Deep Learning Based Channel Prediction for Massive MIMO Systems in High-Speed Railway Scenarios

Xue, Changfeng; Zhou, Tao; Zhang, Haitong; Liu, Liu; Cheng, Tao

doi:10.1109/vtc2021-spring51267.2021.9448982

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Recently, ConvLSTM [39] is proposed to capture the spatiotemporal correlation and has been successfully applied to the high-speed railway and millimeter-wave communications [41], [42]. The major modifications of ConvLSTM are that all of the fully connected operators existed in LSTM are replaced by the convolution operators.…”

Section: B Why Convlstm?mentioning

confidence: 99%

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

Liu

Wang

et al. 2022

IEEE Trans. Commun.

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In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatiotemporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencingattention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

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Section: B Why Convlstm?mentioning

confidence: 99%

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

Liu

Wang

et al. 2022

IEEE Trans. Commun.

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“…With the high-speed development of wireless communication systems, wave MIMO has been deployed around the high-speed railway [1][2][3], which can sharply improve spectral and energy e ciencies [4]. However, training overhead and hardware complexity would be signi cantly increased due to the use of a large number of antennas that are deployed at the base station (BS); it will cost a lot of resources to process data and is very expensive to implement on account of hardware complexity [5,6].…”

Section: Introductionmentioning

confidence: 99%

Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System

Chen

Jiang

2022

Computational Intelligence and Neuroscience

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Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.

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“…Trivedi and Kumar, (2018) used a scheduler based on a standardized SNR for selecting users for data transmission, the scheduler has higher data rate and long-range transmission capabilities without requiring much power or bandwidth. In addition, this paper presents a comparative assessment of the bit error rate (BER) performance of multi-user multiple input multiple output orthogonal frequency division multiplexing (MU-MIMO-OFDM) and MU-MIMO single-carrier frequency-division multiple access (MU-MIMO-SCFDMA) and investigates the impact of various factors, e.g., the CSI imperfections, network heterogeneity, and other factors on the communication transmission; Bai et al (2020) proposed a prediction method based on the long short term memory (LSTM) network and developed an incremental learning scheme for dynamic scenarios, which makes the LSTM predictor run online; (Multiple-Input Multiple-Output, MIMO) system, based on the measured data of 2.35 GHz band in the road-wall scenario, C Xue et al (2021) proposes a Convolutional Long Shore-Term Memory (CLSTM) and CNN combination of Conv-CLSTM channel prediction model for typical channel state information, Les K-factor, RMS delay extension and angle extension characteristics prediction. Son and Han, (2021) proposed the channel adaptive transmission (CAT), which uses the LSTM network for channel prediction and the prediction accuracy is over 97%, indicating that this algorithm can be effectively used for channel prediction.…”

mentioning

confidence: 99%

Channel prediction method based on the data-driving for distribution automation main station

Li,

Zhao,

Feng

et al. 2024

Front. Energy Res.

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A data-driven channel prediction method for distribution automation master is proposed to address the poor quality of communication network and communication system transmission problems in distribution network communication. In this paper, an adaptive broad learning network (ABLN) consisting of a standard broad learning network and a hybrid learning network is introduced to predict the channel state information of the communication system. Among them, the hybrid learning network is used to solve the ill-conditioned solution problem when estimating the output weight matrix of the standard broad learning network. Therefore, the ABLN produces sparse output weight matrices and provides excellent prediction performance. In the simulation analysis, the outdoor and indoor scenes are considered based on OFDM system. The prediction performance of ABLN is subsequently evaluated in one step prediction and multistep prediction. The results show that for the prediction performance is concerned, the maximum improvement of ABLN is about 96.49% as compared to other evaluation models, indicating that the CSI is effectively predicted by the ABLN to support the adaptive transmission of the main station of the distribution automation and to satisfy the quality of the communication network of the distribution network.

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Deep Learning Based Channel Prediction for Massive MIMO Systems in High-Speed Railway Scenarios

Cited by 7 publications

References 10 publications

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System

Channel prediction method based on the data-driving for distribution automation main station

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