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Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G

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Abstract

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.

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Notes

  1. Typical approaches rely on pseudo-random sequences [20], round-trip delays [21], and/or cross-correlations [22] (e.g. in [6], the PDPs were gathered through a correlation method).

  2. Even though typical civilian GNSS receivers have an average accuracy of 3 m, the proliferation of systems similar to Japan’s Quasi-Zenith Satellite System will enable sub-meter accuracies in particular areas. Moreover, there are known DL techniques to deal with noisy labels, such as in [27].

  3. https://github.com/gante/mmWave-localization-learning.

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Acknowledgements

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/CEC/50021/2019, UID/EEA/50008/2019, and PTDC/EEI-HAC/30485/2017, as well as FCT Grant No. FRH/BD/103960/2014.

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Authors and Affiliations

  1. INESC-ID, IST, Universidade de Lisboa, Lisbon, Portugal

    João Gante & Leonel Sousa

  2. Instituto de Telecomunicações, University of Coimbra, Coimbra, Portugal

    Gabriel Falcão

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  1. João Gante
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Correspondence to João Gante.

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Gante, J., Falcão, G. & Sousa, L. Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G. Neural Process Lett 51, 487–514 (2020). https://doi.org/10.1007/s11063-019-10073-1

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