MixerNet: Deep Learning for Eigenvector-Based CSI Feedback

Abstract

Deep learning (DL) methods have been widely used for channel state information (CSI) feedback to reduce the feedback overhead. CSI feedback mainly includes full CSI feedback and the eigenvector-based CSI feedback. This paper focuses on the eigenvector-based CSI feedback and designs a DL-based approach, referred to as MixerNet, where the joint eigenvector composed of multiple subbands is first compressed by an encoder at the transmitter and then recovered by a decoder at the receiver. On the other hand, the compressed information should be quantized before being transmitted to the decoder, therefore uniform quantization (UQ) and vector quantization (VQ) are respectively studied to improve the system performance. Experiment results indicate that the designed MixerNet could recover CSI with high reconstruction quality, however has fewer trainable parameters and lower computation complexity compared with existing DL-based methods. Moreover, VQ method in the MixerNet outperforms UQ method in terms of CSI reconstruction quality.