Publications

2020 -

  • D. H. Tashman, S. Cherkaoui and W. Hamouda, “Maximizing Reliability in Overlay Radio Networks With Time Switching and Power Splitting Energy Harvesting,” in IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 4, pp. 1307-1316, Aug. 2024, doi: 10.1109/TCCN.2024.3375500.
  • D. H. Tashman, S. Cherkaoui, W. Hamouda and S. M. Senouci, “Securing Overlay Cognitive Radio Networks Over Cascaded Channels with Energy Harvesting,” 2023 IEEE Globecom Workshops (GC Wkshps), Kuala Lumpur, Malaysia, 2023, pp. 620-625, doi: 10.1109/GCWkshps58843.2023.10464644.
  • D. H. Tashman, S. Cherkaoui and W. Hamouda, “Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks,” ICC 2024 – IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 293-298, doi: 10.1109/ICC51166.2024.10622458.
  • D. H. Tashman and S. Cherkaoui, “Securing Next-Generation Networks against Eavesdroppers: FL-Enabled DRL Approach,” 2024 International Wireless Communications and Mobile Computing (IWCMC), Ayia Napa, Cyprus, 2024, pp. 1643-1648, doi: 10.1109/IWCMC61514.2024.10592377.
  •  D. H. Tashman, S. Cherkaoui and W. Hamouda, “Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels,” in IEEE Systems Journal, doi: 10.1109/JSYST.2024.3442017.
    Paper link: https://ieeexplore.ieee.org/document/10646359
  • B. Kalfon, S. Cherkaoui, J.-F. Laprade, O. Ahmad, and S. Wang, “Successive data injection in conditional quantum GAN applied to time series anomaly detection,” IET Quantum Communication, vol. 5, no. 3, pp. 269–281, 2024, doi: 10.1049/qtc2.12088.
  • W. Hamhoum, H. Lakhdar, and S. Cherkaoui, “Fortifying Open RAN Security with Zero Trust Architecture and Transformers,” in ICC 2024 – IEEE International Conference on Communications, 2024.
  • H. Moudoud, W. Hamhoum, and S. Cherkaoui, “Strengthening Open Radio Access Networks: Advancing Safeguards Through ZTA and Deep Learning,” in GLOBECOM 2023 – 2023 IEEE Global Communications Conference, 2023.
  • H. Moudoud and S. Cherkaoui, “Multi-tasking federated learning meets Blockchain to foster Trust and Security in the Metaverse”, Ad Hoc Networks, 2023
  • D. H. Tashman, S. Cherkaoui and W. Hamouda, “Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning,” 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 2023, pp. 1160-1165, doi: 10.1109/IWCMC58020.2023.10182973.
  • H. Moudoud and S. Cherkaoui, “Federated Learning Meets Blockchain to Secure the Metaverse,” 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 2023, pp. 339-344, doi: 10.1109/IWCMC58020.2023.10182956.
  • A. Allouis, A. A. Hamza, I. Dayoub and S. Cherkaoui, “Maximum Sum Rate of MCM-NOMA in Future Vehicular Sensor Networks,” in IEEE Sensors Letters, doi: 10.1109/LSENS.2023.3288938.
  • Z. Mlika and S. Cherkaoui, J.F. Laprade, S. Corbeil Letourneau, “User trajectory prediction in mobile wireless networks using quantum reservoir computing”, IET Quantum Communication. Jan 2023, doi: 10.1049/qtc2.12061
  • A. Abouaomar, A. Taik, A. Filali and S. Cherkaoui, “Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks,” in IEEE Communications Magazine, vol. 61, no. 2, pp. 126-132, February 2023, doi: 10.1109/MCOM.007.2200555.
  • Z. Mlika and S. Cherkaoui, “Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communications,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 23597-23612, Dec. 2022, doi: 10.1109/TITS.2022.3190799.
  • A. Filali, B. Nour, S. Cherkaoui and A. Kobbane, “Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning,” in IEEE Communications Standards Magazine, vol. 7, no. 1, pp. 66-73, March 2023, doi: 10.1109/MCOMSTD.0002.2100078.
  • W. Rafique, A. S. Hafid and S. Cherkaoui, “Complementing IoT Services Using Software-Defined Information Centric Networks: A Comprehensive Survey,” in IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23545-23569, 1 Dec.1, 2022, doi: 10.1109/JIOT.2022.3206146.
  • A. Abouaomar, S. Cherkaoui, Z. Mlika and A. Kobbane, “Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach,” in IEEE Systems Journal, vol. 16, no. 4, pp. 5357-5368, Dec. 2022, doi: 10.1109/JSYST.2022.3171232.
  • B. Nour and S. Cherkaoui, “How Far Can We Go in Compute-less Networking: Computation Correctness and Accuracy,” in IEEE Network, vol. 36, no. 4, pp. 197-202, July/August 2022, doi: 10.1109/MNET.012.2100157.
  • A. Taïk, Z. Mlika and S. Cherkaoui, “Clustered Vehicular Federated Learning: Process and Optimization,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25371-25383, Dec. 2022, doi: 10.1109/TITS.2022.3149860.
