39 citations · 92 across the 29 of their papers we have counts for
15 papers · 1 filter
Rydberg Atomic Receivers for Multi-Band Communications and Sensing
Mingyao Cui, Qunsong Zeng, Minze Chen +4
Harnessing multi-level electron transitions, Rydberg Atomic REceivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz. This…
Realizing Quantum Wireless Sensing Without Extra Reference Sources: Architecture, Algorithm, and Sensitivity Maximization
Mingyao Cui, Qunsong Zeng, Zhanwei Wang +1
Rydberg Atomic REceivers (RAREs) have demonstrated remarkable capabilities for radio-frequency signal measurement, enabling advanced quantum wireless sensing. Existing RARE-based s…
Ultra-Low-Latency Edge Intelligent Sensing: A Source-Channel Tradeoff and Its Application to Coding Rate Adaptation
Qunsong Zeng, Jianhao Huang, Zhanwei Wang +2
The forthcoming sixth-generation (6G) mobile network is set to merge edge artificial intelligence (AI) and integrated sensing and communication (ISAC) extensively, giving rise to t…
Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence
Qunsong Zeng, Zhanwei Wang, You Zhou +3
The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In s…
MIMO Precoding for Rydberg Atomic Receivers
Mingyao Cui, Qunsong Zeng, Kaibin Huang
Leveraging the strong atom-light interaction, a Rydberg atomic receiver can measure radio waves with extreme sensitivity. Existing research primarily focuses on improving the signa…
Towards Atomic MIMO Receivers
Mingyao Cui, Qunsong Zeng, Kaibin Huang
The advancement of Rydberg atoms in quantum sensing is driving a paradigm shift from classical receivers to atomic receivers. Capitalizing on the extreme sensitivity of Rydberg ato…
InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization
Zhengyang Hu, Song Kang, Qunsong Zeng +2
Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistic…
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
Hai Wu, Qunsong Zeng, Kaibin Huang
For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simult…
Progressive Feature Transmission for Split Inference at the Wireless Edge
Qiao Lan, Qunsong Zeng, Petar Popovski +2
In edge inference, an edge server provides remote-inference services to edge devices. This requires the edge devices to upload high-dimensional features of data samples over resour…
What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Qiao Lan, Dingzhu Wen, Zezhong Zhang +4
In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main t…
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
Qunsong Zeng, Yuqing Du, Kaibin Huang
Federated edge learning (FEEL) is a widely adopted framework for training an artificial intelligence (AI) model distributively at edge devices to leverage their data while preservi…
Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
Qunsong Zeng, Yuqing Du, Kaibin Huang +1
Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intellig…
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning
Dingzhu Wen, Xiaoyang Li, Qunsong Zeng +2
The 5G network connecting billions of Internet-of-Things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualizat…
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
Qunsong Zeng, Yuqing Du, Kin K. Leung +1
Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framewor…
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
Dongzhu Liu, Guangxu Zhu, Jun Zhang +1
By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, whi…