Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.

认知无线电是为有效使用无线电频谱源而开发的技术。频谱感测功能在认知无线电网络的性能中起关键作用。提出了一种基于在线学习算法的阈值确定新方法，以提高频谱感知方法的频谱感知性能，最大程度地降低总误差概率。在线学习算法使用历史检测数据寻找最佳决策阈值，这是决定主要用户是否存在的最重要参数。详细讨论了基于能量检测和匹配滤波器的频谱感测方法。在具有噪声不确定性的低信噪比条件下，在非衰落和不同衰落信道上测试了该算法的性能。