MMV Subspace Pursuit (MSP) Algorithm for Joint Sparse Multiple Measurement Vectors Recovery In this paper, MMV Subspace Pursuit (MSP) algorithm is proposed for solving joint sparse multiple measurement vectors (MMV) problem. The preselection and backtracking mechanisms are used in MSP, so MSP not only has higher recovery performance than some existing algorithms, but also significantly reduces the iteration number for improving the signal recovery efficiency. Simulations results show that MSP and Simultaneous Compressive Sampling Matching Pursuit (SCoSaMP) have almost identical recovery performance and iteration times, but MSP significantly reduces the computation complexity in per iteration. For example, when sparsity  K  is 5, the computational complexity of MSP is 24.0% of that of SCoSaMP in each iteration.

LowComplexity Multiuser Detection for Generalized MediaBased Modulation Systems Research Institute of Tsinghua University in Shenzhen, Shenzhen 518057, China In this paper, we consider multiuser generalized mediabased modulation (GMBM) system, where the users adopt both generalized spatial modulation (GSM) and mediabased modulation (MBM) schemes to convey extra information via antenna and RF mirror indices. The GSM scheme activates only a subset of available antennas for transmission, and the MBM scheme uses the onoff state of RF mirrors placed around the antennas to create different channel realizations. GMBM improves spectral efficiency for transmission with limited RF resources. We propose two structured compressivesensing algorithms to detect multiuser signals with low complexity. Three types of structure, i.e. the users, the antennas, and the RF mirrors, are recovered during each iteration of the algorithms. The structured subspace pursuit (SP) algorithm for MUGMBM is shown to have better performance for various system settings, and both algorithms benefit from the increase in receive diversity.