About Me
AI/ML for O-RAN, 6G Non-Terrestrial Networks, Wireless Resource Management, and Intelligent Radio Systems
Hossein Mohammadi is a Ph.D. Candidate and Graduate Research Assistant in Electrical Engineering, with a minor in Computer Science, at Mississippi State University. His research focuses on AI-assisted network management for O-RAN and 6G non-terrestrial networks, including federated learning, network slicing, wireless resource allocation, interference mitigation, and robust communication under nonlinear and interference-limited conditions. His background includes wireless communications, MIMO and massive MIMO, machine learning for communication systems, deep reinforcement learning, and SDR-based experimentation.
Ph.D. Candidate in Electrical and Electronics Engineering
Mississippi State University
M.Sc. in Electrical Engineering — Communication Systems
University of Tehran
My research examines how artificial intelligence and machine learning can improve the adaptability, reliability, and efficiency of wireless communication systems.
- AI/ML for wireless communication systems and radio transceivers
- O-RAN and AI-assisted network slicing
- 6G non-terrestrial networks
- Federated learning and distributed resource management
- Deep reinforcement learning for wireless optimization
- MIMO, multiuser MIMO, and massive MIMO
- Spectrum coexistence and interference mitigation
- Millimeter-wave communication and wireless security
Mississippi State University · Graduate Research Assistant · 2021–Present
- AI-assisted network and resource management, federated learning, and network slicing for O-RAN and 6G systems
- AI-driven demodulation and MLP-based self-interference mitigation under receiver nonlinearities and high-power blockers
- Reinforcement-learning-based aerial radio-unit trajectory analysis and DRL-based RFI mitigation for passive radiometry
- 5G security experimentation using multipath communication and srsRAN
University of Tehran · Graduate Researcher · 2016–2019
- In-band full-duplex self-interference management and modulation-performance analysis
- Signal-processing studies involving sampling, windowing, power spectral density, adaptive beamforming, STBC, and MIMO simulation
- Neural-network applications in wireless communication
Programming: Python · MATLAB · C++
AI and machine learning: Machine learning · Deep learning · Multi-layer perceptrons · Deep reinforcement learning · Federated learning
Wireless and communication systems: O-RAN · Network slicing · MIMO and massive MIMO · Adaptive beamforming · Signal processing · Spectrum coexistence · Interference mitigation · Wireless resource allocation
SDR and networking tools: srsRAN · GNU Radio · Wireshark
Scientific and documentation tools: LaTeX · Git · Linux · Adobe Illustrator · Adobe Lightroom · Adobe Photoshop
Selected Participant, CyberPOWER Wireless Experimentation Program
Second Place, Best Research Symposium
Third Place, Best Research Symposium
Selected Participant, COLOSSEUM Open RAN Digital Twin Workshop
Graduate Teaching Assistant · University of Tehran
Random Variables and Stochastic Processes
Led weekly instructional sessions and prepared problem sets to support student learning.