论文标题
通过深厚的增强学习，扩散因子协助洛拉本地化
Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning
论文作者
论文摘要
大多数开发的本地化解决方案都依赖于RSSI指纹识别。但是，在洛拉网络中，由于网络设置中的扩展因子（SF），传统的指纹识别可能缺乏无线电图的代表性，从而导致位置估计不准确。因此，在这项工作中，我们提出了一种新颖的Lora RSSI指纹方法，考虑了SF。绩效评估显示了我们提出的方法的突出性，因为与最先进的方法相比，我们的定位准确性提高了高达6.67％。评估是使用完全连接的深神经网络（DNN）作为基线进行的。为了进一步提高本地化准确性，我们提出了一个深厚的增强学习模型，该模型捕获了洛拉网络和应对的不断增长的复杂性，并具有其可扩展性。获得的结果表明，与基线DNN模型相比，定位精度的提高了48.10％。
Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map, leading to inaccurate position estimates. As such, in this work, we propose a novel LoRa RSSI fingerprinting approach that takes into account the SF. The performance evaluation shows the prominence of our proposed approach since we achieved an improvement in localization accuracy by up to 6.67% compared to the state-of-the-art methods. The evaluation has been done using a fully connected deep neural network (DNN) set as the baseline. To further improve the localization accuracy, we propose a deep reinforcement learning model that captures the ever-growing complexity of LoRa networks and copes with their scalability. The obtained results show an improvement of 48.10% in the localization accuracy compared to the baseline DNN model.
