AI Beam Prediction for Low-Latency 5G Beam Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in reducing overhead and latency associated with beam management, especially in mmWave bands, due to the need for frequent beam training and adjustments.
Innovation Solution
Implementing artificial intelligence (AI)/machine learning (ML) algorithms for beam prediction, where user equipment (UE) and base stations use AI/ML modules to predict future channel quality and beam performance based on past measurements, reducing the need for frequent beam training and adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent beam training and adjustments are performed, then beam management reliability is improved, but overhead and latency increase
Solution Approach 1:
The system performs beam predictions in advance using AI/ML algorithms to forecast future channel quality and optimal beam pairs before actual communication occurs. This preliminary prediction action eliminates the need for frequent real-time beam training and adjustments, thereby reducing overhead and latency while maintaining beam management reliability through proactive beam selection based on predicted channel conditions.
2Loss of time
If AI/ML algorithms are implemented for beam prediction, then overhead and latency are reduced, but device complexity increases
Solution Approach 1:
The patent introduces AI/ML algorithms as intermediary components that mediate between beam management functions and communication protocols. These algorithms process historical beam measurement data and channel state information to generate predictions, acting as a computational layer that reduces the need for frequent manual beam adjustments and signaling overhead, thereby lowering system latency and overhead despite adding computational complexity.
3Measurement precision
If beam predictions are made based on past measurements, then channel quality estimation is improved, but measurement precision requirements increase
Solution Approach 1:
The system implements feedback mechanisms where beam measurement results and channel quality indicators are continuously collected, processed, and fed back into AI/ML prediction models. This feedback loop enables the models to learn from historical data and refine their predictions, improving channel quality estimation accuracy over time. The feedback mechanism allows the system to compensate for measurement variations and maintain high estimation precision without requiring excessively precise individual measurements.
Data Source
AI summary
Aspect are direction to techniques for predicting future beams (e.g., transmit beams and/or receive beams) for communications between a network node and a user equipment (UE). In some examples, the UE may output, for transmission to a network node, beam information of a receive beam. The UE may obtain, via a beam pair comprising the receive beam and a transmit beam, signaling from the network node. In some examples, the UE may output, for transmission to the network node, a measurement of the signaling from a perspective of the receive beam.