The week's pick
Random Articles
Reseach Article
Design of Reasoning Agent for Cognitive Radio Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 171 - Number 9 |
| Year of Publication: 2017 |
| Authors: Gyan Vardhan Singh |
10.5120/ijca2017915119
|
Gyan Vardhan Singh . Design of Reasoning Agent for Cognitive Radio Networks. International Journal of Computer Applications. 171, 9 ( Aug 2017), 24-28. DOI=10.5120/ijca2017915119
Abstract
The main task for a cognitive radio system is to adapt to the transmission parameters in a dynamically changing environment. The nature of wireless channels and the number of transmission parameters to be optimized in various layers add more complexity to the adaptation process. In these situations Case Based Reasoning (CBR) agent along with optimization algorithm provides a robust solution. CBR Agent develops cases as the changes occur in the environment and acquires an understanding of the system. As new situations occur, experience from the previously developed cases is taken into consideration and a solution to the system is provided. In real time with training, the Cognitive Engine (CE) learns the variations occurring in the environment and utilizes the previously existing cases to provide new solution. As well as if abruptly some unexpected situation is encountered then the optimization algorithm is initiated and a solution is designed as per the need of the environment and this case is also added to the case list for future reference. Hence variations in the environment are mapped extensively and the optimization process becomes more efficient. In the present work case based reasoning along with particle swarm optimization is implemented.
References
- A.He, J.Gaeddert, K. K. Bae, J. H. Reed, and C. H. Park, “Development of a Case-Based Reasoning Cognitive Engine for IEEE 802.22 WRAN Applications”, IEEE Transactions, 2007.
- S. Shiu and S. K. Pal, Foundations of Soft Case-Based Reasoning, Wiley Series on Intelligent Systems.Wiley-Interscience, Hoboken, NJ.
- T.R. Newman, A. M. Wyglinski, A. Agah, J. B. Evans, and G. Minden, “Cognitive engine implementation for wireless multicarrier transceiver”, Wireless Communication and Mobile Computing, 7(9):1129–1142, 2007.
- J. L. Kolodner and D. Leake, A tutorial introduction to case-based reasoning, Case-Based Reasoning: Experiences, Lessons and Future Directions, chapter 2, pages 31–65.MIT Press, Cambridge, MA, 1996.
- J. H. Reed et al. Development of a cognitive engine and analysis of WRAN cognitive radio algorithms phase II. Report submitted to ETRI, MPRG, Virginia Tech, December 2006.
- B. Le, T.W. Rondeau, and C.W. Bostian, “Cognitive radio realities”, Wireless Communication and Mobile Computing, 7(9):1037–1048, 2007.
- T. W. Rondeau, B. Le, C. J. Rieser, and C. W.Bostian, “Cognitive radios with genetic algorithms: Intelligent control of software defined radios,” in SDR Forum Technical Conference, pages C–3–C–8, Phoenix, AZ, 2004.
- An He et. al. “Survey Of Artificial Intelligence For Cognitive Radios”, IEEE Transactions On Vehicular Technology, Vol. 59, No. 4, May 2010.
- Mahdi, A.H.; Mohanan, J.; Kalil, M.A.; Mitschele-Thiel, A., "Adaptive Discrete Particle Swarm Optimization for Cognitive Radios," in Communications (ICC), 2012 IEEE International Conference on , vol., no., pp.6550-6554, 10-15 June 2012.
Index Terms
Keywords