Intelligent Cell Switching Decision System Based on Mobility

Abstract

Specifically, the invention relates to an intelligent handover decision system based on mobility, which generates a personalized handover policy for each user equipment by analyzing GPS / A-GPS position data, cell-level measurement reports, velocity vectors, and past handover sequences, and optimizes user equipment handover decisions in LTE (Long Term Evolution) mobile networks using mobility patterns and trajectory prediction.

Description

1 TARIFF Intelligent Cell Switching Decision System Based on Mobility TECHNICAL AREA 5 The invention relates to an intelligent cell switching decision system based on mobility in general. The invention specifically relates to GPS / A-GPS position data, cell-level measurement reports, and speed. vectors, 10 for each user piece of equipment, by analyzing past cell change sequences. Generating a personalized cell replacement policy, LTE (Long-Term Evolution) (Term Evolution) mobile networks user equipment (UE – User) Equipment) mobility of cell handover decisions using mobility patterns and trajectory prediction 15 with a smart cell replacement decision system based on mobility that enables optimization. It is related. STATE OF THE ART Today, the handover system takes into account mobile users' past mobility data and 20 by predicting their future locations, transitioning from one base station to another. (handover) a smart connection that ensures it is performed at the most opportune time. It is a management method. The system adapts to the user's speed and direction of movement using Time-to-Time. The system dynamically adjusts handover parameters such as trigger, hysteresis, and A3 offset. Thus, radio link failures (RLF - Radio Link Failure), failed handover, and 25 Connection quality and experience are improved by reducing unnecessary cell exchanges (ping-pong). Quality of Experience (QoE) is improved. This system is especially useful for 5G / 6G mobile networks. in communication networks, high-speed vehicle communication, autonomous vehicles, intelligent transportation It can be used in systems and mobile networks with high mobility. In current LTE networks, the handover process is mostly reactive. This is done as follows: The RSRP value of the adjacent cell is equal to the RSRP of the serving cell. Handover is initiated when the value exceeds a certain A3 comparison threshold. This the approach takes into account the user's direction of movement, speed and future position 2 because the decision to handover was not made, especially in high-speed user equipment delayed delivery and connection interruption; unnecessary for low-speed users and Repeated ping-pong handover operations can occur. Therefore, not only This structure, based on current signal conditions, is suitable for conditions of varying mobility. This limits handover performance. In traditional systems, Time-to-Trigger (TTT), 5 Handover parameters such as hysteresis and A3 offset are often predetermined. It is configured with fixed values. Therefore, it is suitable for pedestrians, vehicles, or high-speed trains. users with different mobility profiles can use the same parameters It needs to be managed. However, a short TTT (Term-to-Term) is necessary for a high-speed user. While this is possible, a longer TTT on a slow-moving user can result in unnecessary handovers. It can prevent this. Parameters can be dynamically adjusted according to user activity. failure to adjust, handover failures and ping-pong hand changes This leads to an increase in performance and prevents overall network performance from reaching its optimal level. In traditional handover mechanisms, the user will follow the following steps in the future It is generally possible to predict the trajectory or the cells it will pass through. 15 No. The system only shows the user's current location and instantaneous radio conditions. by making a decision based on evaluation, pre-allocating resources in the target cell or Proactive preparation procedures such as acceptance checks cannot be carried out. As a result... When the handover command is given, the user actually reaches the target cell. Timing mismatches may occur, especially among fast-moving 20s The risk of handover failure or RLF may increase for users with current approaches. The target cell is usually the one that provides the highest RSRP value. However, this choice, based solely on signal strength, takes into account the user's direction of movement and It does not take its future position into account. For example, a vehicle moving on a highway, A temporarily stronger signal from a cell located in the opposite direction physically 25 It can receive this data, and the system can select this cell as a target. Also, multipath propagation and transient... Signal changes that appear strong momentarily but are unsuitable for the user. This can lead to the selection of cells. This results in unnecessary handover, ping-pong, and This can lead to problems such as decreased connection stability. Traditional handover handover failures, RLF events, that have occurred in the systems in the past, 30 Early or late handovers and ping-pong transitions are analyzed systematically. This information is not adequately conveyed to subsequent decision-making processes. Therefore, certain recurring coverage gaps, interference zones, or problematic areas in the regions Even if cell transitions are detected, the system will repeat the same handover decisions. 