TOTAL SPECTRAL EFFICIENCY MAXIMIZATION IN MULTI-USERS COGNITIVE RADIO NETWORKS WITH ENERGY-HARVESTING CAPABILITY

Authors

  • E. Obayiuwana Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ile- Ife, Nigeria.
  • O. Ipinnimo Department of Systems Engineering, University of Lagos, Lagos, Nigeria.
  • P. Ayodele Department of Electrical Electronic and Computer Engineering, Cape Peninsula University of Technology, Cape Town, South Africa.
  • F. C. Oluwaseyi Department of Systems Engineering, University of Lagos, Lagos, Nigeria.

DOI:

https://doi.org/10.4314/njt.v43i3.12

Keywords:

Radio resource management, Cognitive radio networks, RF energy harvesting, Spectral efficiency optimization, Underlay spectrum access

Abstract

In this paper, the joint radio resource management issues in a cognitive radio network driven by radio frequency energy harvesting (CRN-RF-EH) functionalities are investigated. For the CRN-RF-EH, the cognitive radio (CR) node first harvests its required energy directly from the transmitter of   spectrum licensed user for its data communication and consequently transmits its data on the licensed frequency of the legacy user using the underlay accessing technique.   Thus, RF-EH is an exciting innovation for energizing low-powered   next-generation wireless networks (NGWNs). Consequently, due to CRN-RF-EH’d low power limitations, the resource allocation for CRN-RF-EH has to be optimized considering the trade-off among spectral efficiency, energy efficiency, and RF energy supply.   Equal allocation of transmission time and/or transmission power may not be efficient for CRN-RF-EH with limited transmission time and power resources. A joint optimal time and power allocation (OTPA) strategy for CRN-RF-EH is proposed to maximise the total spectral efficiency   of the CRN-RF-EH.  The coupled variables in the formulated joint resource allocation problems create a non-convex optimization problem formulation. For analytical tractability, the non-convex optimization formulation is initially converted to its equivalent standard convex optimization formulation using proper variables and next, it is then solved using the convex optimization technique.  The CONOPT solver, a powerful optimization-solving tool for solving convex optimization problems, is utilized to resolve the equivalent standard convex optimization problem formulation. When compared with the baseline biased random time optimum power allocation (BRTOPA) scheme, numerical simulation results show that the OTPA strategy dramatically improves the total spectral efficiency performance. In a severe radio propagation environment with a path loss exponent (PLE)  equal to 3.5 such as in urban areas and less severe radio propagation environment with a path loss exponent (PLE) equal to 2.0, such as in rural areas, the OTPA  outperformed the BROTPA with a mean performance improvement of approximately 23 and,, respectively.

References

[1] Al-Sudani, H. Thabit, A. A. and Dalveren, Y. “Cognitive radio and its applications in the new trend of communication system: A review,” in 2022 5th International Conference on Enginee-ring Technology and its Applications(IICETA), 2022, pp. 419–423.

[2] Ijala, A.D.,Thomas, S. and Adetokun, B. “The Role Of Energy Harvesting In 5G Wireless Networks Connectivity”, IEEE Nigeria 4th International Conference on Disruptive Techn-ologies for Sustainable Development (NIGERCON),pp1-5,2022,ISBN2377-2697, Doi: 10.1109/NIGERCON54645.2022.980300 2

[3] Banerjee, A. and Maity, S. P. “Cognitive radio networks with energy harvesting and eavesdropping-emulation resilience,in - 2020 International Conference on Comunication Systems & Networks (COMSNETS), 2020, pp. 873-878.

[4] Sharmila, A. and Dananjayan, P. “Spectrum sharing techniques in cognitive radio networks: a survey,” in 2019 IEEE International Confere-nce on System, Computation, Automation and Networking (ICSCAN), 2019, pp. 1–4.

[5] S. Sasikumar, S. and Jayakumari, J. “Spectral-Energy Efficiency Tradeoff Enhancement: an Optimal Resource Allocation Framework for 5G Underlay Cognitive Radio Network”, IEEE EUROCON 2021 - 19th International Confere-nce on Smart Technologies, pp.284-289, 2021, Doi 10.1109/EUROCON 52738.2021.9535631

[6] Arora, S., Nijhawan, G., Verma,G. and Patel, R.G., “A systematic survey on various energy harvesting systems for WSN applications”, 2021 International Conference on Industrial Electronics Research and Applications (ICIERA), pp1-5, Doi: 10.1109/ICIERA53202. 2021.9726530

[7] Garman, S. M., Affandy, V. and Smith, J. R. “Harvesting Watts at Ultra-High Frequencies”, IEEE Wireless Power Technology Conference and Expo (WPTCE), pp.1-6, 2023, Doi: 10.11 09/WPTCE56855.2023.10216166

[8] Mhunkaew, T., Kawdungta, S. and Torrungrueng, D. “Dual-band UHF and HF-RFID Tag Antenna for Tracking and Energy Harvesting Applications”, International Electri-cal Engineering Congress (iEECON), pp.72-75, 2023, Doi: 0.1109/iEECON56657.2023.10126 836

[9] Hameed, I., and Koo, I. “Joint optimization of time and power in energy-constrained wireless powered communication network,” in - 2021 International Conference on Information and Communication Technology Convergence (ICTC), 2021, pp. 207–211.

