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A word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Abouaomar, A., Cherkaoui, S., Mlika, Z., & Kobbane, A. (2022). Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach. IEEE Systems Journal, 16(4), 5357-5368. External link
Abouaomar, A., Mlika, Z., Filali, A., Cherkaoui, S., & Kobbane, A. (2021, October). A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks [Paper]. 46th IEEE Conference on Local Computer Networks (LCN 2021), Edmonton, Alberta, Canada. External link
Abouaomar, A., Cherkaoui, S., Mlika, Z., & Kobbane, A. (2021, December). Mean-field game and reinforcement learning MEC resource provisioning for SFC [Paper]. IEEE Global Communications Conference (IEEE GLOBAL 2021), Madrid, Spain. External link
Abouaomar, A., Cherkaoui, S., Mlika, Z., & Kobbane, A. (2021). Resource Provisioning in Edge Computing for Latency Sensitive Applications. IEEE Internet of Things Journal, 8(14), 11088-11099. External link
Filali, A., Mlika, Z., & Cherkaoui, S. (2024). Open RAN Slicing for MVNOs With Deep Reinforcement Learning. IEEE Internet of Things Journal, 3365665 (15 pages). External link
Filali, A., Mlika, Z., Cherkaoui, S., & Kobbane, A. (2022). Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services. IEEE Transactions on Network Science and Engineering, 9(4), 2174-2187. External link
Filali, A., Mlika, Z., Cherkaoui, S., & Kobbane, A. (2020). Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications. IEEE Transactions on Vehicular Technology, 69(12), 15947-15963. External link
Hojatian, H., Mlika, Z., Nadal, J., Frigon, J.-F., & Leduc-Primeau, F. (2024). Learning energy-efficient transmitter configurations for massive MIMO beamforming. IEEE Transactions on Machine Learning in Communications and Networking, 2, 939-955. Available
Hentati, A., Mlika, Z., Frigon, J.-F., & Ajib, W. (2022, April). Energy Harvesting Wireless Sensor Networks: Inter-delivery-aware Scheduling Algorithms [Paper]. 2022 IEEE Wireless Communications and Networking Conference (WCNC 2022), Austin, TX, USA. External link
Mlika, Z., Do, T. N., Larabi, A., Vo, J. D., Frigon, J.-F., & Leduc-Primeau, F. (2024, October). Online energy-efficient beam bandwidth partitioning in mmwave mobile networks [Paper]. IEEE 100th Vehicular Technology Conference (VTC2024-Fall), Washington, DC, USA (6 pages). External link
Mlika, Z., Cherkaoui, S., Laprade, J. F., & Corbeil-Letourneau, S. (2023). User trajectory prediction in mobile wireless networks using quantum reservoir computing. IET Quantum Communication, 4(3), 125-135. Available
Mlika, Z., & Cherkaoui, S. (2022). Deep deterministic policy gradient to minimize the age of information in cellular V2X communications. IEEE Transactions on Intelligent Transportation Systems, 23(12), 23597-23612. External link
Moudoud, H., Mlika, Z., Khoukhi, L., & Cherkaoui, S. (2022). Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model. IEEE Transactions on Network Science and Engineering, 9(5), 2978-2990. External link
Mlika, Z., & Cherkaoui, S. (2021, June). Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks [Paper]. IEEE International Conference on Communications (ICC 2021), Montreal, Qc, Canada (6 pages). External link
Mlika, Z., & Cherkaoui, S. (2021). Massive IoT Access with NOMA in 5G Networks and Beyond using Online Competitiveness and Learning. IEEE Internet of Things Journal, 8(17), 13624-13639. External link
Mlika, Z., & Cherkaoui, S. (2021). Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach. External link
Mlika, Z., & Cherkaoui, S. (2021). Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles. IEEE Network, 35(3), 132-138. External link
Mlika, Z., Ajib, W., Jaafar, W., & Haccoun, D. (2012, September). On the performance of relay selection in cognitive radio networks [Paper]. 76th IEEE Vehicular Technology Conference, VTC Fall 2012, Québec City, QC, Canada. External link
Nour, B., Cherkaoui, S., & Mlika, Z. (2022). Federated Learning and Proactive Computation Reuse at the Edge of Smart Homes. IEEE Transactions on Network Science and Engineering, 9(5), 3045-3056. External link
Triwinarko, A., Mlika, Z., Cherkaoui, S., & Dayoub, I. (2022, October). Deep Reinforcement Learning to Improve Vehicle-to-Vulnerable Road User Communications in C-V2X [Paper]. 8th International Symposium on ubiquitous Networking (UNeT 2022), Montreal, Qc, Canada. External link
Taik, A., Mlika, Z., & Cherkaoui, S. (2022). Clustered Vehicular Federated Learning: Process and Optimization. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25371-25383. External link
Taïk, A., Mlika, Z., & Cherkaoui, S. (2022). Data-Aware Device Scheduling for Federated Edge Learning. IEEE Transactions on Cognitive Communications and Networking, 8(1), 408-421. External link