相关论文: Fast and Accurate Cooperative Radio Map Estimation…
In this paper, a novel framework is proposed to enable air-to-ground channel modeling over millimeter wave (mmWave) frequencies in an unmanned aerial vehicle (UAV) wireless network. First, an effective channel estimation approach is…
Cosmic Microwave Background (CMB) has been a cornerstone in many cosmology experiments and studies since it was discovered back in 1964. Traditional computational models like CAMB that are used for generating CMB temperature anisotropy maps…
A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by…
Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field…
The growing demand for effective spectrum management and interference mitigation in shared bands, such as the Citizens Broadband Radio Service (CBRS), requires robust radar detection algorithms to protect the military transmission from…
In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To alleviate the CSI feedback burden, the channel knowledge map…
Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are two promising technologies in the next generation wireless communication systems. The security of a NOMA CR network (CRN) is important but lacks of study. In this paper, a…
Joint activity detection and channel estimation (JADCE) for grant-free random access is a critical issue that needs to be addressed to support massive connectivity in IoT networks. However, the existing model-free learning method can only…
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore,…
Precise aerial radio environment characterization is vital for low-altitude planning. However, existing datasets and estimation methods lack the high-resolution granularity required for complex aerial spaces. Additionally, current schemes…
In this paper, a novel framework is proposed to perform data-driven air-to-ground (A2G) channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. First, an effective channel…
Robust scatter estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to compute depth-like…
Generative algorithms such as GANs are at the cusp of next revolution in the field of unsupervised learning and large-scale artificial data generation. However, the adversarial (competitive) co-training of the discriminative and generative…
With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices…
Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three-dimensional environments under stringent latency,…
Sampling-based path planning is a popular methodology for robot path planning. With a uniform sampling strategy to explore the state space, a feasible path can be found without the complex geometric modeling of the configuration space.…
Smart Radio Environment (SRE) is a central paradigms in 6G and beyond, where integrating SRE components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a…
Accurate and real-time radio map (RM) generation is crucial for next-generation wireless systems, yet diffusion-based approaches often suffer from large model sizes, slow iterative denoising, and high inference latency, which hinder…
Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured…
In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content transmission delay, we formulate the cooperative caching optimization problem to find the globally optimal…