基于密集连接机制的无线信号调制识别

为提高复杂信道环境下无线通信系统对调制信号的检测识别能力，以及针对当前调制识别方法存在的模型复杂、计算量大、输入数据特征不完备等问题。提出一种改进的深度学习算法模型，对真实无线环境下的9种常见调制信号进行识别研究。该算法通过对原始的同相正交(in-phase quadrature, IQ)数据进行幅度相位计算，以此增加模型输入数据的特征信息，采用改进的密集神经网络(dense neural network, DenseNet)对常见调制信号进行识别分类。实验结果表明：在相同的训练数据样本中，相比其他深度学习调制识别算法，改进算法性能最优。在信噪比为0时，DenseNet平均识别率达到84.6%。改进的IQ输入数据明显提高了无线信号的检测识别率，在信噪比为-10 dB和-5 dB时，调制信号的识别率提高了10%。

Radio Signal Modulation Identification Based on Dense Connection Mechanism

In order to improve the detection and recognition ability of wireless communication systems in complex channel environments, and the problems of complex models, large calculation amounts, and incomplete input data characteristics. We propose an improved deep learning algorithm model to identify nine common modulated signals in real wireless environments. The algorithm calculates the amplitude and phase of the original in-phase quadrature (IQ) data to increase the characteristic information of the input data of the model and uses an improved dense neural network (DenseNet) to identify and classify common modulation signals. The experimental results show that the proposed algorithm has the best performance compared with other deep learning modulation recognition algorithms in the same training data samples. At 0dB, the average recognition rate of DenseNet reaches 84.6%. The improved IQ input data significantly improves the detection and recognition rate of the wireless signal. When the SNR is -10dB and -5dB, the recognition rate of the modulated signal is increased by 10%.