Transformer-Based Fingerprint Positioning for Multi-Cell Massive MIMO-OFDM Systems

Abstract

In this paper, we investigate user terminal (UT) fingerprint positioning for multi-cell massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in non-line-of-sight scenario. We first introduce a refined double beam-based channel model to the positioning problem and extract a energy coupling matrix in the refined beam domain as location-related fingerprint. By taking advantage of refined spatial and frequency beams, the new fingerprint (i.e., energy coupling matrix), contains plentiful and stationary multi-path information, such as power, angle of arrival (AoA), and delay of arrival (DoA), which are favorable to positioning. We then propose a novel deep learning-based fingerprint positioning method to locate the 2-dimension (2D) position of UTs, utilizing multi-BS fingerprint as the input. In particular, we propose a new deep neural network (DNN) architecture in this paper. The DNN first introduce a new network architecture to the fingerprint positioning problem, Transformer, based solely on self-attention mechanisms to sequences of fingerprint patches directly. And it can perform outstandingly on the 2D position coordinates regression. Simulation results show that the proposed positioning method can outperform the existing methods in terms of positioning error.