activity
20122026
most citedDeep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes

31 citations · 69 across the 36 of their papers we have counts for

collaborators
Showing 2025Show all

12 papers · 1 filter

eess.SP2025

Joint Optimization of Pilot Length, Pilot Assignment, and Power Allocation for Cell-free MIMO Systems with Graph Neural Networks

Yao Peng, Tingting Liu, Chenyang Yang

In user-centric cell-free multi-antenna systems, pilot contamination degrades spectral efficiency (SE) severely. To mitigate pilot contamination, existing works jointly optimize pi…

eess.SP2025

Modular PE-Structured Learning for Cross-Task Wireless Communications

Yuxuan Duan, Chenyang Yang

Recent trends in learning wireless policies attempt to develop deep neural networks (DNNs) for handling multiple tasks with a single model. Existing approaches often rely on large…

eess.SP2025

When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?

Jia Guo, Chenyang Yang

Owing to the use of attention mechanism to leverage the dependency across tokens, Transformers are efficient for natural language processing. By harnessing permutation properties b…

eess.SP2025

Adversarial Training: Enhancing Out-of-Distribution Generalization for Learning Wireless Resource Allocation

Shengjie Liu, Chenyang Yang

Unsupervised learning has been extensively adopted to train deep neural networks (DNNs) for learning wireless resource allocation. Yet, the performance of DNNs is vulnerable to dis…

cs.MM2025

Optimizing QoE-Privacy Tradeoff for Proactive VR Streaming

Xing Wei, Shengqian Han, Chenyang Yang +1

Proactive virtual reality (VR) streaming requires users to upload viewpoint-related information, raising significant privacy concerns. Existing strategies preserve privacy by intro…

eess.SP2025

Precoder Learning by Leveraging Unitary Equivariance Property

Yilun Ge, Shuyao Liao, Shengqian Han +1

Incorporating mathematical properties of a wireless policy to be learned into the design of deep neural networks (DNNs) is effective for enhancing learning efficiency. Multi-user p…