[Retracted] Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

Mi, Yin; Lu, Guangyue; Gao, Wenbin

doi:https://doi.org/10.1155/2021/3340525

Security and Communication Networks

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Volume 2021 | Article ID 3340525 | https://doi.org/10.1155/2021/3340525

[Retracted] Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

Yin Mi,^1,2Guangyue Lu ,²and Wenbin Gao²

Academic Editor: Yinghui Ye

Received03 Aug 2021

Revised17 Sept 2021

Accepted03 Nov 2021

Published30 Nov 2021

Abstract

In this paper, we propose a joint sensing duration and transmission power allocation scheme to maximize the energy efficiency (EE) of the secondary user (SU) in a cooperative cognitive sensor network (CSN). At the initial time slot of the frame, the secondary transmitter (ST) performs energy harvesting (EH) and spectrum sensing simultaneously using power splitting (PS) protocol. The modified goodness of fit (GoF) spectrum sensing algorithm is employed to detect the licensed spectrum, which is not sensitive to an inaccurate noise power estimate. Based on the imperfect sensing results, the ST will act as an amplify-and-forward (AF) relay and assist in transmission of the primary user (PU) or transmit its own data. The SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement. Since the SU’s EE function is a nonconvex problem and difficult to solve, we transform the original problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique by applying a nonlinear fractional programming. The closed-form expression of the ST’s transmission power is also derived through Karush-Kuhn-Tucker (KKT) and gradient method. Simulation results show that our scheme is superior to the existing schemes.

1. Introduction

With the popularity of wireless devices in the Internet of Things (IoT) [1], it is expected to reach approximately 15.6 billion devices and connections by 2022. As a result, spectrum resources are becoming increasingly scarce. Motivated by this, cognitive radio (CR) technology is proposed and has attracted promising attention to cope with the spectrum utilization [2]. Generally, there are three models in CSN, including underlay, overlay, and interweave [3, 4]. In underlay CSN, the SUs can be allowed to coexist with the PU on the same frequency bands, but it is necessary to ensure that the interference to the primary receiver (PR) does not exceed the tolerance value, and therefore the SUs usually transmit information at a low transmission power to ensure the PU’s QoS. In addition, the PU has the right to use the licensed spectrum for communication in interweave CSN, while the SUs can only access to the spectrum holes opportunistically which are not occupied by the PU, and they need to detect the spectrum continuously during a period of time. Once the PU returns to the band, it is required for the SUs to evacuate immediately to avoid causing harmful interference to the PR [5]. And in overlay CSN, the ST acts as a relay to assist the PU’s transmission through collaboration [6, 7]; in return, the ST can be allowed to access the licensed spectrum to transmit its own information. In this paper, we assume that the ST will act here as AF relay and assist the PU’s transmission in the overlay CSN.

Meanwhile, it has been particularly noticed that the performance and reliability of wireless communication system are significantly limited by the battery life of energy-limited devices. Simultaneous wireless information and power transfer (SWIPT) has now been widely concerned and is considered a promising technique to solve the energy shortage problem [8], as it can provide perpetual power through radio frequency (RF) EH without the need to recharge or replace the battery. It is well known that the RF signals radiated by surrounding transmitters are always available, so RF EH is more sustainable and flexible than solar and wind energy, and wireless sensor nodes can be allowed to convert the RF signals into energy for their own information transmission. Based on this, two actual receiver structures called time-switching (TS) and PS are proposed and studied in detail [9]. In a PS receiver, energy and information transmission are split into two power streams for EH and information processing, while the two processes are divided over the time slots in a TS receiver.

Recently, RF EH in CSN has become the object of extensive research [10–18]. In [10], a two-phase decoded-and-forward (DF) relay model was constructed by employing adaptive PS at the ST node; the outage probability and throughput were analysed. In [11], the ST employed the TS protocol to harvest energy and decode the PT’s information; the secondary throughput was maximized by optimizing the energy harvesting time and the ST’s relaying power. In [12], the SU’s EE maximization problem was formulated under energy causality and the PU’s QoS requirement constraint; a joint time and power allocation scheme was proposed in the overlay network. In [13], the SU accessed the PU’s channel in a hybrid interweave/underlay mode using the time division multiple access (TDMA) technique to maximize the SU’s throughput. In [14], asymptotic closed-form expressions for the spectrum efficiency (SE) and outage probability were derived at the constraints of the interference temperature and residual energy harvested from PT in an underlay network. Due to the broadcasting nature of wireless communication, the primary user emulation attack (PUEA) and the eavesdroppers may threat network security and defeat the sensing performance; secrecy capacity and secrecy outage probability were analysed in [15–18].

