Nowadays, the demand for mobile wireless communication systems has increased drastically due to its significant use for various real-time applications. This increased demand for communication causes heavy utilization of the radio spectrum to improve ubiquitous computing services. However, systems providing high-speed communication fail to achieve the desired performance due to unsystematic spectrum utilization and resources. The problem addressed by Cognitive Radio Network (CRN) architecture has attracted research and industrial community to enhance the real-time communication systems. Although CRN based real-time communication systems suffer from resource allocation, spectrum sensing, and power consumption issues. In this paper, we introduce a novel approach for resource allocation and sharing based on cooperative game theory, and cooperative node selection ensures maximized payoff. The proposed method optimizes the overhead, energy consumption, and resource utilization. Further, energy consumption and resource allocation issues transformed into an optimization problem. A backtracking search algorithm is applied to reduce the computation complexity and to find the optimal solution for resource utilization. The simulation result obtained achieves better performance compared to the existing energy-aware scheduling approach in CRN.

如今，由于其在各种实时应用中的大量使用，对移动无线通信系统的需求已急剧增加。对通信的这种增加的需求导致无线电频谱的大量利用以改善普遍存在的计算服务。但是，由于系统的频谱利用率和资源不足，提供高速通信的系统无法实现所需的性能。认知无线电网络（CRN）架构解决的问题吸引了研究和工业界来增强实时通信系统。尽管基于CRN的实时通信系统遭受资源分配，频谱感测和功耗问题的困扰。在本文中，我们介绍了一种基于合作博弈理论的资源分配和共享新方法，协作节点选择可确保获得最大回报。所提出的方法优化了开销，能耗和资源利用率。此外，能源消耗和资源分配问题转化为优化问题。应用回溯搜索算法来减少计算复杂度并找到资源利用的最佳解决方案。与CRN中现有的能量感知调度方法相比，获得的仿真结果具有更好的性能。应用回溯搜索算法来减少计算复杂度并找到资源利用的最佳解决方案。与CRN中现有的能量感知调度方法相比，获得的仿真结果具有更好的性能。应用回溯搜索算法来减少计算复杂度并找到资源利用的最佳解决方案。与CRN中现有的能量感知调度方法相比，获得的仿真结果具有更好的性能。