3 This can continue. The inability to learn from past experiences, the system improves itself over time and handover in problematic areas This prevents it from automatically optimizing the parameters or target cell selection. Traditional handover decisions are mostly based on radio signal and connectivity measurements. while relying on the user's implementation, the service's QoS requirements and 5 User priorities are not included in the decision-making process. For example, emergency calls, VoLTE. The interview and high-quality video streaming can be handled using the same handover logic. However, these services have different tolerances for packet loss, delay, and jitter. Therefore, failure to consider the quality of service required by the application is technical. Even if a successful handover is implemented, user experience (QoE) will still be 10%. This can cause drops in signal, increasing packet loss and delay. Based on research conducted under the known state of the art, US20150146689A1 Application number [number] was found. In the said application, the user's history... By leveraging handover behaviors and historical data, we can predict the next 15 years. An approach to predicting handover is described. Specifically, here, The use of past handover data, the ability to record GPS location, current Combining radiographic measurements with past data and future... The handover can be predicted probabilistically. However, here... Actually, by using UE's speed / direction of movement and historical mobility traces, 20 to predict the real-time trajectory and, based on this prediction, TTT, hysteresis, and A3. There is no mention of dynamically changing the offset. As a result, improvements are being made to cell exchange decision systems, Therefore, it will eliminate the disadvantages mentioned above and provide 25% of the existing systems. New structures are needed to provide solutions. THE PURPOSE OF THE INVENTION The present invention meets the aforementioned requirements and overcomes all the disadvantages of 30 with an intelligent cell exchange decision system that eliminates and introduces some additional advantages It is related. 4 The main purpose of the invention is to provide GPS / A-GPS position data, cell-level measurement reports, and speed data. vectors for each user piece of equipment by analyzing past cell change sequences. Generating a personalized cell replacement policy, LTE (Long-Term Evolution) (Term Evolution) mobile networks user equipment (UE – User) Equipment) cell handover decisions mobility 5 using mobility patterns and trajectory prediction Smart cell replacement decision system based on mobility that enables optimization. to provide. One aim of the invention is to improve Kalman filtering and particle filtering. This involves predicting the user's trajectory through particle filtering. Future cell lines are predicted with 85% or higher accuracy. Proactive handover Preparation is made: pre-signaling to the target cell, pre-allocation of resources. Handover Execution delay is reduced by 40-50%. Another objective of the invention is to provide adaptive parameter optimization. Thus, speed-aware trigger timing adjustment: high-speed user The trigger timing for equipment is shortened (40-80 milliseconds), while it is extended for pedestrians. (160-320 milliseconds). Dynamic hysteresis: enhanced in stable environments (3-6 decibels). Reduced during rapid damping (0.5-2 decibels). Handover failure rate reduced from 5-8% to 20%. It will be reduced to 1-2%. Another objective of the invention is to enable intelligent target cell selection. Here, Direction Awareness of cell sorting: correlation between direction of movement and cell position. It is evaluated. Estimated length of stay: the anticipated duration of stay in the cell is taken into account. 25 Multi-criteria scoring: signal quality, predicted dwell time, load balancing, service. Quality is calculated with support components. Ping-pong ratio from 12-18% to 3-5%. is reduced. Another purpose of the invention is to address past handover and radio link failure (RLF) 30 By analyzing the data, the spatial locations of the areas where the failures occurred can be determined. identification of error trends and recurring problem areas The aim is to enable this. In this context, spatial clustering of error locations. by analyzing through various methods, coverage gaps are automatically detected. automatic reporting and handover parameters in areas with error tendencies. and is intended to be adjusted dynamically. Thus, the system's history learning from handover errors can identify recurring errors that may occur in similar circumstances. The aim is to detect and reduce errors in advance. Another aim of the invention is to consider not only radio conditions but also other factors in handover decisions. taking into account the type of service offered and service quality requirements The goal is to implement differentiated handover. In this context, voice call and emergency call... faster and prioritized handover of critical services such as video streaming. In interrupt-sensitive applications, a dual connection or make-before-break approach is used. Improved connectivity and best-effort data traffic handover The aim is to apply these criteria. Thus, they are suitable for different types of services. Packet loss, delay, and connection issues are minimized by creating handover behavior. reducing interruptions and improving user experience quality (QoE) is targeted. 15 Another purpose of the invention is to enable user equipment to be used on railways, highways, and similar high-altitude routes. by identifying that it is in rapid mobility scenarios, handover procedures The goal is to optimize based on the user's speed, direction, and estimated trajectory. In this context, the user's future connection to potential cells is pre-selected by 20 identification, proactive planning of subsequent cell changes and high Automatic handover parameter profiles suitable for rapid mobility conditions. The aim is to implement this. Thus, the problems that may arise due to high speed... late handover, handover failure, radio link failure (RLF), and link The aim is to reduce disruptions. 25 Another aim of the invention is to reduce unnecessary and repetitive handover processes. The goal is to reduce the energy consumption of user equipment. User mobility. Optimizing handover timing by considering the current situation and estimated trajectory. to reduce unnecessary cell scanning, measurement and signaling processes and 30 Optimizing the uptime of user equipment The aim is to create a balance between handover performance and energy consumption. By providing balanced optimization, the battery usage of the user equipment is improved. The aim is to increase its efficiency. 