An FPGA-Based Hardware Architecture for Energy-Efficient Spectrum Sensing in Cognitive Radio Networks
DOI:
https://doi.org/10.31838/NJAP/07.02.32Keywords:
FPGA implementation, spectrum sensing, cognitive radio networks, energy-efficient design, hardware architecture, real-time signal processing, dynamic spectrum accessAbstract
Cognitive Radio Networks (CRNs) have become an interesting approach to alleviating the global spectrum scarcity and electrical bands underutilization. An important feature in CRNs is spectrum sensing that allows secondary users to identify spectrum holes without disturbing the operation of the primary users. But traditional sensing technologies based on software experience severe latency and energy wastage that restricts its application in real-time and low-energy constrained demands. This paper introduces a new hardware architecture of implementing an energy-efficient spectrum sensing based on the energy detection technique through a Field-Programmable Gate Array (FPGA). The suggested architecture utilizes parallel processing, pipelined processing and clock gating in order to achieve lower latency and power usage. The architecture was developed on a Xilinx Artix-7 FPGA and used a 36 percent saving in power dissipation and showed 2.1x increase in throughput over CPU-based implementations. It is compatible with sensing thresholds and sample window sizes that can be configured on the fly and also, adapt to changing Radio frequency (RF) conditions. Its architecture is rather appropriate to be put into practice in Software-Defined Radios (SDRs), IoT-based CRNs, and mobile edge devices with low-latency and power-saving spectrum access needs. Real-time deployment and functional validation attest the compatibility of the design to be used in CRNs that demand low-energy and fast-processing. The findings confirm that the proposed solution of FPGA-based sensing platform is feasible and scalable to the next-generation cognitive radio systems.
References
1. Yucek, T., & Arslan, H. (First Quarter 2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys & Tutorials, 11(1), 116–130, https://doi.org/10.1109/SURV.2009.090109
2. Palanisamy, R., Jayapal, S., Rafi, M. R., Kaliyamoorthy, K., & Kattubadi, J. B. (2023). Consistent information broad cast over cognitive radio AD HOC networks. International Journal of Advances in Engineering and Emerging Technology, 14(1), 222–228.
3. Sipho, T., Lindiwe, N., & Ngidi, T. (2025). Nanotechnology recent developments in sustainable chemical processes. Innovative Reviews in Engineering and Science, 3(2), 35–43. https://doi.org/10.31838/INES/03.02.04
4. Hoang, A. T., & Liang, Y.C. (Sep. 2006). A two-phase spectrum sensing scheme for cognitive radio systems, in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, Finland, pp. 1–5. https://doi.org/10.1109/PIMRC.2006.254209
5. Meymari, B. K., Mofrad, R. F., & Nasab, M. S. (2015). High dynamic range receiver system designed for high pulse repetition frequency pulse radar. International Academic Journal of Innovative Research, 2(2), 28–47.
6. Al Gailani, M. F. (2025). Hardware design and implementation of secure hash algorithm based on FPGA. Journal of Internet Services and Information Security, 15(2), 209226. https://doi.org/10.58346/JISIS.2025.I2.015
7. Luthra, P., & Phadke, K. (2024). An energy conservation technique utilizing FPGA in dynamic wireless sensor networks. International Academic Journal of Science and Engineering, 11(3), 40–43. https://doi.org/10.71086/I AJSE/V11I3/IAJSE1160
8. Zhang, L., Xu, Y., & Hu, W. (May 2009). Real-time spectrum sensing system based on FPGA for cognitive radio, in Proceedings of the IEEE International Conference on Mechatronics and Automation, Chengdu, China, pp. 484-487. https://doi.org/10.1109/ICINDMA.2009.5220214
9. Pragadeswaran, S., Vasanthi, M., Boopathy, E. V., Suthakaran, S., Madhumitha, S., Ragupathi, N., Nikesh, R., & Devi, R. P. (2024). Optimizing VLSI architecture with carry look ahead technology based high-speed, inexact speculative adder. Archives for Technical Sciences, 2(31), 220–229. https://doi.org/10.70102/afts.2024.1631.220
10. Calef, R. (2025). Quantum computing architectures for future reconfigurable systems. SCCTS Transactions on Reconfigurable Computing, 2(2), 38–49. https://doi.org/10.31838/RCC/02.02.06
11. Boregowda, V. K. S., Sahu, B. J. R., Nayaka, R. J., Rai, D. S., Sharma, S., & Vinil, B. (2025). Multi-RAT coordination frameworks in 6G wireless architectures. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 16(2), 755–774. https://doi.org/10.58346/JOWUA.2025.I2.046
12. Fatma, A., & Ayşe, M. (2025). Secure data transmission advances for wireless sensor networks in IoT applications. Journal of Wireless Sensor Networks and IoT, 2(1), 20–30.
13. Muralidharan, J. (2024). Advancements in 5G technology: Challenges and opportunities in communication networks. Progress in Electronics and Communication Engineering, 1(1), 1–6. https://doi.org/10.31838/PECE/01.01.01
14. Sio, A. (2025). Integration of embedded systems in health care monitoring: Challenges and opportunities. SCCTS Journal of Embedded Systems Design and Applications, 2(2), 9–20.
15. Sadulla, S. (2024). Development of a wireless power transfer system for low-power biomedical implants using resonant RF coupling. National Journal of RF Circuits and Wireless Systems, 1(2), 27–36.
16. Zhang, X., & Liang, J. (2019). Design of an FPGA-based spectrum sensing platform for cognitive radios. IEEE Access, 7, 62100–62110.
17. Velliangiri, A. (2025). An edge-aware signal processing framework for structural health monitoring in IoT sensor networks. National Journal of Signal and Image Processing, 1(1), 18–25.
18. Basu, K., Chatterjee, P. B., & Kundu S. (Jan. 2012). Low power VLSI architecture for energy detection in cognitive radio applications, in proceedings of the annual International Conference on VLSI Design, Pune, India, pp. 313–318. https://doi.org/10.1109/VLSID.2012.60
19. Velliangiri, A. (2025). Low-power IoT node design for remote sensor networks using deep sleep protocols. National Journal of Electrical Electronics and Automation Technologies, 1(1), 40–47.
20. Haykin (Feb. 2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.
21. Attar, A., Shahid, A., Khan, M. A., & Safdar, G. A. (2018). Hardware-accelerated energy detection for cognitive radios. In Proceedings of the IEEE International Conference on Communications (ICC), IEEE. pp. 1234-1239. https://doi.org/10.1109/ICC.2018.8422871
22. Ali, W., Ashour, H., & Murshid, N. (2025). Photonic integrated circuits: Key concepts and applications. Progress in Electronics and Communication Engineering, 2(2), 1–9. https://doi.org/10.31838/PECE/02.02.01
