Signal recognition is one of the significant and challenging tasks in the signal processing and communications field. It is often a common situation that there's no training data accessible for some signal classes to perform a recognition task. Hence, as widely-used in image processing field, zero-shot learning (ZSL) is also very important for signal recognition. Unfortunately, ZSL regarding this field has hardly been studied due to inexplicable signal semantics. This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation. The key idea behind SR2CNN is to learn the representation of signal semantic feature space by introducing a proper combination of cross entropy loss, center loss and reconstruction loss, as well as adopting a suitable distance metric space such that semantic features have greater minimal inter-class distance than maximal intra-class distance. The proposed SR2CNN can discriminate signals even if no training data is available for some signal class. Moreover, SR2CNN can gradually improve itself in the aid of signal detection, because of constantly refined class center vectors in semantic feature space. These merits are all verified by extensive experiments with ablation studies.

Hyperspectral imaging (HSI) classification has drawn tremendous attention in the field of Earth observation. In the big data era, explosive growth has occurred in the amount of data obtained by advanced remote sensors. Inevitably, new data classes and refined categories appear continuously, and such data are limited in terms of the timeliness of application. These characteristics motivate us to build an HSI classification model that learns new classifying capability rapidly within a few shots while maintaining good performance on the original classes. To achieve this goal, we propose a linear programming incremental learning classifier (LPILC) that can enable existing deep learning classification models to adapt to new datasets. Specifically, the LPILC learns the new ability by taking advantage of the well-trained classification model within one shot of the new class without any original class data. The entire process requires minimal new class data, computational resources, and time, thereby making LPILC a suitable tool for some time-sensitive applications. Moreover, we utilize the proposed LPILC to implement fine-grained classification via the well-trained original coarse-grained classification model. We demonstrate the success of LPILC with extensive experiments based on three widely used hyperspectral datasets, namely, PaviaU, Indian Pines, and Salinas. The experimental results reveal that the proposed LPILC outperforms state-of-the-art methods under the same data access and computational resource. The LPILC can be integrated into any sophisticated classification model, thereby bringing new insights into incremental learning applied in HSI classification.

With the development of deep learning (DL) technology, the feature extraction capability of networks has gradually been enhanced, and high accuracy has been achieved in signal modulation classification (SMC) tasks. DL requires numerous training samples to achieve high classification accuracy. However, in non-cooperative cases, only a few labeled data are usually available. To solve this problem, we propose a modular few-shot learning framework for SMC, called MsmcNet. MsmcNet comprises an IQ fusion (IQF) module, 1D signal feature processing (1D-SFP) modules, and a classifier. The number of the 1D-SFP modules is variable and is determined by a GCN-based method. Experimental results on RadioML2016.04c and RadioML2016.10a show that the proposed approach outperforms the baselines in all aspects. In particular, for the RadioML2016.10a dataset, when the SNR is 6 dB and the training data account for more than 2% of the test data, the classification accuracy of MsmcNet is greater than 70%. To further meet the requirements in the non-cooperative case, we explore the performance of the proposed MsmcNet in the one-shot learning case and find that it maintains its high performance advantage. In addition to the SMC task, we explore the performance of MsmcNet in the transmitter identification task to verify the universality of the framework. Compared to the baselines, MsmcNet achieves outstanding results.

Multiple-input–multiple-output (MIMO) technology is one of the most widely used communication technologies. However, with the increasing number of antennas, the complexity of the MIMO wireless communication receiver becomes higher and higher. On the other hand, the complex communication channels also raise up a great challenge to the reliability of the communication receiver system. With the rapid development and wide application of deep learning, it has been applied in the field of communication to solve some problems that are difficult to solve by the traditional methods, and thereby, improves the reliability of communication systems. Inspired by this idea, this article reviewed the signal processing process of the MIMO receiver system from the perspective of system reliability. Based on deep learning, the signal processing modules of the receiver system are jointly optimized, which changes the information recovery process of the traditional receiver and proposes the intelligent receiver for MIMO communication. In order to verify the system reliability of the intelligent receiver, this article analyzes it from the aspects of antenna numbers and channel conditions. The influence of different implementation methods of the intelligent receiver on the system reliability is also analyzed. Simulation results show that the proposed intelligent receiver for the MIMO wireless communication can recover information with a lower bit error rate and higher reliability compared with the traditional receiver under different conditions and antenna configurations.

Seamless Internet of Things (IoT) connections expose many vulnerabilities in wireless networks, and IoT devices inevitably face many malicious active attacks. automatic modulation recognition (AMR) is an effective way to combat IoT physical layer threats. In the field of noncollaborative communication, feature representation learning for unlabeled signals is an important task of AMR. However, due to the unavailability of a priori knowledge and the influence of interference during signal transmission, the intercepted unlabeled signals are difficult to perform efficient feature representation. In this article, we propose cooperative contrast learning for unlabeled modulation signal Cooperative Contrast Learning for modulation Signals (CoCL-Sig). Specifically, the CoCL-Sig is trained using both sequence and constellation diagram modalities, and is divided into two parts: 1) modal-level feature representation and 2) instance-level auxiliary feature representation. In modal-level feature representation, two modal projections are matched in the same hyperplane space. To ensure the stability of the feature representation, a sequence auxiliary branch is added to form an instance-level feature representation of the sequence. In addition, the feature representations obtained by the CoCL-Sig can be applied to modulation signals for semi-supervised classification and clustering tasks. We have conducted extensive experiments on two widely used modulation signal data sets, RML2016.10A and RML2016.04C. The results demonstrate the effectiveness of our method in modulation signal feature representation and its superiority compared to other methods.