Reinforcement Learning Beam Selection for Wireless Networks
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Solution Overview
Problem
Beam selection in wireless networks is computationally complex, particularly in determining transmit and receive beams between base stations and user equipment, which affects operational efficiency across various frequency ranges.
Innovation Solution
The implementation of a system that uses machine learning, specifically reinforcement learning, to identify beam pairs by estimating the angle of arrival of signals, allowing base stations and user equipment to select optimal beams from a codebook based on received signal strength measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional beam selection techniques are used, then beam management can be performed, but computational complexity increases and operational efficiency decreases
Solution Approach 1:
The patent replaces traditional computational beam selection algorithms with a machine learning model that has been pre-trained to perform beam selection. The model learns optimal beam selection strategies during training and applies them during inference, substituting complex real-time computations with faster model-based decisions that reduce operational complexity while maintaining or improving efficiency
Solution Approach 2:
The patent performs beam selection training in advance using historical data and simulations. The machine learning model is pre-trained offline to learn optimal beam selection patterns, so that during actual operation, the model can quickly apply learned knowledge without performing complex real-time computations, thereby improving operational efficiency while reducing runtime computational complexity
2Measurement precision
If beam selection accuracy is improved, then spectral efficiency increases, but system complexity increases
Solution Approach 1:
The patent replaces complex traditional beam selection algorithms with a trained machine learning model that achieves high selection accuracy through learned patterns. The model substitutes sophisticated computational methods with a pre-trained neural network or learning-based algorithm that can achieve comparable or superior accuracy with simpler real-time operations
Solution Approach 2:
The patent uses historical beam selection data and signal characteristics as training copies to teach the machine learning model optimal selection strategies. By learning from replicated historical scenarios, the model achieves high accuracy in selecting beam pairs without requiring complex real-time analysis of each new situation
Data Source
AI summary
Apparatuses, systems, and techniques to identify and select a wireless signal beam. In at least one embodiment, a wireless signal beam is identified and selected using a determined angle of arrival of one or more wireless signals at a base station or UE.