Candidate Link Prediction for Low-Latency Wireless Handover
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Solution Overview
Problem
Wireless communications systems face challenges in maintaining service continuity during UE mobility due to high handover frequencies and unsuccessful handovers, particularly in dynamic environments, leading to packet losses and latency issues.
Innovation Solution
Implementing UE mobility prediction-based configurations with validity times and criteria for mobility operations, including handovers and beam switches, to enhance handover performance and reduce failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If handover is performed based on radio measurements without considering UE mobility pattern, then handover selection is simple, but handover failure rate increases
Solution Approach 1:
The system performs preliminary analysis of UE mobility patterns and predicts future target cells before handover is needed. The network entity obtains mobility pattern information from the UE and uses it to predict candidate target cells in advance, so when handover becomes necessary, the prediction results are already available to guide the handover decision, reducing failure rate without adding complex real-time processing
Solution Approach 2:
The handover selection mechanism transitions from static radio measurement-based selection to dynamic prediction-based selection. The system adapts to changing UE mobility conditions by continuously updating mobility patterns and predictions, allowing the handover target selection to dynamically adjust based on predicted UE movement rather than relying solely on current radio conditions
2Ease of manufacture
If trial and error method is used to find suitable target cell, then initial handover selection is simple, but handover failure and service interruption increase
Solution Approach 1:
The network entity obtains mobility pattern information from the UE and predicts candidate target cells in advance based on historical mobility data. This preliminary prediction provides a head start in identifying suitable handover targets, eliminating the need for trial-and-error approaches and reducing service interruption while maintaining configuration simplicity
Solution Approach 2:
The system implements feedback mechanisms where the network entity receives mobility pattern information from the UE, processes it through prediction algorithms, and uses the results to guide handover decisions. The feedback loop continuously refines predictions based on actual handover outcomes and updated mobility patterns, improving service continuity without complicating the overall process
3Reliability
If handover frequency is increased to maintain service continuity in dynamic environments, then service continuity improves, but packet losses and latency increase
Solution Approach 1:
The system performs handover preparations in advance by predicting target cells based on UE mobility patterns before service interruption occurs. By having predictions ready beforehand, the actual handover execution can proceed quickly without extensive measurement and evaluation delays, reducing handover latency while maintaining service continuity in dynamic environments
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for prediction-based mobility management. An example method for wireless communications includes obtaining a first indication of a first prediction of one or more candidate communication links for a communication link modification, and a second indication of a validity time associated with the first prediction, wherein the validity time indicates a time period during which the first prediction is valid; and communicating with a network entity based at least in part on the first prediction during the validity time.