Channel State Information Compression based on Projection Transformation and Curve Fitting

In the Internet of Things (IoT), with the development of channel state information (CSI) based wireless sensing technologies, such as intrusion detection, activity recognition, and indoor localization, a tremendous amount of CSI data need to be transmitted simultaneously, resulting in unacceptable time delay and requirement for additional bandwidth. Therefore, this paper proposes a CSI compression algorithm based on projection transformation and curve fitting (PCFIT), which realizes high-compression-ratio transmission and high-accuracy reconstruction. Concretely, projection matrixes are firstly constructed to perform projection transformation on the original CSI. Next, an adaptive weighted average fitting order judgment algorithm is designed to get the fitting order. And then, the Levenberg-Marquardt (LM) algorithm is used to perform curve fitting on the CSI based on the linear combination of a few sine waves to obtain the fitting parameters. Finally, CSI is reconstructed with these fitting parameters. By analyzing compression indicators such as residual, compression ratio, and parameter estimation accuracy, experimental results show that the PCFIT compression algorithm achieves a better compression ratio than other existing compression algorithms under the same residual and parameter estimation accuracy.