DeepLoRa: Learning Accurate Path Loss Model for Long Distance Links in LPWAN
DeepLoRa: Learning Accurate Path Loss Model for Long Distance Links in LPWAN
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DOI：
10.1109/infocom42981.2021.9488784
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Li Liu;Yuguang Yao;Zhichao Cao;Mi Zhang
中科院分区：
文献类型：
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作者：
Li Liu;Yuguang Yao;Zhichao Cao;Mi Zhang
LoRa（长距离）是一种新兴的无线技术，可实现长距离通信并保持低功耗。因此，LoRa在低功耗广域网（LPWAN）中发挥着越来越重要的作用，LPWAN可以轻松扩展多种场景中的许多大规模物联网（IoT）应用（例如，工业、农业、城市）。在许多通常存在各种类型的土地覆盖的环境中，精确预测LoRa链路的路径损耗具有挑战性。因此，如何部署LoRa网关以确保可靠的覆盖并开发基于指纹的精确定位成为实践中的难题。在本文中，我们提出了DeepLoRa，这是一种基于深度学习的方法，可以准确估计复杂环境中长距离链路的路径损耗。具体来说，DeepLoRa依靠遥感自动识别LoRa链路沿着的土地覆盖类型。然后，DeepLoRa利用Bi-LSTM（双向长短期记忆）开发了一个土地覆盖感知的路径损耗模型。我们实施DeepLoRa，并使用从校园内真实的LoRaWAN部署中收集的数据，从估计准确性和模型可移植性方面广泛评估其性能。结果表明，DeepLoRa将估计误差降低到小于4 dB，比最先进的模型小2倍。
LoRa (Long Range) is an emerging wireless technology that enables long-distance communication and keeps low power consumption. Therefore, LoRa plays a more and more important role in Low-Power Wide-Area Networks (LPWANs), which easily extend many large-scale Internet of Things (IoT) applications in diverse scenarios (e.g., industry, agriculture, city). In lots of environments where various types of land-covers usually exist, it is challenging to precisely predict a LoRa link’s path loss. As a result, how to deploy LoRa gateways to ensure reliable coverage and develop precise fingerprint-based localization becomes a difficult issue in practice. In this paper, we propose DeepLoRa, a deep learning-based approach to accurately estimate the path loss of long-distance links in complex environments. Specifically, DeepLoRa relies on remote sensing to automatically recognize land-cover types along a LoRa link. Then, DeepLoRa utilizes Bi-LSTM (Bidirectional Long Short Term Memory) to develop a land-cover aware path loss model. We implement DeepLoRa and use the data gathered from a real LoRaWAN deployment on campus to evaluate its performance extensively in terms of estimation accuracy and model transferability. The results show that DeepLoRa reduces the estimation error to less than 4 dB, which is 2× smaller than state-of-the-art models.