2 citations · 5 across the 13 of their papers we have counts for
13 papers
Sensing Mutual Information for Communication Signal with Deterministic Pilots and Random Data Payloads
Lei Xie, Hengtao He, Jun Tong +2
The recent emergence of the integrated sensing and communication (ISAC) framework has sparked significant interest in quantifying the sensing capabilities inherent in communication…
ROI-based Deep Image Compression with Implicit Bit Allocation
Kai Hu, Han Wang, Renhe Liu +3
Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions while reducing data redundancy. However, ex…
FedLAM: Low-latency Wireless Federated Learning via Layer-wise Adaptive Modulation
Linping Qu, Shenghui Song, Chi-Ying Tsui
In wireless federated learning (FL), the clients need to transmit the high-dimensional deep neural network (DNN) parameters through bandwidth-limited channels, which causes the com…
Bistatic Target Detection by Exploiting Both Deterministic Pilots and Unknown Random Data Payloads
Lei Xie, Fan Liu, Shenghui Song +1
Integrated sensing and communication (ISAC) plays a crucial role in 6G, enabling innovative applications such as drone surveillance, urban air mobility, and low-altitude logistics.…
The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning
Zhijie Xie, Shenghui Song
In the context of Federated Reinforcement Learning (FRL), applying Proximal Policy Optimization (PPO) faces challenges related to the update order of its actor and critic due to th…
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design
Linping Qu, Yuyi Mao, Shenghui Song +1
One of the most critical challenges for deploying distributed learning solutions, such as federated learning (FL), in wireless networks is the limited battery capacity of mobile cl…
FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
Linping Qu, Shenghui Song, Chi-Ying Tsui
Federated learning (FL) is a powerful machine learning paradigm which leverages the data as well as the computational resources of clients, while protecting clients' data privacy.…
Sensing Mutual Information with Random Signals in Gaussian Channels: Bridging Sensing and Communication Metrics
Lei Xie, Fan Liu, Jiajin Luo +1
Sensing performance is typically evaluated by classical radar metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sen…
Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
Wenhao Zhuang, Yuyi Mao, Hengtao He +4
Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to support high-mobility wireless communications, for which, cost-effective data detectors are…
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning
Ho Man Kwan, Shenghui Song
Recently, innovative model aggregation methods based on knowledge distillation (KD) have been proposed for federated learning (FL). These methods not only improved the robustness o…
Sensing Mutual Information with Random Signals in Gaussian Channels
Lei Xie, Fan Liu, Zhanyuan Xie +2
Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing a…
How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels
Linping Qu, Shenghui Song, Chi-Ying Tsui +1
Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wire…
Client Selection for Federated Policy Optimization with Environment Heterogeneity
Zhijie Xie, Shenghui Song
The development of Policy Iteration (PI) has inspired many recent algorithms for Reinforcement Learning (RL), including several policy gradient methods that gained both theoretical…