scholarly journals Implementation, Adaptation, and Learning

2020 ◽  
pp. 202-237
Author(s):  
Allen C. Amason ◽  
Andrew Ward
1992 ◽  
Vol 39 (6) ◽  
pp. 497-503 ◽  
Author(s):  
T. Fukuda ◽  
T. Shibata ◽  
M. Tokita ◽  
T. Mitsuoka

2003 ◽  
pp. 7-9
Author(s):  
Martin Guirfa ◽  
Randolf Menzel

Author(s):  
Sachin Shetty ◽  
Danda B. Rawat

This chapter describes state-of-the art techniques to improve performance of spectrum sensing and spectrum management in Cognitive Radio Networks (CRN) by leveraging services available in cloud computing platforms. CRNs are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. However, ensuring adaptation and learning in CRN will require availability of large volume of data and fast processing. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity, it may not be able to achieve its full capability. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity.


Sign in / Sign up

Export Citation Format

Share Document