Millimeter Wave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures

Cheng, Long; Yue, Guangrong; Liang, Yueyue; Li, Shaoqian

doi:10.1109/access.2019.2937628

Cited by 21 publications

(11 citation statements)

References 31 publications

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“…Doppler shifts in a time-varying mmWave scenario were considered in [91]. The channel was assumed to have blocksparse and low-rank characteristics, since the change in angle was much slower than that in path gain.…”

Section: Time-varying Channel Modelingmentioning

confidence: 99%

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Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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Section: Time-varying Channel Modelingmentioning

confidence: 99%

“…where H ∈ C M ×N is the channel matrix and n(t) denotes additive Gaussian noise. For mmWave operation, the channel is usually characterized by a geometric model [91]…”

Section: E Mmwave Communication Systemmentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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“…We adopt the geometry channel model, which depicts the channel matrix in a uniform linear array (ULA) and time-varying scenario as [16,17].…”

Section: Time-varying Geometry Channelmentioning

confidence: 99%

Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

Hong

2020

J Wireless Com Network

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Unmanned aerial vehicle (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones. Millimeter wave (mmWave) and massive multiple-input–multiple-output (MIMO) have widely been researched since recent years, which are promising techniques for the next and even the later generation communication system. Hybrid precoding, as a method to reduce the high cost in hardware and power brought by massive antenna array, develops fiercely and is often combined to deep learning, a kind of popular optimization tool, which brings an overwhelming performance. On the other hand, there are not so many attentions about the hybrid precoding in time-varying mmWave massive MIMO, which is necessary to be considered in a UAV-enabled communication scenario because the performance will degrade seriously if the channel changed while the transmitter and receiver use the precoding matrix corresponding to the expired channel, yet. In this paper, we propose a double-pilot-based hybrid precoding system, which completes analog precoding and digital precoding separately—predicting the previous one using deep learning structure and updating equivalent channel frequently for the post one by enhancing the frequency of equivalent channel estimation.

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“…CSI refers to the signal propagation information from transmitter to receiver and shows the mutual signal scattering, signal fading and power consumption effects. Moreover, CSI estimation is very crucial and essential for reliable data transmission with current channel conditions and with high information rates in mm-WAVE communication technology [13][14]. The efficiency of mm-WAVE communication technology is directly depends upon Channel State Information (CSI) of source station and receiver.…”

Section: Introductionmentioning

confidence: 99%

Channel State Information (CSI) based Sparse Reconstruction for Biomedical Applications Using hybrid mm-WAVE MIMO System

et.al¹

2021

TURCOMAT

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The significance of Channel State Information (CSI) is very essential in a hybrid mm-WAVE Multiple Input Multiple Input (MIMO) System due to its direct dependency on medium capacity and energy efficiency of a network. Therefore, a Channel State Information (CSI)-based Sparse Reconstruction (CSISR) technique is adopted for effective evaluation of CSI for future 5G cellular network implementation. A hybrid mm-WAVE MIMO communication system is also employed for effective bandwidth spectrum utilization. Furthermore, a joint sparse coding algorithm is introduced to study the channel matrices of hybrid mm-WAVE MIMO system. The proposed CSISR technique ensure proficient signal reconstruction, signal compression and resource reduction by exploiting sparsity of channel matrix. The proposed CSISR technique under low SNR conditions as well for hybrid mm-WAVE MIMO system with optimization of pre-processors and combiners. The performance throughput of proposed CSISR technique is measured against conventional algorithms considering power consumption, Normalized Mean Square Error (NMSE) and spectral efficiency of the mm-Wave MIMO system. The superiority of proposed CSISR technique is concluded based on simulations considering different system configurations and performance matrices.

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Millimeter Wave Time-Varying Channel Estimation via Exploiting Block-Sparse and Low-Rank Structures

Cited by 21 publications

References 31 publications

Tensor Decompositions in Wireless Communications and MIMO Radar

Tensor Decompositions in Wireless Communications and MIMO Radar

Deep double-pilot-based hybrid precoding in UAV-enabled mmWave massive MIMO

Channel State Information (CSI) based Sparse Reconstruction for Biomedical Applications Using hybrid mm-WAVE MIMO System

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