Wireless Communications and Mobile Computing
Volume 2021 (2021), Article ID 9915974, 10 pages
https://doi.org/10.1155/2021/9915974

Research Article

CR-NOMA Networks over Nakagami- Fading: CSI Imperfection Perspective

Anh-Tu Le¹ and Dinh-Thuan Do²

¹Faculty of Electronics Technology, Industrial University of Ho Chi Minh City (IUH), Ho Chi Minh City 700000, Vietnam
²Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan

Correspondence should be addressed to Anh-Tu Le; leanhtu@iuh.edu.vn

Received 30 March 2021; Revised 5 May 2021; Accepted 8 June 2021; Published 23 June 2021

Academic Editor: Mauro Femminella

Copyright © 2021 Anh-Tu Le and Dinh-Thuan Do. 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.

There are high demands on both massive connections and high spectrum efficiency, and the cognitive radio-based nonorthogonal multiple access (CR-NOMA) system is developed to satisfy such demand. The unreal situation of CR-NOMA is considering the perfect channel state information (CSI) in receivers. This paper indicates impacts of imperfect CSI on outage and throughput performance. In particular, we focus on performance of the secondary network related to the imperfect CSI, and we derive closed-form expressions of outage probability and throughput for the downlink in such a CR-NOMA system. Particularly, a general form of Nakagami- fading channel is adopted to examine the impact of fading on the performance of the CR-NOMA system. As the main achievement, we conduct extensive simulations and provide analyses to demonstrate the outage performance of the CR-NOMA system with CSI imperfections.

1. Introduction

Considering as a promising multiple access technique, nonorthogonal multiple access (NOMA) has been proposed due to the higher traffic adaptation and optimized spectral efficiency (SE) [1]. By sharing frequency/code/time resources, the NOMA-based networks serve multiple users allocated various power allocation (PA) coefficients. In particular, the users with weak channel qualities are required more power compared with the users possessing stronger channel conditions. The strong users benefit from successive interference cancellation (SIC) to detect their own messages [1]. By introducing a cooperative diversity gain, cooperative NOMA (co-NOMA) has been studied in various scenarios to highlight the performance of NOMA [2–5]. To enhance the reliability performance, a co-NOMA scheme was investigated in [5], in which relay nodes are assigned as the users with better channel conditions (namely, NOMA-strong users).

To further improve the SE, an underlay cognitive radio (CR) network is considered as another technique that allows secondary users (SUs) to transmit only if they do not result in reducing performance of primary users (PUs) related to harmful interference [6]. To address some of the 5G challenges including spectrum efficiency and massive connectivity, it is necessary to study the combination of NOMA with CR networks while maintaining reliable transmission of primary and secondary users. In various circumstances of the underlay CR-NOMA developed in [7–10], a superimposed signal is sent by the secondary transmitter to the secondary NOMA destination users. The authors concluded that if the PA factors and target rates are accurately chosen, the performance of NOMA users can be enhanced.

The outage performance of CR NOMA systems has been investigated extensively in the literature [11–15]. Particularly, Nakagami- fading channels are adopted in suitable systems such as [11, 15]. The authors in [16] studied overlay CR-inspired NOMA by introducing expressions of the outage probability. A similar scenario was performed in [17]; however, different from [16], only the best secondary user is chosen to forward the signal to the primary user. To assist in transmission between secondary NOMA users and a secondary base station (SBS), many relays are deployed [18]. In such a system, it is beneficial as the selected relay with the strongest link with the SBS is required to forward signals to the secondary receivers [18]. However, these papers assumed that channel state information (CSI) is available and exact estimation at the receivers. In practice, imperfect CSI occurs as the main reason of degraded performance. It motivates us to study the new system performance analysis for CR-NOMA over Nakagami- fading. In particular, we evaluate numerous new challenges when compared to previous works. These main challenges are summarized as follows.

(i)A cooperative scheme needs to be deployed in the context of NOMA transmission. Such a NOMA scheme has been recognized as a crucial approach to further improve spectral efficiency in CR-NOMA, especially for users with poor channel condition(ii)Spectrum sharing is required at the secondary network in CR-NOMA; then, transmit power constraint is necessary since the SUs are permitted to reuse the licensed spectrum bands belonging to the PU. Spectrum sharing performs well under the conditions that the interference to the PU is guaranteed to be less than an assigned threshold(iii)Outage performance of CR-NOMA network depends on the estimated channels at receiver. However, imperfect CSI make crucial impacts on SNR and then outage probability becomes worse

To address these challenges, we propose the CR-NOMA under a scenario of imperfect CSI by considering the Nakagami- fading channel model. The proposed system in this research is challenging and more realistic compared with [19]. On the comprehensiveness, we implement our proposed method through simulations. We summarize the main contributions of this research as follows.

