• Energy Research

In this paper, we investigate the energy harvesting (EH) technique and accordingly design transceivers for a $K$ link multiple-input multiple-output interference channel. Each link consists of two full-duplex (FD) Internet of Things (IoT) nodes exchanging information simultaneously in a bi-directional communication channel. All the nodes suffer from interference, in particular strong self-interference and inter-node interference, due to operating in FD mode and simultaneous transmission at each link, respectively. Further, we divide the received signal at each node into two parts. While one part of the signal is used for information decoding, the other part is used for EH. We jointly design the transmit and receive beamforming vectors and receiver power splitting ratios by minimizing the total transmission power of the system, subject to both signal-to-interference-plus-noise ratio and EH threshold constraints. Furthermore, the case of multiple-input single-output interference channel is also included for the sake of comparison. We also revisit the above problems for the case when the available channel state information (CSI) at the transmitters is imperfect, where the errors of the CSI are assumed to be norm bounded. Simulation results show that the EH technique can harvest enough energy to support power consumption limited IoT devices by aiding in recharging their respective batteries.

In this paper, we analyze a backscatter network that implements simultaneous wireless information and power transfer. The backscatter devices (BDs) in the network work as relays that receive RF signals from the source, harvest the energy and use it to reflect the signal to the receiver. We assume a non-linear energy harvesting (EH) circuit that takes into account the sensitivity and non-linearity of the electronic components. We formulate an optimization problem to maximize the achievable rate at the receiver of this network. By considering both amplify-and-forward and decode-and-forward modes, we propose algorithms to jointly optimize the power-splitting ratio and the reflection coefficient of the BDs. Simulation results demonstrate the effectiveness of the proposed algorithms with respect to baseline relay networks. In particular, the impact of the number of BDs, source transmit power, and distance of the BDs from the source and destination on the average rate and energy efficiency of the network are illustrated.

The Internet-of-Things (IoT) framework has been considered as an enabler of the smart world where all devices will be deployed with extra-sensory power in order to sense the world as well as communicate with other sensor nodes. As a result, smart devices require more energy. Therefore, energy harvesting (EH) and wireless power transfer (WPT) emerge as a remedy for relieving the battery limitations of wireless devices. In this work, we consider a multi-user amplify-and-forward (AF)-assisted network, wherein multiple source nodes communicate with destination nodes with the help of a relay node. All the source nodes and the relay node have the capability of EH. In addition, to cope with a single point of failure i.e., failure of the relay node due to the lack of transmit power, we consider the WPT from the source nodes to the relay node. For WPT, a dedicated energy control channel is utilized by the source nodes. To maximize the sum rate using a deadline, we adopt a joint approach of power allocation and WPT and formulate an optimization problem under the constraints of the battery as well as energy causality. The formulated problem is non-convex and intractable. In order to make the problem solvable, we utilize a successive convex approximation method. Furthermore, an iterative algorithm based on the dual decomposition technique is investigated to get the optimal power allocation and transfer. Numerical examples are used to illustrate the performance of the proposed iterative algorithm.

We examine the deployment of a large multi-user multiple-input multiple-output (MIMO) system and the resulting Energy Efficiency (EE) considering a 2D rectangular array with increasing antenna elements within a fixed physical space. The resulting increasing mutual coupling and correlation among the base station (BS) antennas are incorporated by deriving a practical mutual coupling matrix which considers coupling among all antenna elements. We also provide a realistic analysis of the energy consumption using a new model, taking into account the circuit power consumption as a function of the number of BS antennas and then present a performance analysis of the system with respect to EE. Our analysis shows that while spectral efficiency increases with increasing number of BS antennas in a massive MIMO system, EE does not increase boundlessly when the increasing number of antennas are to be accommodated within a fixed physical space and the total power consumed is considered to be a function of the BS antennas. Accordingly, analytic expressions for the optimum number of antennas to attain maximum EE are obtained.

Algorithms for joint subcarrier pairing and power allocation are investigated in order to maximize the worst-case energy efficiency (EE) in dual-hop decode-and-forward (DF) relay networks in the presence of an active eavesdropper. Accordingly, we study the impact of number of subcarriers on the trade-off in performance between the EE and the spectrum efficiency (SE). The formulated EE optimization problem is the ratio of the secure SE over the entire power consumption in the network, subject to the constraints of total transmit power and subcarrier pairing. A near-optimal iterative algorithm is proposed to perform the subcarrier pairing and power allocation for achieving the maximum EE in the networks. Furthermore, a suboptimal algorithm is proposed with two-step resource allocation. By considering the subcarrier channel quality of the source-to-relay and relay-to-destination links, the subcarrier pairing is first performed, followed by an energy-efficient iterative power allocation scheme to maximize the EE. Numerical results validate the effectiveness and correctness of the proposed algorithms.