5G Beam Management Optimization

Abstract

The invention relates to an artificial intelligence-based system that provides beamforming and beamforming optimization in dense MIMO systems operating in millimeter wave (mmWave) frequency bands in 5G NR networks, taking into account user mobility and physical obstacles. The system described in this invention proactively predicts trajectories by processing sensor (GPS, IMU, orientation) data and historical position information collected from user equipment using Artificial Neural Networks (LSTM). Mobility patterns are classified using machine learning methods (K-means and SVM), and codebook configurations and SSB patterns are dynamically optimized via a Deep Q-Network (DQN) agent. The system processes camera and LiDAR data using fusion methods and object detection algorithms (YOLOv8), anticipating potential physical obstructions and triggering proactive / soft beam switching orchestration. Interference situations arising from multiple transmission points are modeled using Graph Neural Networks (GNN), dynamically assigning primary and backup beams per user; while autoregressive model-based CSI prediction ensures optimal beamforming pre-coding and uninterrupted connection performance in high-mobility scenarios.

Description

1 TARIFF 5G Beam Management Optimization Technical Area The invention utilizes millimeter wave (mmWave) frequency 5 in 5G New Radio (NR) networks. Beam management and beam in massive MIMO systems operating in bands It is related to beamforming optimization. State of the Art Currently, beam management procedures in 5G NR networks generally employ a reactive approach. It uses user equipment to search for a new beam after beam quality deteriorates. It initiates. In the case of high mobility (Doppler shift >1000 Hz), this situation is the beam. This increases the switching delay (40-80ms) and the risk of radio link loss (RLF). It constitutes. GNodeB and user equipment beamforming codebooks are statically set to 15. It is structured according to the codebook level of detail, based on user mobility patterns. It is not adjustable in an adaptive manner. Synchronization signal block (SSB) periodic beam scan (approximately 1.28 seconds for 64 beams) It creates an excessive load. Dynamic SSB pattern adaptation is not applied. When an obstruction event is detected, an emergency beam search is initiated, but obstruction 20 It lacks predictive capabilities. A reactive approach leads to service disruptions. (>100ms). In multiple transmission point scenarios, inter-point beam coordination is poor. Primary point In case of a malfunction, uninterrupted access to the secondary point is not guaranteed. GPS and IMU sensor data fusion usage is minimal. Future position prediction is integrated. 25 It has not been done, therefore preventive beam alignment is not possible. In high-traffic situations, the channel consistency time is short (e.g., approximately at 300 km / h). 0.5ms @ 28 GHz). Channel state information (CSI) feedback delay (4-8ms) channel This leads to aging. Predictive CSI extrapolation is not applied. Due to the negative aspects described above and the fact that the current solutions are limited to 30% of the subject matter... Due to its shortcomings, it has become necessary to make improvements in the relevant technical field. Purpose of the Invention: The invention is created by drawing inspiration from existing situations and as stated above. It aims to resolve the negative aspects. The main purpose of the invention is to operate in millimeter wave (mmWave) frequency bands in 5G NR networks. The goal is to provide ray management and ray shaping optimization in high-density MIMO systems. 35 2 Another aim of the invention is to provide practical applications, especially in high-speed mobility scenarios (trains, highways, expressways). trains (>120 km / h) artificial ray tracing, ray switching and ray recovery procedures radio communication optimized using intelligent predictive algorithms The goal is to reduce the rate of rollover failure (RLF) and ensure the sustainability of transmission speed. Another objective of the invention is user equipment (UE) trajectory prediction, beamforming code 5. notebook optimization, synchronous signal block (SSB) ray scanning pattern adaptation. and channel status information reference signal (CSI-RS) beam enhancement procedures in real time. LSTM is about dynamically optimizing using network context and historical mobility patterns. Based on an orbit prediction model using GPS coordinates, velocity vector, orientation sensors, and Predict position within the next 500ms-2 seconds using past movement patterns. 10 The goal is to select the optimal beam pair as the interceptor based on the predicted location. Switching delay is reduced from 40ms to 8ms, and the RLF ratio is decreased by 60%; Deep Q-Network (DQN) is a reinforcement learning agent that dynamically optimizes the codebook. (State) The action space includes user equipment speed, angular spread, and blocking frequency. The code space includes the codebook. Detail level adjustment, beam width adjustment, and coverage adaptation. High mobility 15 In the case of wider beams, narrower beams are used for static users; K-means Clustering is the method of grouping mobility patterns. For example, stationary, pedestrian, vehicle, high-speed. Grouping is performed. For each cluster, a customized SSB ray subset and periodicity are determined. This is achieved by reducing the SSB load by 30-40% through the fusion of computer vision and LiDAR. The goal is to predict obstacles. YOLO object detection and pedestrian / vehicle trajectory prediction 20 is being used as an alternative beam shield in the predicted blocking event. It is being prepared. Blocking recovery from 100ms to 20ms, service interruption 80%. It is decreasing; it is about orchestrating multiple transmission points with a graphical neural network (GNN). Primary and backup beams are protected simultaneously per user equipment. Primary beam RSRP Secondary beam activation is triggered when < -80dBm (<10ms interruption); Extended 25 The goal is to combine GPS, IMU, barometer, and magnetometer data using a Kalman filter. Beam selection. Error rate is reduced by 15%; future CSI with autoregressive channel estimation (AR-4). It is about extrapolation. In high mobility situations, the transfer speed increases by 15-25%, block The error rate is reduced by 30%; this involves optimizing the P-3 procedure of 3GPP. Machine learning-based pre-sequential recovery candidate beams reduce beam recovery latency from 60ms to 30ms. It is reduced to 25ms. Figures that will help understand the invention. Figure 1 shows a general representation of the system that is the subject of the invention. 