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.

中文翻译：

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取消 GPS：使用机器学习的低功耗精确 5G 定位系统

在过去几年中，定位系统变得越来越普遍，覆盖了地球的大部分表面。尽管它们对于大量使用来说足够准确，但它们的精度、功耗和硬件要求限制了它们在移动设备中的采用。在本文中，评估了所提出的基于深度学习的毫米波定位方法的能耗，随后将其与精确户外定位系统的最新技术进行了比较。每次定位需要低至 0.4 mJ，与最近的辅助 GPS 实现相比，所提出的方法的能效增益为 $\mathbf {47\times }$ 和 $\mathbf {85\times }$ 用于连续和零星的位置固定（分别），同时估计误差也略低。因此，所提出的方法在存在毫米波网络的情况下显着降低了精确定位所需的能量，从而能够设计出更高效、更准确的支持定位的移动设备。