CSI-RS Beam Management Scheduling via Dynamic Prioritization
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Solution Overview
Problem
Current wireless communication systems face inefficiencies in periodic channel state information reference signal (CSI-RS) beam management, particularly in scheduling and measurement processes, which lead to increased latency and overhead in identifying optimal beam pairs between user equipment (UE) and base stations.
Innovation Solution
The implementation of static and dynamic beam sweeping schedules by user equipment (UE) for measuring CSI-RS, allowing prioritization of receive beams with high historical measurement values and spatial proximity, thereby reducing the need for full beam sweeps and enhancing the efficiency of beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full beam sweeps are performed to identify optimal beam pairs, then measurement precision is improved, but latency increases
Solution Approach 1:
The system performs preliminary beam sweeping to establish historical measurement values for different receive beams. These historical measurements are stored and used to prioritize beams in future measurements, eliminating the need to perform full beam sweeps every period and reducing latency while maintaining identification accuracy.
Solution Approach 2:
The beam measurement approach transitions from static full sweeps to dynamic adaptive sweeping. The system dynamically adjusts which beams to measure based on historical performance data, spatial proximity information, and current channel conditions, optimizing the measurement process for each specific situation.
2Reliability
If comprehensive beam management measurements are performed, then reliability is improved, but overhead increases
Solution Approach 1:
Instead of uniformly measuring all beams with equal effort, the system applies local quality by focusing measurement resources on beams that are spatially proximate to previously identified good beams or have historical measurement values above thresholds. This localized approach maintains reliability by concentrating measurements where they are most needed while reducing overall overhead.
Solution Approach 2:
The system performs partial beam sweeping by selecting only a subset of beams for measurement based on prioritization criteria. Rather than exhaustively measuring all possible beam pairs, it performs sufficient measurements on the most promising beams to maintain reliability while significantly reducing the quantity of measurements required.
3Device complexity
If static beam sweeping schedule is used, then device complexity is reduced, but adaptability decreases
Solution Approach 1:
The system implements dynamic beam sweeping schedules that adapt to changing channel conditions and historical performance data. The scheduling mechanism dynamically adjusts which beams to measure, when to measure them, and prioritization based on real-time information while maintaining manageable complexity through structured algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where historical measurement results are fed back into the beam scheduling decision process. This feedback loop enables the system to adapt its beam sweeping schedule based on past performance, improving adaptability while the feedback structure itself provides a manageable framework that prevents excessive complexity.
4Productivity
If dynamic beam sweeping schedule with prioritization is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary beam sweeping to establish historical measurement values and spatial relationships before actual communication begins. This preliminary action creates a foundation of knowledge that enables faster, more productive beam pair identification during operation without requiring complex real-time calculations, thus improving productivity while managing complexity.
Solution Approach 2:
The dynamic beam sweeping schedule uses prioritization based on historical data and spatial proximity to improve productivity. By dynamically adjusting measurement priorities rather than using fixed schedules, the system identifies optimal beam pairs faster. The complexity is managed through structured prioritization algorithms that build on preliminary measurements rather than requiring complex real-time optimization.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may measure, using one or more receive beams, a channel state information reference signal (CSI-RS) transmitted on a transmit beam of the CSI-RS according to a beam management configuration for periodic CSI-RSs, where the UE performs measurements according to a static beam sweeping schedule or a dynamic beam sweeping schedule associated with the transmit beam of the CSI-RS based at least in part on the beam management configuration. In some aspects, the UE may transmit a measurement report indicating one or more measurements of the CSI-RS based at least in part on measuring the CSI-RS. Numerous other aspects are provided.