Wireless Communications and Mobile Computing

Volume 2018, Article ID 7638215, 14 pages

https://doi.org/10.1155/2018/7638215

## Dynamic Wireless Energy Harvesting and Optimal Distribution in Multipair DF Relay Network with Nonlinear Energy Conversion Model

^{1}Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea^{2}Department of Telecommunication Engineering, FICT, Balochistan University of Information Technology, Engineering and Management Sciences, Pakistan^{3}Department of Computer Engineering, Federal University of Ceará, Sobral, CE, Brazil

Correspondence should be addressed to Min Young Chung; ude.ukks@gnuhcym

Received 14 February 2018; Revised 13 July 2018; Accepted 24 July 2018; Published 6 August 2018

Academic Editor: Enrico Natalizio

Copyright © 2018 Syed Tariq Shah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Wireless energy harvesting has emerged as an efficient solution to prolong the lifetime of wireless networks composed of energy-constrained nodes. In this paper, we consider a multipoint-to-multipoint relay network, where multiple source nodes communicate with their respective destination nodes via intermediate energy-constrained decode-and-forward (DF) relay. The performance of two different transmission modes, namely, delay tolerant and delay nontolerant, is studied. Based on power-splitting relaying protocol (PSR), optimal energy harvesting and distribution schemes for both transmission modes are provided. In addition, for more realistic and practical analysis, we consider a nonlinear energy conversion model for energy harvesting at the relay node. Our numerical results provide useful insights into different system parameters of a nonlinear energy harvesting-based multipair DF relay network.

#### 1. Introduction

Energy harvesting via radio-frequency (RF) signals has emerged as a groundbreaking technique to prolong the network lifetime. The idea is to extend the lifetime of the network via wireless energy harvesting instead of replacing their batteries or recharging the devices through conventional methods [1–3]. Although other ambient energy harvesting methods such as thermoelectric effects, solar, vibrations, and the wind can also be used to recharge the batteries [4, 5], these conventional techniques are not very reliable and highly variable [6]. From the perspective of RF energy harvesting, the main advantage is that RF signals can simultaneously carry both information and energy. Thus, the energy-constrained nodes in the network can scavenge energy and process the information at the same time [7, 8]. Note that, in a wireless energy harvesting enabled network, the nodes can harvest energy from both a dedicated RF source and an ambient RF source.

The idea of wireless energy harvesting offers a practical solution to extend the lifetime of energy constrained networks and also improve communication reliability. Due to these features, recent research works have widely studied its use in state-of-the-art next-generation technologies such as machine-to-machine communications (M2M), Internet of Things (IoT), MIMO, and 5G cellular networks [9–12]. Moreover, it is also well-known that relays can extend the coverage, improve quality-of-service (QoS), and improve capacity of networks by dividing the direct source-to-destination communication channel into two appropriate source-to-relay and relay-to-destination communication paths [13]. In conventional relay networks, relay node uses its own battery power to forward the information received from the source node. However, in the case of energy-constrained relay nodes, the network lifetime is significantly compromised. Luckily, recent advances and state-of-the-art technology in next-generation cooperative networks have paved the way for wireless energy cooperation between communicating nodes in which the idea is to power up the relay node through wireless energy harvesting [14, 15].

##### 1.1. Related Works

The concept to simultaneously transmit both information and energy was first proposed by Varshney in [2], in which the fundamental trade-off for the capacity-energy function was characterized under the assumption of an ideal energy harvesting receiver. However, in practice, it is impossible to design an ideal energy harvesting receiver. Thus, [16] proposed two practical approaches based on power-splitting (PS) and time-switching (TS) mechanisms. Based on [16], Nasir et al. in [17] proposed two different relaying protocols, namely, PS-based relaying protocol (PSR) and TS-based relaying protocol (TSR). The performance of both TSR and PSR was studied for amplify-and-forward (AF) relay based one-way RF energy harvesting relay network, and it was concluded that, at a high transmission rate and low signal-to-noise-ratio (SNR) regions, the TSR protocol performs significantly better than the PSR protocol. Because of the distinct features of the two relaying protocols, the throughput analysis of decode-and-forward (DF) and AF relaying networks is different under energy harvesting constraints [18]. The throughput analysis of a PSR, TSR, and a combined time-power switching relaying (TPSR) [19] protocol for two-way AF relay network was studied in [20, 21], in which the results showed that, at high SNR and low transmission rate, TPSR protocol outperforms TSR and PSR. On the other hand, at high transmission rates and low SNR, the TSR protocol outperforms the TPSR and PSR protocols.

A two-way relay network based on the denoise-and-forward relay with noncoherent differential binary phase-shift keying modulation has been studied in [22], while an energy harvesting and information processing network based on two-way multiplicative relay using PSR protocol was investigated in [23]. The numerical results showed that the proposed scheme outperforms the amplify-and-forward EH relaying technique. Ding et al. in [24] studied different power allocation strategies for power distribution in energy harvesting networks. In [25], Krikidis et al. studied the concept of energy transfer in a cooperative AF relay network based on wireless energy transfer, in which the performance of a greedy switching policy, where the relay node only transmits when its remaining power ensures decoding at the destination, was investigated. In addition, an optimal switching policy with global channel knowledge was also proposed, and it was shown that the greedy switching policy arises as an efficient solution. An outage probability analysis for energy harvesting multirelay networks is provided in [26]. The authors have studied two different relaying algorithms based on best relay selection and cooperative clustered-relying, and it has been shown that the former scheme outperforms the later in terms of outage probability. In [27], an energy harvesting-based weighed cooperative spectrum sensing scheme for cognitive radio networks is proposed. The authors have formulated a joint optimization problem to maximize the spectrum access probability of the secondary users by jointly optimizing the sensing time, a number of cooperative secondary users, and PS factor.

