian optimization. Searches for the values ​​a₁, a₂, a₃, a₄ (constraint: Σa_i = 1). Model retraining trigger: IF (If the estimated MAPE gain is > 25% OR the average data rate gain is < 10%), then model with the new training set. It is retrained. Concept shift detection: monitors the statistical characteristics of the interference distribution, The model is updated when a significant change is detected (Kolmogorov-Smirnov test, p < 0.01). The system utilizes interference-aware dynamic 10 carrier aggregation resources in LTE-Forward networks. It offers a multi-layered AI-powered optimization framework for management. First In the stage, the multi-carrier measurement collector (1) provides comprehensive Layer 1 and for each component carrier. It collects Layer 3 measurements: broadband and subband channel quality indicators, physical resources. RSRP / RSRQ / RSSI values ​​per block, interference plus noise measurements. These raw measurements, The interference analysis engine (2) is analyzed by deep signal processing techniques: FFT-based 15 Spectral analysis for identifying sources of intercellular interference, correlation analysis. Common channel interference characterization, adjacent channel leakage rate with time domain filtering. An estimate is made. Spectral efficiency calculator (3) theoretical capacity based on Shannon-Hartley theorem Accounts and data transfer accessible via actual modulation and encoding scheme mapping 20 estimates the speed. User equipment capability analyzer (4), the device's supported band combinations (according to 3GPP TS 36.306), maximum combined bandwidth, multiple It decomposes the input multiple output (MIMO) capability. Traffic demand estimator (5), buffer state reports, service quality class-based traffic classification and application layer protocols It predicts future traffic requirements by performing packet analysis (light to deep packet inspection). 25 The historical venture database (6) stores historical venture patterns in time series format: spatial heat maps (per geographic region), temporal trends (hourly / daily / weekly) cycles), event-triggered anomalies (mass gatherings, network maintenance). Machine learning. based on interference estimator (7), multilayer long short-term memory neural network architecture (input Layer: 50 attributes, 3 hidden layers: 128-64-32 units, output layer: interference per carrier 30 It predicts future levels of entrepreneurship using (predictive) methods. The model uses online learning. Continuous retraining is carried out, addressing conceptual shifts and seasonal pattern changes. It adapts. The carrier sorting algorithm (8) applies a multi-criteria decision-making approach: Analytic Hierarchy The process determines the criterion weights (spectral efficiency: 0.4, interference level: 0.3, coverage 35). Quality: 0.2, Load Balancing: 0.1), Similarity to Ideal Solution Preference Ranking Technique 8 Carriers are sorted. Secondary cell activation decision engine (9), state machine logic It applies: IDLE → EVALUATION → ACTIVATION → ACTIVE → DISABLED Switches between RELEASE states, hysteresis timers (activation) (Delay: 100ms, deactivation delay: 500ms) prevents the ping-pong effect. The cross carrier timer (10) uses a modified proportional fair timing metric: 5 It adds a penalty term to the traditional proportional fairness metric, channel quality per carrier. Normalizes the indicator for multi-carrier physical resource block allocation optimization. The mixed integer linear programming solver runs. Radio source control signaling. controller (11), 3GPP TS 36.331 compliant RRC link reconfiguration messages It generates: Configures the Secondary Cell Insert / Replace List information element, Abstract Syntax 10 It performs encoding / decoding operations on the notation. Intercellular interference coordination interface (12), adjacent to the X2 application protocol Enhanced Node B enables dual coordination: Load Indicator message exchange. (Upstream / downstream link PRB usage, hardware load), Resource Status Update messages with interference mitigation patterns (Nearly Empty Subframes, reduced power subframes) 15 They negotiate and share High Interference Indicator and Overload Indicator bitmaps. Performance tracker (13) implements closed-loop feedback system: key performance The indicator monitors deviation thresholds and triggers automatic corrective actions (e.g., sorting). (algorithm weight readjustment), different optimizations with A / B test framework They compare their strategies. 20 Unlike traditional blind carrier aggregation, this system is dynamic for each user. It offers an entrepreneurial-aware, predictive, and self-optimizing approach. Machine Thanks to its learning capabilities and multi-purpose optimization techniques, it improves user experience, network efficiency, An optimal balance is struck between spectral efficiency and energy saving objectives. The system, Minimizing service disruptions by making proactive decisions based on real-time intervention conditions 25 and maximizes network capacity.

Claims

9 REQUESTS 1. Carrier aggregation resources in LTE-Forward and LTE-Forward Pro mobile networks It is a system used for initiative-aware dynamic management, and its feature is; • Multiple systems that collect comprehensive Layer 1 and Layer 3 measurements for each component carrier. carrier measurement collector (1), 5 • Interference analysis engine that performs analysis with deep signal processing techniques (2), • Spectral calculation that calculates theoretical capacity and estimates achievable data transfer speed. Productivity calculator (3), • Supported band combinations of the device, maximum combined bands User equipment that separates its width, multiple input multiple output (MIMO) capability 10 talent analyzer (4), • buffer status reports, service quality class-based traffic classification, and Predicting future traffic requirements by performing application layer protocol analysis. traffic demand forecaster (5), • Historical venture data 15, which stores past venture patterns in time series format. base (6), • Future initiatives using a multi-layered long- and short-term memory neural network architecture Machine learning-based venture predictor that estimates levels (7), • carrier ranking algorithm applying a multi-criteria decision-making approach (8), • Secondary cell activation decision engine implementing state machine logic (9), 20 • Cross-carrier timer using a modified proportional fair timing metric (10), • Generates RRC link reconfiguration messages compliant with 3GPP TS 36.

