# Outage Performance of Full-Duplex Relay Networks Powered by RF Power Station in Ubiquitous Electric Internet of Things

Authors: Yu Zhang; Xiaofei Di; Hongliang Duan; Xi Yang; Peng Wu; Baoguo Shan
Journal: Wireless Communications and Mobile Computing
 (2021)

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## Abstract

This paper investigates a full-duplex relay network assisted by radio frequency (RF) power station in ubiquitous electric Internet of Things (UEIoT), where a wireless sensor on high voltage transmission line collects electric data and then transmits data to a destination via a full-duplex decode-and-forward relay. The destination receives and processes the data to inspect the state of high voltage transmission line. Both the sensor and relay do not have fixed energy source; so, they first have to harvest energy from a RF power station and then complete information transmission by using the harvested energy. As the line-of-sight (LOS) propagations often exist in outdoor application scenarios of UEIoT, the Rician fading channel is adopted in the network. To explore the system outage performance, the explicit expressions of the outage probability and throughput of the system are derived by using mathematical analysis. Simulation results validate the correctness of our theoretical analysis and also evaluate the effects of system parameters and deployment position of power station on the outage performance.


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## Body

## 1. Introduction

The ubiquitous electric Internet of Things (UEIoT) aims to realize the interconnection of all things and human-computer interaction in the power system by integrating modern information technologies, including wireless sensor networks, artificial intelligence, and advanced communication technologies [1]. The State Grid Corporation of China has planned to initially build UEIoT by 2021 to support the development of grid business and some other emerging businesses. By 2024, UEIoT will be established to form an energy internet ecosystem with joint construction, cogovernance, and sharing [2, 3]. UEIoT has attracted much attention from academia to industry [4, 5].The UEIoT can be applied to a large number of outdoor and field application scenarios, such as field power line inspection. Unfortunately, although wireless sensors are deployed on the high-voltage line, they cannot be driven directly by the power of the high-voltage line. So, recently passive Internet of Things [6] has attracted attention and become an important part of UEIoT, where the sensors can be wirelessly charged by harvesting energy of the radio frequency (RF) signals emitted by wireless power stations [7–9]. The authors in [7] realized a practical wireless powered sensor network and investigated the efficiency of RF energy harvesting (EH). In [8, 9], an IoT powered by a static and mobile base station was, respectively, studied, where a number of wireless sensors first harvested energies from base station and then transmit their information to the base station. The works in [6–9] considered the linear EH model, while [10, 11] further considered a more practical nonlinear EH model. In [10], a wireless powered cognitive radio sensor network was studied, where two primary users communicate to each other with the help of a sensor node with nonlinear energy harvester and accurate expressions of the outage probability and throughput were derived for the primary system. [11] considered a downlink nonorthogonal multiple access (NOMA) network, where the near user was equipped with a nonlinear energy harvester, and the outage probabilities and throughput for both the far and near users were derived.On the other hand, to complete information delivery, wireless sensors generally communicate by using relaying mode due to the long distance among sensors in IoT [12]. Compared with traditional half-duplex