However, the above researches did not consider the effects of the spectrum sensing results on the SE and EE in CSN. The cognitive base station collected all sensing results from SUs and then made a global decision, so that the SUs could decide whether to transmit or not [19, 20]; the sum secondary throughput maximization problem was formulated under the conditions of finite and infinite battery capacity. In [21], the subchannels were split into two subchannel sets, one for spectrum sensing and the other for EH; they aimed to maximize the sum secondary throughput by jointly optimizing the sensing time, subchannel set, and SU’s transmission power. In [22], the trade-off problem between EE and SE was discussed to determine the optimal sensing time, final decision threshold, and SU’s transmission power, wherein the SUs were divided into two groups, which participated either in spectrum sensing and EH or in PU’s transmission and EH. In [23], spectrum sensing and EH can be realized simultaneously by the ST through PS mode, and residual energy maximization problem based on linear and nonlinear EH models was explored under the constraints of sensing reliability and secondary and secrecy outage probability. In [24], the SU made the decision based on its location, the sensing result, and the battery’s residual energy. Moreover, a greedy policy of the SU was proposed and the optimal detection threshold was found to achieve the maximum throughput.

In an actual wireless communication, spectrum sensing may often be inaccurate. To the best of our knowledge, an imperfect spectrum sensing-based EE analysis has not yet been much investigated with EH in overlay CSN. Moreover, compared with ED and the original GoF algorithm, the modified GoF spectrum sensing algorithm can obtain better detection performance [25]. Since it is insensitive to variance under noise uncertainty, an inaccurate noise power estimate will not affect its detection performance. In addition, because the ranking operation is not required in the method, its computational complexity is significantly reduced, which is beneficial for energy-limited sensors in CSN.

Motivated by this, we propose a joint sensing time and power allocation scheme to maximize the SU’s EE in the modified GoF spectrum sensing-based cooperative CSN with EH, where simultaneous EH and spectrum sensing are done at the ST node using PS mode and it can collaborate with PU’s transmission using AF protocol. The major contributions of this paper are summarized as follows:(i)In spectrum sensing-based cooperative CSN with EH, the ST node employs the modified GoF algorithm to detect the PU’s licensed spectrum. Based on the imperfect decision results, the ST either acts as AF relay and assists in PU’s transmission or transmits its own data. We study the optimal sensing time and power allocation for maximizing the SU’s EE under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement.(ii)By applying a nonlinear fractional programming method, we transform the original nonconvex EE problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique. An iterative optimization algorithm is proposed for seeking the optimal sensing time and power allocation policy jointly. Meanwhile, the closed-form expression of ST’s transmission power is also derived through KKT and gradient method.(iii)Simulations results verify the convergence of our proposed iterative scheme, and considering the linear and nonlinear EH model, we analyse the effect of the ST’s maximal transmission power on the SU’s EE when given different PU’s QoS requirement. With the change of the sensing time and the power splitting ratio, the influence of an idle channel probability and given detection probability on the SU’s EE is also analysed. Moreover, the EE curves are compared with the change of the noise uncertainty, verifying the superiority of the modified GoF algorithm.

The rest of this paper is organized as follows. In Section 2, the system model is formulated. The problem formulation and the proposed solution are introduced in Section 3. Finally, simulation results are provided in Section 4, and Section 5 concludes the paper.

2. System Model

As depicted in Figure 1(a), we consider a modified GoF spectrum sensing-based cooperative CSN with EH that the SU network and the PU network coexist in the same frequency band, where the network is composed of a PT-PR pair and a ST-SR pair. It is assumed that each node is working in half-duplex mode and is equipped with a single antenna. The ST can be regarded as IoT node, which has limited energy and computing power. In our model, simultaneous EH and spectrum sensing are done at the ST node using PS mode. Due to obstacles or deep fading, there is no direct communication link between PT and PR. To access the PU’s licensed spectrum, the SU node must detect whether there is the PU signal or not in the frequency band. Herein, the ST employs the lightweight modified GoF algorithm for spectrum sensing. According to the decision results, the ST will either take part in relaying PU’s transmission or transmit its own data. Let hypothesis 0 (H₀) denote that PU is inactive and the channel is idle and let hypothesis 1 (H₁) denote the presence of PU. The frequently used symbols in this paper are summarized in Table 1.

