We focus on leveraging advanced technologies and UAV-assisted communication to overcome challenges in next-generation wireless networks. Our research addresses issues like eavesdropping, injecting false information, and RIS malfunctions. By integrating optimization techniques and deep neural networks, we enhance the security and efficiency of these systems. Additionally, we address practical concerns such as the lack of standardized protocols, authentication, and security updates, which are critical in environments ranging from smart cities to autonomous systems. We demonstrate improved signal integrity and robustness against physical and network-level attacks through rigorous analysis and simulation.
Publications:
- Shakil Ahmed at. el. “Quantum-Enhanced Zero Trust Framework with Dynamic Anomaly Detection and Adaptive Microsegmentation in 7G Technology” submitted to IEEE Transactions On Quantum Engineering
- S. Ahmed and B. Bash, “Average worst-case secrecy rate maximization via UAV and base station resource allocation,” in Proc. 57th Allerton Conference on Communication, Control, and Computing, Illinois, pp. 1176-1181, Sept. 2019.
- S. Arnab, S. Alam, T. Mahmud, S. Sabuj, and S. Ahmed. “Deep Convolutional Generative Adversarial Networks: Performance Analysis in Wireless Systems,” Discover Internet of Things, Springer (accepted), 2024.
- M. Chowdhury, and S. Ahmed, “A New Conditional Generative Adversarial Network for Massive MIMO Channel Estimation,” submitted to 2024 Arabian Journal for Science and Engineering (AJSE).
- S. Sabuj, S. Ahmed, and A. Khokhar. “Spectrum Scarcity in Vehicular Networks in 6G: Architecture, Challenges and Solutions,” in preparation for IEEE Vehicular Technology Magazine.
- S. Ahmed, M.Z. Chowdhury, S. Sabuj, Y. Jang, and I. Alam. “Energy-efficient UAV relaying robust resource allocation in uncertain adversarial networks,” IEEE Access, vol. 9, pp. 59920-59934, 2021.