大规模认知无线电网络的时延分析

Latency Analysis of Large-Scale Cognitive Radio Networks

时延作为无线网络的最基本的性能之一,对网络信息分发、路由协议设计、节点部署等都具有重要意义.与传统的无线网络不同,认知无线电网络的频谱资源具有动态变化性,该特性会对网络时延产生极大的影响.因此,如何对动态频谱环境下的大规模认知无线电网络进行时延分析,是一项很具挑战性的课题.为此,首先对动态频谱环境进行建模,将认知用户的频谱接入过程建模为一个连续时间的马尔可夫链,并建立认知用户的生存函数来量化授权用户活动以及信道数量对频谱环境的影响;其次,将上述模型与首次通过渗流理论结合起来,研究了大规模认知无线电网络时延的伸缩规律,并获取了更为精确的时延与距离比的上限值.理论分析及仿真结果表明,动态频谱环境与密度一样会对时延产生极大影响.研究结论对认知无线电网络的设计具有重要的指导意义.

As one of the most fundamental properties of wireless networks, latency is important to the information dissemination, routing protocol design and node deployment. Different from the traditional wireless network, the spectrum resource in cognitive radio networks is dynamic which affects the network latency drastically. Thus, how to analyze the latency of large-scale cognitive radio networks under the dynamic spectrum environments is a challenging problem. To address this problem, this paper first constructs a dynamic spectrum environment model in which the process of the licensed spectrum access is defined as a continuous-time Markov chain, and a survival function of secondary users is created to quantify the impact of the number of channels and the activities of primary users. Next, this paper combines the proposed model with the first passage percolation theory to investigate scaling laws of latency in large scale cognitive radio networks. It also derives a tighter upper bound of the ratio of latency to distance. Theoretical analysis and simulation results show that the dynamic spectrum environments have a great impact on the latency of large-scale cognitive radio networks as well as the density. The results provide important guidelines for the design of cognitive radio networks.