Adaptive Weighted Scheduling in Cognitive Radio Networks
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A problem in modern wireless communications is the scarcity of electromagnetic radio spectrum. The traditional fixed spectrum assignment strategy results in spectrum crowding on most frequency bands. Due to limited availability of radio spectrum and high inefficiency in its usage, cognitive radio networks have been seen as a promising solution to reducing current spectrum under-utilization while accommodating for the increasing amount of services demands and applications in wireless networks. Compared with the traditional networks, cognitive radio networks exhibit some distinct features, which result in necessity of further research in the resource allocation and scheduling that have been solved for the traditional networks. In this thesis, we focus on the packet scheduling in a single cell cognitive radio system with a single channel. An adaptive weight factor is introduced to adjust the priority of different cognitive radio users to be selected for service. The purpose of this research is to solve the unfairness problem of the traditional proportional scheduling schemes when used directly in a cognitive radio network, which lead to a user starved for a long time if it experiences a poor channel condition when the channel is available and experiences a good channel condition when the channel is not available. An adaptive weighted scheduling scheme is proposed to improve the performance in terms of throughput and fairness by jointly considering the instantaneous propagation conditions, adaptive weighted factor and the channel availability. The saturated traffic and non saturated traffic cases are considered. Some important performance metrics are investigated in the simulation, such as the system throughput, fairness, and service probability, and are quantified by the impact of weights and channel conditions. Extensive simulations have been conducted to demonstrate the effectiveness and efficiency of the proposed scheduling scheme.