6 Another aim of the invention is to provide a predictive and proactive handover mechanism for mobility. Self-regulating technologies such as Robustness Optimization (MRO) and Mobility Load Balancing (MLB) The goal is to ensure that it works in coordination with the organizing network functions. within the scope, handover failures, RLF, ping-pong hand changes, user Network optimization 5: Information regarding mobility and cell load conditions. sharing and handover parameters among mechanisms and only one Dynamic, taking into account network-wide conditions, not user or cell-specific. The aim is to optimize it in this way. Thus, changing mobility and traffic Network-wide consistent, automated, and adaptable to density and radio conditions. Creating a self-adaptive handover optimization mechanism 10 is the goal. The structural and characteristic features and all the advantages of the invention are given in the figures below. And thanks to the detailed explanation written with references to these figures, it becomes clearer. This will be understood as such. Therefore, the evaluation should also be based on these forms and details. 15 This should be done taking the explanation into consideration. BRIEF DESCRIPTION OF THE FIGURES The best way to utilize the advantages of the existing invention, together with its structure and additional elements. For it to be understood, it should be evaluated together with the figures explained below. 20 is necessary. Figure 1 Block of the intelligent cell switching decision system based on mobility, the subject of the invention. This is a diagram view. REFERENCE NUMBERS 25 1. Mobility Data Collector 2. Speed ​​and Direction Estimator 3. Orbit Prediction Module 4. Cell Array Planner 30 5. Stay Duration Calculator 6. Historical Mobility Data Repository 7. Pattern Recognition Engine 7 8. Handover Parameter Optimizer 9. Target Cell Selector 10. Handover Trigger Controller 11. Handover Policy Manager 12. Radio Link Fault Analyzer 5 13. Performance Monitoring and Feedback Cycle DETAILED EXPLANATION OF THE INVENTION This detailed description explains the preferred 10 cell exchange decision system that is the subject of the invention. These structures are solely for the purpose of better understanding the subject and have no It is explained in a way that will not create a limiting effect. The invention, whose block diagram view is given in Figure 1, specifically concerns GPS / A-GPS. Location data, cell-level measurement reports, velocity vectors, historical cell change 15 Personalized cell replacement for each user's equipment by analyzing their sequences. policy-making, LTE (Long Term Evolution) mobile User Equipment (UE) cell switching in networks Mobility patterns of handover decisions and 20 that enable optimization using trajectory prediction It is an intelligent cell replacement decision system based on mobility. The invention relates to GPS / A-GPS (Global Positioning System) from user equipment. Global Positioning System (GPS) coordinates, RSRP (Reference Signal Received Power - Reference Signal Received Power) and RSRQ (Reference Signal Received Quality - 25 Cell measurements such as Reference Signal Received Quality (TSU) (Tracking Area) Tracking Area Update lists and past cell handover collecting records and RRC (Radio Resource Control) mobility data collector (1) which parses messages the mobility data collected by the collector (1) with speed and past mobility patterns 30 By analyzing this data together, we can predict users' future actions and predicting their trajectories for each user piece of equipment on LTE mobile networks Smart, mobility-based systems that generate personalized and optimized handover decisions. It is a cell exchange system, 8  using sequential position sampling and Doppler shift analysis The Kalman filter analyzes the user's velocity vector, direction of motion, and speed. estimating its magnitude and cross-referencing the resulting velocity estimate with Doppler data. a speed and direction estimator that confirms (2),  Using particle filter methodology, the user's future 5 predicting their positions for time intervals of t+10, t+30 and t+60 seconds, modeling the uncertainty in the process with Gaussian noise (σ=5 meters) and its importance using sampling method to determine the trajectories with the highest probability. orbit prediction module (2),  Using graph-based Dijkstra / A routing algorithms, move cells to 10 A node models the neighborhood relationships between cells as edges. Based on cell topology, edges according to handover possibilities weighting the cell that the user is most likely and most suitable to track in the future Cell array planner (3) which determines the sequence,  For each predicted cell, the distance between the cell boundaries and the cell geometry are 15 and taking into account the projection of the user's speed towards the cell boundary Estimated cell dwell time (T_dwell) depends on whether the cell is hexagonal or irregular. stay duration calculator (5), which calculates by taking into account its geometric structure,  Trajectory containing users' latitude, longitude, and timestamp information traces of handover success, handover failure and radio link failure 20 (RLF) records are spatio-temporal in PostgreSQL and PostGIS databases. historical mobility data repository (6), which stores as  DBSCAN (Density-Based Spatial Clustering) A commonly used movement with the (Clustering of Applications with Noise) algorithm. clustering routes of repeating cells using the Apriori algorithm 25 and determines the sequences and HMM (Hidden Markov Model) pattern recognition that models the probabilities of transitions between cells using engine (7),  TTT (Time-to-Trigger) based on the user's movement speed The trigger value is dynamically determined based on signal variability (30). adapting the hysteresis value and taking into account the user's direction of movement By adjusting the A3 comparison threshold (A3 offset), thus different mobility and handover parameters in real time according to radio conditions optimizing handover parameter optimizer (8), 9  MADM (Multi-Attribute Decision Making) The method assesses the RSRP of candidate cells, their alignment with the direction of movement, and their predicted residence. by normalizing and weighting its attributes such as duration and cell load, each calculates a fitness score for the cell and selects the cell with the highest score. Target cell selector (9), 5 that selects target cell as the target cell for handover.  