Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure

Lin, Xincong; Wu, Sheng; Jiang, Chunxiao; Kuang, Linling; Yan, Jian; Hanzo, Lajos

doi:10.1109/twc.2018.2818142

Cited by 80 publications

(53 citation statements)

References 35 publications

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“…In this regard, we show that the per-2 Note that [25] uses the NNSPL algorithm to exploit the angle domain sparsity of the channel, while [26] uses the NNSPL algorithm to exploit the delay domain sparsity. [23] presents a comprehensive version of the NNSPL algorithm to jointly handle the angle-delay domain sparsity. iteration complexity of STCS is lower than that of NNSPL; we further show that STCS converges much faster than NNSPL.…”

Section: A Related Workmentioning

confidence: 99%

“…Fig. 7, we compare the average NMSE performance of OMP [9], DSAMP [14], L1 LASSO [9], EM-BG-AMP [16], Turbo-CS [17], AMP-NNSPL-FD [23], The performance is averaged by 100 independent trials at each grid point. The sensing matrix is always chosen as partial DFT-RP matrix for a fair comparison.…”

Section: State Evolutionmentioning

confidence: 99%

“…Fig. 9 shows the NMSE performances of STCS-based algorithms and the NNSPL-based algorithms [23] as a function of iteration number at SNR= 10 dB, M N = 0.4 in (a), M N = 0.6 in (b), and M N = 0.8 in (c). From Fig.…”

Section: F Test For More Realistic Channel Datamentioning

confidence: 99%

“…Recently, message-passing algorithms for compressed sensing [15]- [23] have attracted much research interest due to their fast convergence and low computational complexity. Among them, the turbo compressed sensing (Turbo-CS) algorithm [17] and its variants [19], [20] have the state-of-the-art performance in both complexity and convergence rate, especially when partial orthogonal sensing matrices are involved.…”

Section: Introductionmentioning

confidence: 99%

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Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Kuai

Chen

Yuan

et al. 2019

IEEE Trans. Wireless Commun.

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Compressed sensing has been employed to reduce the pilot overhead for channel estimation in wireless communication systems. Particularly, structured turbo compressed sensing (STCS) provides a generic framework for structured sparse signal recovery with reduced computational complexity and storage requirement. In this paper, we consider the problem of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) channel estimation in a frequency division duplexing (FDD) downlink system. By exploiting the structured sparsity in the angle-frequency domain (AFD) and angle-delay domain (ADD) of the massive MIMO-OFDM channel, we represent the channel by using AFD and ADD probability models and design message-passing based channel estimators under the STCS framework. Several STCS-based algorithms are proposed for massive MIMO-OFDM channel estimation by exploiting the structured sparsity. We show that, compared with other existing algorithms, the proposed algorithms have a much faster convergence speed and achieve competitive error performance under a wide range of simulation settings.

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Section: A Related Workmentioning

confidence: 99%

Section: State Evolutionmentioning

confidence: 99%

Section: F Test For More Realistic Channel Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Kuai

Chen

Yuan

et al. 2019

IEEE Trans. Wireless Commun.

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“…However, mmWave communication usually suffers from the severe path loss. Traditional fully-digital precoding with massive antennas can be used to mitigate the severe path loss, but at the cost of high power consumption and hardware cost [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO

Sun

Gao

Wang

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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Millimeter-wave (mmWave) communication is considered as an indispensable technique for the next-generation backhaul/fronthaul network thanks to its large transmission bandwidth. Especially for heterogeneous network (HetNet), the mmWave full-dimension (FD)-MIMO is exploited to establish the backhaul/fronthaul link between phantom-cell base stations (BSs) and macro-cell BSs, where an efficient precoding is prerequisite. Against this background, this paper proposes a principle component analysis (PCA)-based hybrid precoding for wideband mmWave MIMO backhaul/fronthaul channels. We first propose an optimal hybrid precoder by exploiting principal component analysis (PCA), whereby the optimal high dimensional frequency-selective precoder are projected to the lowdimensional frequency-flat precoder. Moreover, the combiner is designed by leveraging the weighted PCA, where the covariance of received signal is taken into account as weight to the optimal minimum mean square error (MMSE) fully-digital combiner for further improved performance. Simulations have confirmed that the proposed scheme outperforms conventional schemes in spectral efficiency (SE) and bit-error-rate (BER) performance.Index Terms-Backhaul/fronthaul, hybrid precoding, wideband FD-MIMO, millimeter wave, principle component analysis.

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Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

Mejri

Hajjaj

Hasnaoui

2020

Trans Emerging Tel Tech

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The hybrid architecture still be the most attractive solution for the analog‐digital beamforming in millimeter wave (mmWave) large‐scale multiple input multiple output (LS‐MIMO) systems, since it provides the best trade‐off between the achievable rate and the power consumption. When maximizing performance metrics, channel knowledge is required to optimally design the hybrid beamforming. The channel estimation (CE) is essential for wideband mmWave LS‐MIMO to substantially enhance data rates, however, is a challenging task. Practically, the antenna number and the bandwidth for such communications are very large. Therefore, the CE scheme should account for the frequency‐dependence of the array response vectors and thereby the beam squint effect. In the first part, this article addresses a structured analysis compressive sensing (CS) for CE in a frequency selective mmWave LS‐MIMO system without neglecting the beam squint effect. Compared to sparse mmWave CE approach that mostly uses on‐grid dictionaries, our proposed approach considers continually distributed angles of arrival and departure. In the second part, we show that the frequency‐selective mmWave MIMO channels share a common sparsity structure within the same subcarrier‐group. Then, we propose a block‐sparsity‐based CS approach that uses the simultaneous orthogonal matching pursuit method to estimate the mmWave channel. Moreover, we prove the partial sparsity structure shared between all the subcarrier‐groups. Hence, we develop our proposed joint subcarrier‐block‐based CE scheme exploiting these two block‐sparsity structures. Numerical results evaluate the performances in terms of normalized mean‐squared error and spectral efficiency to select the most accurate channel estimator with lower overhead.

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Estimation of Broadband Multiuser Millimeter Wave Massive MIMO-OFDM Channels by Exploiting Their Sparse Structure

Cited by 80 publications

References 35 publications

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Structured Turbo Compressed Sensing for Downlink Massive MIMO-OFDM Channel Estimation

Wideband Hybrid Precoding for Next-Generation Backhaul/Fronthaul Based on mmWave FD-MIMO

Structured analysis/synthesis compressive sensing‐based channel estimation in wideband mmWave large‐scale multiple input multiple output systems

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