Search Results for author: Baoyun Wang

Found 6 papers, 1 papers with code

Near-field Localization with Dynamic Metasurface Antennas

no code implementations28 Oct 2022 Qianyu Yang, Anna Guerra, Francesco Guidi, Nir Shlezinger, Haiyang Zhang, Davide Dardari, Baoyun Wang, Yonina C. Eldar

We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy.

Beamforming Design for Integrated Sensing and Wireless Power Transfer Systems

no code implementations27 Oct 2022 Qianyu Yang, Haiyang Zhang, Baoyun Wang

This letter proposes a new concept of integrated sensing and wireless power transfer (ISWPT), where radar sensing and wireless power transfer functions are integrated into one hardware platform.

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever

no code implementations30 May 2022 Jin Chen, Defu Lian, Yucheng Li, Baoyun Wang, Kai Zheng, Enhong Chen

Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss.

Graph Neural Network-Based Scheduling for Multi-UAV-Enabled Communications in D2D Networks

no code implementations15 Feb 2022 Pei Li, Lingyi Wang, Wei Wu, Fuhui Zhou, Baoyun Wang, Qihui Wu

In this paper, we propose a novel graph neural networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.

Scheduling

Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems

no code implementations14 Feb 2022 Liuhang Wang, Nir Shlezinger, George C. Alexandropoulos, Haiyang Zhang, Baoyun Wang, Yonina C. Elda

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments.

Task-Based Graph Signal Compression

1 code implementation24 Oct 2021 Pei Li, Nir Shlezinger, Haiyang Zhang, Baoyun Wang, Yonina C. Eldar

The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation.

Quantization

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