Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications

Pan, Leyuan; Liang, Le; Xu, Wei; Dong, Xiaodai

doi:10.1109/tcomm.2018.2831222

Cited by 16 publications

(23 citation statements)

References 41 publications

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“…Hence, it is of uttermost importance to design a covariance estimator to involve few pilot transmissions. Now, let us recall the covariance estimation technique in [40], [41] which results in the following signal model within the covariance pilot training period (green area): 6…”

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

“…based on the covariance of the baseband received signal vectors R c = E y c y H c as follows [40], [41] Fig. 3: Signal framework with a fixed covariance matrix.…”

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

“…The equation (57) implies that an accurate estimate of R c is highly appealing for the better estimation of R 1 , i.e.R 1 . The authors in [40], [41] suggest the sample covariance matrix R is typically not well-conditioned and may not even be invertible as its rank is upper limited by the number of samples N Q . This can trigger some bad conditions for other scenarios involving the matrix inversion.…”

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

“…In this light, one may devote the entire or the major part of each of these N Q blocks just to estimate the covariance matrix, but with a very slow rate to reduce the variations of analog beamformers. For the design of fast analog beamformers, we refer the interested readers to multiple training technique in [40], [41].…”

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Processing Design for Multipair Massive MIMO Relaying With Channel Spatial Correlation

Fozooni

Ngo

Matthaiou

et al. 2019

IEEE Trans. Commun.

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Abstract-Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-andforward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero-forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the spatial correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits spatial correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results showcase that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.

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Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

“…based on the covariance of the baseband received signal vectors R c = E y c y H c as follows [40], [41] Fig. 3: Signal framework with a fixed covariance matrix.…”

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

Section: Estimate Of Spatial Channel Covariance Matrixmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid Processing Design for Multipair Massive MIMO Relaying With Channel Spatial Correlation

Fozooni

Ngo

Matthaiou

et al. 2019

IEEE Trans. Commun.

View full text Add to dashboard Cite

show abstract

“…Furthermore, many methods are proposed to exploit the sparsity of mmWave channel and most of them have focused on the estimation of the large channel matrix, or equivalently on estimating the channel parameters to acquire the large channel matrix, with widely adopted CS based methods. Additionally, the minimum mean square error (MMSE) criterion is adopted for mmWave CE in [24] and [25], where the CE methods proposed are applicable to both spatially uncorrelated channels and spatially correlated mmWave channels. In [25], the angular domain sparsity is also exploited to improve mmWave CE by incorporating CS method.…”

Section: Introductionmentioning

confidence: 99%

Harmonic Retrieval Based Baseband Channel Estimation for Millimeter Wave OFDM Systems

Sha

Hanzo

Chen

2019

IEEE Trans. Veh. Technol.

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For massive multi-input multi-output (MIMO) enhanced millimeter wave (mmWave) frequency-division multiplexing (OFDM) systems, channel estimation (CE) is challenging. In this paper, the baseband CE of mmWave based MIMO-OFDM systems after beam searching is formulated as a harmonic retrieval (HR) problem, where each path of the channel represents a harmonic with its frequency and strength to be estimated. We propose two methods, a windowed orthogonal matching pursuit (window-OMP) method and a windowed discrete Fourier transform (window-DFT) method, to approximately acquire the maximum likelihood (ML) estimate of the baseband CE. The window-OMP method is capable of approximating the ML estimator with high accuracy, while the window-DFT method has lower complexity and is shown to acquire approximate ML estimate under the assumption that the frequencies of harmonics are sufficiently separated. Theoretical analysis is performed to derive a closed-form Cramér-Rao lower bound (CRLB) as well as to investigate the effect of wrong paths to the estimation accuracy. A simulation study is conducted to investigate the performance of the proposed methods, and the results obtained verify that our methods outperform the existing estimation of signal parameters by rotational invariant techniques (ESPRIT) based HR method and the conventional interpolation method.

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An Introduction on Millimeter Wave Communications

Huang

2019

Wiley 5G Ref

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In the past few years, millimeter wave (mmWave) communication has attracted significant attention both from industry and academia, due to the large available spectrum at mmWave frequency bands. With the significantly increased communication bandwidth, mmWave communication is able to achieve tremendous data rate (e.g. on the order of dozens of Gbps) to satisfy the ever‐increasing traffic demand in 5G‐and‐beyond wireless networks. This article aims to give an introductory overview on the fundamentals of mmWave communication, including the general background of mmWave systems, the propagation characteristics of mmWave channels, followed by the design of hybrid antenna architecture to balance the trade‐off between the hardware cost and the system performance. The state of the art for the design of hybrid precoding/combining and channel acquisition for mmWave systems will be highlighted.

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Framework of Channel Estimation for Hybrid Analog-and-Digital Processing Enabled Massive MIMO Communications

Cited by 16 publications

References 41 publications

Hybrid Processing Design for Multipair Massive MIMO Relaying With Channel Spatial Correlation

Hybrid Processing Design for Multipair Massive MIMO Relaying With Channel Spatial Correlation

Harmonic Retrieval Based Baseband Channel Estimation for Millimeter Wave OFDM Systems

An Introduction on Millimeter Wave Communications

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