Joint Localization and Orientation with Triple-Beam Fingerprints in Massive MIMO-OFDM
For indoor localization in massive MIMO systems, this work addresses the joint estimation of position and motion direction, offering improved accuracy over existing fingerprint-based methods.
The paper proposes a triple-beam fingerprint (TBF) incorporating Doppler information and a Transformer-based network (LOA-Net) for joint localization and orientation estimation in massive MIMO-OFDM systems. In indoor simulations per 3GPP 38.901, the method achieves significantly better localization accuracy than WKNN, 2D/3D CNNs, and satisfactory direction estimation.
With the widespread application of location-based services, fingerprint-based localization has demonstrated advantages in environments with complex signal propagation. Deep learning has significantly improved the efficiency of both offline training and online matching in localization processes. However, existing fingerprints only contain terminal position information without capturing motion states, and neural network designs have not fully incorporated structural features such as fingerprint sparsity. In this paper, we propose a triple-beam fingerprint (TBF) incorporating Doppler information and design a Transformer-based localization and orientation awareness network (LOA-Net) to simultaneously estimate user position and motion direction in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first show the correlation between TBF and multipath information, and investigate the collinearity of different TBFs, demonstrating that TBF is an effective small-size sparse fingerprint. Then, we propose LOA-Net containing a mask-augmented detection Transformer for regression (MaskDETR-Reg) module and a fusion-enhanced Transformer for direction classification (Fusion-TDC) module to process angle-delay domain information and Doppler domain information, respectively. Finally, in the simulation of indoor scenarios defined in 3GPP 38.901, the proposed method achieves significantly better localization accuracy than weighted $K$-nearest neighbors (WKNN), 2D and 3D convolutional neural networks (CNNs), and achieves satisfactory motion direction estimation accuracy.