认知物联网基于边缘计算的频谱感知-分配方法

Spectrum sensing-spectrum allocation strategy based on edge computing for cognitive-radio internet of things network

针对多终端多信道复杂认知无线电-物联网(cognitive-radio Internet of Things,CR-IoT)当中的频谱感知-分配问题,提出了一种基于边缘计算思想的协作频谱感知-分配方法。为了评估边缘计算思想的引入为CR-IoT系统带来的性能提升,设计了一种能够量化对授权用户的干扰以及认知用户的服务质量的性能评估系统(performance evaluation system,PES)。在该系统中,基于排队论以离散时间马尔可夫模型对系统状态进行建模,能够对可配置参数的异构终端进行独立分析,进而对不同的频谱感知-分配方法进行性能评估。使用PES所进行的仿真结果表明,相比于中心感知-分配方法,所提出的边缘计算方案能够实现更低的对授权用户的干扰以及对于认知用户更好的服务质量。这一结果同样证明了,所提出的PES具有帮助网络监管者设计频谱感知-分配方案的能力。

In multi-user multi-channel cognitive radio networks for the internet of things(CR-IoT),a spectrum co-sensing-allocation strategy based on edge computing is proposed.To evaluate the performance of the proposed strategy,a performance evaluation system(PES)capable of quantifying the quality of service(QoS)of cognitive users and the interference to primary users is developed.In the PES,a Markov model is established to describe the CR-IoT system state based on queuing theory.The performance metrics of heterogeneous cognitive users with configurable parameters can be analyzed independently.Thus,various spectrum sensing-allocation strategy can be evaluated using the proposed PES.Numerical results show that the proposed edge computing strategy outperforms central computing strategy in QoS of cognitive users and the quantified interference to primary user.It proves that the PES can help regulators design various spectrum sensing-allocation strategy.