Variational Bayesian Autoencoder for Channel Compression and Feedback in Massive MIMO Systems

In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and t...

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Veröffentlicht in:IEEE International Conference on Communications (2003) S. 6349 - 6354
Hauptverfasser: Zheng, Xuanyu, Bi, Yuanyuan, Guo, Huayan, Lau, Vincent
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.05.2023
Schlagworte:
Costs
CSI compression
deep learning
Knowledge engineering
Massive MIMO
Probability distribution
Rate-distortion
Simulation
Training
variational Bayesian autoencoder
ISSN:1938-1883
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Zusammenfassung:In this paper, we propose a Variational Bayesian Autoencoder (VBA)-based channel state information (CSI) compression and feedback scheme for massive multiple-input multiple-output (MIMO) systems. The proposed scheme incorporates the model-assisted knowledge of low-dimensional feedback features and the sparsity of channel to achieve enhanced compression efficiency. We also design a CsiVBA architecture that outputs distributions of the feedback features and the channel at the encoder and decoder, respectively, which facilitates a Bayesian training formulation exploiting the underlying channel sparsity. In addition, we also propose a low-complexity training scheme for new networks of different bit rates, significantly reducing the retraining cost for new compression requirements. Simulation results show that the proposed scheme achieves better rate-distortion trade-offs than the state-of-the-art solutions.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10278811