relay, full-duplex relay can use the same frequency band to simultaneously receive and transmit information; so, the transmission delay and system spectrum efficiency are greatly improved [13, 14]. Owing to these merits, full-duplex relay has attracted increasing interest [15–17]. In [15], an optimal beamformer design was proposed for a full-duplex decode-and-forward (DF) relay system with self-energy recycling. [16] extended the one-way relay system in [15] to two-way relay one and analyzed outage performance of the system. The authors in [17] studied effect of residual self-interference (SI) on the stability performance of the full-duplex amplify-and-forward (AF) relay system.To inherit the benefits of RF EH and full-duplex relaying, some exiting works have investigated the full-duplex relay networks powered by RF EH, see e.g., [18–21]. In [18], some full-duplex AF relays first harvested energy from a source and then received and forwarded the information of the source to a destination by using the harvested energy. The achievable information rate was maximized by designing optimal power allocation and relay selection policy [18]. The authors in [19] further studied two-way relay system and derived closed-form expressions of the throughput and outage probability over Rayleigh fading channels. In [20], a multidestination relay network was considered, where a source first emitted energy to a full-duplex DF relay and then the relay further used the harvested energy to forward the information of the source to multiple destinations, and the spectral efficiency of system was enhanced by jointly optimizing the time allocation, subcarriers, and transmission power of the source and relay. Different from the works [18–21] considered a relay network consisting of a source, a DF relay and a destination, as well as a power station, where the source and relay first harvested energy from the power station and then cooperatively transmit information to the destination.This paper also considers a full-duplex relay network assisted by a dedicated power station in the UEIoT, as shown in Figure1. A wireless sensor (S) on high voltage transmission line collects electric data and then transmits data to a base station (D) via a dual antenna full-duplex DF relay (R). D receives and processes the data to inspect the state of high voltage transmission line. Both S and R do not have fixed energy source; so, they first have to harvest energy from a RF power station and then complete information transmission by using the harvested energy. Different from [21], the Rician fading channel model is better suited in outdoor roadside scenario with RF energy harvesting, according to the experimental result [22]. As UEIoT is usually deployed in outdoor roadside scenario, the Rician fading channel model is adopted in this paper.Figure 1
System model of full-duplex relay network in UEIoT.For such a network, our contributions are summarized as follows. Firstly, the EH process and information transmission are modeled for the full-duplex relay network powered by RF power station. Then, the explicit expressions of the outage probability and throughput of the system are derived by using mathematical analysis in order to explore the system outage performance. Simulation results validate the correctness of our theoretical analysis, and the effects of system parameters on the outage probability are also discussed. Finally, the optimal deployment position of power station is also investigated, which may provide some useful insights on the future practical applications of UEIoT.The rest of this paper is organized as follows. In section2, the system model is presented. Section 3 derives the outage probability and throughput of the system. Section 4 shows some simulation results to evaluate our theoretical analysis and illustrate the system performance. Finally, this paper is concluded in section 5.


