MU-MIMO Group Selection via Compressed CSI
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Solution Overview
Problem
The actual throughput of MU-MIMO networks varies widely due to sub-optimal grouping of client devices, leading to potential throughput gains not being fully realized, often due to inter-user interference, and existing methods require full channel state information which is costly or not supported in standards like IEEE 802.11ac.
Innovation Solution
The use of compressed channel state information to estimate signal-to-interference-plus-noise ratio (SINR) and consider bandwidth settings to optimize MU-MIMO group selection, employing techniques like exhaustive, pruned-exhaustive, and informed greedy selection processes to reduce processing and communication overhead while achieving optimal groupings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full channel state information is used for MU-MIMO group selection, then grouping accuracy is improved, but processing complexity and communication overhead increase
Solution Approach 1:
The patent extracts only the essential components needed for SINR estimation from full channel state information. Instead of using complete channel matrices, the system extracts compressed CSI that contains sufficient statistics for accurate SINR calculation, thereby reducing processing complexity while maintaining grouping accuracy.
Solution Approach 2:
The patent employs compressed channel state information as a lightweight alternative to full channel state information. This compressed representation is sufficient for the intended purpose (SINR estimation and group selection) without requiring the expensive and complex full CSI processing, effectively using a disposable approximation to solve the problem.
2Productivity
If more client devices are grouped together in MU-MIMO, then throughput is improved, but inter-user interference increases
Solution Approach 1:
The patent uses SINR estimation based on compressed channel state information to continuously monitor and evaluate the quality of MU-MIMO group transmissions. This feedback mechanism allows the system to identify when inter-user interference becomes excessive and adjust group compositions accordingly, enabling the system to maximize throughput while controlling harmful interference levels.
Solution Approach 2:
The patent dynamically adjusts grouping parameters based on estimated SINR values. By changing which client devices are grouped together based on real-time channel conditions and SINR predictions, the system can optimize the balance between throughput and inter-user interference, adapting group compositions to minimize harmful factors while maintaining high productivity.
3Loss of substance
If compressed channel state information is used for SINR estimation, then communication overhead is reduced, but estimation accuracy may deteriorate
Solution Approach 1:
The patent creates a compressed copy of the essential channel state information needed for SINR estimation. This compressed CSI contains the critical statistical properties and channel characteristics required for accurate interference and noise estimation, while discarding redundant information that would increase communication overhead without providing additional estimation accuracy.
Data Source
AI summary
An example communications device includes communications circuitry and control circuitry. The communications circuitry may wirelessly communicate with client devices. The control circuitry may determine signal-to-interference-plus-noise ratios (SINRs) for the client devices based on compressed client-side channel state information received from the client devices. The control circuitry may select, based on the SINRs and in consideration of multiple possible bandwidth settings, a set of multi-user-multiple-input-multiple-output (MU-MIMO) groups each with an assigned bandwidth setting.