Machine Learning based Spectrum Sensing and Spectrum Management using Blockchain

The rapid advancement of wireless technologies like IoT and 5G has exposed the limitations of traditional spectrum management methods, resulting in inefficiencies such as spectrum underutilization and security vulnerabilities. Machine learning (ML)-based spectrum sensing addresses these issues by analyzing and predicting usage patterns, enhancing utilization, and reducing underutilization. Additionally, the centralized nature of traditional systems makes them prone to security breaches and lacks transparency. Blockchain technology mitigates these concerns by offering a decentralized, tamper-proof ledger for managing spectrum allocations. Integrating ML and blockchain optimizes spectrum utilization, enhances security, and maintains the integrity of spectrum management.Countries like the United States, Japan, Korea, China, and Germany have advanced spectral measurement technology using SNR values. The U.S. leads in cognitive radio network research, Japan focuses on urban user identification, South Korea on 5G spectrum allocation, China on reliable sensing amid noise, and Germany on machine learning for spectral accuracy. The model integrates frequency-based spectrum sensing with blockchain for adaptable spectrum allocation. Machine learning enhances utilization and signal identification, while blockchain ensures secure and transparent spectrum management. The Data is preprocessed QoS data for 5G networks and apply ML models to predict vacant bands, evaluating their performance with R-squared scores and mean squared errors.