Eslam Hasan

Eslam Hasan

Assistant Professor in the Department of Computer Science, Engineering & Mathematics, College of Sciences and Engineering

Publications

Journals

  1. E. Mahalal, E. Hasan, M. Ismail, Z.-Y. Wu, M. M. Fouda, Z. M. Fadlullah, and N. Kato, “GAN-based Artificial Noise Generation Against Eavesdropping In Dynamic Indoor LiFi Networks,” Under Review in IEEE Transactions on Wireless Communications.

  2. ​​E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, and N. Kato, “mmWave and Terahertz Indoor Channel Prediction under Data Drift in Real-world Scenarios,” Under Review in IEEE Transactions on Cognitive Communications and Networking.

  3. Eslam B. Ali, Kishk, S. & Abdelhay, E.H. Multi-device Multi-task Computation Offloading in Device to Device Communication. Wireless Pers Commun 123, 1883–1896 (2022).

  4. Eslam B. Ali, Sherif Kishk, Ehab H. Abdelhay, Multidimensional auction for task allocation using computation offloading in fifth generation networks, Future Generation Computer Systems, Volume 108, 2020.

Magazines

  1. E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, and N. Kato, “Towards Robust Channel Prediction in 6G Networks: Mitigating the concept drift using ISAC,” Under Review in IEEE Wireless Communications.

Conferences

  1. E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, and N. Kato, “Sensing-aided Terahertz Channel Prediction: A Robust Deep Learning Approach Against Concept Drift,” Under Review in 2025 IEEE 60th International Conference on Communication (ICC), 2025.
  2. ​E. Mahalal, E. Hasan, M. Ismail, Z.-Y. Wu, M. M. Fouda, and Z. M. Fadlullah, “Deep Learning-based Physical Layer Authentication Against Impersonation Attacks in LiFi Networks,” Under Review in 2025 IEEE 60th International Conference on Communication (ICC), 2025.
  3. E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, and Z. M. Fadlullah, “Communication-aided Terahertz Sensing: A Novel Indoor People Counting System Via Deep Learning,” Accepted in 2024 IEEE 2nd Virtual Conference on Communications (VCC), 2024.
  4. ​E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, and N. Kato, “Occupancy-level-aware Indoor Terahertz Channel Prediction: A Robust Deep Learning Approach,” Accepted in 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall), 2024.
  5. E. Hasan, E. Mahalal, M. Ismail, Z.-Y. Wu, M. M. Fouda, T. Koketsu Rodrigues, and N. Kato, “Robust deep learning-based indoor mmwave channel prediction under concept drift,” in 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 2023, pp. 1–5.