while maintaining the traditional A3 event tracking mechanism, it is orbit-based Cell boundary transition time predicted using predictive triggering logic TTT and early handover when necessary, compared with the execution time. enables the sending of measurement reports via the S1 / X2 interfaces. Sending the Handover Required and Handover Request messages in advance is recommended. handover triggering, which proactively prepares for a handover by triggering it. controller (10),  DSCP (Differentiated Services Code Point) marking and QCI (QoS Class) Based on the Identifier (Service Quality Class Identifier) ​​analysis Classifying services, reducing the TTT (Term Rebound Time) for emergency calls to 20 ms, and prioritizing 15 services. and voice call services that perform accelerated handover. semi-permanent scheduling (SPS) configuration by protecting and creating temporary dual connections in video streaming services. Service quality handover policy manager who performs seamless handover (11), 20  User equipment connection release after radio link failure By analyzing drop-off messages and failure causes, the locations of the failures can be determined. DBSCAN (Density-Based Spatial Clustering) Clustering applications with noise using the Clustering of Applications with Noise method, detecting gaps and RSRP 25 prior to radio link failure (Reference Signal Received Power) change by examining whether the handover procedure was performed too late or too early. radio link failure analyzer (12) and which determined that it did not happen  KPIs based on cell, user mobility category and service quality class (Key Performance Indicator) data 30 continuously collecting and analyzing data, exceeding defined performance thresholds in this case triggers the readjustment of handover parameters or automatically creates records to address coverage gaps and optimized with a control group that works with basic parameters Comparing the experimental group working with parameters within the scope of A / B testing, t- The statistical significance of performance improvement was determined using the test (p<0.05). performance monitoring and feedback loop (13) It includes. The invention describes an intelligent cell switching system based on mobility, on an LTE network. user equipment connected mode handover decisions Predictive analytics and adaptive parameter tuning to optimize It offers a multi-layered approach using a combination of these. In order to realize the invention, the first step in a preferred application is mobility 10 data collector (1), radio source control measurement from user equipment reports (GPS / A-GPS (Global Positioning System)) System) coordinates, RSRP (Reference Signal Received Power - Reference Signal (Received Power) and RSRQ (Reference Signal Quality) Cellular metrics such as Received Quality (RTU) and Tracking Area Update (Tracking 15) Area Update lists and past handover records are collected in real time. Additionally, it provides periodic GPS / A-GPS location updates at intervals of 1-5 seconds. It captures mobility status indicators and past handover sequences. Speed ​​and direction. estimator (2), Extended Kalman Filter on successive location samplings Estimating the velocity state vector [vx, vy] by running the (EKF - Extended Kalman Filter) 20 It does this through Doppler shift measurements (analysis of the rate of change in the power from which the reference signal is received). Velocity is measured across lines. The magnitude and direction of velocity are calculated as: |v| = √(vx² + vy²), θ = arctan(vy / vx). Orbit prediction module (3), particle filtering methodology It applies. Here, 1000 particles are emitted from the current state, Gaussian noise (σ=5 (meters) models process uncertainty with significance sampling and high probability 25 Orbits are maintained. Position predictions are generated for the next 10, 30, and 60 seconds. Probability distribution is obtained with Monte Carlo simulation. Cell array planner (4), It runs the graph transition algorithm (Dijkstra / A*) on the predicted trajectory. Here, in the cell topology diagram (nodes: cells, edges: adjacency relationships) (weighted by handover probability), optimal future cell lineup (next 5-10 30 (cell) determines. Edge costs: cost = 1 / probability_of_switching + direction_mismatch_penalty + load_balancing_term. Stay duration calculator (5), each It estimates the residence time for the predicted cell. Here, the cell boundary... distances (Voronoi mosaic cell geometry), velocity projection vector product. 11 The duration of stay is calculated as minimum_edge_distance / |velocity_projection|. Hexagonal cell model or irregular cell boundaries (real coverage with ray tracing algorithm) (map) is used. Contextual information collected, mobility estimates, and network load data. and measurement reports, time series in the historical mobility data repository (6) It is stored in a database format, for example using InfluxDB or TimescaleDB; every 5 a record, timestamp, user equipment ID, cell ID, measurement values It is indexed with context tags and retained within the last 90 days as part of the retention policy. While the data is being stored, the position is determined thanks to the R-tree spatial indexing mechanism. This enables the rapid execution of queries based on the pattern recognition engine (7). Taking the training dataset from the past mobility data store (6), unsupervised 10 It implements learning algorithms; in this context, the DBSCAN algorithm (ε=50 meters, The Apriori algorithm clusters frequently used path corridors with minPts=10). Repeating cell array patterns with (min_support=0.05, min_reliability=0.7) It extracts and uses Hidden Markov Models (HMM) to determine things like speed category and time of day. The cell transition probability matrix depending on the conditions, P(cell_j | cell_i, speed_category, 15 It learns in the form of (time_of_day). The handover parameter optimizer (8) learns each a parameter mapping function adaptive to the movement speed of user equipment It applies and compares TTT, hysteresis and A3 according to the user's speed category. It dynamically determines the threshold values; in this context, for high-speed users, For example, if the speed is above 80 km / h, then TTT=40 ms and hysteresis=1.20 Earlier handover conditions are applied, with a reduction in dB, while lower speeds are used. Handover