Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) can lead to erroneous results, which greatly reduces the reliability and security of the DL tasks. Considering the wide application of modulation recognition in the communication field and the rapid development of DL, by adding a well-designed adversarial perturbation to the input signal, this article explores the performance of attack methods on modulation recognition, measures the effectiveness of adversarial attacks on signals, and provides the empirical evaluation of the reliabilities of CNNs. The results indicate that the accuracy of the target model reduce significantly by adversarial attacks, when the perturbation factor is 0.001, the accuracy of the model could drop by about 50% on average. Among them, iterative methods show greater attack performances than that of one-step method. In addition, the consistency of the waveform before and after the perturbation is examined, to consider whether the added adversarial examples are small enough (i.e., hard to distinguish by human eyes). This article also aims at inspiring researchers to further promote the CNNs reliabilities against adversarial attacks.

In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict. In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications. Being able to learn from experience is an important way to approach this vision. Transfer learning (TL) encourages new tasks/domains to learn from experienced tasks/domains for helping new tasks become faster and more efficient. TL can help save energy and improve efficiency with the correlation and similarity information between different tasks in many fields of wireless communications. Therefore, applying TL to future 6G communications is a very valuable topic. TL has achieved some good results in wireless communications. In order to improve the development of TL applied in 6G communications, this article performs a comprehensive review of the TL algorithms used in different wireless communication fields, such as base stations/access points switching, indoor wireless localization and intrusion detection in wireless networks, etc. Moreover, the future research directions of mutual relationship between TL and 6G communications are discussed in detail. Challenges and future issues about integrate TL into 6G are proposed at the end. This article is intended to help readers understand the past, present, and future between TL and wireless communications.

The relay is harvesting energy for data transmission and eavesdropping attacks will occur. Considering carried energy and security of relay, this letter studies relay selection in energy harvesting multi-hop D2D networks with an eavesdropper. To improve the secure connectivity performance of the relay, the higher bound of source-to-destination secrecy connectivity probability (SCP) is derived. Then, we derive the factor directly related to the largest SCP. Then, a relay selection algorithm is proposed to find a higher security connectivity relay path. Our theoretical derivation and the proposed algorithm are verified by Monte Carlo simulation results. The simulation results show that the proposed algorithm exhibit significantly higher SCP than the direct transmission method and conventional relay selection algorithms.

With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high‐dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

Automatic modulation classification (AMC) is a key technology in cyber-physical systems (CPSs), which enables the monitoring and identification of communication signals exchanged between devices. One of the most recognized solutions for the AMC is deep learning (DL), which can automatically learn and extract feature representations in signals. However, data-driven DL models are susceptible to adversarial examples, which can cause significant instability in the CPS. To tackle this issue, in this article, we examine the distribution shift between original signals and adversarial examples from a domain distribution perspective and present a system model for addressing the defense problem. We propose the adversarial domain generalization defense (ADGD) framework. The ADGD framework adopts a dual-stream architecture with the AMC as its central task, and extracts and constrains the maximum mean discrepancy distance between the task-relevant features of original signals and adversarial examples to reduce the distribution shift and improve the adversarial robustness. Comprehensive experiments and ablations were conducted to demonstrate the superiority of the proposed ADGD framework on the RML2016.10a and miniRML2018.01a datasets. The results indicate that the ADGD framework shows promising results in improving the adversarial robustness of AMC systems, which is crucial for the stability of the CPS.