Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G

The introduction of 5G's millimeter wave transmissions brings a new paradigm to wireless communications. Whereas physical obstacles were mostly associated with signal attenuation, their presence now adds complex, non-linear phenomena, including reflections and scattering. The result is a multipath propagation environment, shaped by the obstacles encountered, indicating a strong presence of hidden spatial information within the received signal. To untangle said information into a mobile device position, this paper proposes the usage of neural networks over beamformed fingerprints, enabling a single-anchor positioning approach. Depending on the mobile device target application, positioning can also be enhanced with tracking techniques, which leverage short-term historical data. The main contributions of this paper are to discuss and evaluate typical neural network architectures suitable to the beamformed fingerprint positioning problem, including convolutional neural networks, hierarchy-based techniques, and sequence learning approaches. Using short sequences with temporal convolutional networks, simulation results show that stable average estimation errors of down to 1.78 m are obtained on realistic outdoor scenarios, containing mostly non-line-of-sight positions. These results establish a new state-of-the-art accuracy value for non-line-of-sight millimeter wave outdoor positioning, making the proposed methods very competitive and promising alternatives in the field.