能量收集认知无线电网络下的多接入用户信息年龄研究

针对信道资源有限的多接入信道无线传感器网络场景，实时信息的传送需要考虑信道环境和信息新鲜度问题。该文基于认知无线电物联网（Cognitive Radio-Internet of Things， CR-IoT）系统，构建了一个具有频谱访问权限的主用户（Primary User， PU）和两个可共享PU频谱次用户（Secondary User， SU）的网络模型。在考虑PU工作状态和SU数据队列稳定的条件下，提出了一个以最小化节点平均AoI为目标的优化问题。其次使用两种策略进行优化，包括概率随机接入策略（Probabilistic Random Access Policy， PRA），该策略下两个SU节点根据相应的概率分布做出独立的传输决策；以及基于李雅普诺夫优化框架优化时隙内调度决策的漂移加罚策略（Drift Plus Penalty Policy， DPP）。仿真结果可知，DPP策略下得到的平均AoI的值要明显低于PRA策略，表明使用DPP策略对平均AoI的优化更加显著，可以有效提升数据包的时效性和新鲜度。 

Research on Age of Information in Multi-access User with Energy Harvesting Cognitive Radio Network

? ?For the multi-access channel wireless sensor network scenario with limited channel resources， the transmission of real-time information needs to consider the channel environment and information freshness. Based on the cognitive radio-internet of things （CR-IoT） system， this paper constructed a network model of a primary user （PU） with spectrum access rights and two secondary users （SU） that can share the PU spectrum. Under the conditions of considering the PU operating state and the power connection SU node data queue stability， a minimized node average AoI was proposed. Second， two policies were used for optimization， including probabilistic random access policy （PRA）， in which two SU nodes made independent transmission decisions according to the corresponding probability distribution； and the drift plus penalty policy （DPP） based on the Lyapunov optimization framework was used to optimize the scheduling decision in the time slot. The simulation results show that the average AoI value obtained under the DPP policy is significantly lower than the PRA policy， indicating that the use of the DPP policy to optimize the average AoI is more significant， and can effectively improve the timeliness and freshness of data packets.