Closed-Loop Sparse Channel Estimation for Wideband Millimeter-Wave Full-Dimensional MIMO Systems

Liao, Anwen; Gao, Zhen; Wang, Hua; Chen, Sheng; Alouini, Mohamed‐Slim; Yin, Hao

doi:10.1109/tcomm.2019.2942911

Cited by 83 publications

(66 citation statements)

References 42 publications

(126 reference statements)

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“…However, channel estimation is usually necessary before the signal detection. The channel estimation methods mainly include least square (LS) method, MMSE method and some compressive sensing-based methods (for massive MIMO systems) [8], [9]. To further improve the performance, combining them with deep learning (DL) is a feasible solution [10].…”

Section: A Background Of Signal Detection and Channel Estimationmentioning

confidence: 99%

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Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

Wang

Hua

2020

IEEE Access

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In this paper, we propose two deep learning (DL) based receiver schemes in uplink multipleinput multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO receiver using a data-driven full connected neural network. This data-driven receiver can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel. In the second scheme, we adopt a modeldriven network which combines communication knowledge with DL. The model-driven scheme divides the MIMO receiver into channel estimation subnet and signal detection subnet, and each subnet is composed of a traditional solution as initialization and a DL network to further improve the accurate. The simulation results show that both of the two schemes achieve better bit error ratio (BER) performance than traditional methods. In particular, the data-driven scheme can achieve optimal BER performance in low-dimensional MIMO systems, while the model-driven scheme can be trained with fewer trainable parameters and outperforms the data-driven scheme in high-dimension MIMO systems. INDEX TERMS Channel estimation and signal detection, MIMO, deep learning, model-driven, data-driven.

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Section: A Background Of Signal Detection and Channel Estimationmentioning

confidence: 99%

“…, 4 is the binary code element. S in the complex-valued channel model (5) and 6with QPSK is equivalent to binary phase shift keying (BPSK) modulation S = {−1, 1} in the real-valued channel model (8) and (9).…”

Section: A Reparameterizationmentioning

confidence: 99%

Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

Wang

Hua

2020

IEEE Access

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“…Citation information: DOI 10.1109/ACCESS.2020.3017633, IEEE Access based methods. In terms of complexity, it is in the order of 1.8 * 10 5 for 'SBT' (computed from Table 1), 6.4 * 10 7 for [15], and 4.1 * 10 7 for both [19] and [20] (obtained from [38]). Fig.…”

Section: Operation Complexitymentioning

confidence: 99%

Random SBT Precoding for Angle Estimation of mmWave Massive MIMO Systems Using Sparse Arrays Spacing

Lin

Yang²

2020

IEEE Access

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In this paper, we consider the estimation of angle of arrival (AoA) and angle of departure (AoD) for mmWave MIMO channels. For mmWave systems, there is typically a large number of antennas but limited radio frequency (RF) chains. The RF chain limitation indirectly restricts the effective number of antennas or the effective array size. In radar applications, it is known that a larger array size leads to more accurate AoA estimation or more resolvable paths. It is also known that with sparse arrays, which places antennas in a nonuniform sparse manner, an enlarged virtual array can be constructed when the sources are uncorrelated. We show that the idea of sparse arrays can also be used in mmWave systems to have augmented array sizes. The main difficulty in applying sparse arrays to mmWave MIMO channels lies in the fact the signals of different paths can be correlated in mmWave system, but uncorrelatedness is necessary for applying sparse array results. We show that the paths can be decorrelated by employing random precoders and combiners that are submatrices of banded Toeplitz matrices (SBT). When the precoder and combiner are SBT that are designed according to the antenna spacing of a sparse array, we can construct enlarged virtual transmit and receive arrays. With enlarged virtual arrays, accurate estimates can be obtained, as will be demonstrated by simulation examples.

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“…To this end, by exploiting the compressibility of massive MIMO channels represented in the angular domain and/or delay domain, several low X. Ma training overhead channel estimation solutions have been proposed [1], [5]- [8]. Specifically, in [1], a spatially common sparsity based adaptive channel estimation and feedback scheme for FDD massive MIMO was proposed to reliably estimate and feed back the downlink CSI with significantly reduced overhead.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO

Gao

2020

IEEE Trans. Veh. Technol.

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105

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In this paper, we propose a data-driven deep learning (DL) approach to jointly design the pilot signals and channel estimator for wideband massive multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain compressibility of massive MIMO channels, the conceived DL framework can reliably reconstruct the high-dimensional channels from the under-determined measurements. Specifically, we design an endto-end deep neural network (DNN) architecture composed of dimensionality reduction network and reconstruction network to respectively mimic the pilot signals and channel estimator, which can be acquired by data-driven deep learning. For the dimensionality reduction network, we design a fully-connected layer by compressing the high-dimensional massive MIMO channel vector as input to low-dimensional received measurements, where the weights are regarded as the pilot signals. For the reconstruction network, we design a fully-connected layer followed by multiple cascaded convolutional layers, which will reconstruct the highdimensional channel as the output. By defining the mean square error between input and output as loss function, we leverage Adam algorithm to train the end-to-end DNN aforementioned with extensive channel samples. In this way, both the pilot signals and channel estimator can be simultaneously obtained. The simulation results demonstrate that the superiority of the proposed solution over state-of-the-art compressive sensing approaches.

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Closed-Loop Sparse Channel Estimation for Wideband Millimeter-Wave Full-Dimensional MIMO Systems

Cited by 83 publications

References 42 publications

Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

Random SBT Precoding for Angle Estimation of mmWave Massive MIMO Systems Using Sparse Arrays Spacing

Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO

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