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Optimized wireless communication for 6G signal processing using memory-augmented deep unfolding network

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第11期37卷 e5796-e5796页

作者： Eswaramoorthi, R. Prasad, M. Rama, Senthil R. Maheswari, V. Uma KSR Coll Engn Dept Elect & Commun Engn Tiruchengode Tamil Nadu India Vellore Inst Technol Chennai Sch Comp Sci & Engn Chennai Tamil Nadu India DMI Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India Anna Univ Dept Informat & Commun Engn Chennai Tamil Nadu India

In this manuscript, optimized wireless communication for 6G signals processing using memory-augmented deep unfolding network (WC-6G-SP-MADUN) is proposed. The aim of this work is to give a memory-augmented deep unfolding network approach with a focus on belief propagation decoding of error correction codes and multiple-input multiple-output (MIMO) wireless systems. In contrast, compares and tabulates the performance of memory-augmented deep unfolding methodologies with classic principled procedures, which could potentially enable 6G. Here, the wireless communication for 6G signal processing methods was used for a variety of receiver functions, including self-interference cancelation, signal estimation and detection using memory-augmented deep unfolding Network (MADUN), and advanced error correction by clouded leopard optimization algorithm (CLOA). Furthermore, outline the general framework of memory-augmented deep unfolding method for any kind of signal processing application and talk about cutting edge research paths that will make it possible to build communication networks in the future. The proposed approach is implemented in MATLAB;the performance is analyzed with performance metrics such as reliability, complexity, SNR gain, convergence rate, hardware efficiency, and quantization bit width. The proposed approach attains higher SNR gain 17.96%, 24.75%, and 30.09%;lower complexity 17.92%, 23.41%, and 30.13%;and higher convergence 18.01%, 25.41%, and 31.39% when analyzed with existing techniques such as redefined wireless communication for 6G: signal processing meets deep learning and deep unfolding (RWC-6G-SP-DU), lower complication deep unfolded neural network receiver for MIMO under probability data association detector (LC-DUNN-MIMO-PDAD), and deep learning in physical layer communications: evolution with prospects on 5G and 6G networks (DL-PLC-6G), respectively. In this manuscript, optimized wireless communication for 6G signals processing using memory-augme

关键词： clouded leopard optimization algorithm memory-augmented deep unfolding network multiple-input multiple-output signal processing and signal noise ratio

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