[10] Lee, S. and Zhang, R. “Cognitive wireless powered net work: Spectrum sharing models and throughput maximization,” IEEE Transac-tions on Cognitive Communications and Networking, vol. 1, no. 3, pp. 335–346, 2015.

[11] Usman, M. and Koo, I. “Throughput maximization of the cognitive radio using hybrid (overlay-underlay) approach with energy harvesting,” in 2014 12th International Conference on Frontiers of Information Technology, 2014, pp. 22–27

[12] Zheng, K. Liu, X.-Y. Liu, X. and Zhu, Y. “Hybrid overlay-underlay cognitive radio networks with energy harvesting,” IEEE Transactions on Communications, vol. 67, no. 7, pp. 4669–4682, 2019.

[13] Rakovic, V., Denkovski, D., Hadzi-Velkov, Z. and Gavrilovska, L. “Optimal time sharing in underlay cognitive radio systems with rf energy harvesting,” in 2015 IEEE International Conference on Communications (ICC), 2015, pp. 7689–7694.

[14] Xu, C., Zheng, M. Liang, W., Yu H., and Liang, Y.-C. “Outage performance of underlay multihop cognitive relay networks with energy harvesting,” IEEE Communications Letters, vol. 20, no. 6, pp. 1148–1151, 2016.

[15] Xu, C., Zheng, M., Liang, W., Yu, H., and Liang, Y. “End-to-end throughput maximization for underlay multi-hop cognitive radio networks with rf energy harvesting,” IEEE Transactions on Wireless Communicatio-ns, vol. 16, no. 6, pp. 3561–3572, 2017.

[16] Grante, F.,Abib, G.,Muller, M. and Samama, N.,”Super Capacitor and WiFi Speed Optimiza-tion for RF Energy Harvesting”, International Conference on Electrical, Computer, Commu-nications and Mechatronics Engineering (ICECCME), pp.1-6,2022, Doi: 10.1109/ICEC CME55909.2022.9988105.

[17] Boyd, S., and Vandenberghe, L. “Convex optimization Cambridge university press, 2004.

[18] Koch, T., Berthold, T., Pedersen, J., and Vanaret, C. “Progress in mathematical progr-amming solvers from 2001 to 2020” , EURO Journal on Computational Optimization, Vol. 10, pp 100031, 2022, ISBN 2192-4406, https://www.sciencedirect.com/science/article/pii/S2192440622000077, Doi: 10.1016/j.ejco.2 022.100031

[19] Li, Y., Ren, X., Wang, S., Han, Y. and Zhang, T, “ Target Detection with Optimal Power Allocation and Quantization for Distributed MIMO DFRC System”, 20CIE International Conference on Radar (Radar), pp. 2559-2563, 2021, ISBN1097-5764, Doi 10.1109/Radar538 47.2021.10028539

[20] Liu, S., Wang, Z., Tian, Q., and Lin, H. “Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity” Transportation Research Part E: Logistics and Transportation Review. Vol. 181, pp.103376, 2024, ISBN1366-5545, https ://www.sciencedirect.com/science/article/pii/S1366554523003642, doi:10.1016/j.tre.2023.10 3376

[21] Nojavan, S. and A. Attar, A., “Optimal Energy Operation in DC Microgrids Including Hydro-Pumped Storage in the presence Demand Response Program”, 2023 8th International Conference on Technology and Energy Management (ICTEM), pp.1-5, 2023, Doi10.11 09/ICTEM56862.2023.10083898

[22] Lahsen-Cherif, L., Zitoune, L. and Vèque,V., “Energy Efficient Routing for Wireless Mesh Networks with Directional Antennas: When Q-learning meets Ant systems” Ad Hoc Networks, Vol. 121, pp 102589, 2021, ISBN 1570-8705, https://www.sciencedirect.com/Science/article/pii/S1570870521001268, Doi 10.1016/j.adhoc. 2021.102589

[23] Drud, A. S. “Conopt—a large-scale grg code,” INFORMS Journal on Computing, vol. 6, no. 2, pp. 207–216, 1994. [Online].Available: https:/ /EconPapers.repec.org/RePEc:inm:orijoc:v:6: y:1994:i:2:p:207-216

Downloads

Published

2024-09-20

Issue

Section

Computer, Telecommunications, Software, Electrical & Electronics Engineering

How to Cite

TOTAL SPECTRAL EFFICIENCY MAXIMIZATION IN MULTI-USERS COGNITIVE RADIO NETWORKS WITH ENERGY-HARVESTING CAPABILITY. (2024). Nigerian Journal of Technology, 43(3). https://doi.org/10.4314/njt.v43i3.12