ML Beam Sequence Prediction for Low-Overhead Cell Measurements
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately predicting beam sequences for user equipment, leading to inefficiencies in signal quality and increased power consumption due to frequent beam reporting, especially in scenarios involving L1 beam measurement reports.
Innovation Solution
Implementing a system that uses machine learning algorithms to predict beam sequences with confidence intervals, allowing for adaptive adjustments based on measurement data and user feedback to improve accuracy and reduce unnecessary reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam sequence prediction is implemented using machine learning algorithms, then beam prediction accuracy is improved, but device complexity increases
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the measurement data collection and beam sequence determination. The model processes measurement data and generates predicted beam sequences, thereby improving accuracy while isolating the complexity within a dedicated module rather than distributing it throughout the entire system.
Solution Approach 2:
The machine learning model performs preliminary processing of measurement data to generate beam sequence predictions in advance. By pre-processing the data and generating predictions before actual beam switching occurs, the system achieves higher accuracy without requiring complex real-time processing during critical communication moments.
2Reliability
If frequent beam reporting is performed to maintain signal quality, then reliability is improved, but power consumption increases
Solution Approach 1:
Instead of continuous or frequent beam reporting, the system implements periodic beam reporting triggered by specific events or time intervals. The machine learning model predicts when beam switching is likely to occur and schedules reporting accordingly, maintaining signal quality reliability while significantly reducing the frequency of reporting operations and associated power consumption.
Solution Approach 2:
The machine learning model autonomously determines when beam reporting is necessary based on predicted beam sequences and current signal conditions. The system self-regulates the reporting frequency, performing reports only when the prediction indicates potential signal quality degradation, thereby eliminating unnecessary power-consuming reporting operations while maintaining reliability.
3Adaptability or versatility
If beam switching is performed frequently to track signal quality changes, then adaptability is improved, but loss of time increases due to switching overhead
Solution Approach 1:
The machine learning model performs preliminary analysis of measurement data to predict future beam sequences and identifies optimal switching opportunities in advance. By preparing and scheduling beam switches beforehand based on predictions, the system achieves high adaptability to signal changes while minimizing the actual time lost during switching operations.
Solution Approach 2:
The system implements a feedback mechanism where the machine learning model continuously receives measurement data, evaluates prediction accuracy, and adjusts future predictions accordingly. This feedback loop enables the system to adapt to changing signal conditions efficiently, performing beam switching only when predictions indicate genuine improvements, thereby reducing unnecessary switching overhead and time loss.
Data Source
AI summary
There is provided an apparatus comprising means for: receiving (700, 714), from a user equipment, measurement data relating to one or more cells of a network; determining (716), based on the measurement data, a prediction comprising at least one sequence of one or more beams that are predicted to have a highest signal quality for the user equipment at a respective one or more time instances; and sending (718), to the user equipment, the prediction.