Search Results for author: Weifeng Zhu

Found 7 papers, 1 papers with code

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

no code implementations10 Dec 2023 Junyi Yang, Weifeng Zhu, Shu Sun, Xiaofeng Li, Xingqin Lin, Meixia Tao

This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission.

Hierarchical Beam Alignment for Millimeter-Wave Communication Systems: A Deep Learning Approach

no code implementations23 Aug 2023 Junyi Yang, Weifeng Zhu, Meixia Tao, Shu Sun

Fast and precise beam alignment is crucial for high-quality data transmission in millimeter-wave (mmWave) communication systems, where large-scale antenna arrays are utilized to overcome the severe propagation loss.

Cooperative Multi-Cell Massive Access with Temporally Correlated Activity

no code implementations19 Apr 2023 Weifeng Zhu, Meixia Tao, Xiaojun Yuan, Fan Xu, Yunfeng Guan

This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links.

Action Detection Activity Detection +1

Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems

no code implementations8 Sep 2022 Junyi Yang, Weifeng Zhu, Meixia Tao

In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner.

Double-Sided Information Aided Temporal-Correlated Massive Access

no code implementations16 May 2022 Weifeng Zhu, Meixia Tao, Yunfeng Guan

This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames.

Action Detection Activity Detection

A Targeted Universal Attack on Graph Convolutional Network

1 code implementation29 Nov 2020 Jiazhu Dai, Weifeng Zhu, Xiangfeng Luo

The experiments on three popular datasets show that the average attack success rate of the proposed attack on any victim node in the graph reaches 83% when using only 3 attack nodes and 6 fake nodes.

Adversarial Attack

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