is performed by defining different parameter values ​​for users in different categories. The timing is optimized according to user mobility. Target cell selector (9), candidate cells RSRP, alignment with direction of movement, predicted residence time and by evaluating according to multiple criteria such as cell load, 25 for each cell. It calculates a normalized suitability score; this includes signal quality. w1=0.35, directional compliance; w2=0.25, duration of stay; w3=0.20, load balancing. Weights w4=0.20 are assigned, and the user's velocity vector and the target cell's direction vector are used. The highest sum is determined by determining the directional agreement through the cosine of the angle between them. The cell with the specified score is selected as the target cell for handover. Handover trigger 30 The controller (10) continues to follow the standard A3 event logic while adding to it It activates a predictive trigger mechanism based on orbital estimation; neighboring The threshold for comparing the RSRP value of a cell to the RSRP value of the serving cell. and in addition to the standard A3 condition that the sum of the hysteresis values ​​exceeds the amount of the hysteresis value. 12 as, the time remaining until the estimated cell boundary crossing is TTT, handover execution the duration and the safety margin which is dynamically determined between 500-1000 ms If the total is less than the minimum, send the early measurement report. This allows the handover process to be initiated proactively. Service Quality handover policy manager (11), taking into account QCI values ​​of active carriers 5 It differentiates handover processes according to service type; voice services in the QCI=1 class. High priority and dedicated channel for calls by forcing the TTT (Term-to-Time) value to 40 ms. allocation implementation, semi-permanent for IMS signaling in QCI=5 class Continuing scheduling, temporary for video services in QCI=7, 8 and 9. Performing a seamless handover by establishing a double connection and QCI=9 10 Best in class, has higher ping-pong tolerance for data traffic. Enables the use of relaxed handover parameters. Radio link The fault analyzer (12) reports radio link failure events in real time. by collecting UE Context Release Request messages on the S1-AP interface and These are the causes of radioNetwork: radio-connection-with-ue-lost in the Cause IE field. 15 It separates and detects fault locations using the DBSCAN algorithm (ε=100 meters, It groups spatially using minPts=3) and concludes within the scope of root cause analysis. If the RSRP value is below -110 dBm, it indicates a coverage failure, RSRQ the value should be below -15 dB and the RSRP value should be above -100 dBm In the event of a failure of the attempt, also if the handover is too early or too late 20 If implemented, it identifies timing-related failures. Performance monitoring system (13), handover success rate at the end of every five-minute period, ping- such as pong rate, average handover downtime, and radio link failure rate. It calculates key performance indicators; including the handover success rate. The ratio of successful handovers to total handover attempts is 25, the ping-pong ratio. The ratio of the number of handovers performed on the previous cell to the total number of handovers, average downtime, total handover downtimes, number of handovers rate and radio link failure rate of active users of radio link failures It determines it based on the ratio of sessions. The performance monitoring system (13) also, 30 with a control group where 10% of users are managed with static handover parameters The experimental group, 90% of which was managed with adaptive handover parameters It runs an A / B test framework to compare the performance of the two groups. By statistically comparing the metrics using the Mann-Whitney U test, p<0.05 13 When evaluating the significance of the performance difference according to the significance threshold, the obtained The magnitude of the effect is determined using Cohen's d metric. The system described in the invention includes a feedback loop mechanism and a monitoring system. 5 predetermined key performance indicators obtained by If the target thresholds are not met, the handover parameter optimizer (8) and It performs automatic optimization on the orbit prediction module (3); Optimization of hyperparameters using gradient descent method within the scope of learning adapting the speed according to performance and re-designing the model when necessary The triggering of training is ensured and the adaptive learning rate α(t) = α₀ / (1 + 10 The system is updated according to the decay_rate × epoch relationship to reflect changing network and Continuous adaptation to mobility conditions is ensured. Unlike traditional reactive handover mechanisms, the system described in this invention... The system is predictive, proactive, and context-aware. 15 and offers a self-optimizing approach. Orbit Thanks to predictive and machine learning techniques, handover decisions are made more easily than the user's capabilities. It is granted taking into account future movement, cell array and service requirements. This minimizes handover failures, ping-pong effects, and service interruptions. while maximizing user experience quality and network resource utilization efficiency. It is done. The invention is an example of an intelligent cell exchange system based on mobility. In its application, the Radio Access Network of modern LTE / LTE-Advanced mobile operators It is positioned as part of the optimization (RAN Optimization) infrastructure and 25 Within the Self-Organizing Network (SON) ecosystem "Mobility and Robustness Optimization and Predictive Handover Management Module" It is integrated as such. System integration and deployment are exemplified below: 30  Base Station (eNodeB) Layer: The invention enables direct communication with eNodeBs. It establishes S1-AP (S1 Implementation Protocol) and X2-AP (X2 Implementation Protocol). It receives measurement data from eNodeBs via protocols, and performs radio source control. 14 It sends configuration commands. Radio resource management (RRM - Radio) It establishes a strong connection with the Resource Management layer.  