From the 1 of 5 linked papers with an AI index.
5 papers
Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems
Yubo Zhang, Xiaodong Wang
The paper proposes an in-context learning framework using a Transformer backbone to design pilot-to-beamformer mappings for multi-user MISO systems, enabling rapid adaptation to va…
Direct and Ambient Backscatter Communications with a Dual-Function Radar Transmitter
Yubo Zhang, Luca Venturino, Xiaodong Wang
This work considers a system where a dual-function radar transmitter (source) performs direct communication with a reader while simultaneously enabling ambient backscatter communic…
A Semi-amortized Lifted Learning-to-Optimize Masked (SALLO-M) Transformer Model for Scalable and Generalizable Beamforming
Yubo Zhang, Xiao-Yang Liu, Xiaodong Wang
We develop an unsupervised deep learning framework for real-time scalable and generalizable downlink beamforming in multi-user multiple-input single-output (MU-MISO) systems. The p…
Resource Allocation for Positive-Rate Covert Communications Using Optimization and Deep Reinforcement Learning
Yubo Zhang, Hassan ZivariFard, Xiaodong Wang
We aim to achieve keyless covert communication with a positive-rate in Rayleigh block-fading channels. Specifically, the transmitter and the legitimate receiver are assumed to have…
An Encoder-Decoder Network for Beamforming over Sparse Large-Scale MIMO Channels
Yubo Zhang, Jeremy Johnston, Xiaodong Wang
We develop an end-to-end deep learning framework for downlink beamforming in large-scale sparse MIMO channels. The core is a deep EDN architecture with three modules: (i) an encode…