AI in Cognitive Radio Networks

Author(s):  
Preetjot Kaur ◽  
Roopali Garg

This chapter provides a-state-of-art of artificial intelligence (AI) techniques applied to cognitive radio networks. Cognitive radio (CR) is an empowering innovation for various new opportunities, for example, spectrum sensing, access, markets, and self-organizing networks. Its target is to enable the system to exploit the available resources through self-learning and to adapt itself accordingly to the sensed environment. To understand this plethora of applications, CR researchers often make use of several types of AI techniques. By utilizing AI, the network system can immediately complete self-awareness learning, structure association, and scheduling several tasks. To help researchers obtain a healthier knowledge of AI techniques along with CR, this chapter presents several such implementations that have already been applied. Finally, the literature review of the best accomplishments in applying AI techniques to CRs is presented and classified according to the major techniques of artificial intelligence.

Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.


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