ML-Based Beam Prediction for 5G RSRP Reporting Overhead
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Solution Overview
Problem
Current beam management procedures in 5G networks face challenges with increased overhead and latency due to the need for exhaustive beam scanning and feedback, especially with high-dimensional MIMO arrays.
Innovation Solution
Implementing a machine learning-based approach that predicts the best beams by transmitting reference signals on a first set of beams, receiving power measurements from user equipment, and processing these measurements with a machine learning model to identify optimal beams from a second set of beams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive beam scanning is performed to ensure accurate beam selection, then beam selection accuracy is improved, but overhead and latency increase
Solution Approach 1:
The system performs preliminary beam scanning and measurement only for a subset of beams (first set) rather than all possible beams. The machine learning model uses these preliminary measurements to predict the best beam before actual data transmission begins, eliminating the need for exhaustive scanning and reducing latency while maintaining accuracy.
Solution Approach 2:
A machine learning model is introduced as an intermediary between beam measurement and beam selection. The model processes measurements from a subset of beams and predicts the optimal beam choice, serving as a mediator that eliminates the need for exhaustive scanning and reduces the measurement overhead required for accurate beam selection.
2Measurement precision
If reference signal measurements are collected for all beams, then beam prediction accuracy is improved, but measurement reporting overhead increases
Solution Approach 1:
The set of all beams is segmented into two distinct sets: a first set of beams for which reference signals are actually transmitted and measured, and a second set of beams for which predictions are made using the machine learning model. This segmentation allows accurate predictions without requiring measurements for all beams, thereby reducing reporting overhead.
Solution Approach 2:
Instead of measuring all possible beams (excessive action), the system measures only a partial subset of beams (first set) and uses the machine learning model to infer the remaining beam characteristics. This partial measurement approach maintains sufficient prediction accuracy while significantly reducing the quantity of measurements and reporting overhead.
Data Source
AI summary
An apparatus configured to: transmit, to at least one UE, an indication of a measurement reporting scheme; transmit, to the at least one UE, at least one RS on at least one first beam; receive, from the at least one UE, a set of RSRP measurements of one or more of the at least one RS, according to the measurement reporting scheme; and provide the set of RSRP measurements to a machine learning model for prediction of best beams from a second set of beams. An apparatus configured to: receive, from a network node, an indication of a measurement reporting scheme; measure at least one RS on at least one first beam; generate a set of RSRP measurements of one or more of the at least one reference signal based, at least partially, on the measurement reporting scheme; and transmit, to the network node, the set of RSRP measurements.