The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication. With the growing complexity of CSI  in massive MIMO system , traditional compressive sensing based CSI feedback has become a bottleneck problem that is limited in piratical . Recently, numerous deep learning based CSI feedback approaches demonstrate the efficiency and potential. However, the existing methods lack a reasonable interpretation of the deep learning model and the accuracy of the model decreases significantly as the CSI compression rate increases. In this paper , from the intrinsic properties of CSI data itself, we devised the corresponding deep learning building blocks to compose a novel neural network CQNet and experiment result shows CQNet outperform the state-of-the-art method with less computational overhead by achieving an average performance improvement of 8.07 \% in both outdoor and indoor scenarios . In addition, this paper also investigates the reasons for the decrease in model accuracy at large compression rates and proposes a strategy to embed a quantization layer to achieve effective compression, by which the original accuracy loss of 67.19\% on average is reduced to 21.96\% on average, and the compression rate is increased by 8 times on the original benchmark .
The Massive Multiple Input Multiple Output (MIMO) system is a core technology of the next generation communication. With the growing complexity of CSI , CSI feedback in massive MIMO system has become a bottleneck problem, the traditional compressive sensing based CSI feedback approaches have limited performance . Recently, numerous deep learning based CSI feedback approaches demonstrate their efficiency and potential. However, most existing methods improve accuracy at the cost of computational complexity and the accuracy decreases significantly as the CSI compression rate increases. This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI . The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41 \% in both outdoor and indoor scenarios with average 24.1\% less computational overhead. Codes are available at GitHub .
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