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Performance analysis of energyharvestingaware multirelay networks in Nakagamim fading
EURASIP Journal on Wireless Communications and Networking volume 2018, Article number: 63 (2018)
Abstract
This paper investigates the outage performance of an energyharvesting(EH) relayaided cooperative network, where the source node transmits information to its destination node with the help of multiple energyharvesting cooperative nodes. For such a system, we derive an explicit closedform expression of outage probability over Nakagamim fading channels, where the onoff EH model is considered. We simulate the explicit closedform expression of outage probability with different parameters compared with the related Monte Carlo method. It is shown that the simulation results match well with the numerical ones, which demonstrates the correctness of our theoretical analysis. It indicates that to evaluate the system outage performance, our theoretical results can be applied without running the complex Monte Carlo simulation. Via simulations, it is observed that the system outage performance will be improved when natural source EH relays are employed. Besides, more relays, better system performance.
Introduction
Energyconstrained wireless networks, such as sensor networks, are typically powered by batteries, which have limited operation time. Although replacing and recharging the batteries artificially can prolong the lifetime of the network, it is with high cost in practice, especially in largescale networks with hundreds or thousands nodes. Besides in a hazardous environment (e.g., in toxic environments) or for sensors embedded in building structures or inside human bodies, it is also impossible to replace the batteries periodically.
Recently, energy harvesting, which can collect energy (such as solar energy, wind energy, and even radiofrequency signal) from renewable resource in the ambient environment, has become a promising solution to prolong the lifetime of energyconstrained wireless networks [1–6]. It is considered as an appealing approach to power wireless node, especially lowpower devices.
Relayaided wireless networks exploit special diversity to improve system performance [7–9]. To exploit these networks’ advantages, relay nodes harvesting energy from ambient environments have been investigated. For example, in [10], the authors designed a wireless communication device relying exclusively on energy harvesting. The work of [11] focused on the performance analysis of an energyharvesting relayaided cooperative network under slow fading channel from a perspective of outage behavior and deriving the closedform expression of outage probability of the proposed cooperative protocol. In [12], an amplifyandforward (AF) relaying network was studied, where an energyconstrained relay node harvests energy from the received RF signal and uses that harvested energy to forward the source information to the destination. Based on the time switching and power splitting receiver architectures, two relaying protocols were proposed to enable energy harvesting and information processing at the relay [13, 14].
Outage probability, as one of the most commonly used performance metric in wireless cooperative system over the fading channel, has attracted considerable research interests in the past decades [15, 16]. In [17], an opportunistic relaying with the AF strategy was presented and the outage probability was discussed under Rayleigh fading channels. In [18], outage probability of dualhop decodeandforward (DF) relaying scheme was analyzed over mixed Rayleigh and generalized Gamma fading channels. In [19], the authors analyzed the outage probability of opportunistic AF orthogonal frequencydivision multiplexing (OFDM) relaying over Nakagamim fading channel and presented a closedform outage probability of the proposed system at high signaltonoise (SNR) regime. In [20], the paper analyzed the performance of twoway amplifyandforward relaying over Nakagamim frequencyselective fading channels taking consideration of the multipath spread of the channel. In [21], the authors presented a general twoparameter received SNR model for twohop AF relaying.
Although lots of important results have been presented in the above existing works, there still some fundamental issues to be studied for multirelay EH systems [22, 23]. For example, the works of [11] and [12] focused on the research of outage probability with harvesting energy relayaided cooperative networks on the Rayleigh channel, but did not explore it over the Nakagamim fading channnel. The work of [17] and [18] focused on the outage probability of relayaided networks over Rayleigh channel with AF and DF schemes, respectively, but they did not consider the energy harvesting from an ambient environment. The works of [19, 20], and [21] analyzed the performance of twohop AF relaying over Nakagamim fading channels, but did not consider on energy harvesting from an ambient environment.
In this paper, we consider a relayaided cooperative wireless network, where relay nodes are able to harvest energy from ambient environment. An onoff EH model is assumed and Nakagamim fading channels is assumed, as it is a more general channel model, where the Rayleigh model can be consider as a special case of it. Our aim is to study the outage probability.
Our contributions are as follows. We analyze the outage performance of an energyharvesting relayaided cooperative network in Nakagamim fading channel and derive an explicit closedform expression of outage probability for the onoff relayaided cooperative protocol with the help of the Markov property of energy buffer status. Monte Carlo simulations demonstrate the correctness of our derived outage probability express. It is also shown that the outage performance can be significantly improved especially in high SNR regime, and the more the workable relayaided nodes take part in relay aiding, the better system performance can be got.