  • A. Taïk, Z. Mlika and S. Cherkaoui, “Data-Aware Device Scheduling for Federated Edge Learning,” in IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 1, pp. 408-421, March 2022, doi: 10.1109/TCCN.2021.3100574.
  • H. Moudoud, Z. Mlika, L. Khoukhi and S. Cherkaoui, “Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model,” in IEEE Transactions on Network Science and Engineering, vol. 9, no. 5, pp. 2978-2990, 1 Sept.-Oct. 2022, doi: 10.1109/TNSE.2022.3161479.
  • A. Filali, Z. Mlika, S. Cherkaoui, A. Kobbane., “Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services”, IEEE Transactions on Network Science and Engineering, 2022.doi:10.1109/TNSE.2022.3157274.
  • O. A. Dambri and S. Cherkaoui, “Modeling Self-Assembly of Polymer-Based Wired Nano-Communication Channel,” in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 8, no. 2, pp. 107-118, June 2022, doi: 10.1109/TMBMC.2021.3118943.
  • H. Moudoud and S. Cherkaoui, “Toward Secure and Private Federated Learning for IoT using Blockchain,” GLOBECOM 2022 – 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 4316-4321, doi: 10.1109/GLOBECOM48099.2022.10000623.
  • A. Triwinarko, S. Cherkaoui., “Performance of Radio Access Technologies for Next Generation V2VRU Networks”, IEEE International Conference on Communications (IEEE ICC 2022), Seoul, Korea, Republic of (1-6).
  • A. Triwinarko, S. Cherkaoui and I. Dayoub, “Performance of PHY/MAC Cross-Layer Design for Next-Generation V2X Applications,” 2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Indonesia, 2022, pp. 98-104, doi: 10.1109/IoTaIS56727.2022.9975999.
  • A. Triwinarko, Z. Mlika, S. Cherkaoui and I. Dayoub, “Reinforcement Learning to Improve Vehicle-to-Vulnerable Road User Communications in C-V2X”, UNet 2022, Montreal, pp. 138-150 B.
  • Nour, S. Cherkaoui., “Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks”, IEEE International Conference on Communications (IEEE ICC 2022), Seoul, Korea, Republic of (1-6)
  • A. Taik, B. Nour and S. Cherkaoui, “Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning,” in IEEE Wireless Communications, vol. 28, no. 6, pp. 26-33, December 2021, doi: 10.1109/MWC.017.2100187.
  • A. Taik, Z. Mlika, S. Cherkaoui., “Data-Aware Device Scheduling for Federated Edge Learning”, IEEE Transactions on Cognitive Communications and Networking, 2021, doi:10.1109/TCCN.2021.3100574.
  • A. Alalewi, I. Dayoub and S. Cherkaoui, “On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey,” in IEEE Access, vol. 9, pp. 107710-107737, 2021, doi: 10.1109/ACCESS.2021.3100472.
  • O. Chughtai, M. H. Rehmani, L. Musavian, S. -M. Senouci, S. Cherkaoui and S. Mao, “IEEE Access Special Section Editorial: Flying Ad Hoc Networks: Challenges, Potentials, Future Applications, and Way Forward,” in IEEE Access, vol. 9, pp. 74189-74193, 2021, doi: 10.1109/ACCESS.2021.3077816.
  • A. Abouaomar, S. Cherkaoui, Z. Mlika and A. Kobbane, “Resource Provisioning in Edge Computing for Latency Sensitive Applications“, in IEEE Internet of Things Journal. 
  • Z. Mlika and S. Cherkaoui, “Massive iot access with noma in 5g networksand beyond using online competitiveness and learning” IEEE Internet of Things Journal, 2021, doi: 10.1109/JIOT.2021.3068061
  • Z. Mlika and S. Cherkaoui, “Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles“, in IEEE Network, doi: 10.1109/MNET.011.2000591.
  • A. Filali, Z. Mlika, S. Cherkaoui and A. Kobbane, “Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications“, in IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2020.3038918.
  • A. Taïk, S. Cherkaoui, “Federated Edge Learning : Design Issues and Challenges“,accepted for publication in  IEEE Network, Aug. 2020.
  • H. Moudoud,  L. Khoukhi, S. Cherkaoui, “ Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT“, accepted for publication in  IEEE Network, Aug. 2020.
  • A. Filali, A. Abouaomar, S. Cherkaoui, A. Kobbane, M. Guizani, “Multi-Access Edge Computing: A Survey,” in IEEE Access, vol. 8, pp. 197017-197046, 2020, doi: 10.1109/ACCESS.2020.3034136.
  • A. Taïk, S. Cherkaoui , “Electrical Load Forecasting Using Edge Computing and Federated Learning“,  procceding of IEEE ICC 2020, Dublin, June 2020
  • O. A. Dambri, S. Cherkaoui, “Toward a Wired Ad Hoc Nanonetwork“, procceding of IEEE ICC 2020, Dublin, June 2020
Scroll to Top