Deep Learning-based CSI Feedback in Wi-Fi Systems

TL;DR

This paper presents EFNet, a deep learning autoencoder-based framework for Wi-Fi CSI feedback that significantly reduces overhead and enhances throughput, inspired by cellular system advancements.

Contribution

Introduces EFNet, a novel DL-based CSI feedback method for Wi-Fi, achieving high compression and improved throughput over existing standards.

Findings

80.77% reduction in feedback overhead

up to 30.72% increase in throughput

effective in real-world office environment

Abstract

In Wi-Fi systems, channel state information (CSI) plays a crucial role in enabling access points to execute beamforming operations. However, the feedback overhead associated with CSI significantly hampers the throughput improvements. Recent advancements in deep learning (DL) have transformed the approach to CSI feedback in cellular systems. Drawing inspiration from the successes witnessed in the realm of mobile communications, this paper introduces a DL-based CSI feedback framework, named EFNet, tailored for Wi-Fi systems. The proposed framework leverages an autoencoder to achieve precise feedback with minimal overhead. The process involves the station utilizing the encoder to compress and quantize a series of matrices into codeword bit streams, which are then fed back to the access point. Subsequently, the decoder installed at the AP reconstructs beamforming matrices from these bit streams. We implement the EFNet system using standard Wi-Fi equipment operating in the 2.4 GHz band. Experimental findings in an office environment reveal a remarkable 80.77% reduction in feedback overhead compared to the 802.11ac standard, alongside a significant boost in net throughput of up to 30.72%.