(a)

(b)

The instantaneous channel gains, between the PT and the ST link, the ST and the PR link, and the ST and the SR link, are denoted by h_ps, h_sp, and h_ss, respectively. Here, we assume that the links employ Rayleigh flat fading channel model in the cooperative CSN. Let d_ij be the distance from the i-th transmitter to the j-th receiver. Then, the instantaneous channel gain of the i-j link is denoted by where p is the pass loss exponent. Meanwhile, the CSI at the receivers are assumed to be available.

As illustrated in Figure 1(b), the entire frame duration T is divided into three distinct time slots repeating over a frame by frame transmission. Over the first time slot αT, the ST performs EH and spectrum sensing simultaneously using PS protocol, and the received signal power is split by the power splitting device with a ratio of θ: (1-θ) for EH and spectrum sensing, respectively. The modified GoF algorithm is used by the ST to detect the occupancy of the licensed spectrum; the PU is assumed to be either idle or active throughout the entire frame duration. In time slot (1-α)T/2, the PT will transmit its own information to the ST. According to the decision result, the ST will decide to assist PU’s transmission or transmit its own data during the remaining time slot (1−α)T/2, where 0 < α < 1.

During the local spectrum sensing, the i-th (i = 1,2, …, n) instantaneous sample of the received signal y_ST(i) of the SU can be formulated aswhere x_p(i) is the transmitted signal by the PU with zero mean and variance , _ST is assumed to be a circularly symmetric complex Gaussian (CSCG) random variable with zero mean and variance , and ρ is denoted by the signal-to-noise . φ acts as a binary indicator; φ = 1 or 0 indicates the PU being active or idle, respectively. The ST node using PS protocol splits the received signal for spectrum sensing and EH as and , respectively. n represents the number of samples, i.e., n = αTf_s, where f_s denotes the sampling frequency. Without loss of generality, we also assume that x_p and _ST are independent of each other.

One part of the received signal at the ST is used for spectrum sensing. GoF tests measure the distance of the empirical cumulative distribution function (CDF) of the received samples and CDF in H₀ to detect the state of the PU. To be specific, let F_n(y_ST1) denote the empirical CDF of y_ST1 (i). In H₀, F_n(y_ST1) = F₀(y_ST1) according to Glivenko-Cantelli theorem. Alternatively, H₁ is the hypothesis that F_n(y_ST1)≠F₀(y_ST1). Therefore, spectrum sensing can be realized via the distance of F_n(y_ST1) and F₀(y_ST1). Here, the distance of F_n(y_ST1) and F₀ (y_ST1) is defined aswhere .

is equal to the weighted sum squares of a set of test statistics z_nj (j = 1,2, …). Besides, the different component is sensitive to different kinds of deviation, so that each measures some distinctive aspect of the sample distribution. For example, the first component z_n1 is sensitive to mean shift but insensitive to variance shift. Based on this characteristic, the modified GoF method employs z_n1 as the test statisticz_n1 under H₀ follows normal distribution with zero mean and variance unit, and the PDF of z_n1 is an even function. According to the central limit theorem, z_n1 can be approximated under H₀ and H₁. Consequently, the closed-form expression of the false alarm probability P_f is given aswhere Q(·) is the complementary distribution function of the standard Gaussian; it can be given as follows:

Moreover, γ_r = −γ_l = γ. Then, the decision threshold for any P_f can be calculated by

According to (3), the test statistic z_n1 can be calculated using the received signal samples y_ST1(i). Through equation (6), we can get the decision thresholds γ_r and γ_l. Finally, the PU is absent if or . Otherwise, reject H₀ in favour of the PU signal.

Therefore, for a given P_f, the probability of detection P_d can be described asand we can conclude that the CDF of test statistic z_n1, which follows , can be described aswhere

It should be noted that the cooperative spectrum sensing is not considered in our work because only one ST performs spectrum sensing.

