Massive multi-input multi-output (MIMO), which employs large number of antennas at the base station, can significantly boost the spectral efficiency and multiplexing gain. To fully exploit the huge array gain, the accurate channel state information is required at the transmitter side. However, the associated training overhead for downlink channel estimation consumes large amount of communication resource, especially for frequency division duplexing massive MIMO system. To address this issue, a distributed compressed sensing (DCS)-aided channel estimation approach is proposed, which fully exploits slow variation of the channel statistics in consecutive frames and spatially common sparsity within multiple subchannels in the frequency domain. Specifically, by exploiting the slow variation of the channel statistics, a hybrid training structure is proposed to probe the channel in the current frame based on the support information in previous frame. Then, a DCSaided channel estimation algorithm, which combines least square method and DCS method, is proposed to estimate the two parts of channel vector in angular domain among different subcarriers. In addition, to effectively acquire the support information at the beginning of communication, a prior information estimation method is proposed by exploiting the uplink-downlink angular reciprocity. Simulation results demonstrate that the proposed approach outperforms the counterparts and is capable to significantly reduce the training overhead for channel estimation.

Phase noise (PN) introduced by the oscillator at the base station and user side severely degrades the channel estimation performance. This paper investigates the impact of PN on downlink compressive channel estimation in massive multiple-input multiple-output (MIMO) systems. Particularly, the downlink compressive channel estimation with PN is modeled as a sparse signal recovery problem with additive correlated perturbation on the pilot matrix, which is a general formulation for both non-synchronous and synchronous PN. Based on this signal model, the performance of the equivalent sensing matrix is analyzed by invoking restricted isometry property (RIP) in compressive sensing. In addition, the upper bound for $l_{1}$ -minimization based channel estimation method and tight channel estimation bound are derived in the framework of RIP and Oracle least square methodology, respectively. Finally, we propose a PN-aware sparse Bayesian learning (PNA-SBL) algorithm to improve the channel estimation performance in the presence of synchronous PN. Simulation results demonstrate our analysis and superiority of the proposed PNA-SBL algorithm.

Orthogonal time frequency space (OTFS) modulation has been proposed to provide users with stable and reliable services in high-mobility scenarios. The sparse representation of channels in OTFS makes it possible to obtain accurate channel state information with a small number of pilots by compressed sensing (CS) algorithms. However, conventional CS algorithms in MIMO-OTFS channel estimation schemes assume that channel sparsity K is known, which is often not available in practical scenarios. In this letter, we propose a structured sparsity adaptive matching pursuit (SSAMP) algorithm for MIMO-OTFS channel estimation without the prior information of the channel sparsity K. On this basis, we further propose a modified structured sparsity adaptive matching pursuit algorithm to improve both the channel estimation accuracy and reconstruction speed. Simulation results show that the proposed algorithms are effective.

An exact closed-form average bit-error rate expression of a selection combining (SC) scheme is derived for a differential cooperative system employing an amplify-and-forward relay with simultaneous wireless information and power transfer capability. Monte Carlo simulations verify the analytical expressions. In particular, the SC scheme can achieve the same performance as that of a suboptimal maximal ratio combining scheme, and this is achieved with lower implementation complexity and energy consumption for SC.

The combination of orthogonal-time-frequency-space (OTFS) modulation and millimeter wave (mmWave) hybrid beamforming can improve communication performance in air-to-ground high mobility systems. This letter studies a cross-domain channel estimation scheme in OTFS-based mmWave hybrid beamforming systems with off-grid parameters. First, time-domain and delay-Doppler domain signal models are established. Then, channel estimation problems are decomposed into two parallel sparse recovery problems. Finally, a sparse Bayesian learning-based block expectation-maximization algorithm is proposed to obtain the off-grid parameters. Simulation results show the preferable and robust performance of the proposed algorithm compared with existing channel estimation algorithms.

In this paper, the physical layer security of underlay cognitive decode-and-forward (DF) relay networks is investigated, wherein one secondary transmitter (ST) exchanges confidential information with one secondary destination (SD) with the assistance of multiple cognitive decode-and-forward (DF) relays by sharing the licensed spectrum of an primary user (PU), and the relayed transmission is intercepted by multiple passive eavesdroppers. To provide security guarantee for secondary transmission, we propose a novel cooperative scheme aided by artificial noise (AN). In the absence of the eavesdroppers’ channel state information (CSI), two opportunistic relay selection schemes, namely best relay selection (BRS) and conventional relay selection (CRS), are proposed as an incentive to further enhance the physical layer security. To be specific, only the CSI of the main channel is employed in the CRS scheme, whereas the BRS scheme invokes both the CSI of the main channel and interference channel. We address the secrecy performance in terms of secrecy outage probability (SOP), for which the closed-form expressions for both the relay selection schemes are derived. Simulation results are provided to verify our analysis, and show that the proposed BRS scheme significantly achieves better secrecy outage performance, as compared with the CRS scheme, as well as the conventional scheme without jamming.