Before revision:  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 .
After revision:  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 with average 24.1\% less computational overhead. Codes are available at GitHub .