(i)It need be improved performance of far NOMA users in CR-NOMA. Due to poor channel condition, the far user requires to assign a higher power allocation factor. Particularly, the secondary network is affected by interference from the primary nodes; then, relaying scheme should be implemented to achieve expected system improvement(ii)Impacts of interference among two networks in CR-NOMA are considered. Degraded performance can be accepted under the limitation of interference satisfied. In particular, outage probability and throughput are the two most important metrics and we derive the concrete expressions of these metrics under the constraints of transmit power and separated target rates of NOMA users(iii)Simulation results show the performance of the proposed CR-NOMA model using Nakagami- fading channel models with respect to different variable conditions. We mainly focus on the trade-off between the system outage performance and other characteristic parameters such as level of CSI imperfections, total transmit power, and target rates. The results illustrate that the system outage behavior has a satisfied value within a certain range

2. System Model

We examine a general NOMA model as a system model shown in Figure 1 wherein a downlink of CR-NOMA scenario. The relay is necessary to serve two NOMA users and which can operate in the secondary network (SN) containing secondary source (BS) intends and relay . Moreover, the relay operates in a decode-and-forward (DF) mode. It is noted that such SN deals with interference to the primary transmitter (PT) and primary destinations (PD) are operated in the primary network (PN). The transmit source BS in the SN is limited by transmit power of the PN. It is assumed that all nodes are equipped single antenna and operated in half-duplex mode. The Nakagami- fading channel coefficients are adopted in the considered system with parameter . In such a CR-NOMA system, we denote wireless channels from node to node as . In the second hop of the SN, we denote and denote the links from relay to user and , respectively. To guarantee NOMA fairness characteristic, let and be the power allocation factors. It is noted that we can assume that with [20, 21]. , , and are denoted as Additive White Gaussian Noise (AWGN) with variance of .

Figure 1: System model of CR-NOMA.

In a real scenario, the imperfect CSI-related channels are known at the receivers; it is given by [22]

where , represents the estimated channel factor, and stands for the channel estimation error. Moreover, is denoted as the interference from the PN to SN, and is the scaling coefficient of . The CSI of primary transmitters is not available at the secondary receivers as reported in [19].

To enable functions of CR in the context of the CR-NOMA system, the transmit power of secondary node is restricted as [19] , . We denote as the maximum average allowed transmit power, with denoted as the interference temperature constraint (ITC) at node PD.

There are two phases in transmission at the SN. The received signal at the relay in the first phase is computed by

To further evaluate performance, the signal-to-interference-plus-noise ratio (SINR) and signal-to-noise ratio (SNR) before and after performing SIC can be obtained to detect signal and , respectively, at and they are given by

where is defined as the transmit SNR at the BS.

In the second phase, the received signals in , , can be expressed by

Considering signal at , the SINR to detect its signal is given by

Similarly, to detect signal , , we compute the corresponding SINR at user as

3. Performance Analysis

To compute the outage probability (OP) for users , these functions are necessary, and they include the probability density function (PDF) and the cumulative distribution function (CDF) of dedicated Nakagami- channel, respectively, as [15]

where , , and represent for the Gamma function, the lower and upper incomplete Gamma function, respectively ([23], Equations 8.310.1, 8.350.1, and 8.350.2).

It is noted that the PDF of is given as , in which , , and stand for the fading severity factor and mean, respectively. Regarding Nakagami fading, we also assume is an integer number.

3.1. Outage Probability of User

The OP is determined by SNR, i.e., with being the target rate at user , and its unit is BPCU (BPCU is short for bit per channel use). Then, it can be computed the OP of as [19]

Lemma. The first term of (11) is formulated by

Proof. See the appendix.
Base on (7), we can write as

With the help of (9), (10), and some transform, it can be expressed as

where . Similarly, putting (6) into (11) and the close form of can be obtained as

Putting the result of Lemma (12), (14), and (15) into (11), the OP of is written as

□

3.2. Outage Probability of

Similarly, it can be obtained outage probability for as

Then, with the (4), can be expressed as

Similarly, the close form of is expressed as

where . Then, is written as

Finally, the closed-form outage probability of is formulated as

3.3. Asymptotic Analysis

To obtain the insight about CR-NOMA systems, the asymptotic outage probability is presented in high SNR regimes based on the outage probability. In high SNR, we use . Moreover, we can write the incomplete Gamma function as

Base on analysis, the asymptotic outage probability of is obtained as

where , , and are expressed, respectively, as

Similarly, the asymptotic outage probability of is expressed as

where

3.4. Consideration on Throughput

The achieved outage probability leads to computations on throughput to indicate the ability of transmission at fixed target rates. As an extra metric, overall throughput can be given as

4. Simulation Result

The main parameters in simulation are provided in detail. First, the fixed power allocation factors are assigned for two NOMA users, , . In addition, we set channel gains as , , , , (BPCU), , , , and  dB except for specific cases.

