Decision Aided Uplink Compressive Channel Estimation for Massive MIMO Systems

Thank to the observation that in massive multi-input multi-output systems, the channels associated with different base station antennas may share common sparse support, the significant path delays can be accurately captured by only few pilots, leading to a reduction of pilot overhead. However, when the number of pilots is small, the path gains can not be accurately estimated and this limits the system performance. To solve this problem, in this paper we propose a decision aided compressive sensing based channel estimation scheme, which utilizes the decoded data to refine the channel estimation. This scheme can effectively improve the channel estimation without increasing the length of pilot sequence, which is confirmed by both analyses and simulation results

Differential spatial modulation for high-rate transmission systems

This paper introduces a new differential spatial modulation (DSM) scheme which subsumes both the previously introduced DSM and high-rate spatial modulation (HR-SM) for wireless multiple input multiple output (MIMO) transmission. By combining the codeword design method of the HR-SM scheme with the encoding method of the DSM scheme, we develop a high-rate differential spatial modulation (HR-DSM) scheme equipped with an arbitrary number of transmit antennas that requires channel state information (CSI) neither at the transmitter nor at the receiver. The proposed approach can be applied to any equal energy signal constellations. The bit error rate (BER) performance of the proposed HR-DSM schemes is evaluated by using both theoretical upper bound and computer simulations. It is shown that for the same spectral efficiency and antenna configuration, the proposed HR-DSM outperforms the DSM in terms of bit error rate (BER) performance