In the following, we introduce two EH models: including ideal linear EH model and practical nonlinear EH model. In the linear EH model, based on equation (1), the ST splits the received signal by the ratio θ for EH, so the energy gathered at the ST is expressed aswhere 0 ≤ η ≤ 1 means the energy conversion ratio from the harvested energy into its battery; the received RF signals and noise by the ST node are used for energy harvesting during the spectrum sensing phase. We can note that, in the linear EH model, the energy conversion ratio η is independent of the input power P_T. But in the practical nonlinear EH model, the conversion efficiency is greatly relative to the input power level, the harvested power will saturate a constant value when the input power increases to a certain extent, and the sensitivity of the energy harvester is limited [26]. Hence, the amount of the harvested power by the ST is modeled as follows:where is the logistic (or sigmoid) function with respect to the received RF power P_ER. M is a constant denoting the maximum harvested power at the ST when the EH circuit is saturated; a and b are utilized to capture the circuit characteristic. In practice, parameters M, a, and b can be easily obtained by a standard curve fitting tool [26]. In the nonlinear model, the harvested energy by the ST can be expressed as ; here is derived by setting P_ER to . Regardless of the linear or nonlinear EH model, it is supposed that the ST’s harvested energy is enough for the PU’s relaying transmission and its own information transmission.

2.1. ST Cooperates with PU’s Transmission

Because the PT-PR direct link is considerably weak compared to the PT-ST link and ST-PR link, we neglect the PT-PR link data transmission for simplicity. If the PU is considered to exist on the licensed frequency band through the detection of the ST node, it cooperates with PU’s transmission in two equal subslots (1-α)T/2. In the first subslot, the ST node will receive the signal from the PT and then employ AF protocol to amplify the received signal and forward it to the PR in the other subslot. Therefore, the received signal at the PR is shown below:where β is an amplifying power gain and _PR is modeled as CSCG noise at the PR with zero mean and variance .

Here, we take into account the imperfect spectrum sensing in the sensing phase at the ST node, so there are two cases in which the ST node assists in PU’s transmission. The first case denotes that the ST is able to detect correctly when the PU is present, and the PU’s achievable throughput R_P1 is expressed as

In either case, the PU channel is actually idle but it is not detected correctly by the ST node; in the event of false alarm detection, the PU’s achievable throughput R_P2 is expressed aswhere P(H₀) is the probability that the channel is idle, while P(H₁) is the probability that the channel is occupied. To sum up, the PU’s corresponding achievable throughput during the (1-α)T/2 slot is assumed to be .

2.2. SU Transmits Its Own Information

When the PU is detected to be idle, the ST node will transmit its data to the SR node. The received signal at the SR from the ST during (1-α)T/2 is shown below:and here, x_s is the ST transmitted signal with zero mean and variance , which denotes the transmission power of the ST. The noise _ss at the SR node is modeled as CSCG random variable with zero mean and variance . The SU’s transmission in such situation occurs in two specific cases. In case that the PU channel is indeed idle and no false alarm appears, the SU’s achievable throughput in the event of no false alarm R_s1 is given as

In another case where the PU channel is actually active but it is not accurately detected, the primary transmission serves as interference to the SR node. The SU’s achievable throughput in case of missed detection of the PU R_s2 is given as

Therefore, the SU’s corresponding achievable throughput can be given as follows:

In addition, the total energy consumption of the SU is expressed aswhere , and . The SU’s total energy consumption is divided into three parts, the first part P_s represents the spectrum sensing power consumption of the ST, the second part is the ST-assisted transmission power consumption when the PU is considered to exist, and the last part represents the ST’s transmission power consumption when the PU is absent. It should be noted that energy consumption at the receiver is insignificant compared to the ST’s transmission energy consumption, so we ignore this part of the energy consumption for simplicity [27]. Thus, the EE is defined as the ratio of the achievable throughput to the total energy consumption of the SU network, which is represented by

3. Problem Formulation and Proposed Solution

Based on the above analysis, in an imperfect spectrum sensing-based cooperative CSN with EH, the SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement; the sensing time α and the transmission power P_SR are optimized jointly. The EE maximization problem can be formulated as the following fractional programming problem.

