AI-powered Adaptive Spectrum Dynamics (ASD) for Military Radios: Enhancing Spectrum Efficiency and Security
Keywords:
AI, Adaptive Spectrum Dynamics, Military Radios, Machine Learning, Jamming Mitigation, Spectrum Optimization, Communication ResilienceAbstract
The increasing demand for wireless communication in military operations necessitates efficient spectrum utilization. This paper proposes an AI-powered Adaptive Spectrum Dynamics (ASD) framework for military radios, leveraging machine learning and cognitive radio techniques. ASD optimizes spectrum allocation, mitigates interference, and enhances security. Simulation results demonstrate improved spectrum efficiency (25%) and reduced interference (40%) compared to traditional methods. The increasing complexity of modern battlefields demands robust and adaptive communication systems. This paper presents a comprehensive study on AI-Powered Adaptive Spectrum Dynamics (ASD) in military radios, emphasizing its role in optimizing spectrum usage, ensuring resilience against interference, and securing communication channels. By leveraging machine learning algorithms, ASD enables real-time spectrum monitoring, dynamic allocation, and mitigation of jamming threats. The paper discusses architecture, key technologies, challenges, and practical implementations of ASD in military communication systems, demonstrating its potential to revolutionize tactical communications.
Downloads
References
Babu, S. C. V., Artificial Intelligence and Expert Systems, Anniyappa Publications, 2022.
Benzaïd, C., & Taleb, T., "AI for Beyond 5G Networks: A Cyber-Security Defense or Offense Enabler?" IEEE Network, vol. 34, no. 6, pp. 140–147, Nov./Dec. 2020. doi:10.1109/MNET.011.2000088.
Chakrabarty, S., & Engels, D. W., "Secure Smart Cities Framework Using IoT and AI," in 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Dubai, UAE, 2020. doi:10.1109/GCAIoT51063.2020.9345912.
Dhoni, P., & Kumar, R., "Synergizing Generative AI and Cybersecurity: Roles of Generative AI Entities, Companies, Agencies, and Government in Enhancing Cybersecurity," TechRxiv, 2023.
Doshi, P., & Badawy, A., "Machine Learning in Cybersecurity: A Review," Journal of Cybersecurity and Mobility, vol. 8, no. 1, pp. 1–27, 2019.
Haleem, A., Javaid, M., Singh, R. P., Rab, S., & Suman, R., "Perspectives of cybersecurity for ameliorative Industry 4.0 era: A review-based framework," The Industrial Robot, vol. 49, no. 3, pp. 582–597, 2022. doi:10.1108/IR-10-2021-0243.
JPMorgan Chase & Co., "JPMorgan Chase to Use AI in Its Fight Against Fraud," 2018.
Kadel, R., & Kadel, R., "Impact of AI on Cyber Security," International Journal of Scientific Research and Engineering Development, vol. 5, no. 6, 2022.
Kavya Balaraman, "PG&E deploys machine learning to safeguard its grid against California wildfires," Utility Dive, 2020. [Online]. Available: https://www.utilitydive.com/news/wildfires-pushed-pge-into-bankruptcy-should-other-utilities-be-worried/588435/.
Mughal, A. A., "The Art of Cybersecurity: Defense in Depth Strategy for Robust Protection," International Journal of Intelligent Automation and Computing, vol. 1, no. 1, pp. 1–20, 2018.
Panimalar, A., "Artificial Intelligence Techniques for Cybersecurity," 2018.
Qamar, F., Siddiqui, M. U. A., Hindia, M. N., Hassan, R., & Nguyen, Q. N., "Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks," Electronics, vol. 9, no. 9, p. 1416, 2020.
Rony, R. I., Lopez-Aguilera, E., & Garcia-Villegas, E., "Dynamic Spectrum Allocation Following Machine Learning-Based Traffic Predictions in 5G," IEEE Access, vol. 9, pp. 143458-143472, 2021.
Sarker, I. H., "Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective," SN Computer Science, vol. 2, no. 5, p. 377, 2021.
Sheth, K., Patel, K., Shah, H., Tanwar, S., Gupta, R., & Kumar, N., "A Taxonomy of AI Techniques for 6G Communication Networks," Computer Communications, vol. 161, pp. 279-303, 2020.
Soori, M., Arezoo, B., & Dastres, R., "Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, a Review," Cognitive Robotics, vol. 3, pp. 54-70, 2023.
Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., & Wu, K., "Artificial-Intelligence-Enabled Intelligent 6G Networks," IEEE Network, vol. 34, no. 6, pp. 272-280, 2020.
Ahmad, W. S. H. M. W., Radzi, N. A. M., Samidi, F. S., Ismail, A., Abdullah, F., Jamaludin, M. Z., & Zakaria, M., "5G Technology: Towards Dynamic Spectrum Sharing Using Cognitive Radio Networks," IEEE Access, vol. 8, pp. 14460-14488, 2020.
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., ... & Wattam, S., "Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review," Renewable and Sustainable Energy Reviews, vol. 130, p. 109899, 2020.
Bhattacharyya, A., Nambiar, S. M., Ojha, R., Gyaneshwar, A., Chadha, U., & Srinivasan, K., "Machine Learning and Deep Learning Powered Satellite Communications: Enabling Technologies, Applications, Open Challenges, and Future Research Directions," International Journal of Satellite Communications and Networking, vol. 41, no. 6, pp. 539-588, 2023.
Chataut, R., Nankya, M., & Akl, R., "6G Networks and the AI Revolution— Exploring Technologies, Applications, and Emerging Challenges," Sensors, vol. 24, no. 6, p. 1888, 2024.
Dalla Pozza, N., Buffoni, L., Martina, S., & Caruso, F., "Quantum Reinforcement Learning: The Maze Problem," Quantum Machine Intelligence, vol. 4, no. 1, p. 11, 2022.
Dhabliya, D., Al–Jawahry, H. M., Sharma, V., Jayadurga, R., & Jasmin, M., "Long Short-Term Memory (LSTM) Networks for Stock Market Prediction," in 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), 2023, pp. 01-07.
Elsayed, M., & Erol-Kantarci, M., "AI-Enabled Future Wireless Networks: Challenges, Opportunities, and Open Issues," IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 70-77, 2019.
Feriani, A., & Hossain, E., "Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial," IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 1226-1252, 2021.
Hamad, R., "An Assessment of Artificial Neural Networks, Support Vector Machines, and Decision Trees for Land Cover Classification Using Sentinel-2A Data," Sciences, vol. 8, no. 6, pp. 459-464, 2020.
Houssein, E. H., Gad, A. G., Hussain, K., & Suganthan, P. N., "Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application," Swarm and Evolutionary Computation, vol. 63, p. 100868, 2021.
Huang, C., Hu, S., Alexandropoulos, G. C., Zappone, A., Yuen, C., Zhang, R., ...& Debbah, M., "Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends," IEEE Wireless Communications, vol. 27, no. 5, pp. 118-125, 2020.
Kaur, A., & Kumar, K., "A Comprehensive Survey on Machine Learning Approaches for Dynamic Spectrum Access in Cognitive Radio Networks," Journal of Experimental & Theoretical Artificial Intelligence, vol. 34, no. 1, pp. 1-40, 2022.
Kebede, T., Wondie, Y., Steinbrunn, J., Kassa, H. B., & Kornegay, K. T., "Multi-Carrier Waveforms and Multiple Access Strategies in Wireless Networks: Performance, Applications, and Challenges," IEEE Access, vol. 10, pp. 21120-21140, 2022.
Nassar, A., & Yilmaz, Y., "Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities," IEEE Internet of Things Journal, vol. 9, no. 1, pp. 222-235, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.