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Massive multiple-input multiple-output (MIMO) is one of the key techniques toachieve better spectrum and energy efficiency in 5G system. The channel stateinformation (CSI) needs to be fed back from the user equipment to the basestation in frequency division duplexing (FDD) mode. However, the overhead ofthe direct feedback is unacceptable due to the large antenna array in massiveMIMO system. Recently, deep learning is widely adopted to the compressed CSIfeedback task and proved to be effective. In this paper, a novel network namedaggregated channel reconstruction network (ACRNet) is designed to boost thefeedback performance with network aggregation and parametric rectified linearunit (PReLU) activation. The practical deployment of the feedback network inthe communication system is also considered. Specifically, the elastic feedbackscheme is proposed to flexibly adapt the network to meet different resourcelimitations. Besides, the network binarization technique is combined with thefeature quantization for lightweight and practical deployment. Experiments showthat the proposed ACRNet outperforms loads of previous state-of-the-artnetworks, providing a neat feedback solution with high performance, low costand impressive flexibility.

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