Neural Network Beam Management for Wireless Communication Overhead
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Solution Overview
Problem
In wireless communication systems, the use of narrow beams as candidate beams during beam training increases the number of beams to be measured, leading to excessive overhead and potential deterioration in communication quality and device performance.
Innovation Solution
An electronic device applies channel characteristic data from multiple candidate beams to a pre-trained neural network to infer the channel state, allowing for adaptive beam management and reducing the number of beams that need to be measured during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If narrow beams are used as candidate beams during beam training, then beam matching gain between external device and electronic device is improved, but the number of candidate beams requiring signal quality measurement increases, leading to excessive overhead
Solution Approach 1:
The system performs preliminary channel state inference using a neural network before beam training to predict which candidate beams are likely to have good signal quality. This preliminary action allows the electronic device to prioritize measurement of only those predicted high-quality beams, avoiding exhaustive measurement of all narrow candidate beams and thus reducing training overhead while maintaining beam matching gain
Solution Approach 2:
The neural network model is trained offline using historical beam training data and channel state information. Once trained, the model serves itself to automatically infer channel states and guide beam selection without requiring manual configuration or exhaustive measurement, enabling the system to adaptively optimize beam training based on learned patterns from past communications
2Reliability
If the number of candidate beams is increased to improve beam matching accuracy, then communication quality between electronic device and base station is improved, but the overhead during beam training process increases excessively
Solution Approach 1:
The neural network acts as an intermediary between channel characteristic data and beam selection decisions. It processes measured channel characteristics (such as RSRP, channel impulse response) and outputs inferred channel states that guide which candidate beams should be measured. This intermediary enables the system to handle a large number of candidate beams intelligently by filtering down to only those most likely to provide good communication quality, thus managing complexity while maintaining reliability
Solution Approach 2:
The system changes the parameter representation from raw channel measurements to inferred channel states through the neural network. By transforming multiple channel characteristic parameters into a condensed channel state classification, the system simplifies the decision-making process for beam selection, allowing efficient management of numerous candidate beams without proportionally increasing training overhead
Data Source
AI summary
An electronic device includes memory; a communication module comprising an antenna array configured to form a plurality of candidate beams; and a processor connected to the communication module and the memory, the processor comprising a channel state classifier based on a neural network, wherein the processor is configured to: generate channel characteristic data based on information measured from the plurality of candidate beams for a signal received from a base station, input the channel characteristic data to the channel state classifier to infer a channel state between the electronic device and the base station, and reform the plurality of candidate beams based on the inferred channel state.