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

2026-04-15

AI Summary

This work addresses the high channel state information (CSI) feedback overhead that hinders coordinated beamforming in densely deployed Wi-Fi networks, where overlapping basic service sets incur substantial channel sounding costs and elevated data latency. To overcome this limitation, the paper introduces an autoencoder-based CSI compression scheme—integrated into the IEEE 802.11bn MAC layer—as an event-driven mechanism. Leveraging the Sionna RT realistic channel model and event-driven simulation, the proposed approach achieves over 50% reduction in feedback overhead at a 4:1 compression ratio, significantly lowering data latency while improving throughput. This advancement breaks through the performance barriers imposed by conventional compression techniques on multi-AP coordination.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Data Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Coordinated Beamforming
• Channel State Information
• CSI Compression
• Wi-Fi 8
• Multi-Access-Point Coordination

Innovation

• Autoencoder
• CSI compression
• Coordinated Beamforming
• Multi-AP Coordination
• Wi-Fi 8