Abstract
Over the last years positioning systems have become increasingly pervasive, covering most of the planet’s surface. Although they are accurate enough for a large number of uses, their precision, power consumption, and hardware requirements establish the limits for their adoption in mobile devices. In this paper, the energy consumption of a proposed deep learning-based millimeter wave positioning method is assessed, being subsequently compared to the state-of-the-art on accurate outdoor positioning systems. Requiring as low as 0.4 mJ per position fix, when compared to the most recent assisted-GPS implementations the proposed method has energy efficiency gains of $\mathbf {47\times }$ and $\mathbf {85\times }$ for continuous and sporadic position fixes (respectively), while also having slightly lower estimation errors. Therefore, the proposed method significantly reduces the energy required for precise positioning in the presence of millimeter wave networks, enabling the design of more efficient and accurate positioning-enabled mobile devices.
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