In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device typically feeds back downlink channel state information (CSI) to its serving base station (BS), which may then be utilized for beamforming and scheduling purposes. However, the large number of antennas increases the CSI signaling overhead in the uplink, thereby impacting the system's spectral efficiency. To address the feedback over-head problem, this paper proposes a low-complexity beam domain autoencoder that operates on low-dimensional beam domain channel samples. The key idea is to efficiently transform the channel from the antenna-domain to the beam-domain by selecting a small number of beams that preserve most of the channel information. To leverage the beam domain processor, we propose a beam selection method that enables high reconstruction quality with a small number of selected beams. The proposed framework offers a favorable balance between complexity, performance, and memory requirements. Simulations demonstrate the efficacy of the proposed approach in recovering the compressed channel at reduced overhead and complexity, relative to the state-of-the-art methods.
Low Complexity Beam Domain Processing for Autoencoder Based CSI Compression
Low Complexity Beam Domain Processing for Autoencoder Based CSI Compression
Low Complexity Beam Domain Processing for Autoencoder Based CSI Compression
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In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device typically feeds back downlink channel state information (CSI) to its serving base station (BS), which may then be utilized for beamforming and scheduling purposes. However, the large number of antennas increases the CSI signaling overhead in the uplink, thereby impacting the…
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