Beam Management Using Machine Learning for Millimeter Wave Reselection
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Solution Overview
Problem
Current beam management methods in wireless communication systems, particularly in millimeter wave communication, face challenges such as prolonged beam scanning times, late detection of beam mismatches, and low accuracy in reselecting optimal beams, leading to link interruptions and poor communication quality, especially in fast-changing environments.
Innovation Solution
A beam management method utilizing a machine learning model, specifically a reinforcement-learning-based Q learning model with an artificial neural network and bidirectional long short-term memory network, to analyze reference signal receiving powers from multiple receiving beams, predict beam states, and optimize beam selection and switching, thereby improving timeliness and accuracy of beam reselection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple receiving beam scanning is used to periodically traverse each receiving beam and measure RSRP, then the beam management process is simple to implement, but the scanning time is prolonged and beam mismatch detection is delayed
Solution Approach 1:
The system performs preliminary beam measurement and evaluation by using a machine learning model to predict future beam states based on current and historical RSRP measurements. This allows the system to proactively identify potential beam mismatches before they occur, rather than waiting for the complete scanning process to finish and then detecting the mismatch.
Solution Approach 2:
The patent replaces the traditional mechanical sequential beam scanning approach with a machine learning-based predictive system. Instead of physically traversing each beam in sequence and waiting for measurements, the system uses computational models to predict beam performance and identify mismatches, substituting the mechanical scanning process with intelligent computation.
2Measurement precision
If beam scanning is performed sequentially to ensure thorough measurement, then measurement completeness is improved, but the time required for beam reselection increases
Solution Approach 1:
The system extracts the essential information needed for beam management by using the machine learning model to predict beam states from limited measurement data. Instead of requiring complete sequential scanning of all beams to make reselection decisions, the system extracts sufficient information from partial measurements combined with predictive modeling, enabling faster reselection while maintaining decision accuracy.
Solution Approach 2:
The machine learning model performs preliminary analysis of beam measurements and predicts future beam states, allowing the system to identify optimal beams before the complete scanning process finishes. This preliminary prediction enables the system to prepare beam reselection decisions in advance, improving reselection speed without sacrificing measurement thoroughness.
3Device complexity
If traditional beam scanning methods are used, then the system complexity is low, but the accuracy of beam reselection deteriorates due to delayed mismatch detection
Solution Approach 1:
The patent substitutes the simple but inaccurate traditional beam scanning method with a machine learning-based predictive system. This replacement increases system complexity but dramatically improves reselection accuracy by enabling proactive mismatch detection and prediction, allowing the system to select optimal beams based on predicted future states rather than relying solely on current measurements.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously learns from past beam measurements and reselection outcomes. This feedback loop improves the accuracy of mismatch detection and prediction over time, allowing the system to adapt to changing channel conditions and user behavior patterns, thereby improving reselection accuracy without requiring proportional increases in system complexity.
4Adaptability or versatility
If multiple beams are scanned to cover the entire three-dimensional space, then beam coverage is improved, but the time required for beam management operations increases
Solution Approach 1:
The machine learning model performs preliminary prediction of which beams are likely to be optimal based on current channel conditions, user location, and historical data. This allows the system to focus measurements and management operations on a subset of predicted candidate beams rather than scanning the entire three-dimensional space, reducing beam management time while maintaining comprehensive coverage through predictive identification of relevant beams.
Solution Approach 2:
Instead of uniformly scanning all possible beams across the entire three-dimensional space, the system applies local quality assessment by using the machine learning model to identify and prioritize specific beam directions that are most relevant to current user conditions. This localized approach concentrates measurement resources on high-probability candidate beams, reducing overall management time while maintaining effective coverage.
Data Source
AI summary
A beam management method is provided. The beam management method is performed by a user equipment and includes receiving, by the at least one processor, reference signals corresponding to a transmitting beam by using a first receiving beam and a second receiving beam; identifying, by the at least one processor, a first reference signal receiving power (RSRP) of the first receiving beam and a second RSRP of the second receiving beam; analyzing, by the at least one processor, the first RSRP and the second RSRP respectively by a machine learning model; and identifying, by the at least one processor, a beam management operation corresponding to the transmitting beam according to a result of the analyzing.