Centram Centre for Applied Artificial Intelligence

AI & Machine Learning Course for Telecommunication Regulation Management

AI & ML for Telecommunication Regulation Management

Target Audience: Professionals in telecom policy, radio spectrum management, Regulation Agencies, or industry legal and compliance teams)

Course description

In modern Telecom Regulation AI and machine learning methods is requirement to support telecom regulators in spectrum management, monitoring QoS/coverage, detecting fraud/non‑compliance, market and tariff analysis, consumer protection, and network security. Emphasis on applied methods (NLP, supervised/unsupervised learning, anomaly detection, GNNs, RL) plus legal, privacy, interpretability and deployment considerations in regulatory contexts.

Learning objectives

  1. Translate regulatory challenges into ML/AI problem formulations.
  2.  Pre-process telecom datasets (CDRs, QoS probes, spectrum scans, regulatory filings, crowdsourced measurements).
  3. Apply supervised and unsupervised techniques for compliance monitoring, fraud detection, and QoS assessment.
  4. Use NLP for policy analysis, regulatory text mining and stakeholder sentiment.
  5. Model telecom networks with graphs and apply GNNs for coverage and capacity inference.
  6. Design simulation and RL approaches for dynamic spectrum or resource allocation experiments.
  7. Evaluate models under regulatory constraints: interpretability, fairness, privacy, robustness, auditability.
  8. Produce reproducible analyses and policy-ready visualizations/reports.

Course content

Framing regulation problems for AI

  1. Regulatory use cases: spectrum assignment/monitoring, QoS compliance, number portability & fraud, market concentration, tariff review, consumer complaints triage.
  2. Problem framing: detection, forecasting, optimization, simulation, and NLP for documents.
  3.  Project kick-off: pick one regulatory problem and dataset.

Data sources & pre-processing in telecom regulation

  1. Typical datasets: CDRs (aggregates), probe/crowdsourced speed tests (Ookla, M-Lab), network KPI feeds, spectrum occupancy scans, regulatory filings, consumer complaints, billing/tariff data.
  2.  Data cleaning, aggregation, anonymization, feature engineering, geospatial joins, dealing with sampling bias.
  3. Lab: ingest and clean a crowdsourced speedtest dataset; compute per‑cell/zone KPIs.

Supervised learning for QoS compliance & coverage inference

  1. Regression/classification methods (GBMs, neural nets) for estimating KPIs and predicting non‑compliance.
  2. Evaluation metrics relevant to regulators (precision for enforcement, recall to catch violations).
  3. Lab: build a classifier to flag cells/areas likely violating minimum QoS thresholds.

Anomaly detection & fraud identification

  1.  Unsupervised/semi-supervised methods: isolation forest, auto encoders, time-series change point detection.
  2.  Use cases: roaming/fraud detection, SIM-box/fraudulent termination, billing anomalies, sudden QoS drops.
  3.  Lab: unsupervised detection on aggregated CDR-like time series to find anomalous events.

NLP for regulatory documents, complaints and policy analysis

  1. Text mining, topic modelling (LDA), supervised text classification, named entity recognition for filings/complaints.
  2. Sentiment/stakeholder analysis, automated compliance-checking of filings, extraction of tariff parameters.
  3. Lab: classify consumer complaints and extract key issues; summarize a regulatory filing automatically.

Market analysis, competition & tariff modeling

  1.  Demand estimation, clustering operators/markets, Herfindahl-Hirschman Index (HHI) with ML augmentation, causal inference basics for policy impact.
  2. Tariff optimization simulation and consumer welfare estimation.
  3. Lab: cluster geographic markets by competition level and simulate tariff change impacts.

Graphs & network models: GNNs for topology-aware tasks

  1.  Representing networks as graphs: cell coverage graphs, backbone topologies, peering relationships.
  2. Graph Neural Networks for coverage inference, outage propagation prediction, and link importance.
  3. Lab: create a graph of cell sites/edges and predict outage propagation risk with a GNN.
  4. Spectrum management & monitoring with AI

Spectrum sensing data, radio environment maps (REMs), ML for occupancy prediction, spatial interpolation.

  1. ML-assisted dynamic spectrum sharing and conflict detection.
  2. Lab: predict spectrum occupancy in space/time from sparse scans; visualize REM.

