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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]

Abstract Channel state information at transmitter (CSIT) is vital for massive multiple-input-multiple-output (MIMO) system to achieve unprecedented improvement in capacity and spectral efficiency. Unfortunately, CSIT is acquired via channel feedback in frequency-division duplexing (FDD) system, which suffers from large volume of feedback overhead and size of codebook, since they increase with the number of antennas at transmitter. In this paper, by exploiting the channel sparsity in virtual angular domain, a sparsity-aided codebook is proposed for FDD massive MIMO system to alleviate the channel feedback overhead and codebook size. Specifically, a sparsity aided limited channel feedback model is proposed, where the high dimensional channel is decomposed to the limited linear combination with channel coefficients in virtual angular domain. Then, the high dimensional channel can be quantized by the proposed codebook in a more precise way with significantly small codebook size and fed back in low overhead within virtual coherence time. Furthermore, the upper bound of inter-user interference and the per user rate loss incurred by the proposed sparsity-aided codebook are theoretically analyzed, in which the required quantization bits only increase linearly with the channel sparsity level rather than number of BS antennas when the rate loss is limited to a constant level. [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]

In massive multiple‐input–multiple‐output system, the accurate channel state information at the transmitter (CSIT) is required to achieve the high beamforming gain and spectral efficiency. To reduce the feedback overhead for CSIT acquisition in frequency division duplex system, a prior CSIT estimation with 1‐bit feedback per dimension is proposed to alleviate the overhead burden. Specifically, by exploiting the slow variation of channel support between the consecutive frames, the channel support in the previous frame is regarded as the prior information for CSIT estimation of current frame. Then, the received compressed channel measurements at the user side are quantized to 1 bit per measurement and only the sign information is fed back to the base station (BS). At the BS side, the CSIT recovery from the 1‐bit channel feedback information is formulated as a 1‐bit sparse complex‐value signal recovery problem with prior information. Finally, a prior support complex binary iterative hard thresholding algorithm is proposed to perform the CSIT estimation at the BS side. Simulation results demonstrate that the proposed scheme can reduce both the pilot and feedback overhead for CSIT estimation. [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]