Domain Management System (DMS) Layer: DMS It works in coordination with other SON functions in the layer: Mobility Load Mobility Load Balancing (MLB), Mobility and Stability 5 Optimization (MRO - Mobility Robustness Optimization), Random Access Channel Optimization (RACH Optimization), Coverage and Capacity Optimization (CCO - Coverage and Capacity Optimization), Energy Saving Coordination is ensured through its functions. SON Coordination Function Conflict resolution is performed through this method. 10  Network Management System (NMS) Layer: The top layer at the operator policy enforcement level, service level SLA monitoring, a key network-wide performance indicator. It provides data for dashboard visualization and strategic planning. Big data Integration with analytics platforms (Hadoop / Spark). 15 Data Flow and Integration Points are as follows: Southbound Interfaces:  S1-MME: Control plane signaling between the core network and the eNodeB, User equipment context management, monitoring area updates 20  X2: Peer-to-peer communication between eNodeBs, payload information exchange, handover preparation signaling  TR-069 / NETCONF: eNodeB configuration management, parameter update, alarm notifications Northbound Interfaces: 25  REST API: Providing performance metrics, configuration updates, third-party integrations  SNMP (Simple Network Management Protocol): Alarm notifications, trap messages messages), status inquiries  Kafka Streams: Real-time event stream analytics platforms, event-based 30 architectural integration East-West Interfaces:  SON Coordination Protocol: Parameters with other SON functions negotiation, conflict avoidance mechanism  Policy Engine Interface: Operator-defined policy enforcement, business rules engine integration  Big Data Platform Integration: Aggregate data export for Hadoop / Spark transfer, machine learning model training The system described in the invention is a sample application of cloud-native micro-technology. It is deployed with a services architecture:  Data Retrieval Service: Kafka consumers, message parsing, data validation, real-time stream processing  Orbit Prediction Service: Distributed computing clusters, particle filtering 10 motors, Kalman filter units  Optimization Service: High-performance computing cluster, parallel optimization engines, multi-objective optimization solvers  Signaling Service: Radio source control, message generation, ASN.1 Encoding / decoding, protocol adapters 15  Monitoring Service: Prometheus metric collection, Grafana boards, alerts managers, time series database Examples of applications for the system described in this invention are detailed below: 5G NR (New Radio) Beam Level Handover: The invention enables 20% beam level handover in 5G NR networks. mmWave ray tracing, ray failure recovery, and SSB (Synchronization Signal Block - Trajectory prediction in Synchronization Signal Block (SBB) based mobility scenarios and Dynamic beam switching with multiple beam management by providing adaptive beam management. It can be used to optimize processes. V2X (Vehicle-to-Everything) Communication Mode Selection: This is 25 Meeting alongside vehicle speed, traffic density, and safety criticality in cellular V2X communication. Network-assisted Mode 3 and autonomous Mode 4, taking into account network level and coverage status. communication mode transitions between them, RSU (Road Side Unit) It can be used to optimize vehicle selection and convoy management. UAV / Drone Mobility Management: Similarly, the invention relates to UAV (Unmanned Aerial Vehicle - 30 Three-dimensional trajectory estimation and altitude estimation in Unmanned Aerial Vehicle (UAV) and drone systems. conscious cell selection, air corridor optimization, and dynamic handover by providing management of drone fleets and aerial vehicles at different altitude levels It can be used in managing the mobility of vehicles. 16 Satellite-Terrestrial Network Handover: Also, the invention relates to LEO (Low Earth Orbit). Satellite beam handover and terrestrial-to-satellite access in Earth Orbit satellite systems. Inter-RAT (Inter-Radio Access Technologies) between technologies Trajectory prediction and adaptive handover in Access Technology transitions Predicting satellite visibility windows by optimizing parameters 5 and can be used to proactively select a suitable satellite. Private Network vs. Public Network Routing: In addition, the invention enables corporate users Transitions between the private LTE / 5G network and the public network are determined by location and the network used. the application takes into account factors such as security requirements and network capacity to optimize and maintain connectivity during on-campus and off-campus mobility 10 It can be used to provide. Network Selection for Multi-Operator Devices: The invention also includes dual SIM or eSIM enabled devices. Coverage quality, roaming cost, data plan, network on multi-operator supported devices. by evaluating performance and user preferences together, the appropriate operator automatic selection and optimization of handover processes between operators 15 It can enable this. Wi-Fi Offloading and Heterogeneous Network Integration: In addition, the invention combines LTE and Wi-Fi. Handover and offloading between different access technologies such as Fi. their decisions take into account user mobility, signal quality, network load and data cost. to optimize and support seamless Wi-Fi offloading operations 20 It can be used for this purpose. IoT Mobility Management: Finally, the invention relates to IoT (Internet of Things). PSM in the mobility management of NB-IoT and LTE-M devices within the scope of Things. (Power Saving Mode) and eDRX (Extended Continuous Reception) - Integrated with power saving mechanisms such as extended Discontinuous Reception 25 working to make predictive handover decisions and assets with mobile IoT sensors It can be used to optimize connection continuity in tracking applications. The technical details and formulations of the system described in the invention are shared below: Mobility Status Estimation Formula: 30 Mobility_Class = f(N_hand_change, T_evaluation, Speed_threshold) IF N_hand_change > N_high AND Speed ​​> 30 km / h → High Mobility IF N_medium < N_hand_switching ≤ N_high OR 3 < Speed ​​≤ 30 km / h → Medium Mobility 17 IF N_hand_change ≤ N_medium AND Speed ​​≤ 3 km / h → Low Mobility Reference: 3GPP TS 36.304 Section 5.2.4.3 - Mobility Status Assessment A3 Event Condition (Traditional): M_n > M_s + Offset + Hysteresis Here: M_n = Neighboring cell measurement (power received from reference signal), M_s = Service 5 cell measurement that gives Offset = cellIndividualOffset, Hysteresis = hysteresis (in dB) Reference: 3GPP TS 36.331 Section 5.5.4 - Measurement Reporting Modified A3 Event Condition (Predictive): (M_n > M_s + Offset_dynamic + Hysteresis_dynamic) OR (T_boundary_crossing_prediction < 10) (Preparation_minimum) Offset_dynamic = Offset_basis × (1 + β×direction_harmony_term) Hysteresis_dynamics = f(velocity, signal_variability, QoS_class) Load Balancing Index Calculation: Load_Index = Σ(w_i × metric_i) where: 15 metric_1 = PRB_usage / PRB_total (weight: 0.4) metric_2 = Number_of_active_users / Maximum_user_capacity (weight: 0.3) metric_3 = 1 - (Average_User_performance / Peak_performance) (weight: 0.3) Ping-Pong Detection Algorithm: IF (Cell_ID(t) == Cell_ID(t-2T) AND Cell_ID(t) != 20 Cell_ID(tT)) AND (T < T_threshold) THEREFORE Ping_Pong_Detected = True The T_threshold is typically set to 10 seconds (3GPP recommendation). Formula for Calculating Length of Stay: T_stay = d_minimum / (|v⃗| × |cos(θ)|) 25 Here: d_minimum = minimum distance to cell boundary (Voronoi mosaic) v⃗ = velocity vector, θ = angle between the velocity vector and the cell center direction. Security and Privacy Considerations Data Privacy: All user data complies with the European Union General Data Protection Regulation (GDPR). (GDPR - General Data Protection Regulation) and the Turkish Personal Data Protection Law 30 It is processed in compliance with the Law (KVKK). Personally identifiable information (PII) Identifiable Information is anonymized (IMSI / IMEI hash processing). 18 A user consent management mechanism is integrated. Data retention periods comply with the law. It complies with the requirements (maximum 90 days). System Security: Machine learning model poisoning attacks (ML model Anomaly detection is applied against poisoning attacks. Input verification and cleaning. (Input validation and sanitization) is performed on all data inputs. Secure communication 5 TLS 1.3 channels are mandatory on all interfaces. Role-based access control (RBAC) and multi-factor authentication is applied for system access. Scalability and Performance Processing Capacity: The system is capable of handling 1 million+ simultaneous user devices. It can perform time-based optimization. Horizontal scaling with distributed computing architecture 10 Horizontal scaling is supported. Microservices scale independently: data retrieval. The service has a processing capacity of 100K messages per second, and the optimization service has a processing capacity of 10K decisions per second. Latency Requirements:  Data acquisition latency: < 100 milliseconds  Orbital estimation delay: < 300 milliseconds 15  Optimization decision delay: < 2 seconds  End-to-end processing delay: < 5 seconds  Emergency accelerated processing: < 1 second High Availability: The system aims for 99.99% uptime. Active-active. Georedundancy is achieved with the (active-active) distribution model. 20 Automatic failover and disaster recovery mechanisms exist. Standardization and Harmonization 3GPP Standards Compliance:  TS 36.300: E-UTRA and E-UTRAN General Description (Overall Description) 25  TS 36.304: User Equipment Idle Procedures (UE Procedures in (Idle Mode)  TS 36.331: Radio Source Control Protocol Specification (RRC Protocol) Specification)  TS 36.413: S1 Implementation Protocol (S1-AP) 30  TS 36.423: X2 Implementation Protocol (X2-AP)  TS 32.500: Self-Organizing Network Concepts and Requirements (END) Concepts and Requirements)  TS 32.511: Automatic Neighbor Function (ANR) (Relation Function) 35 19 Backward Compatibility: Backward compatible with older user equipment (Release 8 / 9 / 10). It is compatible with the return to static parameters for legacy user equipment. A fallback mechanism is available. Advanced features are only available. Capability-based activation is enabled on supporting user equipment. Machine Learning Model Details 5 Algorithms Used:  Kalman Filter: State transition matrix F(4×4), observation matrix H(2×4), process noise covariance Q and measurement noise covariance R parameters. is configured.  Particle Filter: 1000 particles, Gaussian resampling (Gaussian 10⁻¹⁰) resampling), systematic resampling threshold Its value is 0.5×N_particles.  DBSCAN Clustering: Epsilon distance (ε) = 50 meters, minimum number of points (minPts) = 10, the Haversine distance metric is used for geographic coordinates.  Apriori Algorithm: Minimum support = 0.05, minimum confidence = 0.7, 15 Maximum item set size = 10.  Latent Markov Model: Parameter learning with Baum-Welch algorithm, Viterbi The algorithm derives the most likely sequence of outcomes. Model Training and Update:  Offline training: 20 sessions on aggregated data every 24 hours model retraining  Online learning: Incremental model updating is real with real-time data flow  A / B testing: New model variants are tested with 10% traffic, statistically significant. After significance is confirmed, it is increased to 100% 25  Model versioning: Each model version is tagged, and previous versions are referenced when necessary. It is possible to roll back to a previous version. Operational Considerations Parameter Tuning Strategy: The system is conservative during initial deployment. It starts with parameters (high trigger timing, high hysteresis). Data 30 As data collection progresses and model confidence increases, the parameters are gradually made more aggressive. Safe-guard mechanisms prevent over-optimization. Monitoring and Alert: Real-time monitoring through a comprehensive monitoring infrastructure (Prometheus + Grafana). Timely visibility is provided. Alert rules are used for critical metrics. Defined as: handover failure rate > 3%, radio link failure rate > 1.5%, 35 System latency > 10 seconds. 24 / 7 on-call with PagerDuty / Opsgenie integration. notification. Capacity Planning: System usage and growth trends are continuously monitored. Capacity Predictive models anticipate resource needs for the next 3-6 months. Proactive scaling. This is done via automatic triggers.

