Deep Learning-Based Multi-User Positioning in Wireless FDMA Cellular Networks

In Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV), accessing multiple users and providing high-precision positioning are both vital. This paper aims to design an efficient deep learning approach to extend current Channel State Information (CSI)-based positioning to Frequency Division Multiple Access (FDMA) mode. In FDMA mode, different users are allocated with different subcarriers, making the user CSI have diverse frequency domain characteristics. The diverse frequency domain characteristics bring huge interference to the neural network for stable position inference, and efficient designs are required to handle this challenge. This paper proposes a novel approach named multi-frequency fusion learning for CSI-based positioning. By first using a shareable method to extract position-related features from CSI on each subcarrier independently and then fusing the obtained features, the designed neural network obtains excellent frequency domain flexibility to cope with the diverse frequency address challenge in FDMA mode. Meanwhile, we provide the feasibility analysis of this learning approach in massive Multiple-Input Multiple-Output (MIMO) systems to ensure its stable application. Based on the architecture of multi-frequency fusion learning, we propose two specific positioning schemes with differentiated designs. One is a Multi-Frequency Ensemble Network (MFENet), which extracts and fuses frequency-independent features to ensure the network is utterly unharmed by the complicated frequency domain characteristics. The other is a Multi-Frequency Cumulative Network (MFCNet), which uses sufficient feature accumulation to achieve high precision positioning. The key performance indices and applications on vehicles are comprehensively compared with popular deep-learning methods. Experiment results show the effectiveness and superiority of the proposed schemes.