In Figure 2, we consider the OP versus transmit SNR at the SN. Different OP values are seen for two users at the SN due to different power allocation factors assigned to . As the main achievement, the exact matching can be seen between analytical results and simulation results. Secondly, the OP of two users are better than the OMA case in the whole range of SNR , which confirms the benefit of the CR-NOMA model. In addition, the OP can be improved as increasing of transmit SNR at the BS, which confirms the necessity of improving the quality of transmit signal.

Figure 2: The OP of two users versus transmit SNR with and .

In Figure 3, two cases of which makes impacts on the OP of two users versus the transmit SNR at secondary source are studied. It can be seen clearly that  dB is a better case. The main reason is that  dB leads to higher SINR and then OP will be improved.

Figure 3: Outage performance versus transmit varying with and .

Figure 4 plots OP of two users in different fading parameters; , , and are the better outage behavior. It can be seen clearly that leads to a better channel which plays an important role to improve the performance of destinations. The main reason is that main expressions of SINR depend on channel gains. Therefore, higher channel gains result in higher SINR and the corresponding outage performance can be enhanced. When , such outage performance meets saturation lines sooner for the case of , which confirms that the outage performance can not be adjusted by increasing SNR at the base station at high SNR region.

Figure 4: Outage performance versus SNR varying with .

Moreover, other trend of OP is similar as previous cases.

In Figure 5, the OP decreases significantly for the case of and . Two factors make influence on OP performance which is confirmed in this figure. It is important to examine OP under the impact of imperfect CSI. Therefore, related to the level of imperfect CSI is controlling parameter to target best outage performance for two users. The lower target rate is required at lower CSI imperfection CSI that provides better outage performance. It can be observed the lowest case is the OP of user that corresponds to and .

Figure 5: The OP versus transmit SNR as varying and with .

Regarding impacts of power allocation factors on OP performance, Figure 6 requires we perform simulation for case of . Figure 5 shows the different trends of OPs for two users when we change power factor as . Looking at case , the OP of the user is better than that of . However, opposite trend happens as . The main reason is that power level allocated to each user is strictly related to the OP.

Figure 6: The OP versus power allocation factor with and .

To look on throughput performance, fading parameter makes influence on the performance of two users of such a CR-NOMA, all observations as Figure 7. It can be observed that the highest throughput occurs at and . The ceiling throughput is also seen at a high region on transmit SNR as SNR is greater than 25 dB. Interestingly, the existence of corresponds to maximal throughput as simulation reported in Figure 8.

Figure 7: Throughput of system versus transmit SNR at BS with varying and .

Figure 8: Throughput of system versus with varying .

5. Conclusions

In this paper, we have studied the impact of imperfect CSI on the CR-NOMA as a possible realistic scenario. Two users are evaluated in terms of outage performance to show advantages of a developed CR-NOMA system wherein NOMA users cooperate with the relay nodes. In particular, we derived the closed-form expressions of outage probabilities and throughput to show performance gaps among two NOMA users. Our simulation analysis confirms that the target rates and power allocation factors are main impacts on these metrics. The further simulation results show that CR-NOMA yields better outage performance once we limit imperfect CSI at low level and proper power allocation factor achieved.

Appendix

Proof of Lemma

Next, we further examine the outage event at the BS for and it can be formulated by the following.

It can be recalled that

where , , and . Then, we determine the first and second term of (A.1) which are denoted as and , respectively. Firstly, can be rewritten as

where and .

Then, the first term of is denoted as and it can be obtained as

With the help of (9) and after some manipulations, it can be further achieved as

where . Then, the second term (A.2) is so-called as . With the help of (10), can be given as

The second component is computed as

in which . Similarly, is rewritten as

Putting (A.4), (A.5), and (A.7) into (A.1), we can obtain (12). It completes the proof.

Data Availability

No data were used to support this study.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

Z. Ding, X. Lei, G. K. Karagiannidis, R. Schober, J. Yuan, and V. K. Bhargava, “A survey on non-orthogonal multiple access for 5G networks: research challenges and future trends,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 10, pp. 2181–2195, 2017.

D.-T. Do and A.-T. Le, “NOMA based cognitive relaying: transceiver hardware impairments, relay selection policies and outage performance comparison,” Computer Communications, vol. 146, pp. 144–154, 2019.

T.-L. Nguyen and D.-T. Do, “Power allocation schemes for wireless powered NOMA systems with imperfect CSI: an application in multiple antenna-based relay,” International Journal of Communication Systems, vol. 31, no. 15, article 3789, 2018.