## 2. System Model

We consider a full-duplex relay network assisted by RF power station (PS) in UEIoT, as shown in Figure1, which is composed of a source (S), a relay (R), and a destination (D), as well as a dedicated RF PS. S desires to transmit information to D with the help of R. S and R do not have fixed energy source; so, they have to harvest energy from PS and then use the harvested energy to accomplish information transmission. R adopts the decode-and-forward (DF) protocol. As the decoding processing power required at R is much less than its transmit power, its decoding processing power is thus ignored. PS, S, and D are equipped with single antenna. R is equipped with two antennas, one is the receiving antenna used to receive information from S or harvest energy from PS, and the other is the transmitting antenna used to forward received information to D in the full-duplex mode.Figure2 illustrates the time frame of the transmission protocol. It is assumed that each time frame is with the time period of T, which is divided into two parts, where the first part is used for energy transfer and the second one is for information transmission with full-duplex relaying. Considering there exist line-of-sight (LOS) propagations between the nodes in the outdoor UEIoT, the links between any two nodes are modeled as Rician fading channels. The channel coefficients are assumed to be unchanged in each time frame T, but may change independently between any two adjacent time frames.Figure 2
Time frame of the transmission protocol.Specifically, during the first part with time interval ofαT, PS broadcasts energy to S and R, where α is the time division factor satisfying 0≤α≤1. The energy harvested by S and R can be, respectively, expressed by
(1)ES=αTηPBd1−mh12,(2)ER=αTηPBd2−mh22,where h1 and h2 are the channel coefficients of the links from PS to S (PS-S) and from PS to R (PS-R), respectively. d1 and d2 denote the distances between PS and S and between PS and R, respectively. PB denotes the transmit power of PS. m represents the path loss exponent. η is the energy harvesting efficiency satisfying 0≤η≤1.During the second part with time interval of1−αT, by using the harvested energies, S and R cooperatively transmit information to D with the full-duplex mode; that is, S transmits information to R, and R simultaneously receives information of S and forwards the information to D. From (1) and (2), the available transmit power at S and R can be, respectively, expressed by
(3)PS=ES1−αT=ηαPBd1−mh121−α,(4)PR=ER1−αT=ηαPBd2−mh221−α.LetxS and xR, respectively, represent the signals transmitted by S and R, with ExS2=PS and ExR2=PR. nR denotes the additive Gaussian white noise (AWGN) with the mean of 0 and the variance of N0 at the relay node R. The signal received at R can be expressed as
(5)yR=d3−mh3xS+hRRxR+nR,where the first term of right part d3−mh3xS is the desired signal, and the second term hRRxR is the self interference (SI). h3 represents fading coefficient of the link from S to R (S-R). hRR represents the channel coefficient of the link between the transmitting antenna and receiving antenna of R. d3 is the distance between S and R.R adopts self-interference cancellation (SIC) technology to eliminate SI. However, it is hard to completely eliminate SI in practice, and therefore, the residual self-interference (RSI) always exists [23], which may limit the system performance of full-duplex mode. In order to describe the RSI, denote it as nRR, and then the signal received at R can be transformed into [24]
(6)yR=d3−mh3xS+nRR+nR,where nRR is modeled as a complex Gaussian distribution with mean of 0 and variance of NRSI [25]. NRSI is proportional to the transmit power PR of R, that is,
(7)NRSI=ΩPR,where Ω represents the SIC capability and indicates the strength of the self-interference cancellation capability of relay. The smaller the value Ω is, the stronger the relay SIC capability is.Simultaneously,R also decodes and forwards signal to D while receiving the signal from S. The signal received at D can be expressed as
(8)yD=d4−mh4xR+nD,whereh4 denotes the fading coefficient of the link from R to D (R-D), and d4 represents the distance between R and D. nD represents the AWGN with the mean of 0 and variance of N0 at D.Following (6), the received signal-to-interference and noise ratio (SINR) for decoding at R can be given by
(9)γR=d3−mPSh32NRSI+N0.By substituting (3) and (7) into (9), and using some simple mathematical transformations, we can obtain that
(10)γR=d1−md3−mφ1φ3Ωd2−mφ2+1/Φ,where Φ=ηαγ0/1−α with γ0=PB/N0, and φi=hi2i=1,2,3,4.Similarly, by using (4), (7), and (8), and after some mathematical transformations, the received signal-to-noise ratio (SNR) at D can be given by
(11)γD=Φd2−md4−mφ2φ4.


