Cooperative Spectrum Sensing in Cognitive Radio Networks: A Systematic Review

Background: Spectrum is the backbone for wireless communications including internet services. Now days, the business of industries providing wired communication is constant while the business of industries dealing with wireless communications is growing very fast. There is large demand of radio spectrum for new wireless multimedia services. Although the present fixed spectrum allotment schemes do not cause any interference between users, but this fixed scheme of spectrum allocation do not allow accommodating the spectrum required for new wireless services. Cognitive radio (CR) relies on spectrum sensing to discover available frequency bands so that the spectrum can be used to its full potential, thus avoiding interference to the primary users (PU). Objectives: The purpose of this work is to present an in-depth overview of traditional as well as advanced artificial intelligence and machine learning based cooperative spectrum sensing (CSS) in cognitive radio networks. Method: Using the principles of artificial intelligence (AI), systems are able to solve issues by mimicking the function of human brains. Moreover, since its inception, machine learning has demonstrated that it is capable of solving a wide range of computational issues. Recent advancements in artificial intelligence techniques and machine learning (ML) have made it an emergent technology in spectrum sensing. Result: The result shows that more than 80% papers are on traditional spectrum sensing while less than 20% deals with artificial intelligence and machine learning approaches. More than 75% papers address the limitation of local spectrum sensing. The study presents the various methods implemented in the spectrum sensing along with merits and challenges. Conclusion: Spectrum sensing techniques are hampered by a variety of issues, including fading, shadowing, and receiver unpredictability. Challenges, benefits, drawbacks, and scope of cooperative sensing are examined and summarized. With this survey article, academics may clearly know the numerous conventional artificial intelligence and machine learning methodologies used and can connect sharp audiences to contemporary research done in cognitive radio networks, which is now underway.