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

Five decades of hierarchical modulation and its benefits in relay-aided networking

Hierarchical modulation (HM), which is also known as layered modulation, has been widely adopted across the telecommunication industry. Its strict backward compatibility with single-layer modems and its low complexity facilitate the seamless upgrading of wireless communication services. The specific features of HM may be conveniently exploited for improving the throughput/information-rate of the system without requiring any extra bandwidth, while its complexity may even be lower than that of the equivalent system relying on conventional modulation schemes. As a recent research trend, the potential employment of HM in the context of cooperative communications has also attracted substantial research interests. Motivated by the lower complexity and higher flexibility of HM, we provide a comprehensive survey and conclude with a range of promising future research directions. Our contribution is the conception of a new cooperative communication paradigm relying on turbo trellis-coded modulation-aided twin-layer HM-16QAM and the analytical performance investigation of a four-node cooperative communication network employing a novel opportunistic routing algorithm. The specific performance characteristics evaluated include the distribution of delay, the outage probability, the transmit power of each node, the average packet power consumption, and the system throughput. The simulation results have demonstrated that when transmitting the packets formed by layered modulated symbol streams, our opportunistic routing algorithm is capable of reducing the transmit power required for each node in the network compared with that of the system using the traditional opportunistic routing algorithm. We have also illustrated that the minimum packet power consumption of our system using our opportunistic routing algorithm is also lower than that of the system using the traditional opportunistic routing algorithm

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

A review of relay network on UAVS for enhanced connectivity

One of the best evolution in technology breakthroughs is the Unmanned Aerial Vehicle (UAV). This aerial system is able to perform the mission in an agile environment and can reach the hard areas to perform the tasks autonomously. UAVs can be used in post-disaster situations to estimate damages, to monitor and to respond to the victims. The Ground Control Station can also provide emergency messages and ad-hoc communication to the Mobile Users of the disaster-stricken community using this network. A wireless network can also extend its communication range using UAV as a relay. Major requirements from such networks are robustness, scalability, energy efficiency and reliability. In general, UAVs are easy to deploy, have Line of Sight options and are flexible in nature. However, their 3D mobility, energy constraints, and deployment environment introduce many challenges. This paper provides a discussion of basic UAV based multi-hop relay network architecture and analyses their benefits, applications, and tradeoffs. Key design considerations and challenges are investigated finding fundamental issues and potential research directions to exploit them. Finally, analytical tools and frameworks for performance optimizations are presented