To overcome the limitations of traditional satellite navigation technologies in complex and signal-obstructed industrial environments, this paper presents MSM-Pnet, a novel semi-supervised FM-based positioning framework leveraging FM signals of opportunity. By integrating wavelet packet decomposition with a multi-scale Vision Transformer and a hybrid masking strategy that combines random and time–frequency-aware masking, MSM-Pnet introduces a masked autoencoder architecture capable of robust positioning with limited labeled data. Experimental results demonstrate that MSM-Pnet consistently outperforms conventional supervised learning methods in both indoor and outdoor environments, while also significantly reducing model complexity. These results highlight the method’s potential as a cost-effective and scalable solution for seamless indoor–outdoor positioning for Internet of Things systems.

With the rapid development of deep learning in wireless communication for signal detection, target recognition, and parameter estimation, the vulnerability of models to adversarial examples poses a critical challenge to system robustness and security. Existing adversarial attacks mainly focus on single-task models, limiting their applicability in multi-task scenarios. To address this issue, we propose a Multi-Teacher Distillation-guided Multi-Task Attack (MTDMA) framework. It integrates three high-performance teacher models—direct sequence spread spectrum (DSSS) detection, modulation recognition, and direction-of-arrival (DOA) estimation—to train a unified student model through hybrid knowledge distillation. By fusing soft targets from multiple teachers, the student jointly learns discriminative features of multiple tasks within a shared representation space. Furthermore, we design a Dual-Representation Momentum Attack (DRMA) that generates perturbations in both amplitude and IQ feature spaces with a momentum mechanism, improving attack transferability and stability. Experimental results demonstrate that MTDMA achieves high attack success rates on three task models and transfers effectively to heterogeneous architectures, outperforming FGSM, BIM, PGD, and MIM. This work extends adversarial attacks from single-task to multi-task settings, providing new insights into robustness evaluation and multi-task adversarial sample generation for wireless communication models.

Abstract Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios.

A wireless communications system usually consists of a transmitter which transmits the information and a receiver which recovers the original information from the received distorted signal. Deep learning (DL) has been used to improve the performance of the receiver in complicated channel environments and state-of-the-art (SOTA) performance has been achieved. However, its robustness has not been investigated. In order to evaluate the robustness of DL-based information recovery models under adversarial circumstances, we investigate adversarial attacks on the SOTA DL-based information recovery model, i.e., DeepReceiver. We formulate the problem as an optimization problem with power and peak-to-average power ratio (PAPR) constraints. We design different adversarial attack methods according to the adversary’s knowledge of DeepReceiver’s model and/or testing samples. Extensive experiments show that the DeepReceiver is vulnerable to the designed attack methods in all of the considered scenarios. Even in the scenario of both model and test sample restricted, the adversary can attack the DeepReceiver and increase its bit error rate (BER) above 10%. It can also be found that the DeepReceiver is vulnerable to adversarial perturbations even with very low power and limited PAPR. These results suggest that defense measures should be taken to enhance the robustness of DeepReceiver.