  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%.
