Early Termination Predictor Reporting for UE Decoding Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face inaccuracies in early decoding termination predictions by user equipment (UE), leading to unnecessary utilization of computational resources and power consumption.
Innovation Solution
User equipment (UE) transmits an early termination predictor report (ETPR) to the network entity, indicating the accuracy of early termination predictions, allowing the network entity to optimize power consumption and performance tradeoffs, and update downlink shared channels accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the UE performs early decoding termination prediction to reduce computational resource utilization, then power consumption is reduced, but prediction accuracy deteriorates leading to false alarms
Solution Approach 1:
The UE transmits an Early Termination Predictor Report (ETPR) to the network entity, providing feedback on the accuracy of its prediction algorithm. The network entity uses this feedback to optimize parameters and adjust the prediction configuration, thereby improving prediction accuracy while maintaining power savings. This closed-loop feedback mechanism resolves the contradiction by enabling continuous improvement of prediction accuracy without sacrificing energy efficiency.
Solution Approach 2:
The network entity receives the ETPR and optimizes parameters related to the prediction algorithm based on the reported accuracy. By dynamically adjusting parameters such as prediction thresholds, code block group sizes, or algorithm complexity, the system can balance prediction accuracy and power consumption according to current channel conditions and UE capabilities.
2Reliability
If the UE transmits ETPR reports to improve prediction accuracy, then decoding reliability is improved, but signaling overhead increases
Solution Approach 1:
The ETPR is transmitted selectively rather than continuously. The UE determines when to send reports based on local conditions such as prediction accuracy thresholds, channel state changes, or specific triggering events. This selective reporting approach maintains decoding reliability by providing updates only when necessary, while minimizing signaling overhead by avoiding redundant transmissions.
3Use of energy by moving object
If the network entity optimizes parameters based on ETPR to improve power consumption tradeoff, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The UE autonomously performs the early decoding termination prediction and self-evaluates its accuracy by comparing predictions with actual decoding outcomes. The UE then generates the ETPR based on this self-assessment, eliminating the need for complex network-side monitoring and evaluation mechanisms. This self-service approach improves energy efficiency while keeping system complexity manageable by distributing intelligence to the UE.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. The techniques described herein relate to an early termination predictor report. A user equipment (UE) monitors for a first downlink signal from a network entity. The UE transmits a predictor report indicative of an early decoding termination prediction accuracy associated with decoding the first downlink signal by a decoder of the UE. The UE monitors, in response to transmitting the predictor report, for a second downlink signal from the network entity, the second downlink signal based at least in part on the predictor report.