With the development of Internet of Things(IoT), the demands for communication spectrum have increased rapidly, resulting in the shortage of limited spectrum resources. Cognitive IoT (CIoT) based on cognitive radio (CR) can improve the spectrum utilization by accessing the idle spectrum licensed to a primary user (PU). In this paper, a multichannel spectrum access scheme based on reinforcement learning (RL) is proposed to improve the spectrum access of CIoT, wherein the CIoT can use multiple channels for transmissions to reduce the communication interruptions. The channels are ranked in the decreasing order of their predicted idle probabilities, which can make the CIoT find enough idle channels quickly via decreasing the number of sensing operations and spectrum handoffs. The simulation results show that our proposed scheme is superior to the single-channel spectrum access scheme in terms of throughput, communication interruption, average collision probability and average spectrum switching frequency.

随着物联网的发展，对通信频谱的需求迅速增长，导致频谱资源有限。基于认知无线电（CR）的认知物联网（CIoT）可以通过访问许可给主要用户（PU）的空闲频谱来提高频谱利用率。本文提出了一种基于强化学习的多信道频谱接入方案，以改善CIoT的频谱接入，其中CIoT可以使用多个信道进行传输，以减少通信中断。信道以其预测的空闲概率的降序排列，这可以通过减少感应操作和频谱切换的数量，使CIoT快速找到足够的空闲信道。