• Energy Research

In this paper, we investigate how to apply industrial unmanned aerial vehicles (UAVs) for autonomous power line inspection in smart grid from an energy efficiency perspective. Firstly, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization and the small-timescale optimization. Then, the NP-hard joint optimization problem is transformed to a two- stage optimization problem based on energy consumption magnitude and optimization timescale differences. Next, the first-stage and second-stage problems are solved by exploring dynamic programming (DP) and auction matching, respectively. Finally, the proposed algorithm is verified based on realistic power grid topology. Simulation results demonstrate that the proposed scheme achieves significant energy consumption reduction.

In this work, we consider the practical issues of resource allocation problem in OFDMA two-way relay networks: the inaccuracy of channel-state information (CSI) available to the transmitters. Instead, only the estimated channel status is known by the transmitters. In this context, a joint optimization of subcarrier pairing and allocation, relay selection and transmit power allocation is formulated in the OFDMA two-way amplify-and-forward relay networks. Moreover, to ensure the Quality of Service (QoS) requirement, the energy consumption must be minimized without compromising the QoS. Therefore, by applying convex optimization techniques, energy efficient algorithms are developed with the objective to minimize the total transmit power while guaranteeing the required data rates. Through simulation studies, energy consumption performance of the systems under the proposed schemes are investigated. It is shown that the proposed scheme can improve the energy consumption performance without scarifying the QoS.

Most of the existed works on the radio resource allocation (RRA) problem commonly assume the channel-state information (CSI) can be perfectly obtained by the transmission source. However, such assumption is not practical in the realistic wireless systems. In this work, we consider the practical implementation issues of resource allocation in orthogonal frequency division multiple access (OFDMA) two-way relay networks: the inaccuracy of channel-state information (CSI) available to the source. Instead, only the estimated channel status is known by the source. In this context, a joint optimization of subcarrier pairing and allocation, relay selection, and transmit power allocation is formulated in OFDMA two-way amplify-and-forward relay networks. Moreover, the objective of this work is to minimize the energy consumption of the overall system. Further, to ensure the quality of service (QoS) or data rate requirement, the energy consumption must be minimized without compromising the QoS. Therefore, by applying convex optimization techniques, energy-efficient algorithms are developed with the objective to minimize the total transmit power with guaranteeing the required data rates. Through simulation studies, energy consumption performance of the systems under the proposed schemes is investigated. It can be observed that our proposed scheme can improve the energy consumption performance of the considered system.

In this paper, resource allocation for energy efficiency in heterogeneous Software Defined Network (SDN) with multiple network service providers (NSPs) is studied. The considered problem is modeled as a reverse combinatorial auction game, which takes different quality of service (QoS) requirements into account. The heterogeneous network selection associated with power allocation problem is optimized by maximizing the energy efficiency of data transmission. By exploiting the properties of fractional programming, the resulting non-convex Winner Determination Problem (WDP) is transformed into an equivalent subtractive convex optimization problem. The proposed reverse combinatorial auction game is proved to be strategy-proof with low computing complexity. Simulation results illustrate that with SDN controller, the proposed iterative ascending price algorithm converges in a small number of iterations and demonstrates the trade-off between energy efficiency and heterogeneous QoS requirement, especially ensures high fairness among different network service providers.

On the way toward enabling efficient content distribution, reducing energy consumption and prolonging battery life of mobile equipment, an emerging paradigm, i.e., mobile cloud, which is based on content distribution, was proposed. As a mobile platform that is oriented toward content distribution, mobile cloud is also foreseen as an energy-efficient solution for future wireless networks. The benefits of using CMC for content distribution or distributed computing from social networking perspectives have been studied earlier. In this article, we first present the concepts of CMC and then discuss the energy-efficiency benefits from the system-level point-of-view as well as open challenges in designing a green CMC. Moreover, we investigate the problem of forming mobile clouds for the purpose of content distribution and energy-efficiency. Specifically, given a group of users interested in downloading the same content from an operator, an energy-aware based user selection and scheduling algorithm is proposed. Simulation examples show that a significant energy-saving performance can be achieved without depleting the battery of any user equipment by the proposed scheme. Also, the research potential is discussed in the context of CMC.

This paper proposes an energy-efficient resource allocation scheme for a wireless power transfer (WPT) enabled multi-user massive MIMO system with imperfect channel estimation. In the considered system, the users who have data to be transmitted only can be empowered by the WPT in the downlink from a base station (BS) with a large scale of multiple antennas. The problem of optimizing the energy efficiency objective is formulated with consideration of beamforming design, antenna selection, power allocation and time division protocol based on the practical consideration, i.e., imperfect channel state information (CSI) at the BS. In particular, the proposed antenna selection scheme is to find the optimal number of antennas and then employ the energy beamforming. The nonlinear fractional programming based scheme is utilized to address optimization of energy efficiency and to find the optimal power and time allocation. Performance evaluation is presented to demonstrate the effectiveness of the proposed schemes.

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. peerReviewed

Unmanned aerial vehicle (UAV)-assisted wireless communication systems are able to provide high-quality services and ubiquitous connectivity for massive Internet of Things (IoT) devices. In this paper, we study the Age of Information (AoI) and energy tradeoff in a system where an employed UAV performs data collection for multiple IoT nodes (INs). Bearing in mind the importance of AoI and energy consumption during the data collection process, we present a multi-objective optimization problem to minimize the AoI and UAV energy consumption. To explore the tradeoff between AoI and energy consumption, we jointly optimize the collection time, the UAV trajectory, and the duration of time slots. Due to the non-convexity of the formulated problem, we divide the main problem into three sub-problems and address them by leveraging successive convex approximation (SCA) and Lagrangian dual methods. Finally, we design a multi-variable fixed algorithm to iteratively solve the three sub-problems. Simulations are carried out to investigate the tradeoff between AoI and UAV energy consumption, revealing that reducing AoI and energy consumption simultaneously is unattainable. Furthermore, the convergence and validity of the proposed algorithm are presented and analyzed. peerReviewed

When integrating device-to-device (D2D) communications with densely deployed cellular networks, both energy efficiency (EE) and quality of service (QoS) will be severely degraded by strong intracell and intercell interference. To optimize EE while guaranteeing QoS provisioning, a three-stage energy-efficient resource allocation algorithm is proposed, which combines centralized interference mitigation and distributed power allocation algorithms by exploiting multi-cell cooperations, nonco-operative game, nonlinear fractional programming, and Lagrange dual decomposition. Simulation results have demonstrated that the proposed algorithm achieves a nearly zero infeasibility ratio, and improves EE performance significantly for both cellular and D2D user equipments (UEs) compared to the previous distributed scheme.