35 3 Explanation of Part References 1. Multi-sensor user equipment 2. gNodeB intensive MIMO 3. Multiple transmission points 4. Sensor data collector 5 5. Orbit prediction engine 6. Ray pair estimator 7-channel measurement processor 8. CSI prediction module 9. Beamforming preencoder 10 10. Mobility pattern analyzer 11. Adaptive codebook optimizer 12.SSB pattern configurator 13. Blocking detection 14. Preventive beam switching orchestrator 15 15. Multi-point ray coordinator 16. Radiation management controller 17. Instrument panel Detailed Description of the Invention 20 This detailed description outlines the preferred configurations of the invention, not only for better understanding the subject matter. It is intended to facilitate understanding and will not impose any limiting effects. The invention is shown in Figure 1. As seen, 5G NR networks operate in the millimeter wave (mmWave) frequency bands. To provide ray management and ray shaping optimization in high-density MIMO systems. It has been developed accordingly. 25 In the system that is the subject of the invention, the sensor data collector (4) user equipment (1) GPS, IMU, It collects orientation data (100ms interval). The mobility pattern analyzer (10) uses historical data to analyze K- means clustering training. Adaptive codebook optimizer (11) initializes the DQN model. Orbit estimation engine (5) estimates position for the next 500ms-2s with a sliding window (10s past). It takes position (x,y,z), velocity (vx, vy,vz), and orientation (yaw, slope, roll) as inputs. It receives data. The orbit prediction engine (5) has 2 layers, 128 units and a dropout of 0.3. It uses an LSTM neural network. The output is a predicted (x,y,z) signal for T+500ms, T+1s, T+1.5s, and T+2s. It gives their positions. The estimated RMSE value is 1.8m for T+1s. Ray pair estimator (6) converts the estimated position to global coordinates (according to gNodeB). (azimuth, elevation angles). GNodeB antenna array geometry and beamforming codebook 35 It predicts the optimal Tx beam identity using this method. It ranks the top 3 beam candidates in a multipath environment. 4 The preemptive beam shift orchestrator (14) preemptively triggers the CSI-RS measurement for the predicted beam. The aperiodic CSI-RS configuration is sent to gNodeB. User equipment (1) takes measurements, The CSI report provides feedback. Alternative beam quality (RSRP, SINR) is verified. Current beam RSRP tracking is performed. Threshold determination: RSRP < -80dBm OR estimated_RSRP(T+1s) < -85dBm. Alternating beam RSRP > available + 3dB margin. Decision: beam switching initiated. 5 Layer 1 / Layer 2 based fast beam switching (MAC CE: TCI state activation, <10ms) (delay) is carried out. The adaptive codebook optimizer (11) DQN agent performs continuous learning. Status observation; mobility cluster (SVM classification), speed, channel coherence time, angular spread, It is a multifaceted form of wealth. Action choice is made through ε-greedy (ε=0.1), Q-value evaluation. 10 Action; codebook configuration selection (number of rays: 32 / 64 / 128, ray width: 5° / 10° / 15°, (coverage pattern) is determined. It is implemented with RRC Restructuring. Reward The calculation is: R = transmission_velocity × (1 - λ × beam_switching_frequency), with λ=0.2. The experience repeat buffer is updated with 10K examples. DQN training (batch size 64, learning This is achieved with a rate of 0.001). The learned policy is to have a broader scope in high-mobility situations. The rays are the use of narrow beams in static situations. The SSB pattern configurator (12) provides customized SSBs per cluster. For fixed clusters period=80ms, beam_sub_set=16 (covering dominant aspects based on past usage) It is determined as follows: For the high-speed cluster, period=10ms and beam_subset=64 (full coverage). It is determined. The dynamic SSB pattern is published via SIB (System Information Block). 20 Obstruction detection (13) analyzes camera video stream and LiDAR point cloud. YOLOv8 It performs real-time object detection (pedestrians, vehicles, obstacles). It uses a Kalman filter for object detection. Trajectory tracking is performed. Prediction: The object's trajectory will follow the light path within the next 2 seconds. If it interrupts, an obstruction warning is triggered. As a preventive action, an alternative beam is prepared. Soft beam switching is orchestrated. Blocking recovery time is reduced from 100ms to 20ms. is reduced. Multi-point ray coordinator (15) generates the graph: nodes=transmission points, Edges = interference relationships (distance, according to antenna patterns). Node characteristics; point charge, RSRP is the initiative level. GNN forward transition (3 message passing layers): node aggregation, edge Update. Output; primary point+beam, secondary point+beam 30 per user equipment (1). This is the assignment. Primary ray tracing: RSRP < -80dBm triggers secondary ray activation (<10ms) interruption). The CSI prediction module (8) uses the AR-4 autoregressive model. 3 current and past CSI measurements. It is taken as a time series. Coefficients a_i are estimated according to the Doppler spectrum (high- Mobility: Doppler >500 Hz). Estimated CSI: H(t+Δt) = Σ(a_i × H(t-iΔt)) + noise as 35 It is calculated. The beamforming preencoder (9) uses predictive CSI. High mobility The gain is achieved as a 15-25% increase in transfer speed and a 30% decrease in BLER (Broadcasting Limiters). Adaptive codebook optimizer (11) weekly retraining with new data It performs. Mobility pattern analyzer (10) cluster centre update (incremental K- (means) does. The orbit prediction engine (5) fine-tunes the final orbits. The system itself 5 It works with a development loop.

