Related papers: OpenPathNet: An Open-Source RF Multipath Data Gene…
With the increasing demand for real-time channel state information (CSI) in sixth-generation (6G) mobile communication networks, channel knowledge map (CKM) emerges as a promising technique, offering a site-specific database that enables…
Radio map is an efficient demonstration for visually displaying the wireless signal coverage within a certain region. It has been considered to be increasingly helpful for the future sixth generation (6G) of wireless networks, as wireless…
During the development of the Sixth Generation (6G) networks, the integration of Artificial Intelligence (AI) into network systems has become a focal point, leading to the concept of AI-native networks. High quality data is essential for…
Environment-aware communication and sensing is one of the promising paradigm shifts towards 6G, which fully leverages prior information of the local wireless environment to optimize network performance. One of the key enablers for…
Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are…
With the acceleration of the commercialization of fifth generation (5G) mobile communication technology and the research for 6G communication systems, the communication system has the characteristics of high frequency, multi-band, high…
6G networks are envisioned to support on-demand AI model downloading to accommodate diverse inference requirements of end users. By proactively caching models at edge nodes, users can retrieve the requested models with low latency for…
Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limited modeling fidelity, and cover only narrow scenario types.…
We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting that go beyond translation invariance and appropriately account for the inductive bias with respect to multipath propagation channels, a phenomenon that is…
Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale…
Current limitations in wireless modeling and radio frequency (RF)-based AI are primarily driven by a lack of high-quality, measurement-based datasets that connect RF signals to their physical environments. RF heatmaps, the typical form of…
In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform…
This research delves into the construction and utilization of synthetic datasets, specifically within the telematics sphere, leveraging OpenAI's powerful language model, ChatGPT. Synthetic datasets present an effective solution to…
Environment-aware 6G wireless networks demand the deep integration of multimodal and wireless data. However, most existing datasets are confined to 2D terrestrial far-field scenarios, lacking the 3D spatial context and near-field…
By effectively implementing the strategies for resource allocation, the capabilities, and reliability of non-terrestrial networks (NTN) can be enhanced. This leads to enhance spectrum utilization performance while minimizing the unmet…
Integrated sensing and communications is a key enabler for the 6G wireless communication systems. The multiple sensing modalities will allow the base station to have a more accurate representation of the environment, leading to…
Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, are still not natively supported by these models. Existing…
Deep learning-based RF fingerprinting has recently been recognized as a potential solution for enabling newly emerging wireless network applications, such as spectrum access policy enforcement, automated network device authentication, and…
The advancement of artificial intelligence (AI) hinges on the quality and accessibility of data, yet the current fragmentation and variability of data sources hinder efficient data utilization. The dispersion of data sources and diversity…
Integrating AI into the physical layer is a cornerstone of 6G networks. However, current data-driven approaches struggle to generalize across dynamic environments because they lack an intrinsic understanding of electromagnetic wave…