QCL Type-D CLI Measurement for Optimal Beam Scheduling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face challenges in accurately determining quasi-co-location Type-D assumptions for cross-link interference measurements, which can lead to suboptimal beam selection and increased latency in full-duplex operations.
Innovation Solution
A method and apparatus for determining quasi-co-location Type-D assumptions for cross-link interference measurements, involving a user equipment (UE) receiving a configuration or rule to determine QCL Type-D assumptions, measuring UE-to-UE CLI based on these assumptions, and reporting the results to a network entity, thereby assisting in optimal beam selection and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CLI measurement methods are used without specific QCL Type-D assumptions, then the measurement process is simpler, but beam selection becomes suboptimal and latency increases
Solution Approach 1:
The network entity pre-configures QCL Type-D assumptions and CLI measurement resources before actual measurements are needed. This allows the UE to have measurement resources ready and determined QCL assumptions in advance, reducing the time required when actual CLI measurements and beam selections need to be performed.
Solution Approach 2:
QCL Type-D assumptions act as an intermediary that links reference signals with CLI measurement resources. By introducing this intermediate parameter, the system enables more accurate beam selection without requiring complex real-time calculations, thus improving measurement accuracy while maintaining efficient timing.
2Reliability
If detailed QCL Type-D assumption determination is implemented per CLI resource, then beam selection accuracy improves, but system complexity increases
Solution Approach 1:
The system changes the parameter of QCL assumptions from generic to type-specific (QCL Type-D) for each CLI measurement resource. This parameter refinement enables more accurate beam selection by providing specific spatial relationship information, while the network entity manages the complexity by pre-determining these parameters rather than requiring complex real-time processing at the UE.
Solution Approach 2:
The system segments the CLI measurement process by applying individual QCL Type-D assumptions to each CLI measurement resource rather than using a single generic assumption for all measurements. This segmentation allows each resource to be optimized independently for its specific beam pair, improving overall reliability while the network entity manages the segmentation complexity through centralized configuration.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a cross-link interference (CLI) resource configuration for a UE-to-UE CLI measurement resource. The UE may determine a quasi-co-location (QCL) Type-D assumption per CLI resource based at least in part on a rule or a configuration associated with QCL Type-D assumption determination. The UE may measure UE-to-UE CLI based at least in part on the determined QCL Type-D assumption per CLI resource and the CLI measurement resource. The UE may transmit a CLI report that indicates CLI measurement results associated with the determined QCL Type-D assumption. Numerous other aspects are described.