AI-Based Dynamic Spectrum Prediction and Allocation for IoT Wireless Networks Using Python

G Nagendra Prasad; K Riyazuddin

doi:10.5281/zenodo.17377265

Authors

G Nagendra Prasad Department of Electronics and Communication Engineering, Annamacharya University, Andhra Pradesh, India
K Riyazuddin Department of Electronics and Communication Engineering, Annamacharya University, Andhra Pradesh, India

DOI:

https://doi.org/10.5281/zenodo.17377265

Keywords:

AI-based spectrum management, IoT wireless networks, dynamic spectrum access, spectrum forecasting, reinforcement learning, deep learning, 5G/6G, cognitive radio

Abstract

The advent of the Internet of Things (IoT) has put pressure on scarce spectrum resources, especially in heterogeneous, interference-rich, and latency-critical environments. Static access regimes and rule-based ones cannot handle non-stationary interference and ultra-dense deployments. Leveraging advances in artificial intelligence (AI), this paper investigates and achieves spectrum intelligence: short-horizon spectrum prediction and dynamic, risk-aware allocation at radio timescales. We combine classical machine learning techniques, deep sequence and vision encoders (LSTM/GRU/TCN, spectrogram/REM models), transformers, reinforcement learning, and graph-based surrogates for channel–power assignment. Aside from modeling, reproducibility and deployability are also our focus with Pythonic pipelines: leakage-safe preprocessing, calibration (Brier/ECE), safety shields, and closed-loop evaluation with ns3/ns3-gym. Public datasets and simulators are inventoried with guidelines for splits and metrics (utilization, latency, violation rate, fairness, and energy). We cover engineering trade-offs for deployment on the edge (quantization, ONNX/TorchScript, federation with Flower) and outline open challenges in data sparsity, domain adaptation, explainability, security, and real-time feasibility. The result is an actionable roadmap to bring AI-enabled spectrum management from promising prototypes to robust, scalable IoT systems.

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