Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System

Yang, Yuwen; Gao, Feifei; Li, Geoffrey Ye; Jian, Mengnan

doi:10.1109/lcomm.2019.2934851

Cited by 174 publications

(88 citation statements)

References 36 publications

(105 reference statements)

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“…The channel samples obtained from QuaDRiGa does not have any idealistic feature, e.g., perfect directional reciprocity between the UL and DL channels. This is quite different from previous works where channel samples are obtained based on analytical channel models [15], [16], [18]- [22], [25], [26].…”

Section: A Generating Channel Samplescontrasting

confidence: 68%

“…The purpose of using the NN is to estimate the DL CSI directly from the UL CSI measured at the BS without having any explicit downlink training. More precisely, the previous works in [15], [16] used the UL OFDM channel H(f ul ) as an input and the DL OFDM channel H(f dl ) as an output to train the NN, i.e.,…”

Section: B Nn-based DL Extrapolationmentioning

confidence: 99%

“…The OFDM channels and extracted channel path gains, which are the inputs and outputs of the NN for the CH-learning and proposed PG-learning, respectively, are all complexvalued data. While there are some recent work on NNs that can deal with complex-valued inputs and outputs [16], [18], we exploit well-established NNs using real-valued data.…”

Section: A Input and Output Layers For Nnmentioning

confidence: 99%

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Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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When base stations (BSs) are deployed with multiple antennas, they need to have downlink (DL) channel state information (CSI) to optimize downlink transmissions by beamforming. The DL CSI is usually measured at mobile stations (MSs) through DL training and fed back to the BS in frequency division duplexing (FDD). The DL training and uplink (UL) feedback might become infeasible due to insufficient coherence time interval when the channel rapidly changes due to high speed of MSs. Without the feedback from MSs, it may be possible for the BS to directly obtain the DL CSI using the inherent relation of UL and DL channels even in FDD, which is called DL extrapolation. Although the exact relation would be highly nonlinear, previous studies have shown that a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the BS. Most of previous works on this line of research trained the NN using full dimensional UL and DL channels; however, the NN training complexity becomes severe as the number of antennas at the BS increases. To reduce the training complexity and improve DL CSI estimation quality, this paper proposes a novel DL extrapolation technique using simplified input and output of the NN. It is shown through many measurement campaigns that the UL and DL channels still share common components like path delays and angles in FDD. The proposed technique first extracts these common coefficients from the UL and DL channels and trains the NN only using the path gains, which depend on frequency bands, with reduced dimension compared to the full UL and DL channels. Extensive simulation results show that the proposed technique outperforms the conventional approach, which relies on the full UL and DL channels to train the NN, regardless of the speed of MSs.

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Section: A Generating Channel Samplescontrasting

confidence: 68%

Section: B Nn-based DL Extrapolationmentioning

confidence: 99%

Section: A Input and Output Layers For Nnmentioning

confidence: 99%

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Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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“…In some cases deep learning even exceeds human performance [15]. Deep learning has been also used in physical layer communications [16]- [23]. O'Shea et al [16] considered a communication system at the physical layer as an autoencoder and designed an end-to-end system that optimizes transmitter and receiver simultaneously in one process.…”

Section: Introductionmentioning

confidence: 99%

Deep UL2DL: Data-Driven Channel Knowledge Transfer From Uplink to Downlink

Safari

Pourahmadi

Sodagari

2020

IEEE Open J. Veh. Technol.

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Knowledge of the channel state information (CSI) at the transmitter side is one of the primary sources of information that can be used for efficient allocation of wireless resources. Obtaining downlink (DL) CSI in Frequency Division Duplexing (FDD) systems from uplink (UL) CSI is not as straightforward as in TDD systems. Therefore, users usually feed the DL-CSI back to the transmitter. To remove the need for feedback (and thus having less signaling overhead), we propose to use two recent deep neural network structures, i.e., convolutional neural networks and generative adversarial networks (GANs) to infer the DL-CSI by observing the UL-CSI. The core idea of our data-driven scheme is exploiting the fact that both DL and UL channels share the same propagation environment. As such, we extracted the environment information from UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel. To overcome incorrect latent domain and the problem of oversimplistic assumptions, in this work, we did not use any specific parametric model and instead used data-driven approaches to discover the underlying structure of data without any prior model assumptions. To overcome the challenge of capturing the UL-DL joint distribution, we used a mean square error-based variant of the GAN structure with improved convergence properties called boundary equilibrium GAN (BEGAN). For training and testing we used simulated data of Extended Vehicular-A (EVA) and Extended Typical Urban (ETU) models. Simulation results verified that our methods can accurately infer and predict the downlink CSI from the uplink CSI for different multipath environments in FDD communications.

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“…However, the proposed convLSTM-net has high complexity evident from its design. In addition, authors in [22] claim that in an FDD mMIMO system, the acquisition of downlink CSI is a complex task due to the overheads required for downlink training and uplink feedback. The authors propose a sparse complex-valued neural network (SCNet) system used to map uplink to downlink.…”

Section: Introductionmentioning

confidence: 99%