Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Srivastava, Suraj; Patro, Ch Suraj Kumar; Jagannatham, Aditya K.; Hanzo, Lajos

doi:10.1109/tcomm.2021.3085344

Cited by 27 publications

(27 citation statements)

References 25 publications

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“…However, the above early seminal contributions did not account for the time variation of the mmWave MIMO channel arising due to mobility. The authors of [18]- [20] considered a doubly-selective temporally-correlated block-fading channel in their wideband mmWave MIMO systems, wherein the time-variation of the gains of the multipath components has been modeled by a first-order autoregressive (AR) process. However, the AR model employed requires knowledge of the temporal-correlation coefficient of the channel, which has been popularly evaluated using Jake's model [21], [22].…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Fig. 5(c) demonstrates the NMSE of the channel frequency response (CFR) for mmWave-HB MIMO OFDM systems using the relevant state-of-the-art solutions, such as G-SBL [18], SOMP [17] and DSDS [23]. In particular, the CSI estimation performance of [18] and [17] is poor, since they ignore the effect of Doppler arising within an OFDM symbol.…”

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

“…5(c) demonstrates the NMSE of the channel frequency response (CFR) for mmWave-HB MIMO OFDM systems using the relevant state-of-the-art solutions, such as G-SBL [18], SOMP [17] and DSDS [23]. In particular, the CSI estimation performance of [18] and [17] is poor, since they ignore the effect of Doppler arising within an OFDM symbol. Due to this, their channel estimation models become inconsistent in the presence of Doppler, and their CSI estimation performance degrades further upon increasing the Doppler.…”

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

“…Due to this, their channel estimation models become inconsistent in the presence of Doppler, and their CSI estimation performance degrades further upon increasing the Doppler. It is also important to note that both [18] and [17] exploit only the angular-domain sparsity of the mmWave MIMO channel. The improved performance of the 3-stage scheme developed in [23] is attributed to the fact that it accounts for the Doppler in the channel estimation model derived and also exploits the associated delay-domain sparsity along with the angularsparsity.…”

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

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OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mmWave MIMO Systems

Srivastava

Singh

Jagannatham

et al. 2022

IEEE Trans. Wireless Commun.

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Orthogonal time frequency space (OTFS) waveform based millimeter wave (mmWave) MIMO systems are capable of achieving high data rates in high-mobility scenarios. Hence, transceivers are designed for both analog beamforming (AB) and hybrid beamforming (HB), where we commence by deriving the delay-Doppler (DD)-domain input-output relationship considering a delay-Doppler-angular domain channel model. Subsequently, a novel two-stage procedure is developed for transmit beamformer (TBF)/ precoder (TPC) and receiver combiner (RC) design, and for estimating the DD-domain's equivalent channel state information (CSI). The key feature of the proposed framework is that the RF TBF/ TPC and RC design maximizes the directional beamforming gains. It is also demonstrated that the low-dimensional baseband CSI of the DD-domain becomes sparse for mmWave-AB MIMO OTFS systems, and blocksparse for mmWave-HB MIMO OTFS systems. Subsequently, Bayesian learning (BL) and block-sparse BL (BS-BL) solutions are developed for improved CSI estimation. We also derive the Bayesian Cramer-Rao lower bounds (BCRLB) for benchmarking the mean-squared-error (MSE) of the CSI estimates. Finally, our simulation results demonstrate the improved efficacy of the proposed transceiver designs and confirm the enhanced CSI estimation performance of the BL-based schemes over other competing sparse signal recovery schemes.

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Section: A Literature Reviewmentioning

confidence: 99%

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

Section: B Mmwave-hb Mimo Otfs Systemsmentioning

confidence: 99%

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OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mmWave MIMO Systems

Srivastava

Singh

Jagannatham

et al. 2022

IEEE Trans. Wireless Commun.

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“…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]. González-Coma et al successfully described channel estimation algorithms for the challenging case of multiuser mmWave MIMO systems in [15].…”

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

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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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Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

Roji,

Jayasankar,

Devi

2024

Int J Communication

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SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

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Sparse, Group-Sparse, and Online Bayesian Learning Aided Channel Estimation for Doubly-Selective mmWave Hybrid MIMO OFDM Systems

Cited by 27 publications

References 25 publications

OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mmWave MIMO Systems

OTFS Transceiver Design and Sparse Doubly-Selective CSI Estimation in Analog and Hybrid Beamforming Aided mmWave MIMO Systems

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

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

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