基于协同学习的频谱智能感知方法

An Intelligent Spectrum Sensing Method Based on Collaborative Learning

目前无线通信网络频谱环境时空分布复杂多变,现有多用户协同感知方法数据预处理繁琐,感知效率低下。为此,在由用户感知层和边缘融合层构成的系统架构下,提出了一种基于协同学习的频谱智能感知算法。用户感知层采用多分支卷积循环门控神经网络,利用原始归一化能量信号的底层结构信息,实现本地感知。边缘融合层基于自注意力机制进行消息传播,融合用户感知层中各个非授权用户的感知结果得出最终决策。实验表明,在信噪比为-20 dB以及5个用户协同感知的情况下,该方法能在虚警概率为1.91%时达到18.3%的检测概率,相比对比模型提升了6.1%,且不需要对原始数据额外预处理,降低了算法的复杂度。

The spatial and temporal distribution of current heterogeneous network spectrum environment is complex and variable,the data preprocessing of existing multi-user cooperative sensing methods is cumbersome,and the sensing efficiency is low.For above problems,a cooperative learning-based spectrum intelligent sensing algorithm is proposed under a system architecture consisting of user sensing layer and edge fusion layer.The user-aware layer uses a multi-branch convolutional recurrent gated neural network to realize local sensing by using the underlying structural information of the original normalized energy signal.The edge fusion layer performs message propagation based on a self-attention mechanism and fuses the sensing results of each unauthorized user in the user-aware layer to arrive at the final decision.Experiments show that when the signal-to-noise ratio is-20 dB and five users are sensing cooperatively,the proposed method is able to achieve a detection probability of 18.3%at a false alarm probability of 1.91%,an improvement of 6.1%compared with the comparison model,and does not require additional pre-processing of the raw data,thus reducing the complexity of the algorithm.