Structured Distributed Compressive Channel Estimation Over Doubly Selective Channels

Qin, Qibo; Gui, Lin; Gong, Bo; Ren, Xiang; Chen, Wen

doi:10.1109/tbc.2016.2550761

Cited by 24 publications

(29 citation statements)

References 29 publications

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“…That is, the single antenna on the user has the characteristics of the spatial sparsity of all antennas on the base station. In addition, since the path delay varies much more slowly than the gain associated with the time channel, this sparsity is almost constant during the coherence time [16], forming a temporal correlation.…”

Section: The Spatial Common Sparsitymentioning

confidence: 99%

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

et al. 2019

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Pilot contamination is the reuse of pilot signals, which is a bottleneck in massive multi-input multi-output (MIMO) systems as it varies directly with the numerous antennas, which are utilized by massive MIMO. This adversely impacts the channel state information (CSI) due to too large pilot overhead outdated feedback CSI. To solve this problem, a compressed sensing scheme is used. The existing algorithms based on compressed sensing require that the channel sparsity should be known, which in the real channel environment is not the case. To deal with the unknown channel sparsity of the massive MIMO channel, this paper proposes a structured sparse adaptive coding sampling matching pursuit (SSA-CoSaMP) algorithm that utilizes the space–time common sparsity specific to massive MIMO channels and improves the CoSaMP algorithm from the perspective of dynamic sparsity adaptive and structural sparsity aspects. It has a unique feature of threshold-based iteration control, which in turn depends on the SNR level. This approach enables us to determine the sparsity in an indirect manner. The proposed algorithm not only optimizes the channel estimation performance but also reduces the pilot overhead, which saves the spectrum and energy resources. Simulation results show that the proposed algorithm has improved channel performance compared with the existing algorithm, in both low SNR and low pilot overhead.

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Section: The Spatial Common Sparsitymentioning

confidence: 99%

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

et al. 2019

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“…Therefore, some studies directly use the pilot sequence to complete the frequency domain channel estimation. The distributed compressive sensing-simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm is proposed for channel estimation of single antenna and symbol in [19], and the block simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed for channel estimation of multi symbols in [20]. The system model is further extended to large-scale MIMO systems and the optimal pilot placement scheme is proposed in [21].…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems

Kong

Zhang

2019

Wireless Communications and Mobile Computing

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By exploiting the sparsity of the channel in the delay and angle domains, compressed sensing (CS) algorithms can be used for channel estimation of massive multiple-input multiple-output (MIMO) systems to reduce pilot overhead. Due to the Doppler frequency shift, however, the intercarrier interference (ICI) and the rapid change of the channel state result in the poor estimation effect of doubly selective (DS) channel. In this paper, we propose the block sparsity adaptive matching pursuit (B-SAMP) algorithm to solve this problem. Firstly, the complex exponential basis expansion model (CE-BEM) is used to convert numerous channel tap coefficients into BEM parameter vectors and then the sparsity adaptive channel estimation scheme based on compressed sensing is proposed. Specifically, the ICI-free model is obtained by using the proposed equally placed pilot group scheme, and the B-SAMP algorithm is proposed by using the spatio-temporal common sparsity of the channel to complete the estimation of DS channel. Finally, a linear smoothing method is used to reduce the error caused by CE-BEM, thereby further improving the accuracy of the estimation. The simulation results show that the proposed method not only improves the estimation accuracy compared with the existing scheme but also requires fewer pilots.

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“…Recently, a number of researchers address this problem by applying the compressed sensing (CS) concept to the DS single‐input–single‐output (SISO) channel estimation . They exploit the inherent sparsity of practical wireless channels that has been verified by several experimental results .…”

Section: Introductionmentioning

confidence: 99%

“…Then, they assume a linear approximation for the channel time variations and carry out a smoothing treatment of already estimated channel taps to reduce the BEM modeling error. Exploiting the temporal correlations of a DS channel, Qin et al develop a structured DCS‐based joint multisymbol channel estimation method.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the CE‐BEM results in a strictly banded frequency‐domain channel matrix for DS channel. This property is exploited by some existing CS‐based DS channel estimation approaches to decouple the linear equations of different BEM orders or to get the ICI‐free pilots . However, these approaches cannot simply be extended for accurate BEMs without that property.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

Karani

Beheshti

Fazel

2019

Trans Emerging Tel Tech

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Wireless communications in high-mobility situations face with doubly selective (DS) fading. In addition, many practical wireless channels exhibit a sparse multipath structure. This paper investigates the DS multiuser multiple-input-multiple-output (MIMO) channel estimation via compressed sensing. The DS channel is characterized by a general sparse basis expansion model (BEM). This model reduces the channel estimation complexity as the channel estimation problem is reduced to estimating the BEM coefficients instead of numerous channel parameters. In addition, a pilot pattern is proposed for multiuser MIMO orthogonal frequency division multiplexing systems, which includes guard and silent pilots to reduce intercarrier interference and interuser interference, respectively. Then, two beyond-sparsity features (ie, block and joint sparsity) of the BEM coefficients are exploited to propose a beyond-sparsity-based channel estimation method. Moreover, a pilot design algorithm is proposed to optimize the pilot positions and enhance the estimator performance. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy over the existing approaches, with a much smaller pilot overhead.Trans Emerging Tel Tech. 2019;30:e3636.wileyonlinelibrary.com/journal/ett

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Structured Distributed Compressive Channel Estimation Over Doubly Selective Channels

Cited by 24 publications

References 29 publications

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems

Compressed Sensing-Based Sparsity Adaptive Doubly Selective Channel Estimation for Massive MIMO Systems

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

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