Intelligent Feedback Overhead Reduction (iFOR) in Wi-Fi 7 and Beyond

TL;DR

This paper introduces an unsupervised learning method to significantly reduce feedback overhead in Wi-Fi 7 MIMO systems, leading to substantial throughput improvements.

Contribution

It proposes a novel unsupervised learning approach to minimize sounding feedback duration in Wi-Fi MIMO links, enhancing efficiency and throughput.

Findings

Uses only 8% of feedback bits compared to existing methods

Achieves up to 52% increase in system throughput

Demonstrates effectiveness through simulation results

Abstract

The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today's world. The increasing demand for high data rate in Wi-Fi networks has led to several advancements in the 802.11 standard. Supporting MIMO transmissions with higher number of transmit antennas operating on wider bandwidths is one of the key capabilities for reaching higher throughput. However, the increase in sounding feedback overhead due to higher number of transmit antennas may significantly curb the throughput gain. In this paper, we develop an unsupervised learning-based method to reduce the sounding duration in a Wi-Fi MIMO link. Simulation results show that our method uses approximately only 8% of the number of bits required by the existing feedback mechanism and it can boost the system throughput by up to 52%.