## 3. Outage Performance Analysis

In this section, we analyze the outage probability of the system. AsR adopts DF protocol, it will decode the information of S and forward the information to D if the information is correctly decoded. Let γth be the required SINR for successful transmissions for both S-R link and R-D link. Then, the outage probability of the system can be expressed by
(12)Pout=PrγR<γth+1−PrγR<γthPrγD<γth,where PrγR<γth is the outage probability of the first hop (i.e., S-R link), and PrγD<γth is the outage probability of the second hop (i.e., R-D link) . For convenience, PrγR<γth and PrγD<γth are represented as Pout_R and Pout_D, respectively.DefineRth (in bps/Hz) as a target transmission rate threshold. According to the Shannon formula, we can have that
(13)Rth=1−αlog21+γth,and thus γth can be calculated as γth=2Rth/1−α−1.To calculatePout_R and Pout_D, firstly it can be seen that there are two multiplied random variables φi in (10) and (11). Following the Rician distribution, the probability density function (PDF) fφix and cumulative probability function (CDF) Fφix of the random variable φi are, respectively, given by [26]
(14)fφix=Ki+1e−Kiλi∑l=0∞Ki+1λilKilxll!2e−Ki+1λi·x,(15)Fφix=∫0xfφixdx=1−e−Ki∑l=0∞∑m=0lKilBimxmm!l!e−Bix,where Bi=Ki+1/λii=1,2,3,4. Ki and λi represent the Rician factor and the mean of random variables φi, respectively.In order to facilitate the derivation and analysis, define an auxiliary variableYij=φiφj, and the CDF of Yij can be expressed as
(16)FYijy=PrYij<y=Prφiφj<y=Prφi<yφj=∫0∞Fφiyφjfφjφjdφj.By using (14) and (15), the CDF of Yij in (16) can be further calculated as
(17)FYijy=∫0∞1−e−Ki∑l=0∞∑m=0lKilBiml!m!.yφjm.e−Bi.yφjfφjφjdφj=1−∫0∞e−Ki∑l=0∞∑q=0lKilBiql!q!yφjqe−Bi.yφjAje−Bjφj∑k=0∞BjKjkk!2·φjkdφj=1−2e−Ki+Kj∑l=0∞∑k=0∞∑q=0lKilKjkl!q!k!2BiBjyk+q+12Kk−q+12BiBjy,where Aj=Bje−Ki and Kν· are the modified Bessel function of the second kind [27].Then, by using (10) and (17), the outage probability of the first hop can be derived as
(18)Pout_R=Prd1−md3−mφ1φ3ΩBd2−mφ2+1/Φ<γth=PrY13<γthΩBd2−mφ2+1/Φd1−md3−m=∫0∞fφ2φ2FY13γthΩBd2−mφ2+1/Φd1−md3−mdφ2≜1−Δ,where
(19)Δ=2A2e−K1+K3∑q=0∞∑l=0∞∑k=0∞∑z=0lB2K2qK1lK3kl!z!k!2q!2·∫0∞φ2qe−B2φ2B1B3γthΩBd2−mφ2+1/Φd1−md3−mk+m+12·Kk−z+12B1B3γthΩd2−mφ2+1/Φd1−md3−mdφ2.Similarly, by using (11) and (17), the outage probability of the second hop can be derived as
(20)Pout_D=FY24γthΦd2−md4−m≜1−Θ,where
(21)Θ=2e−K2+K4∑p=0∞∑t=0∞∑n=0pK2pK4tp!n!t!2B2B4γthΦd2−md4−mt+n+12Kt−n+12B2B4γthΦd2−md4−m.Substituting (18) and (20) into (12), we can obtain the outage probability of system as
(22)Pout=PrγR<γth+1−PrγR<γthPrγD<γth=1−Δ+1−1−Δ1−Θ=1−ΔΘ.So, by substituting (19) and (21) into (22), the outage probability of the system is expressed as
(23)Pout=1−4A2e−K1+K2+K3+K4∑q=0∞∑l=0∞∑k=0∞∑z=0lB2K2qK1lK3kl!z!k!2q!2·∫0∞φ2qe−B2φ2B1B3γthΩd2−mφ2+1/Φd1−md3−mk+z+12·Kk−z+12B1B3γthΩd2−mφ2+1/Φd1−md3−mdφ2·∑p=0∞∑t=0∞∑n=0pK2pK4tp!n!t!2B2B4γthΦd2−md4−mt+n+12Kt−n+12B2B4γthΦd2−md4−m.Finally, the system throughput for a delay limited scenario can be expressed as(24)R=Rth1−Pout1−α.


## 4. Simulation Result

This section presents some simulation results to verify our obtained theoretical results and demonstrate the system performances. For clarity, the simulation parameters are listed in Table1. In the figures of the section, the marker of Ana ± denotes the numerical results calculated by the derived closed expression, the marker of Sim ± denotes the simulation results obtained by Monte Carlo method, and Appr ± denotes the numerical asymptote in high SNR regime. In the following, we will discuss the effect of system parameters and PS position on the system performance, respectively.Table 1
System simulation parameter table.
ParameterExplanationDefault value/value rangeηEnergy harvesting efficiency0.8/0-1λiMean ofφi1diDistance3 mmPath loss coefficient2ΩSIC capability-20 dBRthTransmission rate0.05 bits/s/Hz/0.01-0.5 bits/s/HzαTime division factor0.4/0-1γ0Power station transmit power and noise power ratioPB/N00-50 dBKiRician factor2/0-4


