MU-MIMO Stream Grouping via ESINR Segmentation
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently optimizing stream grouping for MU-MIMO transmissions, leading to throughput degradation due to mutual interference between simultaneously transmitted streams, which is complex and time-consuming to analyze.
Innovation Solution
The implementation of an iterative process involving a prolonged and instantaneous computation of effective Signal-to-Interference-and-Noise Ratio (ESINR) for stream grouping, using beamforming reports to dynamically adapt and reduce computational complexity, allowing for rapid identification of high-throughput groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive interference analysis between streams is performed to optimize MU-MIMO grouping, then throughput is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the stream selection process into two distinct phases: a prolonged iterative process that performs comprehensive ESINR-based interference analysis to identify optimal stream groups, and an instantaneous process that rapidly retrieves pre-computed grouping information from lookup tables. This segmentation allows thorough analysis to be performed only when channel conditions change, while normal operations use pre-computed data, thereby resolving the contradiction between comprehensive analysis and computational complexity.
Solution Approach 2:
The patent performs stream grouping analysis in advance during the prolonged iterative process and stores the results in lookup tables before actual MU-MIMO transmissions occur. The instantaneous process then simply retrieves these pre-computed groupings based on current channel conditions. This preliminary action eliminates the need for real-time comprehensive interference analysis during transmissions, significantly reducing computational complexity while maintaining optimal throughput performance.
2Measurement precision
If real-time ESINR computation is performed for all stream combinations, then accurate grouping is achieved, but response time increases
Solution Approach 1:
The patent divides the ESINR computation into a prolonged iterative process that performs accurate but time-consuming calculations to build comprehensive grouping tables, and an instantaneous process that quickly queries these tables during actual transmissions. This segmentation ensures accurate grouping is achieved through thorough ESINR analysis while response time is minimized by using pre-computed results during instantaneous operations.
Solution Approach 2:
The patent performs the computationally intensive ESINR computation and stream grouping analysis in advance during the prolonged iterative process, storing results in lookup tables. During instantaneous operations, the system simply retrieves pre-determined optimal groupings based on current channel state information. This preliminary computation of grouping accuracy eliminates the need for real-time ESINR calculation during transmissions, resolving the contradiction between measurement precision and response time.
3Productivity
If iterative prolonged process is used to compute ESINR for all streams, then optimal stream groups are identified, but computing resources are consumed
Solution Approach 1:
The patent segments the computational workload into a prolonged iterative process that performs comprehensive ESINR computation and stream grouping optimization, and an instantaneous process that retrieves pre-computed results. By segmenting when the intensive computation occurs (only during prolonged phases when channel conditions change) versus when simple retrieval occurs (during instantaneous transmissions), the patent achieves optimal throughput while dramatically reducing ongoing computing resource consumption.
Solution Approach 2:
The patent performs the resource-intensive iterative ESINR computation and optimal stream group identification in advance during prolonged phases, storing results in lookup tables for rapid retrieval. During instantaneous transmission phases, the system simply queries these pre-computed optimal groupings without performing new ESINR calculations. This preliminary identification of optimal groups eliminates repeated computationally expensive operations, resolving the contradiction between throughput optimization and computing resource consumption.
Data Source
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AI summary
A transmitter of a wireless Access Point (AP) comprising a circuitry configured to conduct an iterative prolonged process and an iterative instantaneous process for optimizing streams grouping to enhance MU-MIMO transmissions throughput. The prolonged process comprises receiving beamforming reports corresponding to streams transmitted simultaneously by the AP to one or more stations, computing an Effective SINR (ESINR) for each stream with respect to other stream(s) based on the beamforming reports and updating a grouping table associated with each stream to include one or more groups presenting best ESINR for the respective stream. The instantaneous process comprising computing the ESINR for one or more of the streams using most recent beamforming report(s) received and updating one or more of the groups in the grouping table to include one or more additional streams presenting best ESINR. For each MU-MIMO transmission, the transmitter selects a best ESINR group from the updated grouping table.