PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

Abstract

In a frequency division duplexing multiple-input multiple-output (FDD-MIMO) system, the user equipment (UE) send the downlink channel state information (CSI) to the base station for performance improvement. However, with the growing complexity of MIMO systems, this feedback becomes expensive and has a negative impact on the bandwidth. Although this problem has been largely studied in the literature, the noisy nature of the feedback channel is less considered. In this paper, we introduce PRVNet, a neural architecture based on variational autoencoders (VAE). VAE gained large attention in many fields (e.g., image processing, language models, or recommendation system). However, it received less attention in the communication domain generally and in CSI feedback problem specifically. We also introduce a different regularization parameter for the learning objective, which proved to be crucial for achieving competitive performance. In addition, we provide an efficient way to tune this parameter using KL-annealing. Empirically, we show that the proposed model significantly outperforms state-of-the-art, including two neural network approaches. The proposed model is also proved to be more robust against different levels of noise.

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

“…Recently, as deep learning technologies have received attention for their powerful performance in extracting the principal components of data, there have been many efforts to use this capability for CSI compression [13][14][15][16][17][18] and estimation [19][20][21][22][23]. The autoencoder model is widely used in this field since it best fits the problem context.…”

“…A novel CSI compression scheme, CSINet [13], uses a convolutional autoencoder to solve the CSI compression problem by turning it into a typical 2D image compression problem. Along this line, numerous variants of CSINet have been developed so far [14][15][16][17][18]. RecCsiNet [15] and CSINET-LSTM [16] incorporate LSTM structures into the existing autoencoder model to benefit from the temporal and frequency correlations of wireless channels.…”

“…RecCsiNet [15] and CSINET-LSTM [16] incorporate LSTM structures into the existing autoencoder model to benefit from the temporal and frequency correlations of wireless channels. The authors of PRVNet [17] employ a variational autoencoder to create generative models for wireless channels. In DUalNet [18], the channel reciprocity is utilized for CSI feedback in FDD scenarios.…”

“…A rich body of literature focuses on utilizing the spatial channel correlation used in multi-antenna communication for the same purpose [10][11][12]. Recently, as deep learning technologies have received attention for their superior ability to extract the principal components of data, there have been many efforts to apply them to CSI compression [13][14][15][16][17][18] and estimation [19][20][21][22][23]. In particular, in this field, autoencoders are commonly employed: A receiver compresses CSI data with the encoder of an autoencoder, and the transmitter reconstructs the original CSI using the decoder.…”

“…A novel CSI compression scheme, CSINet [13], is built on a convolutional autoencoder, where the authors regard the CSI compression problem as a typical 2D image compression task. Along this line, numerous variants of CSINet have been developed for various purposes [14][15][16][17].…”