With the thriving artificial intelligence-generated content (AIGC), it is becoming increasingly appealing to exploit generative AI to generate wireless signals for facilitating wireless sensing. However, this is a challenging task, as wireless signals are highly random in general and contain rich physical information. To tackle these challenges, we propose a novel signal disentanglement and generation framework termed SigGen, which is inspired by the Fourier Transform (FT) that converts signals to the frequency domain and accordingly separates objectives by distinct frequency bands. In our proposed framework, we first disentangle the features of objects embedded in the signal and subsequently modify these features to generate the desired signals. Specifically, we devise a neural network based on the vision transformer (ViT) to extract effective features for signal generation. In this neural network, we incorporate both local and global frequency attention modules to adaptively leverage frequency features, and introduce a hybrid patch embedding module to enhance information interaction for the ViT architecture. Furthermore, we propose a novel sequential training method to improve the disentanglement and generation capability of the neural network. Finally, extensive experiments on two benchmark public wireless sensing datasets demonstrate that our framework can effectively decouple wireless signals and generate diverse signals closely resembling real ones, surpassing state-of-the-art methods by 30.83%. A practical case study further demonstrates that our framework can be used as a data augmentation method to improve gesture recognition accuracy by 12.74%.

Human-centered wireless sensing has been significantly advanced by artificial intelligence (AI) technologies. To enhance AI model performance, signal preprocessing, as a fundamental procedure, is widely employed for improving signal quality. However, existing methods are time-consuming, labor-intensive, and exhibit limited generalization. To address this issue, we first investigate the frequency spectrum of various signals. The results demonstrate that, in human-centered wireless applications, human activities significantly affect the low-frequency components in the signal spectrum. Motivated by this observation, we propose Sense+, a concise and versatile signal preprocessing module that can be seamlessly integrated into existing models to enhance sensing performance. Specifically, we transform the raw signals into a unified frequency domain, apply a learnable filter to process their frequency spectra, and then convert them back to the original signal domain. To accurately extract low-frequency features, we further propose a low-pass weight initialization method for the filter. Extensive experiments are conducted across various sensing tasks and signal types, including IR-UWB signals for person identification, mmWave radar signals for gesture recognition, and Wi-Fi signals for action recognition. The results highlight the effectiveness of Sense+ in enabling preprocessing across diverse wireless signals. Specifically, when equipped with Sense+, the average accuracy improves by 21.84% compared to conventional preprocessing methods. Additionally, Sense+ accelerates convergence and exhibits consistent generalization across different neural network models.

Concerns surrounding privacy leakages caused by prevalent vision-based person identifications are countless. A promising privacy-preserving solution is to identify the wireless signals reflecting persons, which, however, faces a major challenge of losing efficacy in multi-environments. In this paper, we work on person identification based on wireless signals using transfer learning, toward tackling the performance deterioration across environments. We investigate the feature variations induced by environmental shifts based on data measurements. Lay our foundation on the feature alignment concept, we propose a novel wireless-based person identification framework using transfer learning. In the framework, we integrate a series of signal processing methods including signal selection, pre-processing, and augmentation, where the first includes a reference environment to assist the feature extraction while the latter two respectively reduce the data noise and improve the data diversity. We also propose a model generalization method where a neural network is employed to align features from different environments, which facilitates the extraction of environment-independent features while incorporating both person and environment information. On a real wireless testbed consisting of an Impulse Radio Ultra-WideBand (IR-UWB) radar, we build and publicly release a dataset with 22,264 samples of ten individuals from three environments, varying in testing distance and obstruction condition. Extensive experimental evaluations demonstrate that the proposed framework can improve the identification accuracy across environments, and surpasses state-of-the-art methods by up to 18.06%.

Radio frequency fingerprint (RFF) identification has become a promising security solution for resource-constrained Internet-of-Things (IoT) devices, which relies on hardware impairments-induced radio frequency features for identification; among which, a hotspot feature is the carrier frequency offset (CFO). Existing research, however, advocates contradictory perspectives on the usage of CFO: For identification and for compensation; the former employs CFO in the feature space while the latter eliminates the CFO from the feature space, both for improving the RFF identification accuracy. In this review, we first discuss the RFF identification procedures and investigate the origination of the CFO and further its relationship with the clock skew of the crystal oscillator. We then provide a review of the state-of-the-art RFF identification schemes, in two categories respectively employing CFO for identification and compensation. Finally, on a real testbed, we experimentally investigate the impact of the usage of CFO on RFF identification accuracy. Experimental results reveal that, the stabilities of CFOs are quite different on hardware platforms from different manufacturers; CFOs can be used for identification when they are relatively distinguishable; compensating CFO alone is inadequate for long-term identification.

Deep learning has significantly advanced wireless sensing technology by leveraging substantial amounts of high-quality training data. However, collecting wireless sensing data encounters diverse challenges, including unavoidable data noise, limited data scale due to significant collection overhead, and the necessity to reacquire data in new environments. Taking inspiration from the achievements of AI-generated content, this paper introduces a signal generation method that achieves data denoising, augmentation, and synthesis by disentangling distinct attributes within the signal, such as individual and environment. The approach encompasses two pivotal modules: structured signal selection and signal disentanglement generation. Structured signal selection establishes a minimal signal set with the target attributes for subsequent attribute disentanglement. Signal disentanglement generation disentangles the target attributes and reassembles them to generate novel signals. Extensive experimental results demonstrate that the proposed method can generate data that closely resembles real-world data on two wireless sensing datasets, exhibiting state-of-the-art performance. Our approach presents a robust framework for comprehending and manipulating attribute-specific information in wireless sensing.

Energy-efficient time synchronizations for wireless sensor networks (WSNs) have been put under the spotlight for years. A promising technique among which is the timestamp-free approach where no timestamps are required to establish the synchronization, thereby sparing the transmissions of the timing messages for conserving significant transmission energy. In this paper, we first investigate the feasibility of adopting timestamp-free time synchronization in practical resource-constrained WSNs; we then identify the issue of inaccuracy in maintaining the pre-defined response interval which affects the foundations of most existing timestamp-free schemes. Based on the investigation and our previously proposed reverse asymmetric time synchronization framework, we further propose an asymmetric timestamp-free time synchronization scheme with two estimation methods tailored for resource-constrained WSNs. We as well introduce the centralized and distributed multi-hop extension methods for the proposed scheme to cover diverse multi-hop scenarios. Experimental results on a real WSN testbed consisting of TelosB motes running TinyOS demonstrate that the proposed scheme achieves high energy efficiency while maintaining microsecond-level time synchronization accuracy compared to three other conventional schemes.