AI Beam Failure Prediction Using UE Reference Signal Monitoring
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Solution Overview
Problem
Existing wireless communication systems struggle to proactively predict and prevent beam failures, leading to disruptions in uplink and downlink transmissions due to the network's inability to anticipate beam quality changes effectively.
Innovation Solution
Implementing an AI-based solution in user equipment (UE) for beam problem prediction by monitoring reference signals, allowing the UE to predict potential beam failures and transmit reporting information to the network, including candidate beams, confidence levels, and time windows for proactive beam adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI-based beam problem prediction is implemented in UE, then proactive beam failure handling capability is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by enabling the UE to predict beam failures before they actually occur. The AI model analyzes historical beam failure data and current channel conditions to forecast potential beam problems, allowing the network to take preventive actions such as switching to alternative beams or adjusting transmission parameters in advance, thereby avoiding the need for complex real-time reactive handling mechanisms.
Solution Approach 2:
The patent introduces an AI model as an intermediary component between the UE and the network. This intermediary processes historical data, current channel state information, and prediction results, facilitating proactive beam management decisions. The AI model acts as a mediator that translates complex channel conditions into actionable predictions, simplifying the overall system architecture while improving reliability.
2Duration of action of stationary object
If beam monitoring and prediction functionality is added to UE, then service continuity is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent applies preliminary action by performing beam quality assessment and failure prediction in advance. The UE continuously monitors channel conditions and uses the AI model to predict potential beam failures before they disrupt service. This allows the network to prepare alternative beams and switch transmissions proactively, maintaining service continuity without requiring complex real-time detection mechanisms during actual failures.
Solution Approach 2:
The patent implements feedback mechanisms where the UE reports predicted beam problems and channel conditions back to the network. This feedback loop enables the network to adjust beam configurations based on predicted performance rather than reacting to actual failures. The feedback mechanism simplifies measurement requirements by using historical data and AI predictions rather than requiring complex real-time measurements during critical moments.
Data Source
AI summary
Embodiments of the present application relate to a method and apparatus of supporting beam problem prediction. An exemplary method may include: receiving configuration information on at least one set of RSs related to beam monitoring; and transmitting reporting information related to at least one beam problem, wherein the at least one beam problem is predicted by at least monitoring the at least one set of RSs related to beam monitoring.