Pruning Overlapping Candidate Resources in Wireless Decoding
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Solution Overview
Problem
Blind decoding in wireless communication systems consumes excessive power and prolongs the end-to-end receive timeline due to ambiguous pruning of overlapping blind decoder candidates, leading to inefficient power consumption and prolonged decoding processes.
Innovation Solution
A user equipment (UE) identifies quality metrics associated with control channel elements (CCEs) of overlapping candidate resources, applies a quality tolerance threshold to prune resources, and performs a first decode operation on higher aggregation level resources before secondary decode operations, thereby optimizing power consumption and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blind decoding is performed on all overlapping candidate resources, then decoding reliability is improved, but power consumption increases and decoding time is prolonged
Solution Approach 1:
The patent applies preliminary action by performing quality metric evaluation and pruning operations before the actual blind decoding process. The UE calculates quality metrics for candidate resources and prunes low-quality candidates in advance, so that only high-quality candidates proceed to full decoding. This preliminary filtering ensures that decoding reliability is maintained for promising candidates while avoiding unnecessary power consumption on poor candidates.
Solution Approach 2:
The patent implements local quality by evaluating and pruning candidate resources based on their individual quality metrics rather than applying a uniform approach to all candidates. Each candidate resource is assessed locally using quality metrics such as signal-to-noise ratio, and pruning decisions are made based on shaping patterns of these metrics. This allows the system to maintain high decoding reliability for quality candidates while reducing power consumption by eliminating low-quality candidates.
2Measurement precision
If blind decoding is performed on all overlapping candidate resources, then decoding accuracy is improved, but the end-to-end receive timeline is prolonged
Solution Approach 1:
The patent applies preliminary action by performing quality metric evaluation and pruning operations before the actual blind decoding process. The UE calculates quality metrics for candidate resources and prunes low-quality candidates in advance, so that only high-quality candidates proceed to full decoding. This preliminary filtering ensures that decoding reliability is maintained for promising candidates while avoiding unnecessary power consumption on poor candidates.
Solution Approach 2:
The patent implements local quality by evaluating and pruning candidate resources based on their individual quality metrics rather than applying a uniform approach to all candidates. Each candidate resource is assessed locally using quality metrics such as signal-to-noise ratio, and pruning decisions are made based on shaping patterns of these metrics. This allows the system to maintain high decoding reliability for quality candidates while reducing power consumption by eliminating low-quality candidates.
3Use of energy by moving object
If quality metric evaluation and pruning is applied to overlapping candidates, then power consumption is reduced, but pruning accuracy may be compromised
Solution Approach 1:
The patent applies parameter changes by utilizing quality metrics such as signal-to-noise ratio and shaping patterns as decision parameters for pruning. By changing from uniform pruning to metric-based pruning, the system achieves more accurate pruning decisions. The quality metrics provide a quantitative basis for determining which candidates to prune, thereby maintaining pruning accuracy while reducing power consumption on low-quality candidates.
Solution Approach 2:
The patent implements feedback by using quality metric evaluation results to guide pruning decisions. The system calculates quality metrics for candidate resources, compares them against thresholds or shaping patterns, and uses this feedback information to determine which candidates to prune. This feedback mechanism ensures that pruning decisions are based on actual signal quality rather than arbitrary criteria, maintaining accuracy while reducing power consumption.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may identify one or more quality metrics associated with one or more respective control channel elements (CCEs) of a first set of candidate resources that partially overlaps with a second set of candidate resources, wherein the first set of candidate resources is associated with a first aggregation level and the second set of candidate resources is associated with a second aggregation level. The UE may prune the second set of candidate resources based at least in part on a shaping pattern of the one or more quality metrics satisfying a quality tolerance threshold. Numerous other aspects are described.