Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points

Wi-Fi channel state information (CSI) provides adequate information for recognizing and analyzing human activities. Because of the short distance and low transmit power of Wi-Fi communications, people usually deploy multiple access points (APs) in a small area. Traditional Wi-Fi CSI-based human activity recognition methods adopt Wi-Fi CSI from a single AP, which is not very appropriate for a high-density Wi-Fi environment. In this article, we propose a learning method that analyzes the CSI of multiple APs in a small area to detect and recognize human activities. We introduce a deep learning model to process complex and large CSI from multiple APs. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi APs exist

Learning Human Activities through Wi-Fi Channel State Information with Multiple Access Points

Wi-Fi channel state information (CSI) provides adequate information for recognizing and analyzing human activities. Because of the short distance and low transmit power of Wi-Fi communications, people usually deploy multiple access points (APs) in a small area. Traditional Wi-Fi CSI based human activity recognition methods adopt Wi-Fi CSI from a single AP, which is not so appropriate for a high-density Wi-Fi environment. In this paper, we propose a learning method that analyzes the CSI of multiple APs in a small area to detect and recognize human activities. We introduce a deep learning model to process complex and large CSI information from multiple APs. From extensive experiment results, our method performs better than other solutions in a given environment where multiple Wi-Fi APs exist.特

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig