The rapid development of communication systems poses unprecedented challenges, e.g., handling exploding wireless signals in a real-time and fine-grained manner. Recent advances in data-driven machine learning algorithms, especially deep learning (DL), show great potential to address the challenges. However, waveforms in the physical layer may not be suitable for the prevalent classical DL models, such as convolution neural network (CNN) and recurrent neural network (RNN), which mainly accept formats of images, time series, and text data in the application layer. Therefore, it is of considerable interest to bridge the gap between signal waveforms to DL amenable data formats. In this paper, we develop a framework to transform complex-valued signal waveforms into images with statistical significance, termed contour stellar image (CSI), which can convey deep level statistical information from the raw wireless signal waveforms while being represented in an image data format. In this paper, we explore several potential application scenarios and present effective CSI-based solutions to address the signal recognition challenges. Our investigation validates that CSI is a promising method to bridge the gap between signal recognition and DL.

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance, and numerous public services. However, the deployment of amateur drones poses various safety, security, and privacy threats. To cope with these challenges, amateur drone surveillance has become a very important but largely unexplored topic. In this article, we first present a brief survey to show the stateof- the-art studies on amateur drone surveillance. Then we propose a vision, named Dragnet, tailoring the recently emerging Cognitive Internet of Things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in detail, accompanied by the technical challenges and open issues. Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over.

Deep learning (DL) has been recognized as an effective solution for automatic modulation cassification (AMC). However, most recent DL based AMC works are based on realvalued operations and representations. In this correspondence, we aim to demonstrate the high potential of complex-valuenetworks for AMC. We present the design of several key building blocks for complex-valued networks, such as complex convolution, complex batch-normalization, complex weight initialization, and complex dense strategies. We then provide a comparison study of three different neural network models and their complex-valued counterparts using the RadioML 2016.10A dataset. Our results validate the superior performance in AMC achieved by the complex-valued networks.

Automatic modulation classification (AMC) plays an important role in both civilian and military applications. Today, increasingly more researchers apply a deep learning (DL) framework in AMC. However, few papers consider that a typical deep model is difficult to deploy on resource constrained devices. In this paper, we propose a new filter-level pruning technique based on activation maximization (AM) that discards the less important convolution filter. Compared to other network pruning techniques, the convolution neural network (CNN) pruned via the AM method obtains equal or higher classification accuracy in the RadioML2016.10a dataset.

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.

While the intersection of blockchain and Industrial Internet of Things (IIoT) has received considerable research interest lately, the conflict between the high resource requirements of blockchain and the generally inadequate performance of IIoT devices has not been well tackled. On one hand, due to the introductions of mathematical concepts, including Public Key Infrastructure (PKI), Merkle Hash Tree, and Proof of Work (PoW), deploying blockchain demands huge computing power. On the other hand, full nodes should synchronize massive block data and deal with numerous transactions in peer-to-peer (P2P) network, whose occupation of storage capacity and bandwidth makes IIoT devices difficult to afford. In this paper, we propose a lightweight blockchain system called LightChain, which is resource-efficient and suitable for power-constrained IIoT scenarios. Specifically, we present a green consensus mechanism named Synergistic Multiple Proof (SMP) for stimulating the cooperation of IIoT devices, and a lightweight data structure called LightBlock (LB) to streamline broadcast content. Furthermore, we design a novel Unrelated Block Offloading Filter (UBOF) to avoid the unlimited growth of ledger without affecting blockchain's traceability. The extensive experiments demonstrate that LightChain can reduce individual computational cost to 39.32% and speed up the block generation by up to 74.06%. In terms of storage and network usage, the reductions are 43.35% and 90.55%, respectively.