An Optimal Spectrum Access Control in Cognitive Radio Networks
Do The Cuong
Kyung Hee University, Department of Computer Engineering
Choong Seon Hong
Spectrum Access Cognitive Radio Networks;
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Cognitive Radio (CR) is a promising technology to solve the spectrum scarcity problem and to increase spectrum efficiency. By using spectrum sensing and databases, one is able to obtain information about and utilize white space spectrum. Spectrum costs, which tend to be high for wireless service providers, can then be reduced significantly by using dynamic spectrum access. Most dynamic spectrum approaches are connected to a three-tier model of the dynamic spectrum market, which includes three network entities: spectrum owners, secondary service providers and Secondary Users. Therefore, wireless service provider should focus on understanding how they can benefit from using cognitive radio. With interest increasingly centered on real-time application, spectrum access control that considers delay as a critical quality of service (QoS) metric has become more important. In this thesis, we focus on the equilibrium behaviors of SUs in a strategic queueing system, where arriving users can take the delay and other metrics into account for strategic decision making. In order to control the spectrum access in an effective manner, an spectrum access control design should take into account of both the traffic statistics of the primary users and the secondary users. In this research track, based on pricing mechanism, queueing theory, game theory and optimization theory, several spectrum access schemes are devised elegantly to achieve optimal performance. In this dissertation, we consider two approaches to derive optimal spectrum access schemes in cognitive radio network. The first approach is based on the pricing approach which is widely used as an effective means for spectrum access control in cognitive radio networks. We study the pricing effect on the equilibrium behaviors of selfish secondary users' data packets which are served by a cognitive radio base station. From the secondary users' point of view, a spectrum access decision on whether to join the queue of the base station or not is characterized through an individual optimal strategy that is joining the queue with a joining probability. This strategy also requires each secondary user to know the average queueing delay, which is a non-trivial problem. Here, we provide queueing delay analysis by using the M/G/1 queue with breakdown. From the base station's point of view, we consider a duopoly market based on the two paradigms: the opportunistic dynamic spectrum access (O-DSA) and the mixed O-DSA & dedicated dynamic spectrum access (D-DSA). In the first paradigm, two co-located opportunistic-spectrum BSs utilize free spectrum-holes to serve the secondary users. Then, we show the advantages of the cooperative scenario due to the unique solution that can be obtained in a distributed manner by using the dual decomposition algorithms. For the second paradigm, there are one opportunistic-spectrum base station and one dedicated-spectrum base station. We study a price competition between two base stations as a Stackelberg game. The cooperative behavior between two base stations is modeled as a bargaining game. In both paradigms, bargaining revenues of the cooperation are always higher than those due to competition in both cases. Extensive numerical analysis is used to validate our derivation. The second approach is queue-based approach that is used in a hybrid overlay/underlay and multiple channels cognitive radio system. The first system, a hybrid overlay/underlay cognitive radio system, is modeled as a M/M/1 queue where the rate of arrival and the service capacity are subject to Poisson alternations. Each packet as a customer arriving at the queue of the system makes a decision to join or balk the queue. Upon arrival, the individual decision of each packet is optimized based on its observation about the queue length and the state of system. It is shown that the individually optimal strategy for joining the queue is characterized by a threshold of queue length. Thus, the individual optimal threshold of queue length is analyzed in detail in this thesis. For the second system, the multiple channels cognitive radio system, we propose is a lightweight probability-based spectrum decision scheme which distributes the packets into multiple channels in a minimizing queueing delay manner. Compare with the sensing-based spectrum decision scheme, the probability-based spectrum decision scheme has been shown to yield a shorter queuing delay time in cognitive radio system. However, the former scheme had cumbersome algorithms and slowly converging speed. In this thesis, by introducing Lagrange function, we propose a lightweight algorithm with the computational effort O(N) to define the optimal distribution probability vector. Numerical results demonstrate a high degree of accuracy for the derived expressions.