PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

Hussien, Mostafa; Nguyen, Kim Khoa; Cheriet, Mohamed

doi:10.36227/techrxiv.13204007.v1

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The CsiNet+ improves the performance but it significantly increases the complexity due to the increase in the convolutional kernel size. Other researchers proposed autoencoder-based CSI feedback to improve the accuracy of the CSI feedback with feedback errors [14][15][16]. However, the work of [11][12][13][14][15][16] focused on reducing the amount of feedback data in the periodic feedback.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers proposed autoencoder-based CSI feedback to improve the accuracy of the CSI feedback with feedback errors [14][15][16]. However, the work of [11][12][13][14][15][16] focused on reducing the amount of feedback data in the periodic feedback. Moreover, the conventional ML structures are too complicated to be used in IoT devices because IoT devices have lightweight processors and small memory capacities.…”

Section: Introductionmentioning

confidence: 99%

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Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Han

Kim

2023

Sensors

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As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Han

Kim

2023

Sensors

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LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks

Lee¹

2022

Computers, Materials &Amp; Continua

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Recently, as deep learning technologies have received much attention for their great potential in extracting the principal components of data, there have been many efforts to apply them to the Channel State Information (CSI) feedback overhead problem, which can significantly limit Multi-Input Multi-Output (MIMO) beamforming gains. Unfortunately, since most compression models can quickly become outdated due to channel variation, timely model updates are essential for reflecting the current channel conditions, resulting in frequent additional transmissions for model sharing between transceivers. In particular, the heavy network models employed by most previous studies to achieve high compression gains exacerbate the impact of the overhead, eventually cancelling out the benefits of deep learning-based CSI compression. To address these issues, in this paper, we propose Lightweight CSI Feedback (LCF), a new lightweight CSI feedback scheme. LCF fully utilizes autoregressive Long Short-Term Memory (LSTM) to generate CSI predictions and uses them to train the autoencoder, so that the compression model could work effectively even in highly dynamic wireless channels. In addition, 3D convolutional layers are directly adopted in the autoencoder to capture diverse types of channel correlations in three dimensions. Extensive experiments show that LCF achieves a lower CSI compression error in terms of the Mean Squared Error (MSE), using only about 10% of the overhead of existing approaches.

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PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

Cited by 2 publications

References 13 publications

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks

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