### 4.1. The Effect of System Parameters on Performance

Figures3 and 4 show the effect of the ratio of the PS transmit power to the noise power (i.e., γ0=PB/N0) on the system outage probability and throughput with different values of the Rician factor, respectively. From Figures 3 and 4, it can be seen that the results calculated by the closed expression match the results obtained by Monte Carlo simulation well, which demonstrates the correctness of our obtained theoretical results.Figure 3
The system outage probability versusPB/N0 for different Rician factor K.Figure 4
The system throughput versusPB/N0 for different Rician factor K.From Figures3 and 4, one can also find that for a fixed Rician factor K, the greater the value of γ0 is, the smaller the system outage probability becomes, and the larger the system throughput becomes. This is because with the increment of γ0 for given N0, the energy harvested by S and R increases, which yields higher SINR of both R and D. It can also be found that for fixed γ0, the greater the value of the Rician factor is, the lower the outage probability of the system becomes, and the larger the system throughput becomes. The reason is that the larger Rician factor means the stronger LOS, which increases the energy harvested by both S and R. Finally, from Figure 3, it can be seen that the outage probability gradually approaches an numerical asymptote in high SNR regime. This indicates that increasing the transmit power of PS cannot infinitely decrease the outage probability of the system.Figures5 and 6 show the effects of the time division factor α on the system outage probability and throughput for different values of γ0, respectively. The results calculated by the closed expression fit well with those obtained by Monte Carlo simulation, confirming that the derived closed-form solution of the system outage probability is completely accurate again. One can see that for a fixed α, the larger γ0 is, the lower outage probability is and the larger throughput is, which agrees with Figures 3 and 4.Figure 5
The system outage probability versus time switching factorα for different γ0.From Figure5, one can see that for a fixed γ0, with the increment of the value of α, the outage probability decreases first and then increases. On one hand, when the value of α is close to 0, the time allocated for energy harvesting is relatively short. In this case, the less energy can be harvested by S and R and therefore, the outage probability of system is close to 1. On the other hand, when the value of α approaches to 1, the time assigned for information relaying transmission becomes very short, and thus the outage probability of system tends to be 1. In addition, it can be found that there is an optimal value of α, which makes the outage probability of system reach the lowest. For example, for γ0=30, when α is equal to about 0.6, the system achieves the best outage performance.From Figure6, one can also see that for a fixed γ0, with the increment of the value of α, the system throughput increases first and then decreases. The reason is as follows. When the value of α is close to 0, S and R do not have energy to transmit information, while when the value of α is close to 1, S and R do not have time to transmit information. Moreover, it can be found that there is an optimal value of α, which makes the system throughput reach the largest. For example, for γ0=30, when α is equal to about 0.08, the system achieves the best throughput performance. Finally, by comparing Figure 6 with Figure 5, it can be found that the optimal values of α are different for the best outage probability and throughput.Figure 6
The system throughput versus time switching factorα for different γ0.Figures7 and 8 discuss the effect of transmission rate threshold Rth on the system outage probability and throughput with respect to γ0, respectively. It can be seen that for fixed transmission rate threshold Rth, as the value of γ0 increases, the outage probability gradually decreases, and the throughput gradually increases, and they tend to be stable, which is consistent with the results in Figures 3 and 4. Moreover, from Figure 7, one can observe that for a fixed γ0, the smaller the value of Rth is, the lower the value of system outage probability becomes. This is because when Rth becomes smaller, γth=2R/1−α−1 also becomes smaller, which further causes lower outage probability. Finally, from Figure 8, it can found that the larger the value of Rth is, the larger the system throughput becomes. The reason is that from (24) one can see that with the decrement of system outage probability, the throughout gradually increases.Figure 7
The system outage probability versusPB/N0 for different transmission rate threshold Rth.Figure 8
The system throughput versusPB/N0 for different transmission rate threshold Rth.


