With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based Automatic Modulation Classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.

Internet of vehicles is a promising area related to D2D communication and the Internet of Things. We present a novel perspective on vehicular communications and social vehicle swarms, to study and analyze a socially aware Internet of vehicles with the assistance of an agent-based model intended to reveal hidden patterns behind superficial data. After discussing its components (its agents, environments, and rules), we introduce supportive technology and methods, deep reinforcement learning, privacy preserving data mining, and sub-cloud computing in order to detect the most significant and interesting information for each individual effectively, which is the key desire. Finally, several relevant research topics and challenges are discussed.

Partial-band noise jamming is an important countermeasure against frequency-hopping spread spectrum technology in wireless sensor networks, and the jamming resource allocation (JRA) problem involved is a high-dimensional combinatorial optimization and also an NP-hard problem. Moreover, the users can dynamically alter their communication status, such as changing the channel spectrum distributions of hopping sets, posing additional difficulties for JRA. This paper develops two methods to address above challenges. First, a deep reinforcement learning (DRL)-based method is proposed for efficient JRA optimization, with the jamming scheme of each jamming node decided sequentially by the policy neural network, and its parameters are updated through trust region policy optimization (TRPO) within a trust region to ensure stable and fast convergence. Second, we propose a meta-TRPO based method to improve the generalization capability of the policy network. After the meta-training process, it can update the meta policy network with just a few fine-tuning steps and quickly obtain a task-specific policy for the fresh task. Extensive simulation results show the proposed DRL-based method converges faster than other DRL methods. Additionally, the proposed meta-TRPO based method can rapidly adapt to unseen jamming tasks with only a small quantity of training trajectories.

Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness. Firstly, a one-dimensional convolutional neural network was designed for recognizing I/Q signals, and a squeeze-and-excitation block with an attention mechanism was added to the network to increase the weights of the feature channels with high efficiency. Meanwhile, the recognition was improved by group convolution and channel shuffle. Then, the network was trained with the joint loss function based on prototype learning to complete the separation of intra-class signals and the aggregation of inter-class signals in the feature space. After the training, the Weibull model was fitted for pre-defined classes by incorporating the extreme value theory. Finally, the classification results were obtained according to the known classes and the Weibull model, effectively completing the open-set recognition. The simulation results showed that the proposed model had a higher recognition performance and robustness compared with other classical models for signals collected from five ZigBee and ten USRP 310 devices.

The advancement of deep learning (DL) techniques has led to significant progress in Automatic Modulation Classification (AMC). However, most existing DL-based AMC methods require massive training samples, which are difficult to obtain in non-cooperative scenarios. The identification of modulation types under small sample conditions has become an increasingly urgent problem. In this paper, we present a novel few-shot AMC model named the Spatial Temporal Transductive Modulation Classifier (STTMC), which comprises two modules: a feature extraction module and a graph network module. The former is responsible for extracting diverse features through a spatiotemporal parallel network, while the latter facilitates transductive decision-making through a graph network that uses a closed-form solution. Notably, STTMC classifies a group of test signals simultaneously to increase stability of few-shot model with an episode training strategy. Experimental results on the RadioML.2018.01A and RadioML.2016.10A datasets demonstrate that the proposed method perform well in 3way-Kshot, 5way-Kshot and 10way-Kshot configurations. In particular, STTMC outperforms other existing AMC methods by a large margin.

Jamming against frequency-hopping spread spectrum (FHSS) in wireless sensor networks (WSNs) has been primarily investigated with the follower jamming mode. However, implementing follower jamming in practical applications encounters manifold challenges, such as stringent requirements on hardware performance, difficulties in attaining accurate synchronization with signals. Diverging from existing works, in this paper, we propose a novel partial-band noise jamming (PBNJ) decision-making algorithm based on asynchronous deep reinforcement learning, which can allocate central jamming frequency and bandwidth more efficiently in FHSS jamming. Firstly, we model the problem of allocating jamming resource of PBNJ to disrupt the FHSS communication in WSNs as a Markov decision process (MDP). Next, considering the interrelationship among decisions made by different jamming nodes (JNs), we construct a multi-step decision framework in a time-division manner, and the Long Short-Term Memory (LSTM) network is leveraged to fully extract decision features from historical data, capturing correlations between jamming strategies of the deployed JNs, and guides future jamming decisions and enhances collaboration among different JNs in jamming resources allocation. Furthermore, to accelerate the convergence, we adopt the asynchronous advantage actor-critic (A3C) algorithm to optimize the allocation of central jamming frequency and bandwidth of JNs, utilizing the architecture of multi-threaded parallel training, and update the actor network and critic network in an asynchronous gradient descent manner. Simulation results show that the proposed LSTM-A3C algorithm converges fast and outperforms various baselines in terms of the convergence speed, jamming success rate and the total reward.