Candidate Cell and Beam Assistance for ML UE Mobility Prediction
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Solution Overview
Problem
Existing wireless communications systems face challenges in accurately predicting user equipment (UE) mobility and handover events due to the lack of effective assistance information exchanged between wireless communication devices for machine learning-based UE mobility prediction.
Innovation Solution
The implementation of various types of assistance information that can be exchanged between wireless communication devices, including assistance information for target or candidate cell prediction, communication failure event prediction, and measurement event prediction, to enhance the accuracy of UE mobility prediction and handover performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning-based UE mobility prediction is implemented without assistance information, then the system can operate with simpler information exchange, but the accuracy of mobility prediction and handover performance deteriorates
Solution Approach 1:
The patent applies preliminary action by having the network entity pre-generate and provide assistance information (including candidate cells, candidate beams, and prediction parameters) to the UE before handover events occur. This pre-computed information enables the ML model to make more accurate predictions without requiring extensive real-time information exchange during mobility events.
Solution Approach 2:
The patent uses assistance information as an intermediary that mediates between the network entity and UE. This intermediary contains pre-processed prediction parameters, candidate cells, and candidate beams that bridge the gap between limited UE measurements and the complex ML prediction requirements, improving accuracy without increasing direct information exchange overhead.
2Device complexity
If traditional handover selection based only on radio measurements is used, then the system operates with simpler processing, but handover failures and ping-ponging increase
Solution Approach 1:
The network entity performs preliminary actions by pre-identifying and providing candidate cells and candidate beams to the UE before handover decisions are needed. This pre-computation allows the ML model to make more reliable handover decisions by considering multiple candidates with associated prediction parameters, rather than relying solely on immediate radio measurements.
Solution Approach 2:
The patent transforms the handover decision process by changing from simple radio measurement thresholds to ML-based predictions using multiple parameters including candidate cell identifiers, candidate beam identifiers, prediction parameters, and confidence scores. This parameter expansion enables more reliable handover decisions while managing complexity through structured information provision.
3Measurement precision
If ML-based mobility prediction with assistance information is implemented, then mobility prediction accuracy improves, but information exchange complexity and processing overhead increase
Solution Approach 1:
The patent segments the assistance information into distinct components: candidate cells, candidate beams, prediction parameters, and confidence scores. This segmentation allows the complex information to be organized in manageable parts, with each component serving a specific function in the ML prediction process, thereby reducing the perceived complexity of information exchange.
Solution Approach 2:
The assistance information structure serves multiple functions simultaneously: it provides candidate cell identification, candidate beam identification, prediction parameters for ML processing, and confidence scoring. This multi-functionality reduces the need for separate information exchanges for each purpose, thereby managing complexity while improving prediction accuracy.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for mobility prediction with assistance information. An example method for wireless communications includes obtaining assistance information associated with user equipment (UE) mobility; predicting one or more candidate communication links for a communication link modification based at least in part on the assistance information; and communicating with a wireless communications device based at least in part on the one or more candidate communication links.