Problem P1:where (20) guarantees that the total energy consumed by the ST does not exceed the harvested energy from the PT; here we mainly focus on the linear EH model; the nonlinear EH model is similar. Equation (20) guarantees the PU’s QoS constraint, where R_th is the PU’s minimum throughput requirement. Generally speaking, the probability of false alarm P_f is much less than the detection probability P_d [28]. By approximation, we can conclude that ; thus the amplifying power gain β can be calculated as

In (20), is a given detection probability, which guarantees the reliability of detection performance. According to (7), the detection probability P_d decreases monotonically with the increase of the decision threshold γ, and hence the maximum EE can be obtained only when P_d acquires its lower bound . In (20), the transmission power P_SR is chosen subject to the transmit power limit; P_max is the ST’s maximal transmission power.

Unfortunately, due to the product of optimization variables in (20), the objective function is still nonconvex problem. To resolve this problem, we seek an optimal transmission power P_SR for a fixed value of sensing time α. Let the partial derivative of η_EE taken with respect to (w.r.t.) α be , and we can conclude that and , which declare there exists a stationary point that maximized η_EE. Since is highly nonlinear, it is difficult to derive a closed-form expression of the optimal sensing time .

Hence, the optimal sensing time can be found using a one-dimensional exhaustive search method over the interval (0, 1). In the following, we focus on deriving the optimal value P_SR when α is fixed. Let denote the maximum EE of the investigated system. Then, we havewhere P_SR and α denote the optimal transmission power and sensing time, respectively.

To transform the problem into a tractable form, motivated by Dinkelbach’s method, we translate the original objective function into a subtractive form by adapting a nonlinear fractional programming, such as . The transformation of the objective function can be summarized in Lemma 1.

Lemma 1. The maximum EE q is achieved if and only if the following condition is satisfied:

Proof. please refer to [29] for the proof.
Then, based on Lemma 1, the original problem P1 can be rewritten as problem P2.
Problem P2:where q is a nonnegative parameter. Note that the optimization problem P2 is convex w.r.t. P_SR, such that we can find the optimal power allocation using convex optimization techniques. To resolve this convex optimization problem, the Lagrangian function of (24) can be expressed aswhere and µ are nonnegative Lagrangian multipliers. By taking the derivative of (25) w.r.t. P_SR, the value of P_SR that maximizes L(P_SR,α,v,μ) can be calculated from the KKT condition ; the transmission power P_SR can be achieved as follows:where , .
After calculating the transmission power P_SR, the Lagrangian multipliers including and µ are associated with equations (20b)–(20c) constraints, which can be updated by the gradient method.where . Then, the transmission power P_SR can be recalculated based on the updated Lagrangian multipliers and μ; the optimal transmission power can be found using this iterative process.
In summary, the overall procedures of the proposed scheme are described in Algorithm 1. Firstly, the initial values of the transmission power P_SR and the dual variable are given; then calculating the amplifying power gain β using equation (21), q is set to . Secondly, P_SR is calculated according to equation (26), and the dual variables, i.e., and μ, are updated iteratively according to equations (27) and (28) until the inner loop (from line 6 to line 9) converges. Finally, for the converged values, R_S (α,P_SR) and E_tot (α,P_SR), are updated, and the convergence of the outer loop (from line 4 to line 11) is checked. If the condition is satisfied, the algorithm can stop and P_SR⁺ is returned. When α varies from 0 to 1, the optimal transmission power P_SR is found and the resulting EE are compared, enabling the optimal values to be found.

(1)	for α = 0 : 1
(2)	Initialize the transmission power P_SR, the maximum tolerance ε, EE q, a given detection probability and Lagrangian multiplier v
(3)	Calculate the amplifying power gain β using (21)
(4)	repeat
(5)	Set
(6)	repeat
(7)	Update the transmission power P_SR according to (26)
(8)	Update the Lagrange multipliers and µ according to equations (27) and (28)
(9)	until convergence of P_SR
(10)	Update R_S (α, P_SR) and E_tot (α, P_SR)
(11)	until
(12)	Return ,
(13)	end for
(14)	, ,

4. Simulation Results

In this section, the performance of the proposed scheme is illustrated via 10⁴ Monte Carlo simulations through MATLAB. For the nonlinear EH model, we set M = 0.024 W, a = 150, and b = 0.014 according to the fitting results [26]. Numerical values of different system parameters are listed in Table 2. To verify the advantages of the proposed scheme, we investigate the effect of different parameters on the scheme’s performance.