Claims

21 REQUESTS 1. GPS / A-GPS (Global Positioning System) from user equipment. Global Positioning System (GPS) coordinates, RSRP (Reference Signal Received Power - Reference Signal Received Power) and RSRQ (Reference Signal Received Quality - 5 Cell measurements such as Reference Signal Received Quality (TSU) (Tracking Area) Update (Tracking Area Update) lists and past cell changes. (handover) collecting records and RRC (Radio Source Control - Radio Including a mobility data collector (1) that parses Resource Control messages, mobility data collected by the mobility data collector (1), speed and past 10 By analyzing mobility patterns, we can use this data to understand users' behavior. predicting future movements and trajectories of LTE mobile personalized and optimized for each user piece of equipment in their networks It is an intelligent cell exchange system based on mobility that generates handover decisions, Feature; 15  estimates the user's velocity vector, direction of motion, and magnitude of velocity, and a speed and direction estimator (2) that cross-validates the obtained speed estimate,  Predicting the user's future locations, reducing uncertainty in the process orbits that model and determine the orbits with the highest probability prediction module (2), 20  cells are nodes, and the relationships between cells are edges. By modeling the cell topology, the user weights the edges and... A cell line that determines the most likely and most suitable cell line to follow in the future. planner (3),  For each predicted cell, the distance between the cell boundaries and the cell geometry are 25 and taking into account the projection of the user's speed towards the cell boundary estimated cell dwell time depending on the cell's hexagonal or irregular geometric shape stay duration calculator (5), which calculates by taking into account the structure,  Trajectory containing users' latitude, longitude, and timestamp information traces of handover success, handover failure and radio link failure 30 (RLF) stores records spatio-temporally in a database. historical mobility data repository (6), 22  repetitive cells that cluster frequently used movement routes determining sequences and modeling transition probabilities between cells pattern recognition engine (7),  TTT (Time-to-Trigger) based on the user's movement speed Dynamically determining the trigger value, based on signal variability. adapting the hysteresis value and taking into account the user's direction of movement By adjusting the A3 comparison threshold (A3 offset), thus different mobility and handover parameters in real time according to radio conditions optimizing handover parameter optimizer (8),  Candidate cells' RSRP, alignment with direction of movement, predicted residence time and 10 by normalizing and weighting attributes such as cell charge, a formula for each cell. It calculates the suitability score and hands over the cell with the highest score. target cell selector (9),  Enables the submission of an early measurement report and Handover Required. By triggering the pre-sending of Handover Request messages, you can proactively... 15 Handover trigger controller (10) which performs handover preparation,  Prioritizing and expediting handover by reducing TTT for emergency calls semi-permanent scheduling (SPS) in voice call services is implemented. preserving the Semi-Persistent Scheduling (SPM) configuration and video streaming By creating a temporary dual connection in services, uninterrupted handover 20 Service quality handover policy manager (11),  User equipment connection release after radio link failure By analyzing drop-off messages and failure causes, the locations of the failures can be determined. clustering, identifying coverage gaps, and radio link failures. RSRP (Reference Signal Power Received) before it By examining the change in Received Power, it is determined that the handover process was performed too late or radio link failure that determines whether it happened too early analyzer (12) and  KPIs based on cell, user mobility category and service quality class (Key Performance Indicator) data 30 continuously collecting and analyzing data, exceeding defined performance thresholds in this case triggers the readjustment of handover parameters or automatically creates records to address coverage gaps and optimized with a control group that works with basic parameters 23 performance by comparing the experimental group working with parameters performance monitoring and evaluating the statistical significance of improvement feedback loop (13) It includes.

2. According to claim 1, it is an intelligent cell exchange system, and its characteristic is; sequential positioning. Using samples and Doppler shift analysis with a Kalman filter It estimates and obtains the user's velocity vector, direction of motion, and magnitude of velocity. a velocity and direction analysis that cross-validates the estimated velocity with Doppler data It includes the estimator (2). 10 3. According to claim 1, it is an intelligent cell exchange system, and its feature is a particle filter. Using this methodology, the user's future locations are determined at t+10, t+30, and t+60. Gaussian noise predicts uncertainty in the process for time intervals of seconds. Modeling with (σ=5 meters) and using significance sampling method, assigning the highest probability to 15 It includes an orbit prediction module (2) that determines the orbits it has.

4. According to claim 1, it is an intelligent cell exchange system, and its characteristic is that it is graph-based. Using Dijkstra / A routing algorithms, cells are routed to nodes, cells Cell topology that models the neighborhood relationships between cells as edges 20 by weighting the edges according to handover possibilities, the user A cell line that determines the most likely and most suitable cell line to follow in the future. It includes a planner (3).

5. According to claim 1, it is an intelligent cell exchange system, the feature of which is that users 25 handover with trajectory tracks including latitude, longitude and timestamp information records of success, handover failure, and radio link failure (RLF). storing data spatio-temporally in PostgreSQL and PostGIS databases. It contains a historical mobility data repository (6).

6. According to claim 1, it is an intelligent cell exchange system, the feature of which is DBSCAN. (Density-Based Spatial Clustering) The algorithm (Applications with Noise) analyzes frequently used traffic routes. clustering, identifying repeating cell sequences using the Apriori algorithm, and 24 Cells using HMM (Hidden Markov Model) It includes a pattern recognition engine (7) that models the transition probabilities between them.

7. According to claim 1, it is an intelligent cell exchange system, the feature of which is; MADM (Multi-Cell Dependent Cell) Candidate 5 was evaluated using the Multi-Attribute Decision Making method. cells' RSRP, alignment with direction of movement, predicted residence time, and cell load By normalizing and weighting attributes such as these, a fitness score is assigned to each cell. The system calculates and selects the cell with the highest score as the target cell for handover. The target cell selector (9) contains the selected target cell.

8. According to Claim 1, it is an intelligent cell exchange system with the feature; DSCP (Differentiated Services Code Point) marking and QCI (QoS Class Identifier - services based on Service Quality Class Descriptor (SQD) analysis classifying and prioritizing emergency calls by reducing the TTT (Term Time Between Calls) to 20 ms. half price in voice call services that perform accelerated handover. preserving the semi-persistent scheduling (SPS) configuration and seamless handover by creating temporary dual connections in video streaming services. The service quality handover policy manager (11) who performs it includes.

9. According to claim 1, it is an intelligent cell exchange system, the feature of which is radio link 20 Following the malfunction, the user equipment receives context release messages and By analyzing the causes of failures, DBSCAN (Density-Based Scanning) determines the locations of failures. Spatial Clustering - Density-Based Spatial Clustering of Applications with clustering using the Noise (NOI) method, identifying coverage gaps, and radio RSRP (Reference Signal Received Power - Reference 25) before the connection failure By examining the change in Signal Received Power (SRP), it is determined that the handover process was performed too late or not. radio link failure that determines whether it happened too early It includes the analyzer (12).

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