Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer

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

A Framework for Enhancing the Energy Efficiency of IoT Devices in 5G Network

A wide range of services, such as improved mobile broadband, extensive machine-type communication, ultra-reliability, and low latency, are anticipated to be delivered via the 5G network. The 5G network has developed as a multi-layer network that uses numerous technological advancements to provide a wide array of wireless services to fulfil such a diversified set of requirements. Several technologies, including software-defined networking, network function virtualization, edge computing, cloud computing, and tiny cells, are being integrated into the 5G networks to meet the needs of various requirements. Due to the higher power consumption that will arise from such a complicated network design, energy efficiency becomes crucial. The network machine learning technique has attracted a lot of interest from the scientific community because it has the potential to play a crucial role in helping to achieve energy efficiency. Utilization factor, access latency, arrival rate, and other metrics are used to study the proposed scheme. It is determined that our system outperforms the present scheme after comparing the suggested scheme to these parameters