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Optimal Resource Allocation for RF-Powered Underlay Cognitive Radio Networks With Ambient Backscatter Communication | IEEE Journals & Magazine | IEEE Xplore

Optimal Resource Allocation for RF-Powered Underlay Cognitive Radio Networks With Ambient Backscatter Communication


Abstract:

In this paper, we study radio frequency (RF)-powered underlay cognitive radio networks (CRNs) with power-domain non-orthogonal multiple access (NOMA). In these networks, ...Show More

Abstract:

In this paper, we study radio frequency (RF)-powered underlay cognitive radio networks (CRNs) with power-domain non-orthogonal multiple access (NOMA). In these networks, by using the harvest-then-transmit (HTT) mode, secondary transmitters (STs) can use the harvested energy to simultaneously transmit data based on power-domain NOMA. However, in this mode, the throughput of the secondary system heavily depends not only on the harvested energy, but also on the stringent interference threshold imposed by the primary users. Furthermore, ambient backscatter communication (ABC) has been introduced as a promising technique which enables STs to transmit information by modulating and reflecting ambient RF signals. Therefore, it has the potentiality to be integrated into the RF-powered underlay CR-NOMA networks to improve the throughput of the secondary system. In these networks, each ST works on either the HTT mode or the ABC mode, but not simultaneously. In order to meet the interference constraint of the primary users, STs control their transmit power by finding the appropriate tradeoff between the HTT mode and the ABC mode. We formulate an optimization problem with the goal of achieving the maximum throughput by finding the optimal time resource allocation between the HTT mode and the ABC mode under the strict transmit power constraint at STs. Then the Lagrangian multiplier iterative algorithm is adopted to solve this optimization problem. Simulation results demonstrate that our proposed scheme can significantly improve the performance of the secondary system by comparing it with the other two baseline schemes.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 69, Issue: 12, December 2020)
Page(s): 15216 - 15228
Date of Publication: 10 November 2020

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I. Introduction

The growing number of both emerging applications and wireless devices has led to an increasing spectrum demand in modern wireless networks [1]. Underlay cognitive radio (CR) paradigm has been confirmed as an effective technique to improve the spectrum utilization efficiency [2]. The key idea of underlay CR is that secondary transmitters (STs) are allowed to transmit their own information as long as they do not create severe interference to the primary users [3]. Meanwhile, with the rapid development of radio frequency energy harvesting (RF EH) technique, RF-powered underlay CR networks (CRNs) have drawn growing attention [4]. In RF-powered underlay CRNs, STs first harvest energy from the primary signals based on the RF EH technique, and then they utilize the harvested energy to perform the data information when they cause tolerable interference to the primary users. It has been shown that RF-powered underlay CRNs can not only improve the spectrum utilization efficiency, but also enhance the energy efficiency [5].

