Article, 2023

Low-Complex AI-Empowered Receiver for Spatial Media-Based Modulation MIMO Systems

IEEE Transactions on Vehicular Technology, ISSN 1939-9359, 0018-9545, Volume 73, 2, Pages 2884-2888, 10.1109/tvt.2023.3316109

Contributors

Kabac, Akif [1] Baaran, Mehmet (Corresponding author) [2] rpan, Hakan Ali [3]

Affiliations

  1. [1] Aalborg University
    2. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] GEN Laboratory, Next-Generation R&D, Network Technologies, 34854, Istanbul, Turkey
    3. [NORA names: Turkey; Asia, Middle East; OECD];
  3. [3] Istanbul Technical University
    4. [NORA names: Turkey; Asia, Middle East; OECD]

Abstract

Next-generation communication systems are expected to integrate artificial intelligence (AI) techniques into multi-antenna setups to improve system performance. One of the most important use cases of AI in wireless communications is multiple-input multiple-output (MIMO) systems, due to the superior AI capability of learning the best possible decision-making in complex transceiver structures. In this study, we propose a deep neural network (DNN) receiver structure for spatial media-based modulation (SMBM)-MIMO systems, which detects the symbols in an end-to-end manner. Instead of a conventional two-stage approach, which handles channel estimation and symbol detection separately, the proposed DNN receiver recovers the transmitted symbols directly, utilizing an offline training process. It is demonstrated that while the proposed DNN receiver structure and conventional maximum likelihood (ML) receiver utilizing linear minimum mean-square error (LMMSE)-based channel estimation, perform similarly for a single receive antenna case, DNN is superior for multiple receive antenna cases. We conclude that using DNNs in SMBM with multi-antenna receivers can provide higher performance and thus permits higher data transmission rates in addition to the reduced receiver complexity due to the removing channel estimation process.

Keywords

AI capabilities, MIMO systems, SMBM, antenna, antenna case, approach, artificial intelligence, capability, cases, cases of AI, channel, channel estimation, channel estimation process, communication, communication systems, complex, conventional maximum likelihood, conventional two-stage approach, data, data transmission rate, decision-making, deep neural networks, detection, end-to-end, end-to-end manner, estimation, estimation process, high performance, improve system performance, intelligence, likelihood, low complexity, manner, maximum likelihood, media-based modulation, modulation, modulation MIMO system, multi-antenna receiver, multi-antenna setup, multiple-input multiple-output (MIMO) systems, multiple-output (MIMO) systems, network, neural network, next-generation communication systems, offline training process, performance, process, rate, receiver, receiver complexity, receiver structure, reduced receiver complexity, setup, spatially, structure, study, symbol detection, symbols, system, system performance, training process, transceiver structure, transmission rate, transmitted symbols, two-stage approach, wireless communication

Funders

  • Scientific and Technological Research Council of Turkey

Data Provider: Digital Science

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