5G SON Beam Coverage Optimization via AI Resource Allocation
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Solution Overview
Problem
5G networks face challenges in optimizing beam coverage and capacity across neighboring cells and resource allocation for diverse services like eMBB, URLLC, and mMTC, which affects network efficiency and user experience.
Innovation Solution
Employing AI-driven Self-Organizing Network (SON) functions to analyze historical performance data and automatically adjust resource allocations and beam management across 5G NR cells, optimizing coverage and capacity by collecting and analyzing RSRP, RSRQ, and SINR measurements, and predicting traffic demands for Network Slice Instances (NSIs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual beam management and resource allocation are used in 5G networks, then network configuration can be controlled precisely, but network efficiency decreases and operational complexity increases
Solution Approach 1:
The patent implements self-service through SON functions that automatically perform beam management and resource allocation without manual intervention. The system collects performance measurements, analyzes them using AI/ML models, and autonomously adjusts network parameters to optimize beam coverage and resource distribution across 5G cells, thereby improving network efficiency while reducing operational complexity
Solution Approach 2:
The patent applies preliminary action by using AI/ML models to predict future network conditions and traffic patterns based on historical performance data. This allows the SON system to proactively adjust beam configurations and resource allocations before performance degradation occurs, maintaining optimal network efficiency without requiring complex real-time manual adjustments
2Productivity
If AI-driven SON functions are implemented to automatically adjust beam management, then network efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where SON functions continuously collect performance measurements (RSRP, RSRQ, SINR, beam quality metrics) from the network, feed this data to AI/ML models for analysis, and use the model predictions to automatically adjust beam management parameters. This closed-loop feedback system improves network efficiency through intelligent automation while managing system complexity through structured data collection and standardized adjustment protocols
Solution Approach 2:
The patent introduces AI/ML models as intermediaries between raw network performance data and beam management decisions. These models process complex performance measurements and translate them into actionable optimization parameters, thereby improving network efficiency while containing system complexity by decoupling data collection from decision-making through the intermediary modeling layer
3Reliability
If resource allocation is optimized for multiple diverse services (eMBB, URLLC, mMTC), then service quality improves, but resource management complexity increases
Solution Approach 1:
The patent applies segmentation by implementing service-specific resource allocation strategies for different 5G service types (eMBB, URLLC, mMTC). The SON system segments network resources and performance optimization according to service requirements, with dedicated beam management and resource allocation parameters for each service category, thereby improving service quality while managing complexity through structured service differentiation
Solution Approach 2:
The patent implements local quality by applying differentiated resource allocation and beam management parameters tailored to specific service requirements and local network conditions. The AI/ML models analyze performance measurements and apply service-specific optimization strategies to appropriate network sectors and time periods, ensuring high service quality for each application type while managing overall resource management complexity through localized rather than uniform optimization
Data Source
AI summary
Techniques discussed herein can facilitate SON (Self Organizing Network) functions for 5G (Fifth Generation) NR (New Radio) systems. One example embodiment comprises an apparatus configured to be employed in a SON (Self Organizing Network) function, comprising: a memory interface; and processing circuitry configured to: collect one or more performance measurements associated with at least one of a RAN (Radio Access Network) or CN (Core Network); analyze the one or more performance measurements; and generate one or more actions to control the behavior of at least one of the RAN or the CN, based on the analyzing of the one or more performance measurements.