The rest of the paper is organized as follows. Section 2 reports about model of the system and the onoff energyharvesting model. Section 3 presents the performance analysis of outage probability. Section 4 presents the numerical results and validates the analytical results through Monte Carlo simulations. Finally, section 5 concludes the paper.
System model
Network model
Consider an energyharvesting relayaided systems where the source node S, communicates with its destination node D with the help of energyharvesting relayaided nodes R_{ k } (1≤κ≤K), where the direct link between S and D exists in the system as illustrated in Fig. 1.
The basebandequivalent discrete time mode for the channels S−D is assumed; therefore, the signal received over the direct link can be expressed by
where N denotes the whole signal block length, and y_{s,d}[n] denotes the received signal at D. h_{s,d}[n] and n_{ d }[n] denotes the channel fading coefficient and additive noise, respectively. P_{ s } denotes source node S’s transmission power.
For the relay node, AF relay operation is employed. Provided that there is harvesting energy used for transmitting current signal block at R_{ k }, information will be transmitted from S to R_{ k } first and then to D from R_{ k }. In order to avoid interference between links S−R_{ k } and R_{ k }−D, halfduplex transmission is assumed, and each signal block is divided into two subphases. In the first subphase, information is transmitted from S to R_{ k }, which is given by
where \(y_{s,r_{k}}[\!n]\), \(n_{r_{k}}[\!n]\), and \(h_{s,r_{k}}[\!n]\) denotes the received signal, additive noise at R_{ k }, and the fading coefficient between S and R_{ k } respectively. Then, the received information is forwarded by R_{ k } to D in the second subphase, which is expressed by
where \(x_{r_{k}}[n]\), P_{ r }, and \(h_{r_{k},d}[n]\) denotes the transmitted signal, the transmit power at R_{ k }, and the channel fading coefficient between R_{ k } and D respectively.
Onoff energyharvesting model
At first, we consider the situation that the relay R_{ k } does not have an energy storage device; it can only work at the current block with the energy E_{ i }, and we assume that E_{ r } is the minimal energy, that is, each relay node activate itself and forward signal. If the energy E_{ i } is less than E_{ r }, transmission outage will occur.
Secondly, we consider the situation where the relay R_{ k } has energy storage with capacity E _{ max }, and S_{ i } denote the value of energy stored at R_{ k } at the beginning of ith signal block, and O_{ i } denotes the energy consumption for ith signal block transmission. Thus, O_{ i } can be expressed as a stationary twovalue random function, i.e.,
where factor K represents the numbers of relay nodes who will be waked up with equal probability. Then, we get the expression of S_{i+1} based on the aforementioned assumptions as
Considering the possible energy consumption flow at R_{ k } from a statistical perspective, which is called as an onoff model and denoted by \(\acute {S_{i}}\). Namely, if S_{ i }≥P_{ r }, P_{ r } can retransmit the signal successfully and the status of \(\acute {S_{i}}\) is defined as on,which is equivalent to the sum set of all the possible original statuses satisfying S_{ i }S_{ i }≥P_{ r }. Otherwise, the status of \(\acute {S_{i}}\) is defined as off, so the transmission outage will occur. Therefore,
and
where f_{ s }(x) denotes the stationary probability distribution of S_{ i }.
That is the relayaided link will wake up if and only if the direct link is too bad, and the current available energy at the relay node is more than E_{ r }, the energyharvesting module at R_{ k } can be characterized by the parameter pair (E_{ r },R_{ k }), we can also say that the energyharvesting module can provide an energy flow that is less than E_{ r } with the probability \(p_{k}^{ex}\).
Outage behavior analysis
Problem analysis
Due to fluctuation of ambient renewable energy, the relay nodes cannot harvest enough energy to maintain working status. So, the outage event will occur.
