Detection of high frequency (HF) signal in the wideband is challenging since the HF environment is chaotic. Recent works adopt deep learning-based object detectors to capture signals in wideband spectrogram, but the task of signal detection exhibits different characteristics from that of generic object detection, which causes the classical deep learning-based detectors to have defects such as limited receptive field and prior anchor mismatch. Based on the task analysis, this letter proposes a deep learning framework which extracts features along the time axis at each frequency bin, and predicts multiple characteristics of the signal, including the center frequency and the shape attributes. The unique advantages of the framework are the utilization of all time features and no prior anchor which adapt to the slender shape of signal with a strong generalization ability. The numerical studies on simulation signal + real-world background prove the superiority of the proposed framework both in accuracy and speed.

Automatic modulation recognition (AMR) is a widely used technique in various communication systems. In this work, we propose a complex-valued transformer (CV-TRN) network for AMR. Considering the in-phase (I) and quadrature (Q) components of the signal are two consistent data with only a phase difference of $\pi $ /2, they can teach the network independently which in disguise augment the training data, but the I/Q components are collectively needed to measure similarity in the multihead self-attention (MHSA). We input the I/Q data individually into the network with shared parameters, and they are transmitted independently in the network except in the MHSA, where a complex-valued MHSA (CMHSA) is proposed to let the information from I/Q components integrate. Moreover, CV-TRN adopts the relative position embedding, with a mathematical analysis of its advantages for AMR. A data augmentation method of random phase offset is introduced to further improve the robustness. Experimental results on RML2016.10a and RML2018.01a data sets demonstrate that the proposed CV-TRN outperforms state-of-the-art AMR methods and is parameter efficient.

Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.

Short-wave radio is an indispensable long-distance means of communication, among which Morse signals, which rely on simplicity and efficiency, plays an import role in military and civilian applications. Automatic Morse detection and recognition have been researched for several years, but some thorny problems in actual communication always restrict the performance of methods. In this paper, by introducing deep learning technology, we propose a network named MorseNet that can simultaneously locate and decode Morse signals in the spectrogram. MorseNet uses shared convolutions to extract shared features for both the detection and recognition branches. The detection branch regresses bounding boxes based on signal centerlines, and the recognition branch decodes Morse fragments cropped from feature maps by a convolutional recurrent neural network (CRNN). The losses of two branches are combined to implement the end-to-end training. Experimental results on four “simulated Morse + real background” datasets demonstrate that the proposed method achieves state-of-the-art performance in both detection and recognition, and it effectively improves four problems that have long been troublesome in accomplishing the tasks. Furthermore, the joint training strategy and architecture give MorseNet advantages over its two-stage deployment in terms of accuracy, speed, and model size.