Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

Majumder, Manjeer; Saxena, Harshit; Srivastava, Suraj; Jagannatham, Aditya K.

doi:10.1109/access.2021.3070921

Cited by 16 publications

(8 citation statements)

References 53 publications

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“…The proposed algorithm requires only limited CSI of the beamspace channel, namely the non-zero coefficients and their respective indices, which substantially reduces the feedback required. Furthermore, in contrast to the existing hybrid transceiver designs [12], [13], [38], the proposed scheme requires no iterations, and hence it is computationally efficient. 5) Our simulation results demonstrate the enhanced performance of our channel estimators, TPC and RC for various practical simulation parameters.…”

Section: )mentioning

confidence: 99%

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Srivastava

Tripathi

Varshney

et al. 2023

IEEE Trans. Wireless Commun.

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Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-ofsight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.

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Section: )mentioning

confidence: 99%

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Srivastava

Tripathi

Varshney

et al. 2023

IEEE Trans. Wireless Commun.

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“…Their technique intelligently employs a virtual array partition to reduce the number of TTD elements that are required to effectively mitigate the beam squint. The effect of beam squint effect has also been considered earlier for mmWave MIMO systems in [23], [24], [26].…”

Section: A Review Of Related Workmentioning

confidence: 99%

“…For the DL, one can concatenate the measurement vectors across all the N c subcarriers, which upon using the result in (26), yields the model…”

Section: Dictionary Learning For Sparse Csi Estimation In Terahertz S...mentioning

confidence: 99%

“…The vectorized channel matrix H(k, d) from ( 23) can be expressed as shown in (35). Upon substituting h b (k, d) = vec(H(k, d)) from ( 35) in (26) followed by collecting the measurement vectors ỹ[k] over all the N c subcarriers, one obtains the quantity Y ∈ C M Lr×Nc as shown in (36). Each column of G is denoted by g[k], ∀k ∈ I(N c ).…”

Section: Crlb Analysis a Unknown Dictionarymentioning

confidence: 99%

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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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“…The combination of OFDM and MIMO is a major trend in mobile communication systems [18][19][20][21][22], such as 5G and next-generations, which are based on the so-called MIMO-OFDM and mMIMO-OFDM approaches [23]. Nonetheless, digital communication systems over wireless channels may suffer severe signal degradations due to multipath propagation, additive white Gaussian noise (AWGN) [24,25], and Doppler effects [26].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Soares

Mayer

Castro

et al. 2021

Sensors

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Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

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Optimal Bit Allocation-Based Hybrid Precoder-Combiner Design Techniques for mmWave MIMO-OFDM Systems

Cited by 16 publications

References 53 publications

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

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

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

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