DocumentCode :
3527554
Title :
Multi-objective reinforcement learning based routing in cognitive radio networks: Walking in a random maze
Author :
Zheng, Kun ; Li, Husheng ; Qiu, Robert C. ; Gong, Shuping
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA
fYear :
2012
fDate :
Jan. 30 2012-Feb. 2 2012
Firstpage :
359
Lastpage :
363
Abstract :
The routing procedure in cognitive radio networks with dynamic spectrum activities is studied. The spectrum statistics are assumed to be unknown. Moreover, the performance is measured using multiple metrics like average delay and packet loss rate. To address the challenges of randomness, uncertainty and multiple metrics, the multi-objective reinforcement learning algorithm is applied for the routing in cognitive radio networks. The effectiveness of the learning procedure is demonstrated by numerical simulations.
Keywords :
cognitive radio; learning (artificial intelligence); radio spectrum management; random processes; telecommunication network routing; cognitive radio networks; dynamic spectrum statistics; multi-objective reinforcement learning; numerical simulations; random maze; routing procedure; Cognitive radio; Delay; Learning; Propagation losses; Routing; Routing protocols;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Networking and Communications (ICNC), 2012 International Conference on
Conference_Location :
Maui, HI
Print_ISBN :
978-1-4673-0008-7
Electronic_ISBN :
978-1-4673-0723-9
Type :
conf
DOI :
10.1109/ICCNC.2012.6167444
Filename :
6167444
Link To Document :
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