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

Energy efficient geographic routing for wireless sensor networks.

A wireless sensor network consists of a large number of low-power nodes equipped with wireless radio. For two nodes not in mutual transmission range, message exchanges need to be relayed through a series of intermediate nodes, which is a process known as multi-hop routing. The design of efficient routing protocols for dynamic network topologies is a crucial for scalable sensor networks. Geographic routing is a recently developed technique that uses locally available position information of nodes to make packet forwarding decisions. This dissertation develops a framework for energy efficient geographic routing. This framework includes a path pruning strategy by exploiting the channel listening capability, an anchor-based routing protocol using anchors to act as relay nodes between source and destination, a geographic multicast algorithm clustering destinations that can share the same next hop, and a lifetime-aware routing algorithm to prolong the lifetime of wireless sensor networks by considering four important factors: PRR (Packet Reception Rate), forwarding history, progress and remaining energy. This dissertation discusses the system design, theoretic analysis, simulation and testbed implementation involved in the aforementioned framework. It is shown that the proposed design significantly improves the routing efficiency in sensor networks over existing geographic routing protocols. The routing methods developed in this dissertation are also applicable to other location-based wireless networks

A Study of Medium Access Control Protocols for Wireless Body Area Networks

The seamless integration of low-power, miniaturised, invasive/non-invasive lightweight sensor nodes have contributed to the development of a proactive and unobtrusive Wireless Body Area Network (WBAN). A WBAN provides long-term health monitoring of a patient without any constraint on his/her normal dailylife activities. This monitoring requires low-power operation of invasive/non-invasive sensor nodes. In other words, a power-efficient Medium Access Control (MAC) protocol is required to satisfy the stringent WBAN requirements including low-power consumption. In this paper, we first outline the WBAN requirements that are important for the design of a low-power MAC protocol. Then we study low-power MAC protocols proposed/investigated for WBAN with emphasis on their strengths and weaknesses. We also review different power-efficient mechanisms for WBAN. In addition, useful suggestions are given to help the MAC designers to develop a low-power MAC protocol that will satisfy the stringent WBAN requirements.Comment: 13 pages, 8 figures, 7 table