论文标题
使用多代理QLEALNING的认知无线电资源调度
Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE
论文作者
论文摘要
在本文中，我们建议，实施和测试两种新型的下行链接LTE调度算法。这些算法的实现和测试是在MATLAB中，它们基于增强学习的使用，更具体地说，是用于安排两种用户的Qlearning技术。第一种算法称为协作调度算法，第二个算法称为竞争调度算法。预定用户的第一种类型是主要用户，他们是为其服务付费的许可订户。预定用户的第二种类型是二级用户，他们可能是不用执行的订户，这些用户不为其服务付费，设备通信或传感器。每个用户是主要还是次要的用户被视为代理。在协作调度算法中，主要用户代理将协作，以做出有关将资源块分配给每个人的共同计划决定，然后二级用户代理人将在他们之间进行竞争以使用其余资源块。在竞争时间表算法中，主要用户代理人将在可用资源上竞争，然后二级用户代理将在其余资源中竞争。实验结果表明，调度算法都融合到频谱的近90％利用率，并提供了用户之间的频谱公平份额。
In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.
