Sparse Bayesian Learning (SBL)-Based Frequency-Selective Channel Estimation for Millimeter Wave Hybrid MIMO Systems

Srivastava, Suraj; Patro, Ch Suraj Kumar; Jagannatham, Aditya K.; Sharma, Govind

doi:10.1109/ncc.2019.8732197

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…The SBL algorithm begins by assigning the following parameterized Gaussian prior to the unknown beamspace channel vector h h h b [c] [29,30]:…”

Section: Conventional Sbl Algorithmmentioning

confidence: 99%

“…where Γ Γ Γ (k−1) [c] denotes the matrix of the hyperparameter matrix Γ Γ Γ[c] estimated in the (k − 1)-th iteration. Then, the Mstep maximizes (23) with respect to the hyperparameter vector γ γ γ[c] as [30]…”

Section: Conventional Sbl Algorithmmentioning

confidence: 99%

“…where the matrices J J J D and J J J P denote the FIMs of the observation vector y y y p [c] and the parameter vector h h h b [c], respectively. Upon applying the results from [29,30,44] along with some further simplification, J J J D and J J J P can be formulated as…”

Section: E Bayesian Cramer-rao Bound Of Multi-layer Sbl Channel Estim...mentioning

confidence: 99%

“…Specially in mmWave communications, the spatial sparsity can be effectively exploited to estimate the channel coefficients of the significant spatial paths. Based on this observation, the sparse mmWave channels can be estimated with a considerably decreased pilot overhead using the CS-based methods, such as the sparse Bayesian learning (SBL) [28][29][30], orthogonal matching pursuit (OMP) [31,32], approximate message passing (AMP) [33], etc. For the SBL and OMP methods, the angles of arrival (AoA) and angles of departure (AoD) spaces are divided into a finite number of grid-points to obtain the beamspace representation of mmWave channels.…”

mentioning

confidence: 99%

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Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

Liu

Zhang

et al. 2022

IEEE Trans. Veh. Technol.

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The efficiency of link adaptation in wireless communications relies greatly on the accuracy of channel knowledge and transmission mode selection. In this paper, a novel deep learning based link adaptation framework is proposed for the orthogonal frequency-division multiplexing (OFDM) systems with compressed-sensing-assisted index modulation, termed as OFDM-CSIM, communicating over millimeter-wave (mmWave) channels. To achieve link adaptation, a novel multi-layer sparse Bayesian learning (SBL) algorithm is proposed for accurately and instantaneously providing the required channel state information. Meanwhile, a deep neural networks (DNN)-assisted adaptive modulation algorithm is proposed to choose the best possible transmission mode to maximize the achievable throughput. Simulation results show that the proposed multi-layer SBL algorithm enables more accurate channel estimation than the conventional techniques. The DNN-based adaptive modulator is capable of achieving a higher throughput than the learning-assisted solution based on the k nearest neighbor (k-NN) algorithm, and also the classic average signal-to-noise ratio (SNR)-based solutions. Moreover, analysis shows that both the multi-layer SBL algorithm and the DNN-assisted adaptive modulator achieve better performance than their respective conventional counterparts while at a significantly lower computational complexity cost.

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“…The SBL algorithm begins by assigning the following parameterized Gaussian prior to the unknown beamspace channel vector h h h b [c] [29,30]:…”

Section: Conventional Sbl Algorithmmentioning

confidence: 99%

Section: Conventional Sbl Algorithmmentioning

confidence: 99%

Section: E Bayesian Cramer-rao Bound Of Multi-layer Sbl Channel Estim...mentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

Liu

Zhang

et al. 2022

IEEE Trans. Veh. Technol.

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“…The authors of the path-breaking work in [6] presented a LASSO-based sparse channel estimation technique for mmWave MIMO systems. Proceeding further, Bayesian learning based sparse channel estimation techniques for mmWave MIMO systems were explored in [7] and [8]. The authors of [9]- [12] described sparse channel estimation schemes for FDD massive MIMO systems, and the framework was extended to mmWave hybrid MIMO OFDM systems in other papers such as [13], [14].…”

Section: A Review Of Related Workmentioning

confidence: 99%

Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

et al. 2022

Self Cite

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This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems.To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the nonidealities.

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Time Domain Channel Estimation for Time and Frequency Selective Millimeter Wave MIMO Hybrid Architectures: Sparse Bayesian Learning-Based Kalman Filter

K¹,

Sampath²

2020

Wireless Pers Commun

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Sparse Bayesian Learning (SBL)-Based Frequency-Selective Channel Estimation for Millimeter Wave Hybrid MIMO Systems

Cited by 12 publications

References 12 publications

Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

Deep Learning Assisted Adaptive Index Modulation for mmWave Communications With Channel Estimation

Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

Time Domain Channel Estimation for Time and Frequency Selective Millimeter Wave MIMO Hybrid Architectures: Sparse Bayesian Learning-Based Kalman Filter

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