A geometric water-filling [28, 29] based on optimal power allocation scheme for cognitive radio (CR) multiple-input-multiple-output (MIMO) systems with energy harvesting capabilities was proposed in [30]. The authors showed that the proposed algorithm has finite computations with a low degree of polynomial computational complexity. In [31], an extended version of a geometric water-filling approach proposed in [29] was used to maximize the throughput and minimize the transmission completion time of a hybrid energy source system. Their considered hybrid energy source system consists of two energy sources: energy harvesting from the environment and energy from the power grid. It has been shown that the optimal power allocation can be achieved by adopting the proposed sequence of water-filling algorithms. A similar geometric water-filling based resource scheduling scheme for the cellular network has been studied in [32].

Eirini Eleni Tsiropoulou et al. in [10] have introduced a wireless energy harvesting-based concept of joint consideration of interest-, physical-, and energy-related properties in the clustering and resource management processes of M2M communication networks. In their proposed approach, the cluster-head supports its respective nodes to harvest and store energy in a stable manner via RF energy harvesting. The simulation results show that the proposed scheme significantly prolongs the operation of the overall M2M network. In [11], the authors have investigated the performance of ambient energy harvesting in a nonorthogonal multiple access (NOMA) based dual hope DF relay network. The relay nodes in the considered network are assumed to be energy constrained. With the help simulation results, the impact of relay selection under different successive interference cancellation (SIC) techniques is shown and it is concluded that the overall system performance is highly influenced by the efficiency of SIC techniques.

In [12], Vamvakas et al. have studied the issue of user-centric energy-efficient power management in an RF energy harvesting-based wireless sensor network. A low complexity, distributed, and adaptive energy transfer algorithm is proposed. The proposed algorithm aims to determine the optimal transmission power of power stations in the network. Their numerical results show that the proposed algorithm outperforms the existing state-of-the-art approaches in terms of network energy efficiency.

An interesting approach to extend the network lifetime via placing relay nodes at optimal locations is studied in [33]. The authors have addressed the problem of relay placement under the location constraint; i.e., the relay nodes can only be at set of candidate locations. The performance of an RF energy harvesting-based full duplex MIMO relay network is studied in [34]. With the help of numerical results it has been shown that the use of multiple antennas at both source and destination nodes can significantly improve the overall system performance. A joint optimization of positioning and routing for designing wireless body area sensor networks with traffic uncertainty is developed in [35].

##### 1.2. Objective and Contribution

The idea of relays for coverage extension in wireless sensor networks has been well established and widely accepted [36]. In cooperative wireless networks, the battery power of the cooperating nodes (such as relay nodes) is usually limited, and to actively perform their role in the network, these nodes may need to rely on an additional charging mechanism [11, 12]. In this paper, we propose a dynamic wireless energy harvesting and optimal power distribution scheme for a multipoint-to-multipoint energy harvesting-based DF relay network. The main contributions of the present paper are summarized as follows:(i)Unlike most of the works discussed in the previous section where, in a typical point-to-point relaying network, two source nodes exchange data via a relay node, this paper considers a network where multiple source nodes communicate with their respective destination nodes via intermediate energy-constrained DF relay.(ii)Since the relay is an energy-constrained node and has no energy of its own, it harvests energy from multiple received source signals and then distributes the harvested energy among all possible destination nodes. For energy harvesting at the relay node, we adopt PSR protocol [17].(iii)For an in-depth performance analysis of the proposed system model, two different transmission modes, namely, delay tolerant mode and delay nontolerant mode, are considered. For both of these transmission modes, we have proposed dynamic energy harvesting and optimal power distribution schemes. More specifically, in delay tolerant transmission mode, optimal energy is harvested and distributed by relay node for each individual source-to-destination link. In delay nontolerant transmission mode, the relay node dynamically harvests the energy from all source signals power based on their received signal-to-noise-ratio. Then this harvested energy is distributed among all possible destination nodes using the geometric water-filling technique [28]. More details about both of these transmission modes are provided in Section 4.(iv)Furthermore, unlike most of the previous studies [3–18, 37, 38], this paper considers a more realistic and practical approach of nonlinear energy efficiency for energy harvesting at relay [39].(v)With the help of numerical results, useful and detailed practical insights of our proposed scheme are provided.

##### 1.3. Organization

The remainder of the paper is organized as follows. The considered system model is presented in Section 2. The generalized procedures for information processing and energy harvesting using PSR protocol are explained in Section 3. The details on our proposed optimal power distribution and energy harvesting scheme for both delay tolerant and delay nontolerant transmission modes are provided in Sections 4. Section 5 provides the detailed discussion of numerical results. Finally, the paper is concluded in Section 6.

#### 2. System Model

We consider an RF energy harvesting-based multichannel multipair DF relay network as shown in Figure 1. In the proposed network, we define and as the sets of source and destination nodes in the network with cardinality and , respectively. In our proposed scheme, information is transmitted from source node (where ) to its respective destination node (), via an intermediate energy-constrained DF relay node R using orthogonal channels. It is assumed that there is no direct link between the source and destination nodes, and the respective SNRs of the channels between the communicating nodes are less than the minimum required threshold SNR for effective communication. Therefore, to assist the information transmission between communicating nodes, an intermediate relay node (R) is used [40].