Efficient Compressive Channel Estimation for Millimeter-Wave Large-Scale Antenna Systems - 2018


Giant-scale antenna systems are thought of as a viable technology to catch up on huge path loss in millimeter-wave (mmWave) communications. But, due to the huge antennas, the channel state information (CSI) acquisition is costly and challenging. During this Project, we develop a completely unique compressive channel estimation framework based mostly on multiple measurement vectors (MMV). Compared with conventional single measurement vector (SMV)-primarily based approach, the proposed framework exploits structural sparsity exhibited within the relatively made local scattering mmWave channels to greatly cut back the coaching and computational overheads. Moreover, we propose a channel subspace matching pursuit (CSMP) algorithm based on the MUltiple SIgnal Classification (MUSIC) as an MMV solver. By leveraging the advantages of MUSIC, the proposed CSMP can properly exploit the diversity gain from structural sparsity, and more improve the estimation quality via the superresolution capability. Meanwhile, an economical implementation technique of the proposed CSMP is additionally presented. Compared to the conventional MMV solver, the proposed CSMP exhibits abundant lower complexity. Finally, several simulation results show that the MMV-primarily based CSMP achieves significant performance gains over different estimation algorithms, especially when the angular resolutions are high. Regarding the computational price, the simulation result shows that the MMV-based mostly estimation algorithms are approximately 2 orders of magnitude smaller than the SMV-based estimation algorithms.

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