Proactive Beam Management via Channel Prediction
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Solution Overview
Problem
Current wireless communication systems, including 5G NR, face challenges in maintaining high-quality communication links due to varying channel conditions, often leading to increased latency and reduced throughput, as they rely on reactive beam failure detection and recovery methods that require resource overhead and may disconnect users before remedial actions can be taken.
Innovation Solution
Implementing a method where user equipment (UE) predicts future channel conditions and transmits an indication to the base station, allowing proactive beam management to adjust communication parameters or handover to a different node before channel failure occurs, thereby reducing resource requirements and disconnection time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive beam failure detection and recovery methods are used, then the system can detect and respond to channel failures, but latency increases and throughput is reduced due to resource overhead and disconnection time
Solution Approach 1:
The patent applies preliminary action by predicting future channel conditions before beam failure actually occurs. The UE uses machine learning models to analyze historical channel data and predict degradation trends, enabling the system to take preventive measures (such as switching to alternative beams or adjusting transmission parameters) before the failure happens, thereby avoiding latency and disconnection time associated with reactive recovery methods
2Reliability
If reactive beam failure recovery methods are used, then the system can restore communication after failure, but resource overhead increases due to recovery procedures
Solution Approach 1:
The system performs preliminary channel condition prediction and identifies alternative beams or communication paths in advance. When channel degradation is predicted, the UE and base station can seamlessly switch to pre-prepared alternative configurations without triggering resource-intensive recovery procedures, thereby reducing resource overhead while maintaining reliability
3Reliability
If proactive channel condition prediction is implemented, then beam failure frequency is reduced and resource requirements decrease, but the system complexity increases due to prediction mechanisms
Solution Approach 1:
The UE autonomously performs channel condition prediction using its own processing capabilities and local machine learning models. The prediction mechanism leverages the UE's existing hardware (processors, memory, sensors) to analyze channel data and generate predictions without requiring additional dedicated prediction hardware or complex network-side infrastructure, thereby managing device complexity while achieving beam failure prevention
Data Source
AI summary
In an aspect of the disclosure, methods, a computer-readable media, and apparatus are provided. An apparatus may be a wireless communication device. The apparatus may predict a future channel condition for a wireless communication channel between a wireless communication device and the UE. The apparatus may transmit an indication of the future channel condition to the wireless communication device.