Figure 2 shows that the convergence of the SU’s EE under different channel gains. In our considered cooperative CSN, we assume that the instantaneous channel gain h_ss employs Rayleigh flat fading channel model. Meanwhile, the CSI at the SR is assumed to be available, and our proposed scheme is independent of the channel fading. It can be observed that the convergence of the SU’s EE can be achieved very well within five iterations, which illustrates the effectiveness of the proposed algorithm.

Figure 3 illustrates the variations of the SU’s EE when the sensing time α varies under different PU’s minimum throughput requirements R_th. It is seen that the SU’s EE is a unimodal function of sensing time α; the optimal EE value is noted at α = 0.55 for R_th = 0.2. When less time is spent on spectrum sensing, the SU’s throughput is small due to the high false alarm probability P_f which reduces the SU’s EE. As the sensing time α increases, the SU’s EE value increases until the optimal sensing time α is reached. In addition, when α exceeds its optimal value, more power is required to assist PU’s transmission to meet its minimum throughput requirement; thus the SU’s EE will decline accordingly. Moreover, the optimal sensing time α gradually increases with the increase of R_th, which means that the ST node will take more time to collect energy and perform spectrum sensing.

In Figure 4, we compare the SU’s EE of the two EH models when the ST’s maximal transmission power P_max varies from 0 to 1W under different PU’s minimum throughput requirements R_th (R_th = 0.2,0.5,0.8). It is interesting to note that as P_max increases, the SU’s EE increases with the decrease of R_th. The reason is that high R_th requires the ST to be more likely to transmit sufficient power to satisfy throughput requirement, while more energy consumption of the ST node will lead to lower EE. In addition, we can also observe that the SU’s EE increases rapidly when P_max is small; then it converges to its optimal value, which indicates that increasing P_max to a certain value can no longer improve the EE; on the contrary it causes a loss. As the power splitting ratio θ increases, the SU’s EE will be improved because of harvesting more energy. Compared with the linear EH model, the EE’s improvement adopting the nonlinear EH model is more obvious, because it can accurately reflect the nonlinear behaviors in the practical EH circuits.

(a)

(b)

Figure 5 shows the SU’s EE curves of the two EH models as an idle channel probability P(H₀) changes under different sensing time α (α = 0.3,0.4,0.5). The PU’s minimum throughput requirement R_th = 0.2. It is seen that the SU’s EE curves are all unimodal functions of P(H₀) values, and for different sensing time, P(H₀) corresponding to the optimal value of SU’s EE is increasing. For example, in Figure 5(a), the maximum value of SU’s EE is taken in the case of α = 0.3, P(H₀) = 0.3, and when α = 0.5, the maximum value is obtained when P(H₀) = 0.6. It means that when α is limited, in order to achieve the optimal EE, the probability of the PU occupying the channel is as small as possible. However, when the PU’s absence is severe, it will restrict the ST’s energy harvesting, resulting in a decrease of SU’s EE. From the nonlinear EH model in Figure 5(b), we can clearly see that the SU’s EE improves significantly with the increase of the power splitting ratio θ.

(a)

(b)

From Figure 6, it can be observed that the SU’s EE decreases with the increase of a given detection probability when the sensing time α takes different values (0.3,0.4,0.5). For the same detection performance, the shorter the sensing time, the lower the SU’s EE; the reason may be that the SU’s energy consumption has increased accordingly. Similarly, the power splitting ratio θ is proportional to the SU’s EE; it means that the ST node will harvest enough energy for the PU’s assist transmission or its own data transmission.

In Figure 7, when the noise uncertainty varies from 0 dB to 3 dB, we compare the SU’s EE of the modified GoF algorithm, CM GoF, and ED algorithm. Because the modified GoF algorithm is insensitive to an inaccurate noise power, its false alarm probability P_f is constant and unchanging for different noise uncertainties (0 dB∼3 dB), so the SU’s EE curve is a straight line. While the SU’s EE using the CM and ED algorithms are all decreasing slowly with the increase of the noise uncertainty, the false alarm probability of these two algorithms exceeds the given false alarm probability value, resulting in the decline of EE, which violates the requirements of the IEEE 802.22 standard.

