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    Data detection in decentralized and distributed massive MIMO networks
    (Elsevier, 2022) Albreem, Mahmoud A.; Alhabbash, Alaa; Abu-Hudrouss, Ammar M.; Almohamad, Tarik Adnan
    In order to meet the user demands in performance and quality of services (QoS) for beyond fifth generation (B5G) communication systems, research on decentralized and distributed massive multiple-input multiple output (M-MIMO) is initiated. Data detection techniques are playing a crucial role in realization and implementation of M-MIMO networks. Although most of detection techniques were proposed for centralized M-MIMO, there is a notable trend to propose efficient detection techniques for decentralized and distributed M-MIMO networks. This paper aims to provide insights on data detection techniques for decentralized and distributed M-MIMO to generalists of wireless communications. We garner the detection techniques for decentralized and distributed M-MIMO and present their performance, computational complexity, throughput, and latency so that a reader can find a distinction between different algorithms from a wider range of solutions. We present the detection techniques based on the following architectures: decentralized baseband processing (DBP), feedforward fully decentralized (FD), and feedforward partially decentralized (PD), FD based on coordinate descent (FD-CD), and FD based on recursive methods. In addition, the role of expectation propagation algorithm (EPA) in decentralized architectures is comprehensively reviewed. In each section, we also discuss the pros, cons, throughput, latency, performance, and complexity profile of each detector and related implementations. Moreover, the energy efficiency of several decentralized M-MIMO architectures is also illustrated. The cell-free M-MIMO (CF-M-MIMO) architecture is discussed with an overview of deployed detection schemes. This paper also illustrates the challenges and future research directions in decentralized and distributed M-MIMO networks.