A Spatialxe2x80x93Temporal SubspaceBased Compressive Channel Estimation Technique in Unknown Interference MIMO Channels Spatialxe2x80x93temporal (ST) subspacebased channel estimation techniques formulated with   ell 2   minimum mean square error (MMSE) criterion alleviate the multiaccess interference (MAI) problem when the interested signals exhibit lowrank property. However, the conventional   ell 2  ST subspacebased methods suffer from mean squared error (MSE) deterioration in unknown interference channels, due to the difficulty to separate the interested signals from the channel covariance matrices (CCMs) contaminated with unknown interference. As a solution to the problem, we propose a new   ell 1   regularized ST channel estimation algorithm by applying the expectationmaximization (EM) algorithm to iteratively examine the signal subspace and the corresponding sparsesupports. The new algorithm updates the CCM independently of the slotdependent   ell 1   regularization, which enables it to correctly perform the sparseindependent component analysis (ICA) with a reasonable complexity order. Simulation results shown in this paper verify that the proposed technique significantly improves MSE performance in unknown interference MIMO channels, and hence, solves the BER floor problems from which the conventional receivers suffer.

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.