### 4.2. The Effect of Power Station Position on Performance

In order to discuss the effect of power station position on performance, we consider a coordinate system, as shown in Figure9. S is located at the origin of coordinate, R is located at the positive X-axis, and their distance d3 is set to be 10 m. D is located in the fourth quadrant and its distance d4 from R may be set to 5 m, 10 m, 20 m, or 30 m. PS moves at a straight line whose distance h from the X-axis is set to be 10 m. The X-axis coordinate of PS is denoted by x, which is used to represent the position of PS. Besides, the ratio of PS transmit power to the noise power γ0 is set to be 40 dB, and other system parameters are set according to Table 1.Figure 9
The considered coordinate system.Figures10 and 11 show the system outage probability and system throughput with respect to PS position x for different distances between R and Dd4, respectively. It can be found that with the increment of the X-axis coordinate of PS x, the outage probability first decreases and then increases while the throughput first increases and then decreases. And there is an optimal PS position whose X-axis coordinate is located between S and R, which makes the outage probability of system reaches the lowest or system throughput reach the largest. The reason is when the X-axis coordinate of PS is located between S and R, and both hops are balanced, which leads to lower outage probability and larger throughput. It can be also seen that with the increment of distance between R and Dd4, the outage probability increases, and the throughput decreases, which is caused by larger path loss from R to D. Finally, one can see that with the increment of distance between R and Dd4, the optimal PS position is gradually close to R. This is because when PS is closer to R, it can supply more energy to R to alleviate the effect of larger path loss from R to D.Figure 10
The system outage probability versus PS positionx for different distances between R and Dd4.Figure 11
The system throughput versus PS positionx for different distances between R and Dd4.


## 4.1. The Effect of System Parameters on Performance

Figures3 and 4 show the effect of the ratio of the PS transmit power to the noise power (i.e., γ0=PB/N0) on the system outage probability and throughput with different values of the Rician factor, respectively. From Figures 3 and 4, it can be seen that the results calculated by the closed expression match the results obtained by Monte Carlo simulation well, which demonstrates the correctness of our obtained theoretical results.Figure 3
The system outage probability versusPB/N0 for different Rician factor K.Figure 4
The system throughput versusPB/N0 for different Rician factor K.From Figures3 and 4, one can also find that for a fixed Rician factor K, the greater the value of γ0 is, the smaller the system outage probability becomes, and the larger the system throughput becomes. This is because with the increment of γ0 for given N0, the energy harvested by S and R increases, which yields higher SINR of both R and D. It can also be found that for fixed γ0, the greater the value of the Rician factor is, the lower the outage probability of the system becomes, and the larger the system throughput becomes. The reason is that the larger Rician factor means the stronger LOS, which increases the energy harvested by both S and R. Finally, from Figure 3, it can be seen that the outage probability gradually approaches an numerical asymptote in high SNR regime. This indicates that increasing the transmit power of PS cannot infinitely decrease the outage probability of the system.Figures5 and 6 show the effects of the time division factor α on the system outage probability and throughput for different values of γ0, respectively. The results calculated by the closed expression fit well with those obtained by Monte Carlo simulation, confirming that the derived closed-form solution of the system outage probability is completely accurate again. One can see that for a fixed α, the larger γ0 is, the lower outage probability is and the larger throughput is, which agrees with Figures 3 and 4.Figure 5
The system outage probability versus time switching factorα for different γ0.From Figure5, one can see that for a fixed γ0, with the increment of the value of α, the outage probability decreases first and then increases. On one hand, when the value of α is close to 0, the time allocated for energy harvesting is relatively short. In this case, the less energy can be harvested by S and R and therefore, the outage probability of system is close to 1. On the other hand, when the value of α approaches to 1, the time assigned for information relaying transmission becomes very short, and thus the outage probability of system tends to be 1. In addition, it can be found that there is an optimal value of α, which makes the outage probability of system reach the lowest. For example, for γ0=30, when α is equal to about 0.6, the system achieves the best outage performance.From Figure6, one can also see that for a fixed γ0, with the increment of the value of α, the system throughput increases first and then decreases. The reason is as follows. When the value of α is close to 0, S and R do not have energy to transmit information, while when the value of α is close to 1, S and R do not have time to transmit information. Moreover, it can be found that there is an optimal value of α, which makes the system throughput reach the largest. For example, for γ0=30, when α is equal to about 0.08, the system achieves the best throughput performance. Finally, by comparing Figure 6 with Figure 5, it can be found that the optimal values of α are different for the best outage probability and throughput.Figure 6
The system throughput versus time switching factorα for different γ0.Figures7 and 8 discuss the effect of transmission rate threshold Rth on the system outage probability and throughput with respect to γ0, respectively. It can be seen that for fixed transmission rate threshold Rth, as the value of γ0 increases, the outage probability gradually decreases, and the throughput gradually increases, and they tend to be stable, which is consistent with the results in Figures 3 and 4. Moreover, from Figure 7, one can observe that for a fixed γ0, the smaller the value of Rth is, the lower the value of system outage probability becomes. This is because when Rth becomes smaller, γth=2R/1−α−1 also becomes smaller, which further causes lower outage probability. Finally, from Figure 8, it can found that the larger the value of Rth is, the larger the system throughput becomes. The reason is that from (24) one can see that with the decrement of system outage probability, the throughout gradually increases.Figure 7
The system outage probability versusPB/N0 for different transmission rate threshold Rth.Figure 8
The system throughput versusPB/N0 for different transmission rate threshold Rth.


