Abstract

Advancements in wireless technology and the increased demand for high-speed internet access necessitate improved and efficient spectrum utilization. Cognitive Radio Networks (CRNs) enable opportunistic access to and use of previously unexploited frequency bands, alleviating the spectrum access problem. The purpose of this paper is to investigate how CRN uses dynamic spectrum access to improve the performance of internet delivery. Secondary users periodically transmit and receive in the spectrum 'holes' used by primary users. Secondary users enhance spectrum utilization by adding to the overall system capacity. In unserved and underserved rural and remote areas, the densely populated primary users representing the broadband system are overwhelmed. More timely broadband access in rural and remote areas is enabled by CRN's ability to quickly and efficiently detect, learn, and adapt to environmental changes. This paper analyzes the system's architectural framework, the applicable functional elements, and the recognized primary challenges in spectrum utilization. Challenges included are the extent to which the Spectrum can be sensed and the degree to which spectrum policy and operational specification compliance can be achieved. This paper documents the application of hand-optimized and emerging machine learning and artificial intelligence techniques to cognitive control of spectrum access and use in CRNs. CRNs operating in complex multiservice networks are analyzed and described in this paper. In closing, the research informs us about the implementation of cognitive radio to address the spectrum access problem and about the technology's versatility in providing efficient and equitable Internet access in heterogeneous environments.