MRFNet: A Deep Learning-Based CSI Feedback Approach of Massive MIMO Systems

Hu, Zhengyang; Guo, Jianhua; Liu, Guanzhang; Zheng, Hanying; Xue, Jiang

doi:10.1109/lcomm.2021.3099841

Cited by 31 publications

(9 citation statements)

References 19 publications

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“…Meanwhile, the complexity of ConvLSTM is the lowest. Furthermore, the visualization analysis in the same way as [51] is used to reveal the reason behind the performance gain of ConvLSTM. An arbitrary sample in the testing dataset is selected, and the output of different basic DL models and the ground truth are visualized.…”

Section: Appendixmentioning

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: Appendixmentioning

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 the literature, ML and deep learning (DL) have been applied to improve the precision of acquired CSI at BS. The authors of [8], [16]- [34] exploit autoencoder for CSI feedback. Particularly, inherent features of autoencoder, i.e., encoder and decoder, are used to compress and recover CSI, respectively.…”

Section: A Csi Feedback: Motivation and State-of-the-artmentioning

confidence: 99%

Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Shehzad

Rose

Azam

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality.

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“…MRFNet [53] The MRFBlock has three parallel paths, with 5 × 5, 7 × 7, and 9 × 9 convolution kernels;…”

Section: Multiple Resolutionsmentioning

confidence: 99%

“…The NN architecture in [53], called MRFNet, reveals the reason for the success of the multiple resolution strategy via feature visualization. The encoder in MRFNet is the same as that in CsiNet, and the main modifications are employed to the decoder at the BS.…”

Section: Well-designed Preprocessingmentioning

confidence: 99%

Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

Guo¹,

Wen²,

Jin³

et al. 2022

Preprint

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Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user terminal) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of the transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on bitlevel CSI feedback and joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including training dataset collection, online training, complexity, generalization, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

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MRFNet: A Deep Learning-Based CSI Feedback Approach of Massive MIMO Systems

Cited by 31 publications

References 19 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

Real-Time Massive MIMO Channel Prediction: A Combination of Deep Learning and NeuralProphet

Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems

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