Open set recognition (OSR) in radio frequency fingerprint (RFF) is critical for securing Internet of Things (IoT) systems, where previously unseen or malicious devices may attempt unauthorized access. A widely used approach treats all unknown devices as a single additional class and assumes that they will produce low confidence scores during inference. However, due to the inherently subtle and highly similar RFF features across devices, this assumption often fails, leading to high false acceptance rates. To address this challenge, we propose a novel framework, called Multiple Unknown Classes Generation (MUCG), which replaces the single-class modeling of unknowns with a more expressive structure that simulates multiple distinct unknown classes. MUCG employs a conditional generative adversarial network (cGAN) guided by ideal signal priors to produce diverse and realistic unknown samples. Furthermore, we introduce a soft label perturbation (SLP) strategy that blends label semantics using Feature-wise Linear Modulation (FiLM), encouraging the generator to embed richer feature variations. Experiments on three public IoT datasets demonstrate that MUCG consistently outperforms state-of-the-art (SOTA) methods in OSR tasks, achieving superior accuracy and robustness under varying signal conditions.

Radio frequency fingerprint identification (RFFI) leverages signal distortions caused by hardware impairments to identify transmitters, thereby enhancing IoT security. However, current radio frequency fingerprints (RFFs) modelings typically focus on partial hardware impairments, risking incomplete RFF extraction and limited RFF understanding. This study aims to refine the modeling of RFFs and guide the development of robust and accurate RFFI approaches based on this model. Specifically, we propose a comprehensive time-domain signal distortion model based on hardware impairments in wireless transmission circuit components, revealing that RFFs can be categorized into two types: 1) fine-grained RFF and 2) coarse-grained RFF. The former encompass localized distortions, such as mismatches, intersymbol interference, and nonlinear distortions; the latter relate to global features, including frequency spurs, phase noise, and crystal oscillator frequency offset. Subsequently, we analyze the impact of interference on the RFF model and propose necessary methods to mitigate the interference. Combining the comprehensive analysis of the RFF model and interference, we summarize three primary characteristics of RFFs: 1) multiscale; 2) fixedness; and 3) ubiquity. These characteristics indicate that convolutional neural networks (CNNs) from the visual domain cannot be directly transferred or simply adapted in terms of input data shape for application in RFFI. Therefore, we propose an enhanced CNN architecture with grouped convolutions and channel fusion modules for effective RFF extraction. To demonstrate the generalizability of our approach, we conduct extensive experiments using three public IoT signal datasets. Experimental results demonstrate that our method exhibits excellent identification performance and robustness against interference across various environments.

Radio Frequency Fingerprinting (RFF)-based identification methods have the potential to enhance the security of the Internet of Things (IoT). However, conventional fingerprinting techniques based on standard sample rates face limitations related to noise and device scale. The utilization of high sample rate receivers offers a promising solution to mitigate these constraints. Nonetheless, the challenge lies in extracting RFFs from the collected ultra-long signals. Image-based methods, which accumulate signals in the time domain, reduce the difficulty of extracting RFFs from long signals but overlook the fine-grained RFFs in the time domain. To solve this problem, this paper proposes RFF modeling for long signals, emphasizing the importance of obtaining short-term time-varying RFFs and time-invariant RFFs. Combining an analysis of the inductive biases of convolutional neural networks, we propose a backbone network named GResNet, which is capable to effectively extract these two types of RFFs. An information fusion module is added to improve identification performance. Extensive experiments are conducted with 100 LoRa devices, demonstrating that our method outperforms existing RFFI techniques based on standard sample rate or high sample rate signals. Furthermore, our approach maintains robust performance within a wide range of SNRs.

The rapid expansion of the Internet of Things (IoT) has heightened the necessity for device identity authentication to ensure security. Radio frequency fingerprint identification (RFFI) has emerged as a promising solution for this purpose, which leverage unique signal distortions caused by hardware impairments to authenticate device identities. However, most RFFI methods operate under a closed-set assumption and usually mistakenly identify unknown devices from the open set as known devices. In this paper, we propose a Synthetic Feature Constrain for open-set Recognition (SFCR) method to maintain classification performance on known devices and identify unknowns. Specifically, we modify the nonlinear characteristics of known devices based on the power amplifier nonlinearity model of radio frequency fingerprints (RFF) to synthesize signals for unknown devices. Furthermore, we propose a synthetic feature constraint to calibrate the position of synthetic devices in the feature space, such that they lie between the feature centers of the collective synthetic and originating known devices. As synthetic devices represent only a subset of the real unknown devices, we also introduce a calibration method for the prediction results. Experiments on a publicly available LoRa device dataset have validated the effectiveness of our approach. The code is released on github.com/wzyxwqx/SFCR.

With the fast development of multi-agent reinforcement learning, communication among agents has become a new research hotspot for its significant role in promoting the cooperation of automated devices. However, in real-world scenarios, agents such as unmanned vehicles and robots are likely to suffer from communication resource constraints, making designing efficient communication protocols essential. In this paper, we propose to quantize messages and reduce discrete entropy to achieve effective multi-agent communication under bandwidth limits. Achieving this goal requires solving two challenges: The first one is that the gradients of discrete entropy remain zero except for several discontinuous points wherein the gradients are undefined, making it hard to reduce discrete entropy via gradient-based training. To overcome it, we design Surrogate Entropy Minimization (SEM) scheme and confirm its effectiveness theoretically. The second challenge is maximizing cooperation performance under a given bandwidth limit. We model it as a constrained optimization problem and design Soft Barrier Method (SBM). Our proposed scheme is evaluated alongside four other methods in six environment settings and five different bandwidth limits, and demonstrates outstanding performance. Specifically, it manages to reduce bandwidth consumption by up to 90% with little or no loss of cooperation performance.

Communication in multi-agent reinforcement learning has been drawing attention recently for its significant role in cooperation. However, multi-agent systems may suffer from limitations on communication resources and thus need efficient communication techniques in real-world scenarios. According to the Shannon-Hartley theorem, messages to be transmitted reliably in worse channels require lower entropy. Therefore, we aim to reduce message entropy in multi-agent communication. A fundamental challenge is that the gradients of entropy are either 0 or ∞, disabling gradient-based methods. To handle it, we propose a pseudo gradient descent scheme, which reduces entropy by adjusting the distributions of messages wisely. We conduct experiments on two base communication frameworks with six environment settings and find that our scheme can reduce message entropy by up to 90% with nearly no loss of cooperation performance.

Sparse fast Fourier transform (SFFT) achieves spectrum sensing with sublinear computational and sample complexity, which has raised widely attention in the signal processing community recently. However, SFFT ignores the structure characteristics of spectrum. In this paper, we optimize the SFFT algorithm for band-limited spectrum sensing. The optimized permutation theory is given and proven first. Then, based on the optimized permutation theory, the optimized sparse Fourier transform for band-limited signal (OB-SFT) is designed. OB-SFT is a universal algorithm with deterministic parameters, and the computational and sample complexity are less than SFFT. Finally, numerical simulations verify the effectiveness and advantages of OB-SFT.