Muhammad Fayaz

About

Muhammad Fayaz (Member, IEEE) received his Ph.D. degree in computer science from Queen Mary University of London, U.K., in 2023. He is currently a lecturer in the Department of Computer Science and IT, University of Malakand, Pakistan, since 2023.

His research interests include mobile ad hoc networks, the Internet of Things (IoT), NOMA, and machine learning for optimization in 5G and beyond wireless communication systems.

Employment

• University of Malakand Assistant Professor
  2023 - Present

Education

• Queen Mary University of London PhD in Computer Science
  2019 - 2023

Projects & Funding

• PhD Scholarship under University of Malakand, Pakistan Faculty Development Program Higher Education Commission, Pakistan

Publications (14)

• Intent-Based Networking With Deep Reinforcement Learning for Detecting Decreased Rank Attacks in Low-Power and Lossy IoT Networks
  Muhammad Haqdad, Muhammad Fayaz, Pervez Khan +4 more
  IEEE Internet of Things Journal 2026 DOI: 10.1109/JIOT.2025.3596097
  +Save
• A Federated Learning-Based Hybrid Architecture for Multi-Class Attack Detection in UAVs Communications
  IEEE Open Journal of the Communications Society 2026 DOI: 10.1109/OJCOMS.2026.3698792
  +Save
• Hybrid Learning Framework for Throughput Enhancement in IoT Networks With Semi-Grant-Free NOMA
  IEEE Transactions on Cognitive Communications and Networking 2026 DOI: 10.1109/TCCN.2026.3714800
  +Save
• Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA
  IEEE Transactions on Consumer Electronics 2025 DOI: 10.1109/TCE.2024.3442568
  +Save
• Learning Meets Logic: Fuzzy Logic Based Deep Matching for Intelligent User Association in 6G IoT Networks
  IEEE Open Journal of the Communications Society 2025 DOI: 10.1109/OJCOMS.2025.3615411
  +Save
• Toward Autonomous Power Control in Semi-Grant-Free NOMA Systems: A Power Pool-Based Approach
  IEEE Transactions on Communications 2024 DOI: 10.1109/TCOMM.2024.3361535
  +Save
• Semi-Centralized Optimization for Energy Efficiency in IoT Networks With NOMA
  IEEE Wireless Communications Letters 2023 DOI: 10.1109/LWC.2022.3227135
  +Save
• Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework
  IEEE Transactions on Vehicular Technology 2023 DOI: 10.1109/TVT.2023.3254138
  +Save
• Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
  Electronics 2022 DOI: 10.3390/electronics11020185
  +Save
• Transmit Power Pool Design for Grant-Free NOMA-IoT Networks via Deep Reinforcement Learning
  IEEE Transactions on Wireless Communications 2021 DOI: 10.1109/TWC.2021.3086762
  +Save
• Transmit Power Pool Design for Uplink IoT Networks with Grant-free NOMA
  IEEE International Conference on Communications 2021 DOI: 10.1109/ICC42927.2021.9500849
  +Save
• Transmit power pool design for grant-free NOMA-IoT networks via deep reinforcement learning
  arXiv 2020
  +Save
• Effect of terrain size and pause time on the performance of reactive routing protocols
  Jurnal Teknologi 2016 DOI: 10.11113/jt.v78.8258
  +Save
• Performance analysis of TCP/AQM under Low-Rate Denial-of-Service attacks
  Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016 2016 DOI: 10.1109/INVENTIVE.2016.7830116
  +Save