ML-Based Beam Management for 5G Network Latency
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Solution Overview
Problem
Current beam management methods in 5G telecommunications networks are inefficient, requiring extensive reference signal measurements that lead to delays in user and control-plane data transmission due to the need for sweeping multiple beams, which consumes significant time and power.
Innovation Solution
Implementing Machine Learning (ML) and Reinforcement Learning (RL) to process environment property measurements and suggest optimal beam options for data exchange, reducing the number of reference signal transmissions and enhancing beam selection speed and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam sweeping is performed to select the best beam for UE connection, then beam selection accuracy is improved, but transmission delay increases
Solution Approach 1:
The system performs preliminary beam sweeping and stores beam measurement results in advance. When a UE needs to connect, the pre-computed beam information is retrieved and used immediately, eliminating the need to perform full beam sweeping at the moment of connection establishment. This preliminary action resolves the contradiction by preparing beam selection data beforehand, achieving both accurate beam selection and reduced transmission delay.
2Reliability
If multiple reference signals are transmitted for beam measurement, then beam selection reliability is improved, but power consumption increases
Solution Approach 1:
The system extracts and utilizes environmental property measurements (such as UE position, velocity, and channel characteristics) to predict suitable beams without transmitting multiple reference signals for each beam. By taking out the essential information needed for beam selection from environmental measurements rather than from extensive reference signal transmissions, the system achieves reliable beam selection with reduced power consumption.
3Adaptability or versatility
If comprehensive beam sweeping is performed across all directions, then coverage completeness is improved, but system complexity increases
Solution Approach 1:
Instead of performing uniform beam sweeping across all directions, the system determines the UE's location and environmental properties to identify the relevant local area, then performs beam sweeping only in that specific direction. This local quality approach ensures coverage completeness for the UE's actual position while reducing system complexity by avoiding unnecessary beam sweeps in other directions.
Data Source
AI summary
Methods and apparatus for beam management are provided. A computer-implemented method for beam management includes obtaining measurements of one or more properties of an environment, wherein the environment contains one or more User Equipments (UEs). The method further includes initiating transmission of the obtained property measurements to a machine learning (ML) agent hosting a ML model, and receiving the transmitted property measurements at the ML agent. The method also includes processing the received property measurements using the ML model to suggest one or more beam options for exchanging data with the one or more UEs, from among a plurality of beam options, and selecting, using the one or more suggested beam options, at least one of the one or more suggested beam options. The method additionally includes exchanging data with the one or more UEs using the selected beam options.