DocumentCode :
1674419
Title :
PSO of Neural Networks to Predict Busy Times of Cellular Traffic for Assignment to TV Idle Channels by Cognitive Radio
Author :
Winston, Ojenge ; Thomas, Abu ; Patrick, Ogao ; William, OkelloOdongo
Author_Institution :
Tech. Univ. of Kenya, Kenya
fYear :
2013
Firstpage :
48
Lastpage :
52
Abstract :
Kenya has identified radio spectrum as a keydriver in its development. Yet, globally, radio spectrum is inefficiently utilized due to ITU´s static spectrum allocation.In Kenya, mobile operators are running short of bandwidth due to deployment of 4G services, which enable super fast mobile broadband/internet. In the USA and UK, FCC and Ofcom, respectively, have made effort to allow opportunistic ´poaching´ of licensed spectrum as long as communication of licensed user is not interfered with. This has focused research on use of cognitive radio, which would use its sensor networks to establish which TV channels are idle in order to allocate them temporarily to cellular networks.Enabling the cognitive radio to predict which channels shall lie idle at what times introduces better planning and more temporally-efficient allocation. This study explores the viability of predicting the times of mobile telephony traffic jam for a mobile service operator with poor QoS rating within a cell of perennial mobile traffic jam in order to explore whether those times can map well with the TV spectrum holes. The times of the TV spectrum holes shall be determined in a later study.
Keywords :
cellular radio; channel allocation; cognitive radio; neural nets; particle swarm optimisation; quality of service; radio spectrum management; telecommunication traffic; 4G services; FCC; ITU static spectrum allocation; Internet; Kenya; Ofcom; PSO; TV idle channels; TV spectrum holes; busy times; cellular networks; cellular traffic; cognitive radio; licensed spectrum; licensed user; mobile operators; mobile service operator; mobile telephony traffic jam; neural networks; opportunistic poaching; perennial mobile traffic jam; poor QoS rating; radio spectrum; sensor networks; super fast mobile broadband; Cognitive radio; Hidden Markov models; Mathematical model; Mobile communication; Neural networks; TV; Training; Cognitive Radio; Mobile Telephony Traffic; NN; PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modelling Symposium (EMS), 2013 European
Conference_Location :
Manchester
Print_ISBN :
978-1-4799-2577-3
Type :
conf
DOI :
10.1109/EMS.2013.8
Filename :
6779820
Link To Document :
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