## 4.2. The Effect of Power Station Position on Performance

In order to discuss the effect of power station position on performance, we consider a coordinate system, as shown in Figure9. S is located at the origin of coordinate, R is located at the positive X-axis, and their distance d3 is set to be 10 m. D is located in the fourth quadrant and its distance d4 from R may be set to 5 m, 10 m, 20 m, or 30 m. PS moves at a straight line whose distance h from the X-axis is set to be 10 m. The X-axis coordinate of PS is denoted by x, which is used to represent the position of PS. Besides, the ratio of PS transmit power to the noise power γ0 is set to be 40 dB, and other system parameters are set according to Table 1.Figure 9
The considered coordinate system.Figures10 and 11 show the system outage probability and system throughput with respect to PS position x for different distances between R and Dd4, respectively. It can be found that with the increment of the X-axis coordinate of PS x, the outage probability first decreases and then increases while the throughput first increases and then decreases. And there is an optimal PS position whose X-axis coordinate is located between S and R, which makes the outage probability of system reaches the lowest or system throughput reach the largest. The reason is when the X-axis coordinate of PS is located between S and R, and both hops are balanced, which leads to lower outage probability and larger throughput. It can be also seen that with the increment of distance between R and Dd4, the outage probability increases, and the throughput decreases, which is caused by larger path loss from R to D. Finally, one can see that with the increment of distance between R and Dd4, the optimal PS position is gradually close to R. This is because when PS is closer to R, it can supply more energy to R to alleviate the effect of larger path loss from R to D.Figure 10
The system outage probability versus PS positionx for different distances between R and Dd4.Figure 11
The system throughput versus PS positionx for different distances between R and Dd4.


## 5. Conclusion

This paper has studied the communication outage performance of full-duplex relay networks powered by RF energy harvesting in UEIoT, where the outdoor Rician fading channel model is adopted and full-duplex relaying protocol is employed. The explicit expression of the outage probability of the system has been derived. Simulation results have validated the correctness of our theoretical analysis and also evaluated the effects of the transmit power of the power station, the Rician factor, the time division factor, and system transmission rate threshold on the system outage probability and throughput. The optimal deployment position of power station is also investigated, which provides some useful insights on the future practical applications of UEIoT.



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