# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 1 Introduction  
With the rapid development of communication technologies such as 5 th generation mobile networks (5G) and satellite communications, the electromagnetic spectrum, and space have become very crowded and complex due to the increasing number of different radiation sources and advanced waveforms in modern society. This has resulted in the situation that signal processing systems may be affected by multiple intentional or unintentional interfering signals injected simultaneously into their receivers [1]. Therefore, the safe use and effective control of the electromagnetic spectrum have become a priority task for radio monitoring [2]. In the cognitive radio (CR) communication system, the signal blind detection technology can achieve the accurate discovery, information restoration, and user identity adjudication of illegal authorized users, and further conduct location monitoring, spectrum suppression, and investigation and forensics to protect the safe use of radio spectrum resources by legitimate users, which is of great practical importance. In this paper, the main goal of blind detection of communication signals is the detection and preliminary automatic modulation identification (AMC) of communication signals under blind information conditions (i.e., reception of non-cooperating party signals) [3, 4, 5, 6]. Normally, many broadband signal detection algorithms in previous studies have been based on energy detection (ED) [7]. In 1967, [PERSON] [8] first investigated howto detect unknown deterministic signals. That is, the energy distribution is modeled as a variable non-central basis distribution random, and the threshold for determining the detection is determined by the degrees of freedom of this basis distribution and the non-central parameters. [PERSON] et al. addressed the cognitive radio spectrum perception problem by constructing a matched filter detector using sub-user signal sequences and an energy detector using primary user signal energy and fused the results of both detectors for verdict detection, effectively mitigating the impact of sub-user out-of-connection on spectrum perception [9]. When using the spectrum-based energy detection method, simple parameters such as the number of signals, carrier frequency, and signal bandwidth can be roughly obtained. However, it is still difficult to accurately obtain the signal's moment of appearance, modulation type, and other relevant parameters [2; 10]. Deep learning (DL) techniques, developed in recent years, provide new ideas for blind detection and identification of communication signals in complex environments [11]. Some existing studies have obtained good results on AMC tasks by transforming signals into time-frequency images and then inputting them into classifiers built by neural networks [12; 13]. [PERSON] et al. proposed a spatial-temporal hybrid feature extraction network for AMC, which maps wireless communication signals into spatial feature space and temporal feature space, respectively, to improve the effectiveness in the few-sample AMC task [5]. After the development in recent years, AMC techniques based on deep learning have become mature [12; 14]. In 2020, the Cascaded Human-Object Interaction Recognition [15] proposed by [PERSON] et al. effectively improved the quality of the target detection model. In ref. [3], [PERSON] et al. first introduced deep learning methods into a multi-signal detection task. Based on the Single Shot MultiBox Detector (SSD) target detection method [16; 17], the papers performed blind detection and modulation identification on broadband signals using time-frequency images and constellation images as input of the neural network.