# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 1 Introduction  
With the rapid development of wireless mobile communication, satellite communication, and other communication technologies, the electromagnetic spectrum space has become very complex and crowded. The safe use and effective control of the electromagnetic spectrum have turned into the critical task of radio monitoring, particularly in the non-cooperative electromagnetic spectrum monitoring field. Carrier signal detection is the first and most crucial step of non-cooperative signal processing. Through accurately detecting only the signal in the spectrum, we can further perform modulation recognition, channel coding identification, source coding identification, specific emitter identification, and other information analysis processes. Few algorithms [1; 2; 3; 4; 5; 6] are available for carrier signal detection, and these algorithms are mainly based on threshold values and human intervention, although some improvements have been noted using the double-thresholds method [7; 8]. [PERSON] et al. [9] proposed the use of a slope tracing-based algorithm to separate the interval of signal elements based on signal properties, such as amplitude, slope, deflection width or distance between neighboring deflections. For the practical application of these methods, many restrictions exist due tothe fact that some parameters cannot be accurately estimated as well as the high complexity of computations. Recently, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications. Moreover, deep learning is one of the most exciting and fast-growing techniques with numerous improvements, particularly in domains, such as computer vision and natural language processing [10]. Meanwhile, many researchers have performed considerable exploration of AI and its application in wireless communication [11; 12; 13]. [PERSON] et al. [14] discussed several deep learning applications for the physical layer. They introduced a new way of thinking about communications as an end-to-end reconstruction optimization task, using autoencoders to jointly learn transmitter and receiver implementations as well as signal encodings without prior knowledge. Deep learning has been applied to carrier signal detection. For example, [PERSON] and [PERSON] [15] applied a fully connected neural network for the detection of FSK signals. Moreover, [PERSON] et al. [16] used generative adversarial network (GAN) and CNN for blind detection of underwater acoustic communication signals. In contrast, [PERSON] et al. [17] used deep learning for blind morse signal detection in wideband spectrum data. Inspired by two-dimensional (2D) image semantic segmentation [18; 19; 20], we employed an FCN-based model [21] to solve the carrier signal detection problem in the broadband power spectrum. By regarding the broadband power spectrum sequence as a 1D image and each subcarrier on the broadband as the target object, we can transform the problem into a semantic 1D image segmentation problem. On the basis of the FCN-based model, the 1D deep CNN model was designed to categorize each point on a broadband power spectrum array into two categories (i.e., subcarrier or noise), then we can locate the subcarrier signals' position on the broadband power spectrum. We use a simulated and real satellite broadband power spectrum dataset to train and validate the 1D CNN model, respectively. The experimental results demonstrate that the FCN-based method can successfully detect the subcarrier signal and achieve higher accuracy than the slope tracing method. In accordance with [22], a deep learning-based framework named SigdetNet is applied to the multi-signal detection task, which focuses on each point classification of the power spectrum and is essentially based on FCN. However, the FCN-based methods [21; 22] cannot handle the carrier signal detection very well in these situations.