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1.
C. Luo, G. Min, F. R. Yu, M. Chen, L. T. Yang and V. C. M. Leung, "Energy-efficient distributed relay and power control in cognitive radio cooperative communications", IEEE J. Sel. Areas Commun., vol. 31, no. 11, pp. 2442-2452, Nov. 2013.
2.
F. Mehmeti and T. Spyropoulos, "Performance analysis comparison and optimization of interweave and underlay spectrum access in cognitive radio networks", IEEE Trans. Veh. Technol., vol. 67, no. 8, pp. 7143-7157, Aug. 2018.
3.
S. Arzykulov, G. Nauryzbayev, T. A. Tsiftsis and M. Abdallah, "On the performance of wireless powered cognitive relay network with interference alignment", IEEE Trans. Commun., vol. 66, no. 9, pp. 3825-3836, Sep. 2018.
4.
B. Lyu, H. Guo, Z. Yang and G. Gui, "Throughput maximization for hybrid backscatter assisted cognitive wireless powered radio networks", IEEE Internet Things J., vol. 5, no. 3, pp. 2015-2024, Jun. 2018.
5.
D. T. Hoang, D. Niyato, P. Wang, D. I. Kim and Z. Han, "Ambient backscatter: A new approach to improve network performance for RF-powered cognitive radio networks", IEEE Trans. Commun., vol. 65, no. 9, pp. 3659-3674, Sep. 2017.
6.
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 J. Sel. Areas Commun., vol. 35, no. 10, pp. 2181-2195, Oct. 2017.
7.
Y. Liu, F. R. Yu, X. Li, H. Ji, H. Zhang and V. C. M. Leung, "Joint access and resource management for delay-sensitive transcoding in ultra-dense networks with mobile edge computing", Proc. IEEE Int. Conf. Commun., pp. 1-6, May 2018.
8.
S. Arzykulov, G. Nauryzbayev, T. A. Tsiftsis and B. Maham, "Performance analysis of underlay cognitive radio nonorthogonal multiple access networks", IEEE Trans. Veh. Technol., vol. 68, no. 9, pp. 9318-9322, Sep. 2019.
9.
L. Lv, J. Chen, Q. Ni, Z. Ding and H. Jiang, "Cognitive non-orthogonal multiple access with cooperative relaying: A new wireless frontier for 5G spectrum sharing", IEEE Commun. Mag., vol. 56, no. 4, pp. 188-195, Apr. 2018.
10.
D. Do, A. Le and B. M. Lee, "NOMA in cooperative underlay cognitive radio networks under imperfect SIC", IEEE Access, vol. 8, pp. 86 180-86 195, 2020.
11.
Z. Song, X. Wang, Y. Liu and Z. Zhang, "Joint spectrum resource allocation in NOMA-based cognitive radio network with swipt", IEEE Access, vol. 7, pp. 89 594-89 603, Jul. 2019.
12.
X. Wang et al., "Energy efficiency optimization for NOMA-based cognitive radio with energy harvesting", IEEE Access, vol. 7, pp. 139 172-139 180, 2019.
13.
H. D. Thai, D. Niyato, P. Wang, D. I. Kim and Z. Han, "The tradeoff analysis in RF-powered backscatter cognitive radio networks", Proc. IEEE Glob. Commun. Conf., pp. 1-6, Dec. 2016.
14.
G. Yang, D. Yuan, Y. Liang, R. Zhang and V. C. M. Leung, "Optimal resource allocation in full-duplex ambient backscatter communication networks for wireless-powered IoT", IEEE Internet Things J., vol. 6, no. 2, pp. 2612-2625, Apr. 2019.
15.
V. Liu, A. Parks, V. Talla, S. Gollakota, D. Wetherall and J. R. Smith, "Ambient backscatter: Wireless communication out of thin air", Comput. Commun. Rev., vol. 43, no. 4, pp. 39-50, Aug. 2013.
16.
R. Kishore, S. Gurugopinath, P. C. Sofotasios, S. Muhaidat and N. Al-Dhahir, "Opportunistic ambient backscatter communication in RF-powered cognitive radio networks", IEEE Trans. Cogn. Commun. Netw., vol. 5, no. 2, pp. 413-426, Jun. 2019.
17.
H. Guo, Y. Liang, R. Long and Q. Zhang, "Cooperative ambient backscatter system: A symbiotic radio paradigm for passive IoT", IEEE Wireless Commun. Lett., vol. 8, no. 4, pp. 1191-1194, Aug. 2019.
18.
W. Wang, D. T. Hoang, D. Niyato, P. Wang and D. I. Kim, "Stackelberg game for distributed time scheduling in RF-powered backscatter cognitive radio networks", IEEE Trans. Wireless Commun., vol. 17, no. 8, pp. 5606-5622, Aug. 2018.
19.
X. Liu, Y. Gao and F. Hu, "Optimal time scheduling scheme for wireless powered ambient backscatter communications in IoT networks", IEEE Internet Things J., vol. 6, no. 2, pp. 2264-2272, Apr. 2019.
20.
X. Lu, P. Wang, D. Niyato, D. I. Kim and Z. Han, "Wireless networks with RF energy harvesting: A contemporary survey", IEEE Commun. Surveys Tut., vol. 17, no. 2, pp. 757-789, Apr.–Jun. 2015.
21.
V. Rakovic, D. Denkovski, Z. Hadzi-Velkov and L. Gavrilovska, "Optimal time sharing in underlay cognitive radio systems with RF energy harvesting", Proc. IEEE Int. Conf. Commun., pp. 7689-7694, Jun. 2015.
22.
S. K. Nobar, K. A. Mehr and J. M. Niya, "RF-powered green cognitive radio networks: Architecture and performance analysis", IEEE Commun. Lett., vol. 20, no. 2, pp. 296-299, Feb. 2016.
23.
Y. H. Bae and J. W. Baek, "Performance analysis of delay-constrained traffic in a cognitive radio network with RF energy harvesting", IEEE Commun. Lett., vol. 23, no. 12, pp. 2177-2181, Oct. 2019.
24.
C. Luo, G. Min, F. R. Yu, Y. Zhang, L. T. Yang and V. C. M. Leung, "Joint relay scheduling channel access and power allocation for green cognitive radio communications", IEEE J. Sel. Areas Commun., vol. 33, no. 5, pp. 922-932, May 2015.
25.
D. T. Hoang, D. Niyato, P. Wang, D. I. Kim and L. B. Le, "Overlay RF-powered backscatter cognitive radio networks: A game theoretic approach", Proc. IEEE Int. Conf. Commun., pp. 1-6, May 2017.
26.
D. Munir, S. T. Shah, K. W. Choi, T.-J. Lee and M. Y. Chung, "Performance analysis of wireless-powered cognitive radio networks with ambient backscatter", EURASIP J. Wireless Commun. Netw., vol. 1, pp. 1-13, Feb. 2019.
27.
K. H. Park, D. Munir, J. S. Kim and M. Y. Chung, "Integrating RF-powered backscatter with underlay cognitive radio networks", Proc. Int. Conf. Inf. Netw., pp. 288-292, Jan. 2017.
28.
D. Wang and S. Men, "Secure energy efficiency for NOMA based cognitive radio networks with nonlinear energy harvesting", IEEE Access, vol. 6, pp. 62 707-62 716, 2018.
29.
S. Mao, S. Leng, J. Hu and K. Yang, "Power minimization resource allocation for underlay MISO-NOMA swipt systems", IEEE Access, vol. 7, pp. 17 247-17 255, 2019.
30.
N. Van Huynh, D. T. Hoang, X. Lu, D. Niyato, P. Wang and D. I. Kim, "Ambient backscatter communications: A contemporary survey", IEEE Commun. Surv. Tut., vol. 20, no. 4, pp. 2889-2922, Dec. 2018.
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References

References is not available for this document.