Media‐based modulation (MBM) plays a crucial role in enhancing the spectral efficiency and energy efficiency of massive MIMO (mMIMO) systems for 5G and beyond wireless communications. In MBM, multiple parasitic elements, also termed as radio frequency (RF) mirrors, are placed near the transmit antennas for generating different channel fade realizations. These realizations are obtained by ON/OFF switching of RF mirrors. One of those channel fade realizations is selected (using a part of the incoming information bits) for transmitting a part of the information bits utilizing a symbol chosen from the conventional constellation set (using another part of the incoming information bits). Transmission of a symbol through one of the available channel realizations constitutes a sparse transmit vector for each user in MBM‐mMIMO. The sparse nature of transmitted symbols from multiple users and inter‐user interference makes the symbol detection in uplink MBM‐mMIMO challenging. Therefore, in this article, the problem of symbol detection in MBM‐mMIMO is analyzed from a graph‐theoretical point of view, and a graph‐traversal aided low‐complexity symbol detection algorithm is proposed inspired by socio‐cognitive learning of swarm optimization. Also, the convergence characteristic of the proposed technique is investigated theoretically. Further, an analytical expression of upper bound on bit error rate performance is derived and corroborated through simulations. Viability and robustness of the proposed technique are also justified through simulations over state‐of‐art detection techniques, under both perfect and imperfect channel state information scenarios.

中文翻译：

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基于群体优化的社会认知知识的上行MBM大规模MIMO中的图遍历辅助检测

基于媒体的调制（MBM）在提高大规模MIMO（mMIMO）系统的5G及无线通信系统的频谱效率和能效方面发挥着至关重要的作用。在MBM中，多个寄生元件（也称为射频（RF）镜）放置在发射天线附近，用于生成不同的信道衰落实现。这些实现是通过RF镜的ON / OFF切换获得的。选择那些信道衰落实现中的一个（使用一部分输入信息比特），以利用从常规星座图中选择的符号（使用另一部分输入信息比特）来发送一部分信息比特。通过可用信道实现之一传输符号构成了MBM-mMIMO中每个用户的稀疏传输矢量。来自多个用户的传输符号稀疏和用户之间的干扰使上行链路MBM-mMIMO中的符号检测具有挑战性。因此，本文从图论的角度分析了MBM-mMIMO中的符号检测问题，并从群体优化的社会认知学习出发，提出了一种图遍历辅助的低复杂度符号检测算法。此外，从理论上研究了所提出技术的收敛特性。此外，通过仿真得出并证实了误码率性能上限的解析表达式。通过在完善和不完善的信道状态信息场景下，通过对最新检测技术的仿真，也证明了所提出技术的可行性和鲁棒性。