Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding

Huang, Hexin; Song, Yiwei; Ye, Jie; Gui, Guan; Adachi, Fumiyuki

doi:10.1109/tvt.2019.2893928

Cited by 420 publications

(281 citation statements)

References 24 publications

(27 reference statements)

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“…The gap between the MO algorithm and the DL frameworks is explained by the corruptions in the DL input which causes deviations from the label data (obtained via MO) at the output regression layer. Note that our DLHB methods improve upon other DL-based techniques such as MLP [36], which lacks a feature extraction stage provided by convolutional layers in our networks. Among the DL frameworks, F2 and F3 exhibit superior performance than F1 because the channel estimated by MC-CENet and SC-CENet has higher accuracy.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Elbir

Mishra

2019

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

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Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for joint channel estimation and hybrid beamforming for frequencyselective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the received pilot signal as input and yield the hybrid beamformers at the output. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.Index Terms-Channel estimation, deep learning, hybrid beamforming, mm-Wave, wideband massive MIMO.

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Section: Numerical Simulationsmentioning

confidence: 99%

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Elbir

Mishra

2019

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

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“…CNN-MIMO is also compared with the DL-based approach MLP proposed in [27]. MLP is designed as described in [27] but adapted for the multi-user scenario with the same training data used for CNN-MIMO. As another benchmark and denoted as "No interference" in the simulations, we present the performance of fullydigital beamforming and combining where the interference is completely eliminated.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

Elbir

Papazafeiropoulos

2020

IEEE Trans. Veh. Technol.

132

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In multi-user millimeter wave (mmWave) multipleinput-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.

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“…Massive multiple-input multiple-output (MIMO) has been widely recognized as a promising technology in future wireless communications due to its high spectrum efficiency and large beamforming gain [1]. Moreover, to mitigate the hardware cost and power consumptions imposed by fully-digital massive MIMO, the hybrid MIMO architecture with much reduced number of radio frequency (RF) chains has been proposed by dividing the beamforming into the cascaded analog domain and digital domain operations [2]- [5].…”

Section: Introductionmentioning

confidence: 99%

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

Gao

2020

IEEE Trans. Veh. Technol.

116

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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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Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding

Cited by 420 publications

References 24 publications

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems: A Deep Learning Approach

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

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