T.-L. Nguyen and D.-T. Do, “Exploiting impacts of intercell interference on SWIPT-assisted non-orthogonal multiple access,” Wireless Communications and Mobile Computing, vol. 2018, Article ID 2525492, 12 pages, 2018.

H.-P. Dang, M. S. van Nguyen, D.-T. Do, H.-L. Pham, B. Selim, and G. Kaddoum, “Joint relay selection, full-duplex and device-to-device transmission in wireless powered NOMA networks,” IEEE Access, vol. 8, pp. 82442–82460, 2020.

S. Arzykulov, G. Nauryzbayev, T. A. Tsiftsis, B. Maham, and M. Abdallah, “On the outage of underlay CR-NOMA networks with detect-and-forward relaying,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 795–804, 2019.

Y. Liu, Z. Ding, M. Elkashlan, and J. Yuan, “Non-orthogonal multiple access in large-scale underlay cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 65, no. 12, pp. 10152–10157, 2016.

D. T. Do, A. T. le, and B. M. Lee, “NOMA in cooperative underlay cognitive radio networks under imperfect SIC,” IEEE Access, vol. 8, pp. 86180–86195, 2020.

Y. Song, W. Yang, Z. Xiang, N. Sha, H. Wang, and Y. Yang, “An analysis on secure millimeter wave NOMA communications in cognitive radio networks,” IEEE Access, vol. 8, pp. 78965–78978, 2020.

G. Im and J. H. Lee, “Outage probability for cooperative NOMA systems with imperfect SIC in cognitive radio networks,” IEEE Communications Letters, vol. 23, no. 4, pp. 692–695, 2019.

J. Men, J. Ge, and C. Zhang, “Performance analysis of nonorthogonal multiple access for relaying networks over Nakagami-m fading channels,” IEEE Transactions on Vehicular Technology, vol. 66, no. 2, pp. 1200–1208, 2017.

X. Wang, J. Wang, L. He, and J. Song, “Outage analysis for downlink NOMA with statistical channel state information,” IEEE Communications Letters, vol. 7, no. 2, pp. 142–145, 2018.

J. Lee, H. Wang, J. G. Andrews, and D. Hong, “Outage probability of cognitive relay networks with interference constraints,” IEEE Transactions on Wireless Communications, vol. 10, no. 2, pp. 390–395, 2011.

Y. Zou, Y.-D. Yao, and B. Zheng, “Outage probability analysis of cognitive transmissions: impact of spectrum sensing overhead,” IEEE Transactions on Wireless Communications, vol. 9, no. 8, pp. 2676–2688, 2010.

C. Zhong, T. Ratnarajah, and K.-K. Wong, “Outage analysis of decode-and-forward cognitive dual-hop systems with the interference constraint in Nakagami-m fading channels,” IEEE Transactions on Vehicular Technology, vol. 60, no. 6, pp. 2875–2879, 2011.

L. Lv, J. Chen, Q. Ni, and Z. Ding, “Design of cooperative nonorthogonal multicast cognitive multiple access for 5G systems: user scheduling and performance analysis,” IEEE Transactions on Communications, vol. 65, no. 6, pp. 2641–2656, 2017.

L. Lv, J. Chen, and Q. Ni, “Cooperative non-orthogonal multiple access in cognitive radio,” IEEE Communications Letters, vol. 20, no. 10, pp. 2059–2062, 2016.

L. Bariah, S. Muhaidat, and A. Al-Dweik, “Error performance of NOMA-based cognitive radio networks with partial relay selection and interference power constraints,” IEEE Transactions on Communications, vol. 68, no. 2, pp. 765–777, 2020.

S. Arzykulov, T. A. Tsiftsis, G. Nauryzbayev, and M. Abdallah, “Outage performance of cooperative underlay CR-NOMA with imperfect CSI,” IEEE Communications Letters, vol. 23, no. 1, pp. 176–179, 2019.

X. Li, Q. Wang, M. Liu et al., “Cooperative wireless-powered NOMA relaying for B5G IoT networks with hardware impairments and channel estimation Errors,” IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5453–5467, 2021.

X. Li, Q. Wang, Y. Liu, T. A. Tsiftsis, Z. Ding, and A. Nallanathan, “UAV-aided multi-way NOMA networks with residual hardware impairments,” IEEE Wireless Communications Letters, vol. 9, no. 9, pp. 1538–1542, 2020.

Z. Yang, Z. Ding, P. Fan, and G. K. Karagiannidis, “On the performance of non-orthogonal multiple access systems with partial channel information,” IEEE Transactions on Communications, vol. 64, no. 2, pp. 654–667, 2016.

D. Zwillinger, Table of Integrals, Series, and Products, 7 Ed, Academic Press, 2007.