Off-Grid Aware Channel and Covariance Estimation in mmWave Networks

Anjinappa, Chethan Kumar; Gürbüz, Ali Cafer; Yapıcı, Yavuz; Güvenç, İsmail

doi:10.1109/tcomm.2020.2980829

Cited by 20 publications

(7 citation statements)

References 51 publications

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“…where ς 2 is the received signal power. We evaluate the CCM estimation performance via the relative efficiency metric (REM), which is widely adopted [28]- [30] and defined as…”

Section: Simulation Resultsmentioning

confidence: 99%

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Wang¹,

Fang²,

Huang³

et al. 2022

Preprint

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We consider the problem of spatial channel covariance matrix (CCM) estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication systems. Spatial CCM is essential for two-timescale beamforming in IRS-assisted systems; however, estimating the spatial CCM is challenging due to the passive nature of reflecting elements and the large size of the CCM resulting from massive reflecting elements of the IRS. In this paper, we propose a CCM estimation method by exploiting the low-rankness as well as the positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation of the CCM is formulated as a semidefinite programming (SDP) problem and an alternating direction method of multipliers (ADMM) algorithm is developed. Our analysis shows that the proposed method is theoretically guaranteed to attain a reliable CCM estimate with a sample complexity much smaller than the dimension of the CCM. Thus the proposed method can help achieve a significant training overhead reduction. Simulation results are presented to illustrate the effectiveness of our proposed method and the performance of two-timescale beamforming scheme based on the estimated CCM.

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“…where ς 2 is the received signal power. We evaluate the CCM estimation performance via the relative efficiency metric (REM), which is widely adopted [28]- [30] and defined as…”

Section: Simulation Resultsmentioning

confidence: 99%

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Wang¹,

Fang²,

Huang³

et al. 2022

Preprint

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show abstract

“…For comparison purposes, two typical methods to generate dictionaries, known as the uniform sampling of the physical domain (USPD) and the uniform sampling of the virtual domain (USVD) [11], are reproduced, i.e.,…”

Section: B Dictionary Designmentioning

confidence: 99%

“…Motivated by the spherical Fibonacci grid (SFG) [9] that provides uniform grids on a sphere and the Bayesian tools [10], this letter develops a channel estimation technique for hybrid FD-MIMO systems. Specifically, we first design a new dictionary based on the SFG in the CS framework, which provides minor angular errors than traditional dictionaries [11]. Then, a Bayesian inference-aided greedy pursuit algorithm is developed for accurate channel estimation by integrating the Bayesian inference into the general CS framework.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Compressive Channel Estimation for Hybrid Full-Dimensional MIMO Communications

Huang¹,

Wu²,

Xia³

2021

Preprint

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Efficient channel estimation is challenging in fulldimensional multiple-input multiple-output communication systems, particularly in those with hybrid digital-analog architectures. Under a compressive sensing framework, this letter first designs a uniform dictionary based on a spherical Fibonacci grid to represent channels in a sparse domain, yielding smaller angular errors in three-dimensional beamspace than traditional dictionaries. Then, a Bayesian inference-aided greedy pursuit algorithm is developed to estimate channels in the frequency domain. Finally, simulation results demonstrate that both the designed dictionary and the proposed Bayesian channel estimation outperform the benchmark schemes and attain a lower normalized mean squared error of channel estimation.

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“…Recently, frequency-selective channels with OFDM-based communications leading to a more complex estimation problem have also been considered, with different approaches to exploit the sparse channel characteristics [4,7,8]. Several model-based signal processing techniques for mmWave channel estimation under various system settings can be found in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Site-specific millimeter-wave compressive channel estimation algorithms with hybrid MIMO architectures

Thoota¹,

Martí²,

Demir³

et al. 2021

ITU-J FET

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In this paper, we present and compare three novel model-cum-data-driven channel estimation procedures in a millimeter-wave Multi-Input Multi-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communication system. The transceivers employ a hybrid analog-digital architecture. We adapt techniques from a wide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the unknown virtual beamspace domain dictionary, as well as the delay-and-beamspace sparse channel. We train the model-based algorithms with a site-specific training dataset generated using a realistic ray tracing-based wireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site's test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show that model-based approaches combined with data-driven customization unanimously outperform the state-of-the-art techniques by a large margin. The proposed algorithms were selected as the top three solutions in the "ML5G-PHY Channel Estimation Global Challenge 2020" organized by the International Telecommunication Union.

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Off-Grid Aware Channel and Covariance Estimation in mmWave Networks

Cited by 20 publications

References 51 publications

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Bayesian Compressive Channel Estimation for Hybrid Full-Dimensional MIMO Communications

Site-specific millimeter-wave compressive channel estimation algorithms with hybrid MIMO architectures

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