A Deep Reinforcement Learning for Adaptive Spectrum Access in Geo-Enabled Cognitive Radio Networks - Elesa Ntuli; Du Chunling (AI Society & STMIK Indonesia, 2026)
The radio spectrum is getting more crowded as more people and devices connect wirelessly. Cognitive Radio Networks (CRNs), which can find and use empty spectrum, help solve this problem. One common way they do this is by using a Geo-Location Spectrum Database (GLSD) to check which parts of the spectrum are free. But these databases are not always updated in real time, so they sometimes miss chances to use the spectrum or cause interference. This study looks at a better way to handle this by using Deep Reinforcement Learning (DRL). The system we designed learns from the environment and makes smart decisions about when and where to use the spectrum. We used a Deep Q-Network (DQN) to test it in a simulated environment. Our results show that this new method improved spectrum use by 27%, reduced interference by 35%, and made decisions 22% faster than older methods that rely only on the database. The model reached about 91% accuracy in its decisions over many tests. This means that adding DRL to geo-location systems can make spectrum use more efficient, especially in busy areas or places with limited internet access. We suggest trying this setup in the real world using Software Defined Radios (SDRs) to see how it performs outside of simulations.