331. radio source control signal controller (11), • Dual coordination with neighboring enhanced nodes B via X2 implementation protocol 25 intercellular interference coordination interface (12), • Performance monitor implementing closed-loop feedback system (13) It includes.

2. The system is compliant with Claim 1 and its feature is; a multi-carrier measurement collector (1), each component 30 Collecting periodic and event-triggered measurement reports for the carrier, broadband CQI (all (single value for carrier), sub-band CQI (for each source block group), RSRP / RSRQ / RSSI Separating the values, the measurement reporting period is between 200ms and 10240ms. Configuration, processing of RRC Measurement Report messages according to 3GPP TS 36.

331. It includes. 35 3. The system is compliant with claim 1 and its feature is that the interference analysis engine (2) analyzes the collected measurements. SINR calculation per carrier, separation of interference components, fast Fourier transform. Identifying interference sources through spectral analysis based on (FFT), interference-thermal ratio (IoT). It involves making classifications.

4. The system is compliant with Claim 1 and its feature is that the spectral efficiency calculator (3) is Shannon-5 Application of Hartley's theorem, comparison of theoretical spectral efficiency with real MCS, LTE Using the MCS table (3GPP TS 36.213), the expected MCS index from SINR and data mapping the speed and generating a normalized spectral efficiency score for each carrier. It includes.

5. The system is compliant with Claim 1 and its feature is; user equipment capability analyzer (4), UE 10 It separates the capability information element, checks the category capabilities, and determines the UE's power class. It includes the determination.

6. The system complies with Claim 1 and its feature is; traffic demand estimator (5), Buffer State It analyzes its reports (BSR), classifies traffic priority according to QCI values, Identifying the service type through application layer protocol analysis, autoregressive integrated moving average 15 Predicting future traffic demand for 60 seconds using the ARIMA (Average Average) model, real-time traffic. The load involves setting thresholds.

7. The system complies with Claim 1 and its feature is that it contains a database of past initiatives (6), with timestamps. storing enterprise records, using a time series database structure, data storage policy implementation, optimizing geographical querying with spatial indexing, data 20 It includes the implementation of compression algorithms for size management.

8. The system complies with claim 1 and its feature is; machine learning based interference predictor (7), LSTM trains its neural network architecture, learns from past interference patterns, and prepares for the future. predicting enterprise levels, providing continuous retraining through online learning, The concept involves adapting to seasonal pattern changes through perception shift. 25 9. The system is compliant with claim 1, and its feature is that the carrier sorting algorithm (8) is for each user and Multidimensional score calculation for each carrier, carriers sorted in descending order of Total_Score. The ranking process involves selecting the first N carriers as candidate carriers.

10. The system is compliant with claim 1 and its feature is the secondary cell activation decision engine (9), State machine-based decision logic application, IDLE state, EVALUATION 30 Status, ACTIVATION status, ACTIVE status, DEACTIVATION status It includes.

11. The system is compliant with claim 1 and its feature is that the cross carrier timer (10) is available to each user. and a modified proportional fairness metric calculation for each carrier k, for each timing During the period (1ms subframe), the user with the highest PF_i,k value for each carrier is selected (35). 11 selection, physical resource block (PRB) allocation, multi-carrier coordination It includes providing assurance.

12. The system complies with Claim 1, and its characteristic is that it is a radio source control signal controller. (11) Generate a customized RRC Connection Reconfiguration message for each UE, The SCellToAddModList information element must be populated according to ASN.1 PER (Packed Coding Rules) 5. Message encoding, S1-AP: Downlink NAS Transport message from eNodeB to UE sending the message, waiting for message success confirmation (RRC Connection Reconfiguration Complete), It includes resending the message in case of a timeout.

13. The system complies with Claim 1 and its characteristic feature is the inter-cell intervention coordination interface. (12), exchanging information with neighboring eNodeBs via X2 protocol, LOAD 10 Sending an INDICATION message, attempting to initiate a RESOURCE STATUS UPDATE process. ensuring coordination with ABS via ENB CONFIGURATION UPDATE message. negotiating its configuration, additional for coordinated multi-point (CoMP) scenarios It involves providing signaling.

14. The system compliant with claim 1, and its feature is that the performance tracker (13) monitors the performance every 5 minutes. calculating key performance indicators, monitoring target KPI thresholds, deviation Automatic corrective actions are triggered when detected, A / B test frameworks are run, Mann- Checking for statistical significance with the Whitney U test, visualizing the results on the dashboard. Its purpose is to demonstrate and provide a decision support system.

Images