A Survey on Collaborative Optimization Technologies for Edge Computing and Cognitive Radio Convergence
DOI:
https://doi.org/10.54097/yvqmnb13Keywords:
Cognitive radio, edge computing, spectrum sensing and allocation, task offloading, energy efficiency optimization.Abstract
This paper provides a systematic review of collaborative optimization technologies integrating edge computing (EC) and cognitive radio (CR), exploring their pivotal role and challenges in 6G integrated sensing, communication, and computing (ISAC). Edge computing, with its distributed computational support, significantly enhances CR's capabilities in real-time spectrum sensing and dynamic allocation. Meanwhile, CR's dynamic spectrum access technology offers flexible communication resources for edge computing task offloading, effectively alleviating spectrum scarcity while reducing communication latency and energy consumption. The study focuses on spectrum sensing and allocation, task offloading, and energy efficiency optimization, and analyzing the strengths and limitations of existing technologies. It also identifies future research directions, including key challenges such as security in dynamic resource scheduling, standardized architecture design, and lightweight AI model development. This paper aims to provide theoretical references and technical pathways for the deep integration of edge computing and cognitive radio in 6G networks, advancing the synergistic development of communication, sensing, and computing.
Downloads
References
[1] K. Meng, C. Masouros, A. P. Petropulu, and L. Hanzo, “Cooperative ISAC Networks: Opportunities and Challenges,” IEEE Wireless Communications, vol. 32, no. 3, pp. 212–219, June 2025
[2] “Global IoT connections forecast to rise above 40 billion in 2034.” Accessed: Aug. 07, 2025. [Online]. Available: https://transformainsights.com/news/global-iot-connections-forecast-above-40-billion-2034
[3] F. Liu, G. Tang, Y. Li, Z. Cai, X. Zhang, and T. Zhou, “A Survey on Edge Computing Systems and Tools,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1537–1562, Aug. 2019
[4] A. Ali and W. Hamouda, “Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications,” IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1277–1304, 2017
[5] T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116–130, 2009
[6] Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A Survey on Mobile Edge Computing: The Communication Perspective,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2322–2358, 2017
[7] G. I. Tsiropoulos, O. A. Dobre, M. H. Ahmed, and K. E. Baddour, “Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 824–847, 2016
[8] M. El Tanab and W. Hamouda, “Resource Allocation for Underlay Cognitive Radio Networks: A Survey,” IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1249–1276, 2017
[9] A. Islam, A. Debnath, M. Ghose, and S. Chakraborty, “A Survey on Task Offloading in Multi-access Edge Computing,” Journal of Systems Architecture, vol. 118, p. 102225, Sept. 2021
[10] B. Liu, J. Wang, S. Ma, F. Zhou, Y. Ma, and G. Lu, “Energy-Efficient Cooperation in Mobile Edge Computing-Enabled Cognitive Radio Networks,” IEEE Access, vol. 7, pp. 45382–45394, 2019
[11] J. Gao, X. Yi, C. Zhong, X. Chen, and Z. Zhang, “Deep Learning for Spectrum Sensing,” IEEE Wireless Communications Letters, vol. 8, no. 6, pp. 1727–1730, Dec. 2019
[12] P. Bhattacharya et al., “A Deep-Q Learning Scheme for Secure Spectrum Allocation and Resource Management in 6G Environment,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4989–5005, Dec. 2022
[13] H. Peng, Q. Ye, and X. Shen, “Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 7, pp. 3001–3012, July 2020
[14] FZ. Benidris, S. Limam, “A novel distributed spectrum management in mobile edge computing based cognitive radio Internet of Things networks.” Multiagent and Grid Systems. 2021;17(4):367-382.
[15] S. Wang, X. Zhu, H. Sun, M. Zhang, and T. Bian, “Spectrum sensing-spectrum allocation strategy based on edge computing for cognitive-radio internet of things network,” Journal of Electronic Measurement and Instrumentation, vol. 37, no. 7, pp. 81–92, Mar. 2023
[16] B. Liang, R. Fan, H. Hu, H. Jiang, J. Xu, and N. Zhang, “Joint Task Offloading and Resource Allocation in Multi-User Mobile Edge Computing with Continuous Spectrum Sharing,” IEEE Transactions on Vehicular Technology, vol. 73, no. 5, pp. 7234–7249, May 2024
[17] [17] A. A. Olawole, F. Takawira, and C. S. Chabalala, “Task Offloading and Computation in Hybrid uRLLC and eMBB Cognitive Radio Enabled 5G Based Multiuser Edge Computing,” IEEE Access, vol. 13, pp. 79871–79893, 2025
[18] B. Liu, W. Li, Y. Ma, J. Wang, and G. Lu, “Wireless Powered Cognitive-Based Mobile Edge Computing with Imperfect Spectrum Sensing,” IEEE Access, vol. 7, pp. 80431–80442, 2019
[19] D. Wang, F. Zhou, W. Lin, Z. Ding, and N. Al-Dhahir, “Cooperative Hybrid Nonorthogonal Multiple Access-Based Mobile-Edge Computing in Cognitive Radio Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 1104–1117, June 2022,
[20] W. Ejaz and M. Ibnkahla, “Multiband Spectrum Sensing and Resource Allocation for IoT in Cognitive 5G Networks,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 150–163, Feb. 2018
[21] A. A. Elnaim et al., “Energy Consumption for Cognitive Radio Network Enabled Multi-Access Edge Computing,” in 2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA), Oct. 2023, pp. 1–5
[22] P. Si, H. Liang, W. Wu, and Y. Zhang, “Joint Resource Management in Cognitive Radio and Edge Computing Based Industrial Wireless Networks,” in Globecom 2017 - 2017 IEEE Global Communications Conference, Dec. 2017, pp. 1–6
[23] C. Xu, P. Zhang, and H. Yu, “Lyapunov-Guided Resource Allocation and Task Scheduling for Edge Computing Cognitive Radio Networks via Deep Reinforcement Learning,” IEEE Sensors Journal, vol. 25, no. 7, pp. 12253–12264, Apr. 2025
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
