Titlebook: Sparse Signal Processing for Massive MIMO Communications; Zhen Gao,Yikun Mei,Li Qiao Book 2024 Beijing Institute of Technology Press 2024

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Compressive Sensing Single-User Signal Detection in Massive MIMO Systems with Spatial Modulation,systems. The goal is to address the high complexity of the optimal maximum likelihood (ML) detector in massive SM-MIMO, while also avoiding the performance loss associated with state-of-the-art low-complexity detectors for small-scale SM-MIMO. The adopted signal detector leverages the structured spa

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Compressive Sensing Multi-User Detection in Massive MIMO Systems with Spatial Modulation, SM to increase the throughput. By using hundreds of AEs and a limited number of RF chains, i.e., adopting the hybrid MIMO architecture, the BS can efficiently serve multiple users while also reducing power consumption. However, due to the large number of AEs of multiple users and limited RFs at the

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Compressive Sensing Massive IoT Access in Massive MIMO Systems with Media Modulation,d detection performance. However, reliable active device detection and data decoding present a serious challenge in such an mMTC scenario. To address this problem, an efficient CS-based massive access solution is introduced, leveraging the sparsity of the UL massive access signals received at the BS

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Sparse Channel Estimation in TDS-OFDM Systems,ce challenge in the presence of doubly selective fading channels. To tackle the issue, an overlap-add technique in the time-domain transmission symbol (TS) processing is adopted to coarsely estimate the channel length, path delays, and path gains. Even though the severe fading channel has a long del

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978-981-99-5396-7Beijing Institute of Technology Press 2024