About Me
Wireless Communications and AI/ML Research — 5G/6G RAN, Dynamic Spectrum Sharing, and Machine Learning
Hossein Mohammadi is a Ph.D. Candidate and Graduate Research Assistant in Electrical Engineering, with a minor in Computer Science, at Mississippi State University, working at the intersection of 5G/6G radio access networks, dynamic spectrum sharing, and machine learning. He has first-authored IEEE publications spanning VTC, WCNC, ICC, MILCOM, DySPAN, and CCNC, and contributes to two NSF-funded programs on active–passive spectrum coexistence and secure 5G communications. His work centers on designing deep-learning and reinforcement-learning methods for interference mitigation, spectrum selection, and network resource management, validated through system-level simulation and SDR-based 5G testbeds.
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-enabled wireless systems and radio transceivers
- O-RAN, 5G/6G, network slicing, and resource management
- Dynamic spectrum sharing and active–passive coexistence
- RF-interference mitigation and passive radiometry
- Secure and resilient 5G communications
- Software-defined radio and 5G COTS testbeds
- MIMO, beamforming, full-duplex, and interference-limited communications
Mississippi State University · Graduate Research Assistant · 2021–Present
NSF SWIFT-SAT / INTERACT — Learning-Based RFI Mitigation for Active–Passive Spectrum Coexistence (2024–Present)
- Designed a deep-reinforcement-learning (DDQN) spectrum-selection framework identifying interference-dominant 5G NR subbands for passive radiometer receivers operating in shared spectrum.
- Developed neural successive-interference-cancellation modules — U-Net time–frequency masking and a two-stage MLP — to suppress structured 5G OFDM interference while preserving passive radiometric baselines.
- First-authored the IEEE DySPAN 2025 paper on AI-assisted successive interference cancellation and two follow-on manuscripts extending the framework.
NSF Convergence Accelerator Track G — Combating Vulnerability and Unawareness in 5G Network Security (2023–Present)
- First-authored the IEEE MILCOM 2024 paper on defending 5G networks against jamming attacks with multipath communications.
- Contributed to an error-pattern steganographic communication framework concealing covert data within standard 5G traffic, and to hardware-in-the-loop validation on a 5G Standalone COTS testbed (srsRAN, Open5GS, USRP B210).
O-RAN, Federated Learning, and 6G Network Management (Dissertation Research, 2021–Present)
- Developed SliceFed, a federated constrained multi-agent deep-reinforcement-learning framework (Lagrangian primal–dual PPO with federated averaging) for dynamic spectrum slicing with URLLC latency guarantees in dense 6G radio access networks (submitted to IEEE DySPAN 2026).
- Evaluated reinforcement-learning schemes for trajectory optimization of aerial radio units in non-terrestrial 6G deployments (IEEE ICC 2023).
AI-Enabled Radio Transceivers and Nonlinear Receivers
- Developed AI-driven demodulation approaches for nonlinear receivers operating in shared spectrum with high-power blockers (IEEE WCNC 2022).
- Investigated opportunities and challenges of artificial-neural-network architectures for radio transceivers (IEEE VTC-Fall 2021).
University of Tehran · Graduate Researcher · 2016–2019
- Developed self-interference management techniques for in-band full-duplex wireless systems (M.Sc. thesis).
- Implemented and compared adaptive beamforming algorithms and simulated space–time block coding and MIMO systems.
- Conducted signal-processing studies on resampling, windowing, and power-spectral-density estimation, and explored neural-network methods for wireless applications.
Programming and scientific computing: Python (proficient) · MATLAB (proficient) · Bash · Linux
AI and machine learning: Deep learning · Multi-layer perceptrons · U-Net architectures · Deep reinforcement learning (DDQN, PPO) · Constrained MDPs · Federated learning
ML frameworks: TensorFlow/Keras · PyTorch · Sionna
Wireless and communication systems: 5G NR · O-RAN/Open RAN · Network slicing · Wireless resource allocation · Non-terrestrial networks · Spectrum sharing · RF sensing and passive radiometry · Interference mitigation · Wireless security · MIMO, MU-MIMO, and massive MIMO · Millimeter-wave communications · Adaptive beamforming · Full-duplex communications
Signal processing and analysis: Digital and statistical signal processing · Information theory · STFT and PSD analysis · OFDM waveform analysis · Channel modeling (CDL) · Estimation theory · Adaptive filtering · Monte Carlo evaluation
Testbeds and platforms: srsRAN · GNU Radio · USRP B210 · MATLAB 5G Toolbox · Wireshark · POWDER wireless testbed · Hardware-in-the-loop 5G experimentation
Research and documentation: LaTeX · Git/GitHub · Overleaf · IEEE manuscript preparation · Technical writing · Reproducible research
Selected Participant, CyberPowder Fellows Program
Second Place, ECE Research Symposium
Third Place, ECE 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.