Autoencoder-Based CSI Compression for Beyond Wi-Fi 8 Coordinated Beamforming

TL;DR

This paper introduces an autoencoder-based CSI compression method for Wi-Fi 8 that significantly reduces feedback overhead, enabling more efficient coordinated beamforming and improving network throughput and latency.

Contribution

It presents a novel autoencoder-based CSI compression integrated into IEEE 802.11bn, improving feedback efficiency and enabling better coordinated beamforming in dense Wi-Fi deployments.

Findings

AE-based compression reduces CSI feedback by over 50%.

A compression ratio of 1/4 offers optimal accuracy and latency.

AE compression enhances Co-BF performance over legacy methods.

Abstract

Coordinated beamforming (Co-BF) is a key multi-access-point coordination (MAPC) technique for dense Wi-Fi deployments, but its performance can be hindered by the large channel state information (CSI) feedback required through channel sounding across overlapping basic service sets (OBSS). This work proposes an autoencoder (AE)-based CSI compression mechanism integrated into a standards-aligned IEEE 802.11bn MAC design. Using an event-driven simulator with realistic channels generated through Sionna RT, we evaluate the tradeoff between AE reconstruction accuracy and feedback size by measuring their impact on channel sounding overhead and data latency. Our results show that AE-based compression reduces channel sounding overhead by more than 50% compared to IEEE 802.11 CSI compression, with a compression ratio of 1/4 providing the best accuracy/feedback-size tradeoff for lowest data latency. Compared to legacy transmissions without MAPC, IEEE 802.11 CSI compression limits Co-BF due to high channel sounding overhead, causing it to underperform the legacy in some situations. However, AE-based CSI compression enables better Co-BF performance with substantial gains in throughput and data latency compared to legacy, demonstrating its promise as an enabler of efficient MAPC operation in future Wi-Fi systems.