论文标题
神经网络体系结构用于物联网中的位置估算
Neural Network Architectures for Location Estimation in the Internet of Things
论文作者
论文摘要
在许多实际情况下，用于无线位置估计的人工智能（AI）解决方案可能会占上风。在这项工作中，我们首次证明了Cramer-Rao上限如何在定位精度上限制可以促进有效的神经网络解决方案，以实现无线位置估计。特别是，我们演示了如何智能选择网络的神经元的数量，从而导致AI位置解决方案并不耗时运行，并且不太可能被过度拟合所困扰。提供了我们方法的实验验证。在许多情况下，我们的新算法直接适用于包括物联网以及车辆GPS坐标不可靠或需要验证的车辆网络。我们的工作代表了通信问题的第一个成功的AI解决方案，其神经网络设计基于基本信息理论结构。我们预计我们的方法对于超出位置估计的广泛沟通问题将很有用。
Artificial Intelligence (AI) solutions for wireless location estimation are likely to prevail in many real-world scenarios. In this work, we demonstrate for the first time how the Cramer-Rao upper bound on localization accuracy can facilitate efficient neural-network solutions for wireless location estimation. In particular, we demonstrate how the number of neurons for the network can be intelligently chosen, leading to AI location solutions that are not time-consuming to run and less likely to be plagued by over-fitting. Experimental verification of our approach is provided. Our new algorithms are directly applicable to location estimates in many scenarios including the Internet of Things, and vehicular networks where vehicular GPS coordinates are unreliable or need verifying. Our work represents the first successful AI solution for a communication problem whose neural-network design is based on fundamental information-theoretic constructs. We anticipate our approach will be useful for a wide range of communication problems beyond location estimation.
