5 papers
Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds
Xinping Yi, Gaojie Jin, Xiaowei Huang +1
Understanding the generalization behavior of deep neural networks remains a fundamental challenge in modern statistical learning theory. Among existing approaches, PAC-Bayesian nor…
Learning to Unfold Fractional Programming for Multi-Cell MU-MIMO Beamforming with Graph Neural Networks
Zihan Jiao, Xinping Yi, Shi Jin
In the multi-cell multiuser multi-input multi-output (MU-MIMO) systems, fractional programming (FP) has demonstrated considerable effectiveness in optimizing beamforming vectors, y…
Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network
Jinshuo Zhang, Yafei Wang, Xinping Yi +4
Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses…
Revisiting Topological Interference Management: A Learning-to-Code on Graphs Perspective
Zhiwei Shan, Xinping Yi, Han Yu +2
The advance of topological interference management (TIM) has been one of the driving forces of recent developments in network information theory. However, state-of-the-art coding s…
A Tensor-Structured Approach to Dynamic Channel Prediction for Massive MIMO Systems with Temporal Non-Stationarity
Hongwei Hou, Yafei Wang, Yiming Zhu +4
In moderate- to high-mobility scenarios, CSI varies rapidly and becomes temporally non-stationary, leading to severe performance degradation in the massive MIMO transmissions. To a…