论文标题
用于动态认知临时网络的分布式算法
Distributed Algorithm for Dynamic Cognitive Ad-hoc Networks
论文作者
论文摘要
认知临时网络允许用户访问未经许可/共享的频谱，而无需通过中央控制器进行任何协调，并且正在设想未来派超密集的无线网络。网络的临时性质要求每个用户学习并定期更新各种网络参数，例如渠道质量和用户数量，并使用学习的信息来改善频谱利用率并最大程度地减少碰撞。对于这样的学习和协调任务，我们提出了一种基于多武器多臂强盗方法和新型信号传导方案的分布式算法。所提出的算法不需要对网络参数（用户，频道）的先验知识及其检测和适应网络参数的更改的能力，从而使其适用于静态和动态网络。理论分析和广泛的仿真结果验证了所提出的算法优于现有的最新算法。
Cognitive ad-hoc networks allow users to access an unlicensed/shared spectrum without the need for any coordination via a central controller and are being envisioned for futuristic ultra-dense wireless networks. The ad-hoc nature of networks require each user to learn and regularly update various network parameters such as channel quality and the number of users, and use learned information to improve the spectrum utilization and minimize collisions. For such a learning and coordination task, we propose a distributed algorithm based on a multi-player multi-armed bandit approach and novel signaling scheme. The proposed algorithm does not need prior knowledge of network parameters (users, channels) and its ability to detect as well as adapt to the changes in the network parameters thereby making it suitable for static as well as dynamic networks. The theoretical analysis and extensive simulation results validate the superiority of the proposed algorithm over existing state-of-the-art algorithms.
