Time-Series Beam Management for Faster 5G Beam Alignment
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Solution Overview
Problem
Existing wireless communication systems, particularly 5G NR, face challenges in efficiently managing beams for optimal communication due to high path loss and the need for precise beam alignment, especially in millimeter wave frequencies, which affects latency and reliability.
Innovation Solution
Implementing spatial and frequency domain beam management using time series information and machine learning algorithms to predict optimal beams based on historical data patterns, allowing for efficient beam selection and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam management is performed using traditional methods in 5G NR, then communication can be established, but path loss is high and beam alignment precision is insufficient, especially in millimeter wave frequencies
Solution Approach 1:
The system performs preliminary beam measurements and collects time series data before actual communication. By measuring signals on a second subset of beams over a time window and inputting these measurements to a computational model in advance, the system predicts channel measurements for the first subset of beams, enabling proactive beam alignment optimization rather than reactive adjustment.
Solution Approach 2:
The patent replaces traditional mechanical beam sweeping and exhaustive search methods with machine learning-based prediction. Instead of systematically testing each beam direction, the computational model predicts optimal beams based on historical time series data patterns, substituting computational intelligence for brute-force mechanical search.
2Measurement precision
If exhaustive beam measurement is performed on all beams, then accurate beam selection is achieved, but measurement time and system complexity increase
Solution Approach 1:
The system extracts and utilizes only the necessary measurements from a second subset of beams to predict channel measurements for the first subset of beams. By selecting representative beams for actual measurement and using computational prediction for the rest, the system achieves accurate beam selection without the time cost of measuring all beams exhaustively.
Solution Approach 2:
The computational model serves multiple functions: it predicts channel measurements for unmeasured beams, identifies optimal beams for communication, and adapts to changing channel conditions over time. This single predictive system replaces multiple separate measurement and decision-making processes.
3Reliability
If frequent beam measurements are performed to maintain alignment, then beam alignment precision is maintained, but latency increases
Solution Approach 1:
The system continuously collects time series measurement data from the second subset of beams and maintains an updated computational model. This continuous data collection and model updating enable the system to predict beam measurements without performing frequent exhaustive measurements, maintaining alignment precision while reducing latency through predictive rather than reactive operations.
Data Source
AI summary
In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be wireless equipment. The wireless equipment selects a first subset of beams to be utilized for beam management. The beams are from a set of first type of beams used for communication with a base station or a UE. The wireless equipment measures signals transmitted on a second subset of beams. The beams are from the set of first type of beams or from a set of second type of beams. The wireless equipment measures the signals over a time window. The wireless equipment inputs the measurements to a computational model. The wireless equipment receives predictions of channel measurements on the first subset of beams from the computational model.