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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 …