Compressed Beamforming for MU-MIMO Grouping Optimization
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Solution Overview
Problem
In wireless communication systems, multiple-input multiple-output (MIMO) operations face challenges with high packet error rates due to inter-user interference in multi-user MIMO transmissions, where STAs with high channel correlation are grouped together, leading to inefficient modulation and coding scheme (MCS) adaptation.
Innovation Solution
The use of compressed beamforming information to estimate multi-user signal-to-interference-plus-noise (SINR) metrics for each station in candidate groups, accounting for expected interference, allows for the formation of efficient MU transmission groups and accurate MCS determination based on SINR metrics and correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If STAs are grouped into MU-MIMO groups based on availability and traffic, then multi-user transmission efficiency is improved, but packet error rate increases due to inter-user interference when channel correlation is high
Solution Approach 1:
The system uses compressed beamforming feedback from STAs to calculate channel correlation metrics. This feedback mechanism enables the AP to continuously monitor channel conditions and adjust MU-MIMO groupings accordingly, resolving the contradiction by using real-time channel state information to prevent high-correlation STAs from being grouped together.
Solution Approach 2:
The invention changes the grouping parameter from simple availability/traffic status to include channel correlation metrics derived from compressed beamforming feedback. By incorporating SINR metrics and channel correlation coefficients as grouping parameters, the system optimizes both transmission efficiency and reliability simultaneously.
2Measurement precision
If compressed beamforming information is used to calculate MU SINR metrics for all candidate STAs, then grouping accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary channel sounding and collects compressed beamforming feedback from all STAs before MU-MIMO transmission. This preliminary action pre-calculates channel correlation metrics and SINR values, reducing the computational burden during actual transmission scheduling while maintaining high grouping accuracy.
Solution Approach 2:
The invention extracts only the essential components from compressed beamforming feedback (channel correlation coefficients and SINR metrics) needed for grouping decisions, rather than processing the entire feedback matrix. This extraction approach maintains measurement precision while significantly reducing computational complexity.
Data Source
AI summary
Methods, systems, and devices are described for wireless communication. In one aspect, a method of wireless communication includes receiving, by a first wireless device, compressed beamforming information from each of a plurality of stations, the compressed beamforming information including a feedback signal-to-noise ratio (SNR) value and compressed feedback matrix. The method also includes determining a multi-user signal-to-interference-plus noise ratio (SINR) metric for each of the plurality of stations based at least in part on the received feedback SNR values and the received compressed feedback matrices.