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Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave) can provide unprecedented performance improvement for realizing future wireless communications. However, acquiring accurate channel state information in wideband mmWave massive MIMO systems with hybrid transceiver architectures is even challenging, especially in high-mobility scenarios with severe Doppler effects. In this paper, we propose a tensor decomposition-based method to estimate the time-varying and frequency-selective (TVFS) mmWave MIMO channels. Specifically, by exploiting the sparse scattering nature of TVFS channels, we model the frequency-domain received signal as a third-order tensor that admits a canonical polyadic (CP) decomposition format. Then, we analyze the uniqueness condition of the proposed CP decomposition-based channel estimation problem and propose a novel estimator to acquire TVFS channel parameters including angle of departure/arrival (AoD/AoA), time delay, path gain, and the Doppler shift. To address the sophisticated coupling among unknown parameters, we further propose a joint AoD and Doppler shift estimation (JADE) algorithm that provides reliable initial and iteratively refined estimates. The derived analysis and simulation results verify that the proposed JADE algorithm achieves higher estimation accuracy and guarantees the superiority of the proposed TVFS channel estimator over existing schemes. [ABSTRACT FROM AUTHOR]

The large antenna arrays with hybrid analog and digital (HAD) architectures can provide a large aperture with low cost and hardware complexity, resulting in enhanced direction-of-arrival (DOA) estimation and reduced power consumption. This paper investigates the trade-off between DOA estimation and power consumption in large antenna arrays with HAD architectures. Particularly, the DOA estimation problem of fully-connected, sub-connected (SC), and switches-based (SE) hybrid architectures is formulated into a unified expression, with the compression matrix in a time-varying form. Based on this model, we derive a dynamic maximum likelihood (D-ML) estimator that is suitable for both HAD and conventional fully digital (FD) structures, and the closed-form expression of Cramér-Rao bound (CRB) to evaluate the performance limit of the D-ML estimator for different HAD structures. The theoretical CRB analysis in the single-source case reveals that, the SC structure has the ability to achieve approximately the same performance as the FD structures at DOAs around zero, but suffers from the inherent angle ambiguity because of the antenna grouping. In addition, we propose a dynamic SC (D-SC) structure that is proved to eliminate the angle ambiguity with time-varying phase shifters, and a switch optimization (SWO) algorithm to minimize the CRB of SE structures. Finally, we introduce a new metric, DOA efficiency, to measure the trade-off between the DOA estimation performance and power consumption of different structures. Simulation results verify our theoretical analysis and the superiority of the proposed D-SC structure and the SWO algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Channel state information is essential for base station (BS) to fully exploit the merits of massive multiple input multiple output, which consumes large amount of pilot overhead attributed to tremendous number of BS antennas. Accordingly, huge computational complexity of correlation operation tends to be an obstacle for the implementation of compressive channel estimation algorithms, especially for greedy algorithms. In this paper, pilot overhead problem lightens by exploiting common support property due to the close space of BS antenna array. Furthermore, a low complexity correlation algorithm is proposed for each iteration of greedy algorithm, which exploits the inherent of pilot distribution and sensing matrix composed of pilot sequence. Complexity of proposed algorithm related to pilot distribution is also investigated. Performance analysis and simulation results prove that the proposed algorithm maintains the same performance, while achieves much less computational complexity than the original greedy algorithm. [ABSTRACT FROM AUTHOR]

Massive multi-input multi-output (MIMO) has been recognized as a key technology for 5G communication system due to the high array gain and spectral efficiency. To redeem the merits, the base station (BS) requires the accurate channel state information (CSI), which consumes large amount of pilot overhead for downlink CSI acquisition in frequency division duplex system. To address this issue, a block Bayesian matching pursuit (BBMP) based channel estimation approach is proposed, which fully exploits the common support of channels from multiple antennas at the BS. Specifically, the channel estimation problem is formulated as the block sparse recovery problem, where the prior probability for block support set is accordingly derived. Then, based on the prior knowledge and equivalent sensing matrix, a selection metric is defined to select the block index into the dominant support by exploiting the maximum likelihood criterion. Furthermore, by utilizing a matching pursuit based approach, the update criterion is derived for selection metric when the dominant support set augments with the iteration. Simulation results demonstrate that the proposed BBMP algorithm is capable to significantly reduce the pilot overhead for channel estimation and robust to the channel sparsity information. [ABSTRACT FROM AUTHOR]

Summary: The multiuser millimeter wave (mmWave) MIMO system has been considered as a promising technology for future 5G cellular networks since high volume of data rates can be achieved by simultaneously supporting multiple users. However, the channel estimation is challenging due to the multiuser interference in frequency‐selective hybrid mmWave MIMO system. In this paper, we focus on the multiuser uplink wideband channel estimation with frequency selectivity. Specifically, a layer‐precoder reuse strategy is proposed, in which each transmission frame is divided into several subframes and the users share a common precoder at each subchannel of different subcarriers. Then, instead of only estimating channel of one user, multiuser frequency‐selective channel can be jointly estimated via a sparse recovery formulation. Furthermore, by exploiting the common sparsity among subchannels in frequency‐selective mmWave system, a distributed compressed sensing (DCS)–based channel estimation is proposed to jointly acquire the channel state information of all users via weighted least square to further reduce the pilot overhead. In addition, the Cramer‐Rao lower bound (CRLB) is derived for multiuser frequency‐selective channel estimation. Simulation results demonstrate that the proposed DCS‐based method can asymptotically approach the CRLB of channel estimation. A layer‐precoder reuse strategy is proposed, in which each transmission frame is divided into several subframes and the users share a common precoder at each subchannel of different subcarriers. Multiusers frequency‐selective channel can be jointly estimated via a sparse recovery formulation via the proposed distributed compressed sensing‐based channel‐estimation algorithm by exploiting the common sparsity. The Cramer‐Rao lower bound (CRLB) is derived for multiuser frequency‐selective channel estimation. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained a mistake in “Algorithm 1” under Section 3. The ellipsis in Algorithm 1 appears as alpha “L” in the published article [ABSTRACT FROM AUTHOR]