With the rapid development of Internet of Things (IoT), accurate and robust two-dimensional direction of arrival (2-D DOA) estimation has become more and more crucial. This paper addresses the significant challenge of 2-D DOA estimation with high degrees of freedom (DOF) under impulsive noise in practical IoT systems. Firstly, an augmented extended coprime parallel array is proposed by integrating an extended coprime array (ECA) with an augmented unfolded coprime array. The proposed design exploits non-circular signal characteristics, achieving an enhancement in DOF. Furthermore, a generalized versoria function (GVF) is introduced to suppress impulsive noise, and a GVF-based covariance matrix is developed to achieve robust DOA estimation. Simulation results confirm the proposed method achieves higher estimation accuracy and DOF under impulsive noise, making it suitable for reliable IoT systems.

Due to the uniqueness and stability of radio frequency fingerprints (RFF), radio frequency fingerprint identification (RFFI) is an important physical layer authentication method in the security of Internet of Things (IoT). However, existing deep-learning-based RFFI methods require a large number of labeled samples to achieve ideal performance. Besides, when the signal length changes, the network structure needs to be redesigned and the entire training process needs to be reconducted. To address this issue, we propose a few shot RFFI method on the basis of self-distillation with no labels (SDINO). Within it, a network termed DPformer is designed, which can adapt signals of varying lengths and is more lightweight. When the signal length changes, there is no need to retrain the network, and it is more lightweight. Simulation results show that, compared with existing methods, the proposed method achieves better recognition performance and more lightweight on LoRa dataset with 30 classes.

The rapid development of wireless sensing technology has opened new possibilities for non-contact health monitoring. Among them, respiration is crucial information for assessing vital signs. However, traditional methods face challenges of signal interference and overlap in multi-person environments. In this work, we propose a novel method for multi-person respiration detection using a digital programmable metasurface (DPM). This method takes advantage of the modulation characteristics of the DPM in the time and space domain. It divides the Channel State Information (CSI) from the Wi-Fi transmitter into multiple sub-signals in the time domain and modulates the radiation directions of the sub-signals for space redistribution. These signals are received by the Wi-Fi receiver and recombined to restore the CSI in each direction, thus enabling the accurate extraction of respiration signals from targets in different directions. Experimental results show that this method can directionally sense the human respiration information in a specific direction under the static working mode. Under dynamic scanning mode, it can effectively separate and detect the respiration information of four people from different directions. This system has great potential for applications in wireless communication, healthcare, and smart home environments.

Blind modulation classification has emerged as a promising technology in many military and civilian applications, such as cognitive radios, satellite systems, etc. However, it is very challenging to support this blind mechanism within non-Gaussian noise environments, which recently have been identified in a variety of electromagnetic communication networks. Also, start-of-the-art classification methods are mainly based on neural networks or deep learning, which inevitably induces heavy computation loads and thus cannot proactively learn from wireless data in real time. To address the challenges, this paper introduces a series of low- computation radio frequency analytics, including generalized cyclic spectrum (GCS), principal component analysis (PCA), and support vector machine (SVM), which enables the blind modulation classification under non-Gaussian noise. First, based on raw sensory signals and the designed bounded nonlinear function, GCS is extracted as the radio frequency feature to facilitate discrimination of modulation schemes. This GCS can also effectively suppress the burstiness impact of non-Gaussian noise. Then, PCA method is adopted to optimally reduce the dimensionality of GCS features, and a simple and efficient SVM classifier is employed to identify the exact modulation of received signals. Both Monte Carlo simulations and real- data experiments confirm that the proposed design outperforms existing solutions with higher classification accuracy and robustness, i.e., at least 13\% improvement of recognition accuracy in very low (-2 dB) generalized signal-to-noise ratio scenario.

During the last few years, automatic modulation classification (AMC) has attracted widespread attention in both civilian and military applications. Conventional AMC schemes are primarily developed under Gaussian noise assumptions. However, recent empirical studies show that non-Gaussian noise has emerged in a variety of wireless networked systems. The bursty nature of non-Gaussian noise fundamentally challenges the applicability of the conventional AMC schemes. In order to improve the classification performance under non-Gaussian noise, in this paper, a novel modulation classification method is proposed by using cyclic correntropy spectrum (CCES) and deep residual neural network (ResNet). First, CCES is introduced to effectively suppress non-Gaussian noise through the designated Gaussian kernel. CCES also provides significantly different CCES graphs with respect to different modulation schemes, enabling AMC to directly operate with the graphs without further feature extraction. Next, based on the CCES graphs, an end-to-end deep ResNet-based AMC is developed to recognize the correct modulation by iteratively evaluating the residual information in a cascade of multiple learning layers. Experimental results confirm that the proposed algorithm outperforms existing designs with much higher classification accuracy, i.e., 3 dB less in the required generalized signal to noise ratio for 100% accuracy, in non-Gaussian noise environments.