Joint Localization and Orientation Assisted by Massive MIMO-OFDM Triple-Beam Fingerprints

Wireless communication systems offer strong potential for accurate localization, and deep-learning-based fingerprinting has shown good adaptability in complex propagation environments. However, existing fingerprints are limited to static location features, and current neural network designs do not fully utilize their structural properties. To improve this, we introduce a dynamic triple-beam fingerprint (TBF) for massive multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems and develop a localization and orientation awareness network (LOA-Net) designed to align with its sparse structural characteristics for joint localization and orientation estimation. We analyze the advantages of TBF for localization and orientation awareness through channel modeling. Localization is formulated as a regression task and enhanced with a masking mechanism, while orientation estimation is modeled as a multi-class classification problem using the estimated coordinates as prior information. Simulations in standard 3GPP scenarios demonstrate the high localization accuracy and the potential for effective orientation awareness.