Browsing by Author "Oloyede, Abdulkarim"

Now showing 1 - 2 of 2
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    ENERGY EFFICIENT BID LEARNING PROCESS IN AN AUCTION BASED COGNITIVE RADIO NETWORK
    (BAYERO JOURNAL OF ENGINEERING AND TECHNOLOGY (BJET), 2016-02-08) Oloyede, Abdulkarim; David, Grace
    his paper proposes a learning based auction model for cognitive radio network using the concept of Bayesian and Q-learning. A learning process is introduced to aid energy efficiency in an auction based cognitive system. By using Q-learning to learn the bid price, this paper showed that for the learning users, the amount of energy consumed per file sent can be reduced when compared to the non-learning users. Furthermore, to overcome the deficiencies of tra- ditional Q-learning we bias the exploration process with Bayesian learning. This helps the exploration process to converge faster, thereby further reducing the energy consumption by the learning users in the system and the system delay.
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    Spectrum Pricing and Cognitive Radio
    (International Journal on Wireless Communication, 2014-05-05) Oloyede, Abdulkarim; David, G; Nasir, Faruk
    Wireless service providers (WSP) are facing continuous challenge of enhancing network coverage and capacity to handle ongoing upsurge in data traffic and increased user demand for ubiquitous high quality service. Several economic models have been proposed to provide flexible prices to users and also serve as a control to the network resource. However, the existing pricing schemes available today could still not mitigate the problem of unfairness and congestion. This is expected to be persistent as the demand for data applications keeps increasing. In this paper, we propose a real-time dynamic pricing scheme based on game model that incooperated the current demand, the state of the network and the transmitting power of the users. The model determines the pricing based on the relationship and conflict of interest between the service provider and the user. Results of analysis show that Nash Equilibrium can be achieved when both users and the service providers adopt the use of learning to approximate the utility of each other. The model demonstrates how pricing can be used to simulate the cooperation of users and the service provider to generate a socially, optimal allocation mechanism. Furthermore, we found that if learning is adopted by both the users and the service providers, then, the utility and energy consumed by the users can be improved significantly when compared to a non-learning system.