AI-Driven Adaptive mmWave RF Systems Fundamentals Training by Tonex
Modern wireless frontiers are shifting—this program equips engineers to architect, deploy, and govern AI-enabled adaptive mmWave RF systems that perform under real-world dynamics. You’ll learn how learning-based beamforming, predictive blockage handling, and closed-loop control lift link reliability and spectral efficiency. Security is addressed end-to-end: adversarial robustness in models, integrity of control loops, and resilience against spoofing or RF deception. Cybersecurity implications are woven throughout, covering attack surfaces created by AI inference at the edge and protecting data, models, and telemetry. The result is a practical framework for building trustworthy, high-performance mmWave systems ready for scale.
Learning Objectives
- Explain mmWave propagation, phased arrays, and hybrid beamforming
- Design AI pipelines for beam prediction and tracking
- Implement real-time blockage detection and autonomous beam recovery
- Validate model performance, uncertainty, and drift in the field
- Architect human-on-the-loop oversight and safety interlocks
- Apply threat modeling to AI control paths ensuring cybersecurity throughout
Audience
- RF Engineers and Architects
- Wireless System Designers
- AI/ML Engineers working in communications
- Network Planners and Solution Engineers
- Product Managers and Technical Leaders
- Cybersecurity Professionals
Course Modules
Module 1 – mmWave RF Essentials
- Propagation characteristics and path loss
- Phased arrays and beam steering basics
- Hybrid analog–digital architectures
- Link budgets and blockage sensitivities
- Antenna calibration and impairments
- Regulatory bands and deployment contexts
Module 2 – AI Beam Prediction
- Feature engineering from CSI and sensors
- Supervised vs. reinforcement approaches
- Sequence models for mobility dynamics
- Online learning and model adaptation
- Uncertainty estimation and fallback logic
- KPI alignment: SNR, EVM, throughput
Module 3 – Real-Time Blockage Detection
- Multi-sensor fusion for occlusion cues
- Fast classifiers vs. lightweight CNNs
- Edge inference constraints and latency
- Thresholding, hysteresis, false alarms
- Dataset curation and drift handling
- Telemetry for continuous improvement
Module 4 – Autonomous Beam Recovery
- Candidate beam set generation strategies
- Exploration vs. exploitation policies
- Micro-reacquisition under mobility
- Controller stability and loop timing
- Interference awareness and coexistence
- Fail-safe reversion and watchdogs
Module 5 – Trust and Verification
- Test plans: lab, OTA, and field A/B
- Robustness against adversarial RF
- Data integrity, signing, and lineage
- Model cards, governance, and audit
- Explainability for operational teams
- Performance SLAs and acceptance gates
Module 6 – Human-on-the-Loop Operations
- Decision boundaries and escalation paths
- Operator UIs and actionable telemetry
- Incident response for AI control faults
- Rollback, blue–green, and canary releases
- Compliance, safety, and documentation
- Lifecycle: versioning, updates, retirement
Ready to build resilient, high-performance mmWave systems with trustworthy AI at the core? Enroll now to master the tools, patterns, and governance needed to deliver adaptive RF that is fast, secure, and dependable.