7 papers
Learning Wideband User Scheduling and Hybrid Precoding with Graph Neural Networks
Shengjie Liu, Chenyang Yang, Shengqian Han
User scheduling and hybrid precoding in wideband multi-antenna systems have never been learned jointly due to the challenges arising from the massive user combinations on resource…
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…
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…
Quantization Design for Deep Learning-Based CSI Feedback
Manru Yin, Shengqian Han, Chenyang Yang
Deep learning-based autoencoders have been employed to compress and reconstruct channel state information (CSI) in frequency-division duplex systems. Practical implementations requ…
Distributed Resource Block Allocation for Wideband Cell-free System
Yang Ma, Shengqian Han, Chenyang Yang
This paper studies distributed resource block (RB) allocation in wideband orthogonal frequency-division multiplexing (OFDM) cell-free systems. We propose a novel distributed sequen…
Learning of Uplink Resource Allocation with Multiuser QoS Constraints
Manru Yin, Shengqian Han, Chenyang Yang
In the paper the joint optimization of uplink multiuser power and resource block (RB) allocation are studied, where each user has quality of service (QoS) constraints on both long-…