According to aforementioned descriptions, that is to say, if the direct link channel is good, or the relay link’s energy is more about E_{ r }, and the relay link channel is good, the system can work well. We can express the overall successful communication events as
Correspondingly, if the direct link channel is not good, at the same time, the relay link channel is not good, or the relay link node’s energy is small than E_{ r }, the system outage will occur. The overall outage events can be expressed as
Assume the energyharvesting modules owned by the relay nodes are independent from each other. The overall outage probability in (9) following the transmission protocol can be given by
where \(P_{sd}^{\mathrm {{(out)}}}\) denotes the outage probability of the direct link can not work, \(P_{sr_{k}d}^{\mathrm {{(out)}}}\) denotes the outage probability of the relay link is not good or the energy of the relay node is small than E_{ r }, and \(p_{k}^{ex}\) is the probability of relayaided link do not have enough energy to active itself to work, which have been discussed on the previous chapter.
In order to calculate ptotal(out), we have a detail analysis about \(P_{sd}^{\mathrm {{(out)}}}\) and \(P_{sr_{k}d}^{\mathrm {{(out)}}}\) in the next step.
Calculation of \(P_{sd}^{\mathrm {{(out)}}}\)
In direct transmission scenario, only the direct link between S and D is selected as a working link where all relay nodes do not work, and all the time slots in a signal block are used by the direct link.
The mutual information for the direction link of S−D achieved by zeromean circularly symmetric complex Gaussian input is defined as:
where \(\gamma =\frac {P_{s}}{N_{D}}\) denotes SNR, and for h_{0}=h_{ sd }^{2}, h_{0} follows the Nakagamim distribution, whose PDF and outage probability is expressed as respectively
Assume that the minimum acceptable rate equals to R_{0}, the outage probability \(P_{sd}^{\mathrm {{(out)}}}[R_{s,d}<R_{0}]\) under direct transmission protocol can be expressed as
where \(\gamma _{th}^{sd}=\frac {2^{R_{0}}1}{\gamma }\).
Calculation of \(P_{sr_{k}d}^{\mathrm {{(out)}}}\)
For cooperative relay system [24, 25], if someone relay aided a link with enough energy among K’s relay, it will be selected as a work node on the condition that the direct link cannot provide information transmission because of deep fading. The cooperative relay works as

Step 1:
the system prefers to consider the direct link to transmit information with. If h_{s,d}^{2} is bigger than the threshold value, the direct link shown in (1) will be selected to work for transmitting information. And the relay link will not need to be set up. Otherwise, go to Step 2.

Step 2:
some relay link among K’s, as shown in (2) and (3), will be selected and activated to work instead of a direct link. If it also fails, then there will be an outage event occurred.
Since the relay node R_{ k } is deployed to amplifyandforward relay scheme. Considering the relationship between received and signal \(y_{s,r_{k}}(t)\) and retransmitted signal \(x_{r_{k}}(t)\) at R_{ k }, as shown in (2) and (3), we can get
Correspondingly, the destination D’s received instantaneous SNR [21] can be express as
Then we can get the mutual information for the S−R_{ k }−D link as
Given γ_{ eq } in (15), which is the random variable denoting the received SNR at destination node D, then we can derive exact expressions for the CDF of γ_{ eq }. And the CCDF of γ_{(eq)} is \(\bar {F}_{\gamma _{eq}}(\gamma)\) [26, 27]
So, CDF of γ_{(eq)} is \(F_{\gamma _{eq}}(\gamma)\), which is given by
System overall outage probability
Substituting (13) and (19) into (10), the explicit closedform expression of outage probability for the relayaided cooperative transmission protocol by the harvested energy can be explained as follows
Simulation results
This section studies how the outage probability changes with different parameters. It is assumed that the available frequency bandwidth for the whole network is W=2 Mhz, and the minimum acceptable rate R_{0}=200 kbps, P_{ s }=P_{ r }=P_{0}=1w. Monte Carlo simulations were carried out by 2×10^{8} sample points with intel(R) Core(TM) i76700HQ, 4 cores CPU@ 2.60 GHz and 16.0 GB RAM.
In Fig. 2, K=1. It shows the outage probability of cooperative networks as a function of SNR \(\phantom {\dot {i}\!}\left (\text {i.e.}, SNR=P_{0}/\sigma _{0}^{2}\right)\) when the energyexhausted probability \(P_{1}^{ex}\) at R is 1, 0.1, 0.01, and 0 with m_{ sd }=1, \(m_{s_{r}k}=1\), and \(m_{r_{k}d}=1\). Obviously, when \(P_{1}^{ex}=1\), it is equivalent to the traditional system with direct transmission protocol. In addition, when \(P_{1}^{ex}=0\), it is equivalent to the system in which the relay node is powered by a constant power system. At last, we can observe in Fig. 2 that the energyharvesting relay node can improve the outage performance of the system significantly compared with traditional direct transmission protocol, which has no relay link.
In Fig. 3, K=1. It shows the outage probability of cooperative networks as a function of SNR(i.e.,\(SNR=P_{0}/\sigma _{0}^{2}\)) when the energyexhausted probability \(P_{1}^{ex}\) at R is 1, 0.1, 0.01 and 0, respectively with m_{ sd }=1,\(m_{s_{r}k}\)=1,\(m_{r_{k}d}\)=1, it is coordinated to the curve of the work [11] at the Rayleigh channels parameters.
In Fig. 4, K=1. It shows the outage probability of cooperative networks as a function of SNR\(\left (\text {i.e.}, SNR=P_{0}/\sigma _{0}^{2}\right)\) when the energyexhausted probability \(P_{1}^{ex}\) at R is 1, 0.1, 0.01, and 0, with \(m_{sd}=3, m_{s_{r}k}=3, m_{r_{k}d}=3\phantom {\dot {i}\!}\), Similarly, when \(P_{1}^{ex}=1\), it is equivalent to the traditional system with direct transmission protocol. In addition, when \(P_{1}^{ex}=0\), it is equivalent to the system in which the relay node is powered by constant power system. At last, we can observe in Fig. 4 that the energyharvesting relay node can improve the outage performance of the system significantly compared with traditional direct transmission protocol during the concerned SNR range. But comparing with the results from Fig. 2, we can find that the downward trend is more fast with the increasing of SNR. That is to say, with the increment of relay link, we can get better performance comparing with the scenario in the Fig. 2, which has one relay link only.
In Fig. 5, it shows the outage probability of cooperative networks as a function of SNR when the number of available relay nodes K is 0, 1, 2, 3, respectively with m_{ sd }=3, \(m_{s_{r}k}=3\), \(m_{r_{k}d}=3\), and \(\phantom {\dot {i}\!}\left (P_{1}^{ex},P_{2}^{ex}\ldots P_{N}^{ex}\right)=0.1\). It can be observed that the system performance can be significantly improved with the increment of the number of available relay nodes K.
In Fig. 6, it shows the outage probability of cooperative networks as a function of K when the SNR is 15, 10, and 5 dB, respectively with \(m_{sd}=3, m_{s_{r}k}=3, m_{r_{k}d}=3\phantom {\dot {i}\!}\), and \(\phantom {\dot {i}\!}\left (P_{1}^{ex},P_{2}^{ex}\ldots P_{N}^{ex}\right)=(0.1,0.1\ldots,0.1)\). It can be observed that the system performance can improved with the linear increment of the SNR. In Table 1, it shows that the numerical calculate time is more less than the Monte Carlo time, the theoretical results can be applied without running the complex Monte Carlo simulation in the practical application.
Conclusions
In this paper, we presented the performance of cooperative networks aided by an onoff Markov model energyharvesting relay node in Nakagamim fading scenario. With the help of some approximations, we got the explicit closedform expression of outage probability of cooperative system based on the nonidentical Nakagamim fading channels. The result of Monte Carlo method correlated with numerical analytical results reported by previous studies. Via simulations, it was shown that the system outage performance have been improved when natural source EH relays were employed. Besides, the more relays, the better system performance.
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Acknowledgements
This work was supported by the National Science Foundation of China(NSFC), no. 61672217,61304208, and also by the Foundation of the Science and technology project of Hunan Provincial Department of Education, no. 15C1414.
Funding
This paper was supported by the National Natural Science Foundation general projects, China (No. 61672217), by the National Natural Science Foundation general projects, China (No. 61304208), and also by the Foundation of the Science and technology project of Hunan Provincial Department of Education (No. 15C1414).
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SZ has fulfilled all the system modeling, analysis, simulation, and drafting the article. RL has helped revise the manuscript. HH has given critical revision of the article and has helped revise the manuscript. All authors read and approved the final manuscript.
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Correspondence to Renfa Li.
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Zhong, S., Huang, H. & Li, R. Performance analysis of energyharvestingaware multirelay networks in Nakagamim fading. J Wireless Com Network 2018, 63 (2018). https://doi.org/10.1186/s1363801810504
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Keywords
 Energy harvesting
 Nakagamim fading channel
 Outage probability
 Relayaided cooperative networks