Xiangxue Ma, Changbin Tian, Wenbo Chen, Bo Peng, and Xin Ma
Massive IoT access, unmanned aerial vehicle, spatial modulation, compressed sensing, channel estimation
Unmanned aerial vehicle (UAV) could be seen as an important technique to efficiently support wide-area massive Internet of Things (IoT) access because of inherent properties, such as flexibility, mobility, and adaptive altitude. This paper investigates an efficient massive access scheme with multiple-input multiple-output (MIMO)- enabled UAV-based aerial base station (BS) and spatial modulation (SM)-enabled devices. Because the majority of data transmissions are periodic or event driven, only a part of IoT devices are active in a given time. Considering the sporadic transmission, two-phase sparsity structure, as well as the complicated propagation condition of UAV-ground channels, we propose a low-complexity compressed sensing (CS)-based algorithm called prior information joint block gradient pursuit (PI-JBGP) algorithm to jointly activate devices identification, channel estimation (CE) and SM symbol detection. By analysing simulation results, the proposed algorithm performs much better than the existing CS-based algorithms.
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