The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

中文翻译：

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CR网络中基于深度学习的功率控制和频谱共享的共识性能

认知无线电网络（CRN）旨在通过观察和测量可用资源，通过学习和调整来增强系统。由于CRN中的频谱感应功能，它应该可行且快速。观察和重新配置的能力是CRN的关键功能，而当前的机器学习技术与系统自适应算法结合使用时效果很好。本文介绍了CRN中频谱共享的共识性能和功率控制。（1）CRN用户被认为是非合作用户，因此预定义了主要用户（PU）的功率控制策略，而使次要用户（SU）不知道PU的功率控制策略。为了更有效地共享频谱，已经开发了深度学习功率控制策略。该算法基于CRN节点处的接收信号强度。（2）为CR用户的共识表现引入了基于代理的方法。（3）所有代理在将近100秒后都达到了稳态值。但是，建立时间长。整个操作内部的检测延迟为0.4秒是相同的。假定的方法足以表示CR网络中的大规模感测延迟。