CSI of Each Subcarrier is a Fingerprint: Multi-Carrier Cumulative Learning Based Positioning in Massive MIMO Systems

Viewing channel state information (CSI) as a fingerprint to infer user position is a promising technology. Recently, with the help of deep learning (DL) methods, the performance of CSI-based positioning has been further improved. However, the performance of DL methods is highly dependent on the number of training samples. Thus, achieving high performance with limited training samples has become an important topic. Analyzing the prior properties of the task and introducing the prior into the neural network is an effective means. In this paper, by analyzing the physical correlation between CSI and position, we point out that the CSI of each subcarrier is a valid fingerprint of user position in a massive multiple-input multiple-output (MIMO) system. With this property, we propose a positioning scheme based on multi-carrier cumulative learning neural network (MCCNet). Instead of directly learning the mapping from the entire MIMO-orthogonal frequency division multiplexing (OFDM) CSI to position, MCCNet first learns to map from the CSI of each subcarrier to position and then accumulates the extracted features from each subcarrier for the final position inference. This a priori design makes the feature extraction from the entire CSI downsize to the CSI of each subcarrier, reducing the learning burden. Simulation experiments on line-of-sight and non-line-of-sight scenarios both show that compared to the existing methods, MCCNet can reduce the averaged position error by at least 53% with the same training samples or achieve the same performance with only a quarter of training samples.