Claims

6 REQUESTS 1. Dense MIMO operating in millimeter wave (mmWave) frequency bands in 5G NR networks. It is a system that provides beam management and beam shaping optimization in its systems, feature; The 5G NR 5 is equipped with GPS, IMU, gyroscope, accelerometer, magnetometer, and camera sensors. user equipment (1), 5G base station operating in mmWave FR2 bands, equipped with a 64 / 128 / 256 antenna array. station (2), Providing multiple point connections through a heterogeneous distribution of macrocells and small cells. multiple transmission points (3), 10 GPS coordinates, IMU readings, orientation data from user equipment (1) Sensor data collector (4) that collects and pre-processes periodically (100ms interval), Long-Short Term Memory (LSTM) neural network inputs position / velocity history for the last 10 seconds. It takes the field as data and outputs the estimated positions at T+500ms, T+1s, T+1.5s, and T+2s. orbit prediction engine (5), 15 Converting the estimated user equipment (1) position into azimuth / elevation angles and Ray pair estimator (6) which estimates the optimal Tx / Rx ray identity. Processing SSB-RSRP, CSI-RSRP, CRI, RI, PMI, CQI measurements and ray-specific RSRP. channel measurement processor (7) which stores its history, Calculating optimal beamforming weights with estimated CSI (hybrid beam 20 formatting) CSI prediction module (8), Optimal ray shaping with CSI predicted by CSI prediction module (8) ray shaping preencoder (9) which calculates their weights, Past trajectories were clustered using K-means clustering (K=4) as stationary, pedestrian, vehicle, and high-speed. Mobility pattern analyzer 25 that groups and performs real-time SVM classification. (10), With the DQN agent, the state (mobility set, coherence time, angular spread), action (code) (notebook configuration) and reward (transmission speed - beam switching penalty) parameters adaptive codebook optimizer that dynamically optimizes the codebook using (11), 30 Customized SSB configuration per cluster (fixed: 80ms, 16 beams; high-speed: 10ms, SSB pattern configurator (12) providing 64 rays, YOLOv8 uses camera and LiDAR fusion to detect objects and predict their trajectories. Obstruction detection (13) gives an obstruction warning if it cuts the beam path. 7 Preventive beam replacement based on predicted beam distortion / interference conditions. orchestrated by and triggering an interceptor that prevents the alternating beam CSI measurement. beam shift orchestrator (14), A Graphical Neural Network (GNN) orchestrates multiple transmission points, with nodes acting as transmission points. defining points, edges as interference and primary and 5 per user equipment (1) Multi-point beam coordinator assigning backup beam (15), Orchestrating all beam control modules, RRC signaling, MAC CE commands, and DCI managing beam management controller (16), Beam change frequency heat map, RLF ratio trend, transmission velocity vs mobility scattering and Dashboard visualizing the blocking timeline (17) 10 It includes.

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