5. Conclusions

In this paper, a joint sensing time and power allocation scheme is suggested to maximize the SU’s EE in sensing-based cooperative CSN with EH under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement, where simultaneous EH and spectrum sensing can be done at the ST node using PS mode. The ST employs the modified GoF spectrum sensing algorithm to detect the spectrum usage, because of its ability to overcome noise uncertainty. Since the SU’s EE maximization function is a nonconvex problem, we iterate for the optimal solutions by applying a nonlinear fractional programming, with the aid of Dinkelbach’s method and convex optimization techniques. Simulation results show that the SU’s EE can be improved with the decrease of R_th and is proportional to the power splitting ratio. Moreover, the SU’s EE are all unimodal functions of α and P(H₀). Finally, the SU’s EE adopting different sensing algorithms are compared as the noise uncertainty changes, verifying the superiority of the modified GoF algorithm.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant nos. 61701399 and 61801382) and the Science and Technology Innovation Team of Shaanxi Province for Broadband Wireless and Application (Grant no. 2017KCT-30-02).

References

Q. Wu, G. Ding, Y. Xu et al., “Cognitive internet of things: a new paradigm beyond connection,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 129–143, 2014.
View at: Publisher Site | Google Scholar
J. Mitola III, “Cognitive radio for flexible mobile multimedia communications,” Mobile Networks and Applications, vol. 6, no. 5, pp. 435–441, 2001.
View at: Publisher Site | Google Scholar
B. Beibei Wang and K. J. R. Liu, “Advances in cognitive radio networks: a survey,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 1, pp. 5–23, 2011.
View at: Publisher Site | Google Scholar
Q. Qinyu Zhang, B. Bin Cao, Y. Ye Wang, A. Naitong Zhang, A. Xiaodong Lin, and A. Limin Sun, “On exploiting polarization for energy-harvesting enabled cooperative cognitive radio networking,” IEEE Wireless Communications, vol. 20, no. 4, pp. 116–124, 2013.
View at: Publisher Site | Google Scholar
Y. Mi, G. Lu, Y. Li, and Z. Bao, “A novel semi-soft decision scheme for cooperative spectrum sensing in cognitive radio networks,” Sensors, vol. 19, no. 11, Article ID 2522, 2019.
View at: Publisher Site | Google Scholar
Y. Chen, R. Shi, and W. Feng, “AF relaying with energy harvesting source and relay,” IEEE Transactions on Vehicular Technology, vol. 66, no. 1, pp. 874–879, 2017.
View at: Publisher Site | Google Scholar
X. Zhou and Q. Li, “Energy efficiency for SWIPT in MIMO two-way amplify-and-forward relay networks,” IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 4910–4924, 2018.
View at: Publisher Site | Google Scholar
X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: architecture design and rate-energy tradeoff,” IEEE Transactions on Communications, vol. 61, no. 11, pp. 4754–4767, 2013.
View at: Publisher Site | Google Scholar
R. Zhang and C. K. Ho, “MIMO broadcasting for simultaneous wireless information and power transfer,” IEEE Transactions on Wireless Communications, vol. 12, no. 5, pp. 1989–2001, 2013.
View at: Publisher Site | Google Scholar
Q. Li, X. Zhang, A. Pandharipande, and J. Zhang, “Cooperative spectrum sharing on SWIPT-based DF relay: an energy-aware retransmission approach,” IEEE Access, vol. 7, pp. 120802–120816, 2019.
View at: Publisher Site | Google Scholar
F. Li, H. Jiang, and R. Fan, “Optimal cooperative strategy in energy harvesting cognitive radio networks,” in Proceedings of the 86th Vehicular Technology Conference (VTC-Fall), pp. 1–6, IEEE, Toronto, Canada, September 2017.
View at: Publisher Site | Google Scholar
F. Yan, J. Zhao, H. Qu, and X. Xu, “Energy‐efficient cooperative strategy in RF energy harvesting cognitive radio network,” Chinese Journal of Electronics, vol. 28, no. 3, pp. 651–657, 2019.
View at: Publisher Site | Google Scholar
A. F. Tayel, S. I. Rabia, A. H. A. El-Malek, and A. M. Abdelrazek, “An optimal policy for hybrid channel access in cognitive radio networks with energy harvesting,” IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 11253–11265, 2020.
