Search Results for author: Li Wei

Found 15 papers, 2 papers with code

Holographic MIMO Communications with Arbitrary Surface Placements: Near-Field LoS Channel Model and Capacity Limit

no code implementations11 Apr 2023 Tierui Gong, Li Wei, Chongwen Huang, Zhijia Yang, Jiguang He, Mérouane Debbah, Chau Yuen

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits.

A Generalized Electromagnetic-Domain Channel Modeling for LOS Holographic MIMO with Arbitrary Surface Placements

no code implementations15 Mar 2023 Tierui Gong, Li Wei, Zhijia Yang, Mérouane Debbah, Chau Yuen

Holographic multiple-input multiple-output (H-MIMO) is considered as one of the most promising technologies to enable future wireless communications in supporting the expected extreme requirements, such as high energy and spectral efficiency.

Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions

no code implementations2 Dec 2022 Tierui Gong, Panagiotis Gavriilidis, Ran Ji, Chongwen Huang, George C. Alexandropoulos, Li Wei, Zhaoyang Zhang, Mérouane Debbah, H. Vincent Poor, Chau Yuen

In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems.

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations

no code implementations14 Oct 2022 Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nicholas Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel

We reconcile these notions and show that the tension is due to differences in distributions of users where items are relevant, and break down the important factors of the user's recommendations.

Fairness Recommendation Systems

Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design

no code implementations22 Jan 2021 Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang, Merouane Debbah

We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.

Improvement of Classification in One-Stage Detector

1 code implementation20 Nov 2020 Wu Kehe, Chen Zuge, Zhang Xiaoliang, Li Wei

In this paper we proposed an object confidence task for this problem, and it shares features with classification task.

Classification General Classification

Model-Driven Channel Estimation for OFDM Systems Based on Image Super- Resolution Network

no code implementations 23-25 Octorber 2020 Xin Ru, Li Wei, and Youyun Xu*

Reliable channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) systems to achieve high data rate.

Image Super-Resolution

Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method

no code implementations20 Sep 2020 Chongwen Huang, Zhaohui Yang, George C. Alexandropoulos, Kai Xiong, Li Wei, Chau Yuen, Zhaoyang Zhang

Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.

Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

no code implementations4 Aug 2020 Li Wei, Chongwen Huang, George C. Alexandropoulos, Chau Yuen, Zhaoyang Zhang, Mérouane Debbah

We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station.

TEAM: An Taylor Expansion-Based Method for Generating Adversarial Examples

no code implementations23 Jan 2020 Ya-guan Qian, Xi-Ming Zhang, Wassim Swaileh, Li Wei, Bin Wang, Jian-hai Chen, Wu-jie Zhou, Jing-sheng Lei

Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples. Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation. Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem. The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples.

Recommending what video to watch next: a multitask ranking system

no code implementations RecSys 2019 Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

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