Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains. However, in a frequency division duplex (FDD) massive MIMO system, the huge CSI feedback overhead becomes restrictive and degrades the overall spectral efficiency. In this paper, we propose a deep learning based channel state matrix compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. Simulation results demonstrate that DeepCMC significantly outperforms the state of the art compression schemes in terms of the reconstruction quality of the channel state matrix for the same compression rate, measured in bits per channel dimension.
The architecture in REF , which was composed of convolutional layers followed by quantization and entropy coding blocks, presented promising performance.
Deep Convolutional Compression For Massive MIMO CSI Feedback
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)
Engineering Computer Science Mathematics