View at: Publisher Site | Google Scholar
Y. Liu, S. A. Mousavifar, Y. Deng, C. Leung, and M. Elkashlan, “Wireless energy harvesting in a cognitive relay network,” IEEE Transactions on Wireless Communications, vol. 15, no. 4, pp. 2498–2508, 2016.
View at: Publisher Site | Google Scholar
P. Yan, Y. Zou, and J. Zhu, “Energy-aware multiuser scheduling for physical-layer security in energy-harvesting underlay cognitive radio systems,” IEEE Transactions on Vehicular Technology, vol. 67, no. 3, pp. 2084–2096, 2018.
View at: Publisher Site | Google Scholar
L. Ni, X. Da, H. Hu, M. Zhang, and K. Cumanan, “Outage constrained robust secrecy energy efficiency maximization for EH cognitive radio networks,” IEEE Wireless Communications Letters, vol. 9, no. 3, pp. 363–366, 2020.
View at: Publisher Site | Google Scholar
D. Wang and S. Men, “Secure energy efficiency for NOMA based cognitive radio networks with nonlinear energy harvesting,” IEEE Access, vol. 6, pp. 62707–62716, 2018.
View at: Publisher Site | Google Scholar
M. Zhang, K. Cumanan, J. Thiyagalingam et al., “Energy efficiency optimization for secure transmission in MISO cognitive radio network with energy harvesting,” IEEE Access, vol. 7, pp. 126234–126252, 2019.
View at: Publisher Site | Google Scholar
S. Biswas, S. Dey, and A. Shirazinia, “Sum throughput maximization in a cognitive multiple access channel with cooperative spectrum sensing and energy harvesting,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 2, pp. 382–399, 2019.
View at: Publisher Site | Google Scholar
A. Banerjee and S. P. Maity, “On residual energy maximization in DF cognitive radio networks with multiple eavesdroppers,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 718–727, 2020.
View at: Publisher Site | Google Scholar
X. Liu, F. Li, and Z. Na, “Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio,” IEEE Access, vol. 5, pp. 3801–3812, 2017.
View at: Publisher Site | Google Scholar
S. Chatterjee, S. P. Maity, and T. Acharya, “Energy-spectrum efficiency trade-off in energy harvesting cooperative cognitive radio networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 2, pp. 295–303, 2019.
View at: Publisher Site | Google Scholar
A. Banerjee and S. P. Maity, “On residual energy maximization in cognitive relay networks with eavesdropping,” IEEE Systems Journal, vol. 13, no. 4, pp. 3836–3846, 2019.
View at: Publisher Site | Google Scholar
K. Zheng, X. Liu, Y. Zhu, K. Chi, and Y. Li, “Impact of battery charging on spectrum sensing of CRN with energy harvesting,” IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7545–7557, 2020.
View at: Publisher Site | Google Scholar
Y. Mi and G. Lu, “Performance analysis of decomposed cramer-von mises detector for blind spectrum sensing under noise uncertainty,” in Proceedings of the 10th International Conference on Wireless Communications and Signal Processing (WCSP 2018), pp. 1–6, IEEE, Hangzhou, China, September 2018.
View at: Publisher Site | Google Scholar
H.-V. Tran, G. Kaddoum, and K. T. Truong, “Resource allocation in SWIPT networks under a nonlinear energy harvesting model: power efficiency, user fairness, and channel nonreciprocity,” IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8466–8480, 2018.
View at: Publisher Site | Google Scholar
T. T. Tran, D. H. N. Nguyen, T. Do-Hong, and S. Ngo-Van, “Achievable rates with cooperative spectrum sharing in the modulation-based dimension,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 654–670, 2019.
View at: Publisher Site | Google Scholar
C. Stevenson, G. Chouinard, Z. Zhongding Lei, H. Wendong, S. Shellhammer, and W. Caldwell, “Ieee 802.22: the first cognitive radio wireless regional area network standard,” IEEE Communications Magazine, vol. 47, no. 1, pp. 130–138, 2009.
View at: Publisher Site | Google Scholar
S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, U.K., 2004.

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Copyright © 2021 Yin Mi 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.

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