Overlapping PDCCH Candidate Rules Across Aggregation Levels

The patent segments the PDCCH candidate set by aggregation level, creating distinct groups (first aggregation level candidates and second aggregation level candidates). This segmentation allows the UE to process and interpret candidates from different aggregation levels separately, eliminating the ambiguity that arises when candidates overlap without such differentiation.

The patent applies different monitoring and interpretation rules to different subsets of PDCCH candidates based on their aggregation levels. Specifically, candidates of the first aggregation level are monitored and interpreted according to first rules, while candidates of the second aggregation level are monitored and interpreted according to second rules. This local quality approach ensures that each candidate is processed with the appropriate interpretation rules for its specific aggregation level, resolving the ambiguity problem.

The patent segments PDCCH candidates into different groups based on aggregation level (first aggregation level vs. second aggregation level). This segmentation simplifies the monitoring process by allowing the UE to apply different interpretation rules to different segments, rather than trying to uniformly interpret all candidates, thus reducing overall complexity while maintaining robust monitoring of multiple candidates for repetitions.

The patent implements local quality by applying specific monitoring and interpretation rules tailored to each aggregation level group. Candidates of the first aggregation level are handled with first rules, while candidates of the second aggregation level are handled with second rules. This localized approach reduces the cognitive load and processing complexity at the UE by providing clear, context-specific interpretation guidelines for each candidate group.