Second-Order Statistics-Based Semi-Blind Techniques for Channel Estimation in Millimeter-Wave MIMO Analog and Hybrid Beamforming

Singh, Prem; Srivastava, Suraj; Jagannatham, Aditya K.; Hanzo, Lajos

doi:10.1109/tcomm.2020.3016010

Cited by 11 publications

(4 citation statements)

References 28 publications

(75 reference statements)

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“…To improve the sparse estimation performance, the optimal baseband RC and pilot matrices can be obtained by minimizing the total coherence of the dictionary matrix Φ p,u . Toward this, set the RF TPC and the RF RC as the discrete Fourier transform (DFT) matrices of size N T ×N T and M R × M R [41]- [42]. Furthermore, since the matrix Φ p,u is blockdiagonal, the total coherence minimization of Φ p,u reduces to minimizing the coherence of each sub-matrix…”

Section: Volume 13mentioning

confidence: 99%

An Affine Precoded Superimposed Pilot-Based mmWave MIMO-OFDM ISAC System

Gupta,

Jafri,

Srivastava

et al. 2024

IEEE Open J. Commun. Soc.

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A new affine-precoded superimposed pilot (AP-SIP) scheme is conceived for both wireless channel and radar target parameter estimation in a millimeter wave (mmWave) multiple-input multipleoutput (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems. The AP-SIP scheme leads to enhanced estimation accuracy and improved utilization of spectral resources. Initially, the pilot-assisted radar (PAR) and data-assisted radar (DAR) parameter estimation models are separately developed for the estimation of the radar target parameters. Subsequently, these are combined into a joint pilot-data radar (JPDR) model for simultaneously harnessing both the signals to further boost the estimation accuracy. The sparse Bayesian learning (BL)-based joint-BL (J-BL) technique is developed for this system that efficiently exploits the sparsity of the radar scattering environment. Next, a group sparse BL (G-BL) technique is also derived that exploits the group sparsity across subcarriers for the estimation of the wireless beamspace channel vector, which outperforms the competing techniques, including conventional sparse BL. The optimal pilot, transmit precoder (TPC) and receive combiner (RC) are determined at the dual-function radar-communication (DFRC) base station (BS) and also at the user equipment (UE) for maximizing the performance attained. The Bayesian Cramer-Rao bounds (BCRB) are explicitly derived to benchmark the performance of the wireless channel and radar target parameter estimation. Simulation results are provided to demonstrate the improved performance of the proposed schemes considering multiple metrics, such as the normalized mean squared error (NMSE), bit error rate (BER) and achievable spectral efficiency (ASE).INDEX TERMS Integrated sensing and communication (ISAC), dual-function radar-communication (DFRC), millimeter wave (mmWave), affine-precoded superimposed-pilot (AP-SIP), Bayesian Learning (BL).

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Section: Volume 13mentioning

confidence: 99%

An Affine Precoded Superimposed Pilot-Based mmWave MIMO-OFDM ISAC System

Gupta,

Jafri,

Srivastava

et al. 2024

IEEE Open J. Commun. Soc.

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“…To handle 𝑁 𝑠 ≤ 𝑁 𝑅𝐹 parallel data streams, assume a single user mm-Wave hybrid massive MIMO system having 𝑁 𝑅 receiving antennas, 𝑁 𝑇 transmitting antennas, and 𝑁 𝑅𝐹 ≤ min (𝑁 𝑇 , 𝑁 𝑅 ) RF chain at transmitter and receiver, respectively. Figure 1 represents a hybrid MIMO architecture system [19]. An analog RF domain precoder with a series of digital MIMO baseband precoders, indicated by matrices 𝐅 RF ∈ ℂ N T ×𝑁 𝑅𝐹 and 𝐅 BB ∈ ℂ N RF ×𝑁 𝑠 , respectively, make up the hybrid precoder 𝐅 = 𝐅 RF 𝐅 BB ∈ ℂ N 𝑻 ×N 𝒔 .…”

Section: System Modelmentioning

confidence: 99%

Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)

Shailender

Khera²,

Singh³

et al. 2022

J. Phys.: Conf. Ser.

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In a millimeter-wave (mm-Wave) large MIMO system, hybrid precoding is a critical component for lowering radio-frequency hardware costs as compared to the traditional full-digital precoding strategy. Knowledge of channels is required for hybrid precoding. Though, estimation of the channel is problematic for the mm-Wave system because of the usage of a massive array of antenna and hybrid architecture with analog precoding. Due to the extremely directed nature of wireless propagation, wireless channels have spatial sparsity. In this paper, we exploit this spatial sparsity nature of wireless channels to develop orthogonal matching pursuit technique-based estimation of channels for hybrid millimeter-wave (mm-Wave) wireless systems. Genie (Ideal) channel estimation is also performed in which we presume that actual angle of departures (AoD) and angle of arrivals (AoA) are known to us. Finally, the simulation results reveal that suggested OMP algorithm-based channel estimation has a significant advantage over conventional approaches like least-squares channel estimation.

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“…In MIMO millimeter-wave systems, the channel has a high dimension and large bandwidth, making its estimation challenging. In [409], channel estimator for millimeter-Wave MIMO systems is proposed by using matrix perturbation theoretic techniques. One of the channel estimation difficulties is CFO, which impairs the received signal.…”

Section: ) Millimeter-wave Systemsmentioning

confidence: 99%

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in singlecarrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, nonorthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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Second-Order Statistics-Based Semi-Blind Techniques for Channel Estimation in Millimeter-Wave MIMO Analog and Hybrid Beamforming

Cited by 11 publications

References 28 publications

An Affine Precoded Superimposed Pilot-Based mmWave MIMO-OFDM ISAC System

An Affine Precoded Superimposed Pilot-Based mmWave MIMO-OFDM ISAC System

Estimation of Channel for Millimeter-Wave Hybrid Massive MIMO Systems using Orthogonal Matching Pursuit (OMP)

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

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