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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Indonesian Journal of Electrical Engineering and Computer Science
Jayashankar, Parinitha
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Economics, Econometrics & Finance

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Deep learning-based channel estimation with application to 5G and beyond networks Jayashankar, Parinitha; Nanjundaiah, Shobha Byrappa
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp271-277

Abstract

Channel state information (CSI) feedback estimation for a downlink medium in a massive multiple input multiple output (MIMO) system is an essential and critical task to improve channel capacity and performance yield, especially in a frequency division duplex (FDD) multiplexing system. However, spectral efficiency degradation is a massive issue due to high channel feedback overhead. This work proposes a deep learning-based channel estimation (DLCE) model to improve channel reconstruction efficiency and channel overhead reduction accuracy. The proposed deep learning (DL) mechanism consists of encoder and decoder network where encoder network is utilized to compress CSI matrices whereas decoder network is used to decompress obtained CSI matrices. Here, inverse discrete Fourier transform (IDFT) method is utilized to convert CSI matrices of frequency domain into CSI matrices of delay domain. Simulation results are evaluated between uplink and downlink medium in the massive MIMO system considering a co-operation in science and technology (COST) 2,100 model. Here, a significant improvement in correlation and normalized mean square error (NMSE) results is observed. The proposed DLCE model shows superior performance against varied channel estimation techniques in terms of NMSE and correlation efficiency.
A novel deep learning based spatial delay feature aware encoder decoder module for enhanced CSI feedback in massive MIMO Jayashankar, Parinitha; Rangaswamy, Chigalakkappa; Shobha, Byrappa N.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1862-1869

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.
Co-Authors Nanjundaiah, Shobha Byrappa Rangaswamy, Chigalakkappa Shobha, Byrappa N.