Specific emitter identification (SEI) separates the radio frequency fingerprint (RFF) from signals, which is of great significance in solving Internet of Things (IoT) security problems. However, the scarcity of high-quality, diverse, and labeled data in real-world scenarios limits the application of SEI. Under such conditions, the SEI is referred to as few-shot SEI (FS-SEI). To surmount this challenge, we propose a diffusion model-based data augmentation method capable of generating a substantial volume of diverse, high-quality data. Specifically, we develop a multi-scale convolutional block attention module denoising diffusion probabilistic model (MSCBAM-DDPM), which enhances feature capture capabilities, laying the foundation for the generation of diverse data. Furthermore, we propose an adaptive two-stage multi-domain loss function that guides the model to learn the characteristics of the original data and further derive other similar features, thereby achieving the goal of generating diverse and high-quality data. Finally, we theoretically derive the feasibility of the proposed loss function and further demonstrate the excellent diversity and quality of the data generated by our method, as well as its considerable gain for FS-SEI, through extensive experiments on real-world signal datasets.

In the context of future 6G networks, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are emerging as pivotal technologies for enhancing signal quality and eliminating coverage blind spots. This paper addresses the issue of secure and resilient transmission in RISassisted NOMA systems. Specifically, the base station transmits private signals to multiple legitimate users while dealing with the threat of potential eavesdropping. To model this challenge, we optimize the beamforming vectors and the RIS phase-shift matrix to maximize the sum secrecy rate while satisfying the user quality of service (QoS) requirements and the power constraints of the base station. Since the problem involves high-dimensional variables and non-convex objective functions, it is difficult to be solved by traditional optimization methods. Therefore, the twin delayed deep deterministic policy gradient algorithm (TD3) based on deep reinforcement learning (DRL) is proposed in this paper to effectively address the complexity of the original problem. Numerical results show that the proposed scheme exhibits satisfactory performance in improving communication security, transmission efficiency, and resistance to channel errors.

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

With the rapid development of emitter individual identification technology in cognitive radio networks, electromagnetic emitter individual target identification based on deep learning has received much attention. However, the confusion of unintentional features (i.e., individual fingerprint features) and modulation features resulting from the received signal might lead to low identification accuracy. In order to address this narrow, we propose an emitter individual identification network based on the competitive collaboration framework, called Specific Emitter Identification with Adaptive Decomposition and Extraction of individual fingerprint features (SEI-ADE), which can adaptively decompose and extract individual fingerprint features. Firstly, a signal adaptive decomposition network is proposed to distinguish the emitter signal and the interference signal by adopting the gradient inversion layer and the non-sequential characteristics of the signal. Then, in order to distinguish and extract corresponding features, the feature extractor and the training loss constraints are constructed for individual fingerprint feature signals, modulation signals, and external emitter interference signals, respectively. The proposed framework can continuously adjust the gradient loss, classification loss, and timing coding contrast loss, thus minimizing the entire training loss. For the separation of the modulation signal and individual fingerprint feature signal, the signal is transformed into the feature domain, and a mask prediction network is proposed to locate the domain of the individual fingerprint feature. The obtained experimental results show the outstanding performance of our proposal, compared with the current benchmarks. All our models and code are available athttps://github.com/jn-z/SEI-ADE.

With the emerge of the network function virtualization (NFV) and software-defined network (SDN), the SDN/NFV-enabled network has been recognized as one of the most promising technologies to efficiently achieve resource allocation for network service. By introducing the SDN/NFV technology, each service can be represented by a service function chain (SFC), which can deploy the virtualized network functions (VNFs) and chain them with corresponding flows allocation. Considering the dynamic and complex nature of mobile terminals in cloud networks, how to efficiently embedding SFCs remains as a challenging problem. However, the traditional methods (e.g., exact, heuristic, meta-heuristic, and game, etc.) are subjected to the complexity of cloud network scenarios with dynamic network states, high-speed computational requirements, and enormous service requests. Recent studies have shown that deep reinforcement learning (DRL) is a promising way to deal with the limitations of the traditional methods. However, DRL agent training easily suffers from the problem of slow convergence performance. In order to overcome this narrow, in this paper, we design a novel DRL framework based on the enhanced deep deterministic policy gradient (E-DDPG) for the efficient SFC embedding in the dynamic and complex cloud network scenarios. Simulation results validate the high efficiency of the proposed DRL framework as it not only converges faster than currently baseline algorithms, but also reduces the end-to-end delay down to at least 28.3% compared to the benchmarks. All our proposed algorithms and code are available at https://github.com/ jn-z/.

Estimating the Direction of Arrival (DOA) of a single source signal from a single observation of an array data still plays an important part in practical application scenarios. To address the problem, this letter proposes two iterative, fast and accurate approaches namely Q-Shift based DOA Estimation Algorithm (QS-DOAEA) and Tradeoff between A&M and Q-Shift based DOA Estimation Algorithm (TAQ-DOAEA). QS-DOAEA adopts the interpolation of shifted DFT coefficients to iteratively obtain the near-optimal estimation. TAQ-DOAEA employs the error function by simultaneously mixing theq-shifted and half-shifted DFT coefficients, which only requires two iterations. Numerical results reveal that QS-DOAEA achieves significant improvement in terms of estimation accuracy and TAQ-DOAEA expedites the convergence. The proposed estimation algorithms demonstrate that they have an asymptotic variance that is at least 1.0013 times asymptotic Cramér-Rao bound (ACRB), and provide more than 80× reduction in the computational cost, compared to the baseline method. All our proposed algorithms and code are available at https://github.com/jn-z/.

Multitask joint learning technology continues gaining more attention as a paradigm shift and has shown promising performance in many applications. Depth estimation and semantic understanding from monocular images emerge as a challenging problem in computer vision. While the other joint learning frameworks establish the relationship between the semantics and depth from stereo pairs, the lack of learning camera motion renders the frameworks that fail to model the geometric structure of the image scene. We make a further step in this article by proposing a multitask learning method, namely DPSNet, which can jointly perform depth and camera pose estimation and semantic scene segmentation. Our core idea for depth and camera pose prediction is that we present the rigid semantic consistency loss to overcome the limitation of moving pixels from image reconstruction technology and further infer the segmentation of moving instances based on them. In addition, our proposed model performs semantic segmentation by reasoning the geometric correspondences between the pixel semantic outputs and the semantic labels at multiscale resolutions. Experiments on open-source datasets and a video dataset captured on a micro-smart car show the effectiveness of each component of DPSNet, and DPSNet achieves state-of-the-art results in all three tasks compared with the best popular methods. All our models and code are available at https://github.com/jn-z/DPSNet: Multitask Learning Using Geometry Reasoning for Scene Depth and semantics.