A novel deep learning based spatial delay feature aware encoder decoder module for enhanced CSI feedback in massive MIMO
Abstract
The algorithm presented in this study addresses the challenge of reconstructing downlink channel state information (CSI) in massive multiple input multiple output (MIMO) systems with a focus on enhancing efficiency and accuracy. It begins by acquiring both downlink and uplink CSI data alongside other critical parameters such as the number of iterations and convolutional filter specifications. The process initiates with the vectorization of downlink CSI data followed by compression through a fully connected layer, effectively reducing dimensionality to manage computational complexity. The iterative reconstruction phase then unfolds, where each iteration updates an intermediary variable using a refined formula that incorporates the compressed CSI representation and correction factors. This iterative refinement aims to progressively enhance the accuracy of the reconstructed CSI. A pivotal aspect of the algorithm involves an optimized Encoder-Decoder framework designed to handle spatial-delay features inherent in MIMO systems. This framework employs thresholding operations to eliminate insignificant features, ensuring that the reconstructed CSI accurately reflects the crucial aspects of the channel. Simultaneously, an information module utilizes uplink CSI data to adjust weights during reconstruction, thereby further refining the accuracy of the downlink CSI estimation.
Keywords
Channel state information; Multiple input multiple output; Normalized mean square error; Spatial delay feature aware; encoder-decoder; Spatial delay feature extraction
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PDFDOI: http://doi.org/10.11591/ijeecs.v38.i3.pp1862-1869
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES).