The emerge of Internet of Things (IoT) brings up revolutionary changes to wireless communications. Cognitive radio (CR) can be seen as one of the prominent solutions to spectrum scarcity in IoT, where multi-band cooperative spectrum sensing (CSS) is the key. However, lack of centralized control and increase in number of devices place a room for many challenges. One of the main challenges is secondary users’ (SUs’) scheduling to sense a subset of channels in heterogeneous distributed CR networks (CRNs). To overcome the aforementioned challenge, in this paper, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. The proposed scheme allows heterogeneous SUs to sense multiple channels and consists of two stages. We formulate a mathematical model to optimize leader-selection for each channel in the first stage. We then formulate another optimization problem to determine corresponding cooperative SUs to sense these channels in the second stage. After that, diffusion learning is used to decide on the availability of channels. Simulations illustrate that the proposed scheme improves detection performance and CRN throughput, is scalable in terms of detection performance, and provides fair energy consumption for CSS on all channels compared to existing multi-band CSS schemes.

中文翻译：

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分布式物联网系统基于可扩展学习的异构多频带多用户协作频谱感知

物联网（IoT）的出现带来了无线通信的革命性变化。认知无线电（CR）可以看作是物联网中频谱稀缺的重要解决方案之一，其中以多频带协作频谱感知（CSS）为关键。然而，缺乏集中控制和设备数量的增加为许多挑战提供了空间。主要挑战之一是二级用户（SU）的调度，以感知异构分布式CR网络（CRN）中的一部分信道。为了克服上述挑战，在本文中，我们提出了一种新颖的异构多频带多用户CSS（HM2CSS）方案。所提出的方案允许异构SU感测多个信道并且包括两个阶段。我们在第一阶段制定了数学模型，以优化每个渠道的领导者选择。然后，我们制定另一个优化问题，以确定相应的协作SU，以在第二阶段感知这些通道。之后，使用扩散学习来确定渠道的可用性。仿真表明，与现有的多频带CSS方案相比，该方案可提高检测性能和CRN吞吐量，在检测性能方面可扩展，并为所有通道上的CSS提供合理的能耗。