Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility. This work was supported in part by the National Key R&D Program of China under Grant 2017YFE0112300. The authors are with the Department of Electronic Engineering, Tsinghua University, and Beijing National Research Center for Information Science and Technology (BNRist), Beijing 100084, China. Massive multiple-input multiple-output (MIMO) is widely regarded as one of the key techniques in the fifth-generation wireless communication system [1]. With larger antenna array, massive MIMO is able to boost both spectrum and energy efficiency [2]. The downlink channel state information (CSI) needs to be obtained at the base station (BS) so that the MIMO system can acquire the performance gain with beamforming [3]. In frequency division duplexing (FDD) mode, downlink CSI is usually estimated at the user equipment (UE) and fed back to the BS due to the channel non-reciprocity. However, the dimension of CSI matrix is sharply increased in massive MIMO system.