Automatic modulation recognition is essential for large-scale wireless communications, but traditional methods often ignore unknown signals in open-set conditions, leading to their incorrect classification as known types and thereby compromising system reliability and communication security. To handle this challenge, a novel automatic modulation open set recognition (AMOSR) model based on deep metric learning with embedding enhancement is proposed in this article. First, for each known example, deep neural model is employed to separately extract the original in-phase and quadrature (IQ) signal and its instantaneous features, which are then fused to obtain embedding. Second, random erasing is employed to the original IQ signal and instantaneous features separately to obtain an augmented example, which has similar structure but different semantics with known example, and the embedding of this example is obtained by using first step. Then, the embedding space, in which tuplet loss and margin loss are combined with the embeddings of known and augmented examples, is trained to improve the overall performance of the model. Finally, after training, AMOSR is implemented using the class centers of known classes. Experiments on three automatic modulation datasets show that our model has better average performance than several mainstream methods in the field of computer vision.

Modulation recognition of communication signals is of great importance in the context of the Internet of Everything (IoE), as wireless communication technology is a key foundation for implementing the IoE. Recently, graph neural networks (GNNs) have been successfully applied to modulation recognition tasks due to their ability to merge messages transmitted between adjacent nodes in the graph. However, GNN-based models are more computationally intensive when processing long signals, potentially reducing their practicality. In this article, we explore a novel signal representation from a graph perspective and propose a graph-powered modulation recognition framework. We first propose the dynamic time series graph (DTSG) algorithm, which segments the signals and maps each segment into a patch graph, with corresponding patches from different signals sharing connected edges. By integrating DTSG with both GNNs and recurrent neural networks (RNNs), we have designed an end-to-end signal classification framework, DTSG-Net, for modulation recognition. Experimental results on four datasets: 1) RML2016.10a; 2) RML2018.01a; 3) Sig2019-12; and 4) HKDD_AMC36—demonstrate that our DTSG-Net can achieve high signal modulation classification accuracy (Acc) with minimal computational resources, outperforming existing methods based on signal graph representation in terms of computational resource savings and higher accuracy.

Automatic Modulation Open Set Recognition (AMOSR) has practical significance in detecting unknown classes. However, a challenge arises when unknown samples closely resemble known samples, posing a formidable task for accurate detection. A novel AMOSR framework based on multi-view discriminators’ joint judgment is proposed to handle this challenge. Firstly, utilizing signal domain knowledge, multi-dimensional features are extracted through varied signal time-frequency transforms and encoders, baesd on which multiple discriminators are created. Secondly, Constrained Clustering Prototype Loss and Geodesic Contrastive Loss are introduced to pretrain these discriminators, providing more space for unknown signals. Then, collaborative learning is employed to further fine-tune the aforementioned discriminators, enhancing information sharing between modalities. Furthermore, a set of indicators is constructed, and multi-criteria fusion is performed using the TOPSIS algorithm to evaluate the discrimination capabilities of different classifiers in both closed-set and open-set scenarios. Furthermore, a decision tree is constructed to segregate test signals into known and unknown classes, in which discriminators with higher confidence levels are given precedence. Finally, TOPSIS hierarchical ensemble pruning algorithm that considers diversity and open-set recognition capabilities is adopted to reduce model complexity while maintaining original performance. Extensive experiments conducted on modulation datasets demonstrate the superiority of this framework over state-of-the-art AMOSR results.

N:m sparsity has received increasing attention due to its remarkable performance and latency trade-off compared with structured and unstructured sparsity. How-ever, existing N:m sparsity methods do not differentiate the relative importance of weights among blocks and leave important weights underappreciated. Besides, they di-rectly apply N:m sparsity to the whole network, which will cause severe information loss. Thus, they are still sub-optimal. In this paper, we propose an efficient and effective Multi-Axis Query methodology, dubbed as MaxQ, to rectify these problems. During the training, MaxQ employs a dynamic approach to generate soft N:m masks, considering the weight importance across multiple axes. This method enhances the weights with more importance and ensures more effective updates. Meanwhile, a spar-sity strategy that gradually increases the percentage of N:m weight blocks is applied, which allows the network to heal from the pruning-induced damage progressively. During the runtime, the N:m soft masks can be precom-puted as constants and folded into weights without causing any distortion to the sparse pattern and incurring ad-ditional computational overhead. Comprehensive experi-ments demonstrate that MaxQ achieves consistent improve-ments across diverse CNN architectures in various com-puter vision tasks, including image classification, object detection and instance segmentation. For ResNet50 with 1:16 sparse pattern, MaxQ can achieve 74.6% top-1 ac-curacy on ImageNet and improve by over 2.8% over the state-of-the-art. Codes and checkpoints are available at https://github.com/JingyangXiang/MaxQ.

Abstract This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model.

Automatic modulation recognition (AMR) of radio signal is an important research topic in the area of non-cooperative communication and cognitive radio. Recently deep learning (DL) techniques enable significant progress in AMR. However, the techniques of adversarial machine learning cause the threats of adversarial attacks in DL-based AMR. In this paper, we aim to make AMR model robust, accurate and lightweight, thus propose a multi-distillation mechanism for robust training of DL-based AMR models, namely Adversarial Multi-Distillation (AMD). In the framework of AMD, by knowledge distillation, two powerful teacher models transfer the learned classification knowledge and defense knowledge, respectively, to the student model to form robust training. Our experiments with public dataset RML2016.10a show that the proposed method can significantly improve the defense of AMR models to against adversarial perturbations and keep relatively high classification accuracy, which enables robust decision making with lightweight models under adversarial attacks.

Deep learning technology has found a promising application in lightweight model design, for which pruning is an effective means of achieving a large reduction in both model parameters and float points operations (FLOPs). The existing neural network pruning methods mostly start from the consideration of the importance of model parameters and design parameter evaluation metrics to perform parameter pruning iteratively. These methods were not studied from the perspective of network model topology, so they might be effective but not efficient, and they require completely different pruning for different datasets. In this article, we study the graph structure of the neural network and propose a regular graph pruning (RGP) method to perform a one-shot neural network pruning. Specifically, we first generate a regular graph and set its node-degree values to meet the preset pruning ratio. Then, we reduce the average shortest path-length (ASPL) of the graph by swapping edges to obtain the optimal edge distribution. Finally, we map the obtained graph to a neural network structure to realize pruning. Our experiments demonstrate that the ASPL of the graph is negatively correlated with the classification accuracy of the neural network and that RGP has a strong precision retention capability with high parameter reduction (more than 90%) and FLOPs reduction (more than 90%) (the code for quick use and reproduction is available at https://github.com/Holidays1999/Neural-Network-Pruning-through-its-RegularGraph-Structure).

Although OSS development is very popular, ultimately more than 80% of OSS projects fail. Identifying the factors associated with OSS success can help in devising interventions when a project takes a downturn. OSS success has been studied from a variety of angles, more recently in empirical studies of large numbers of diverse projects, using proxies for sustainability, e.g., internal metrics related to productivity and external ones, related to community popularity. The internal socio-technical structure of projects has also been shown important, especially their dynamics. This points to another angle on evaluating software success, from the perspective of self-sustaining and self-governing communities.

Software defect prediction aims to automatically locate defective code modules to better focus testing resources and human effort. Typically, software defect prediction pipelines are comprised of two parts: the first extracts program features, like abstract syntax trees, by using external tools, and the second applies machine learning-based classification models to those features in order to predict defective modules. Since such approaches depend on specific feature extraction tools, machine learning classifiers have to be custom-tailored to effectively build most accurate models.