This letter proposes the Dynamic Margin Meta Metric Learning (DM-MML) framework to address unknown class interference and sample scarce challenges in few-shot open set specific emitter identification. In DM-MML, the dual-loop meta-training phase adopts a hierarchical margin strategy with triple-optimization, where in the inner loop, strong margins enhance feature separability for source domain emitters and the outer loop applies weak margins to calibrate target domain distributions. This strategy simultaneously preserves open decision boundaries for potential unknown class samples. During meta testing, a collaborative decision mechanism combines distance metrics and confidence scores, enabling precise known-class identification while maintaining open-space rejection capacity for unknown classes. Experiments on the WiSig dataset demonstrate superior performance over existing benchmarks under few-shot conditions.

Specific Emitter Identification (SEI) is crucial for ensuring the security of physical layer communication. However, signal characteristics can be affected by various factors such as environmental and equipment variations. An effective SEI system must continuously learn and adapt to these changes to maintain accurate signal recognition. This study proposes an advanced domain incremental learning (DIL) framework for SEI, named Adaptive Domain-Incremental Learning with Knowledge Replay and Domain Alignment (ADIRA). ADIRA employs knowledge replay and distillation strategies, along with adaptive coefficients, to balance the model’s performance in recognizing signals across both new and old domains. To address the variations in signal data feature distributions across different domains, we introduce a domain alignment strategy based on adversarial training. This approach integrates embedding distillation loss with supervised contrastive loss, significantly enhancing the model’s adaptability to domain changes. Experimental results on two benchmark datasets demonstrate that ADIRA achieves performance only 0.42% and 1.71% lower than joint training, with replay samples constituting just 1.1% and 1.5% of the training set, effectively mitigating catastrophic forgetting.

Abstract Aiming at the problems of weak robustness of single feature and limited recognition range in modulation recognition, this paper proposes a modulation recognition algorithm based on cascaded feature fusion and multi‐classifier combination, in which time‐frequency map and instantaneous amplitude spectral density features are extracted from low intermediate frequency (IF) signal, constellation map and high‐order cumulant features are extracted from zero IF signal, and a three‐level recognition algorithm is designed through the decision fusion of decision tree, convolutional neural network, and support vector machine. In order to verify the performance of the algorithm, a modulation recognition system is designed and built based on USRP2974. The low IF and zero IF modulation signals are received through two channels, and the RF signals are received and processed in real time with the help of the built‐in CPU of the receiver. The recognition of 13 kinds of analog modulation and digital modulation signals is realized. Under the condition of wireless reception, the recognition rate of this system is more than 93% at 5 to 10 dB.

In recent years, enterprises are increasingly moving from private WANs to hybrid WANs, for the purpose of cost saving, better scalability and improved user experience. To utilize network resource of both private and public networks, existing traffic engineering (TE) solutions for hybrid WANs perform traffic classification at the application level, i.e., sending traffic of high-priority applications to private WANs while letting other traffic go to the Internet. Unfortunately, such a strategy falls short of achieving optimal performance and has inherent limitation in making desired tradeoff between cost and performance. In this paper, we present comboTE, a mixed-link based TE framework to optimize both performance and cost over hybrid WAN. Specifically, comboTE designs a fine-grained TE strategy that considers link(s) from both types of networks at every hop when deciding the routing path for each traffic demand. To address the scalability issues, we leverage Lagrangian relaxation, a decomposition technique for solving large scale integer linear programs, combined with a novel augmented-graph-based approach to derive the most cost-efficient link compositions within a segment. Our comboTE achieves the near-optimal solution with a theoretically proven gap that is better than linear programming relaxation. The experimental results on realistic topologies and traffic matrices show that comboTE scales well and achieves solutions of high quality. Compared to prior TE algorithms, comboTE achieves a solution with tighter gap in limited time, and takes 2.2× less execution time without a time budget.