Simulation & reinforcement learning for resource allocation

  1. Designing simulators (ns‑3 or simplified custom env) for dynamic spectrum or RAN parameter tuning.
  2.  RL basics, reward shaping for regulatory goals (fairness, minimum service guarantees), safe RL considerations.
  3. Lab: small RL experiment for dynamic channel assignment in a simulated environment.

Privacy, fairness, explainability & legal constraints

  1. Location data privacy, de‑identification limits, GDPR/sectoral rules, differential privacy basics.
  2. Model interpretability (SHAP, LIME), auditability and explainability for enforcement decisions.
  3.  Ethical/regulatory implications of automated decision systems and human-in-the-loop design.
  4.  Lab: apply explainability tools on a QoS non-compliance classifier; produce an auditable report.

Operationalization & monitoring of AI systems in regulators

  1.  Deployment options, monitoring model drift, reproducibility, pipelines for model updates, incident response.
  2.  Procurement, vendor models, standards and transparency obligations.
  3.  Workshop: design a lifecycle plan for a regulatory ML system including KPIs and SLA for model Behavior.
  4. Project presentations + policy implications & future trends

Final project demos and policy-ready recommendations.

  1. Discussion: algorithmic regulation, AI for spectrum auctions, open data initiatives, cross-border coordination.

Assessments

  1.  Weekly labs/programming exercises: 30%
  2. Midterm: short project milestone or take-home assignment: 20%
  3. Final project (report + demo + policy brief): 35%
  4. Participation / quizzes / reading summaries: 15%

Labs / practical assignments

  1. Clean and aggregate crowdsourced speed test data; produce coverage heatmap.
  2. QoS compliance classifier with explainability report.
  3. Anomaly detection on aggregated CDR time series (simulated or sanitized).
  4. NLP pipeline to extract tariff components and compare operator filings.
  5. Spectrum occupancy interpolation and REM generation from sparse measurements.
  6. GNN-based outage propagation prediction on a simplified network graph.
     Small RL experiment for dynamic spectrum or channel allocation in a toy simulator.

Data sources & Datasets

  1. Ookla Speedtest public datasets (where available) / M-Lab (network measurement datasets).
  2. Public reports & datasets from national regulators (FCC, Ofcom, TRA, ANATEL) and BEREC.
  3. GSMA Intelligence (subject to license), ITU datasets and statistics.
  4. Synthetic/simulated CDRs and network KPI datasets (to avoid privacy issues).
  5. Radio spectrum scan repositories (where public) or research datasets for REMs.
    Consumer complaints published by regulators.

Tools & Libraries

  1. Python stack: pandas, NumPy, scikit-learn, XGBoost/LightGBM.
  2. NLP: spaCy, NLTK, Hugging Face Transformers.
  3. Deep learning: PyTorch or TensorFlow; PyTorch Geometric or DGL for GNNs.
  4. Anomaly & time series: Prophet, tsfresh, ruptures, sklearn.
  5. Explainability: SHAP, LIME, ELI5.
  6. Simulation/network: ns-3 (advanced), custom simulators, NetworkX for graphs.
  7. GIS/visualization: GeoPandas, Folium, Kepler.gl, QGIS.
  8.  Deployment / MLOps: Docker, Airflow, MLflow (for reproducibility/audit trails).

Readings & Policy resources

  1. Sutton & Barto — Reinforcement Learning (selected).
  2. Select ML for networks and GNN papers (instructor-provided).
  3. Regulator whitepapers: FCC/Ofcom research reports on broadband mapping and QoS, BEREC policy papers.
  4. ITU and GSMA guidance on data privacy and regulatory practices.
  5.  Recent academic papers on spectrum sensing, REMs, and ML for network inference.

Ethics, Privacy & Legal considerations (must-cover in course)

  1. Handling personally identifiable telecom data (legal constraints, aggregation thresholds).
  2. Transparency and appeal mechanisms when automated systems affect operators or consumers.
  3. Risk of bias in enforcement (disparate impacts across regions/demographics).
  4.  Vendor procurement accountability and open-data vs confidentiality trade-offs.

Final project ideas

  1. Automated QoS compliance monitoring dashboard with explainable alerts.
  2. Spectrum occupancy interpolation and sharing recommendation engine (simulated).
  3. Consumer complaint triage using NLP to speed regulator response and trend detection.
  4. Fraud detection pipeline for roaming/SIM-box activity using aggregated CDR-like logs.
  5.  Market concentration analysis and simulated tariff-change welfare impacts.