Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ

Energy-Efficient Optimization for Wireless Information and Power Transfer in Large-Scale MIMO Systems Employing Energy Beamforming

In this letter, we consider a large-scale multiple-input multiple-output (MIMO) system where the receiver should harvest energy from the transmitter by wireless power transfer to support its wireless information transmission. The energy beamforming in the large-scale MIMO system is utilized to address the challenging problem of long-distance wireless power transfer. Furthermore, considering the limitation of the power in such a system, this letter focuses on the maximization of the energy efficiency of information transmission (bit per Joule) while satisfying the quality-of-service (QoS) requirement, i.e. delay constraint, by jointly optimizing transfer duration and transmit power. By solving the optimization problem, we derive an energy-efficient resource allocation scheme. Numerical results validate the effectiveness of the proposed scheme.Comment: 4 pages, 3 figures. IEEE Wireless Communications Letters 201

Low energy indoor network : deployment optimisation

This article considers what the minimum energy indoor access point deployment is in order to achieve a certain downlink quality-of-service. The article investigates two conventional multiple-access technologies, namely: LTE-femtocells and 802.11n Wi-Fi. This is done in a dynamic multi-user and multi-cell interference network. Our baseline results are reinforced by novel theoretical expressions. Furthermore, the work underlines the importance of considering optimisation when accounting for the capacity saturation of realistic modulation and coding schemes. The results in this article show that optimising the location of access points both within a building and within the individual rooms is critical to minimise the energy consumption

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial