Výsledky vyhledávání - "Zhu, Huiling"

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Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

Autor: Zhang, Cui, Xu, Xiao, Wu, Qiong, Fan, Pingyi, Fan, Qiang, Zhu, Huiling, Wang, Jiangzhou

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency.Due to different amounts of local data,computing c

Externí odkaz: http://arxiv.org/abs/2404.08444

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Spectral Efficiency Maximization for Active RIS-aided Cell-Free Massive MIMO Systems with Imperfect CSI

Autor: Eskandari, Mahdi, Zhu, Huiling, Wang, Jiangzhou

A cell-free network merged with active reconfigurable reflecting surfaces (RIS) is investigated in this paper. Based on the imperfect channel state information (CSI), the aggregated channel from the user to the access point (AP) is initially estimate

Externí odkaz: http://arxiv.org/abs/2402.14025

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Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

Autor: Wu, Qiong, Wang, Wenhua, Fan, Pingyi, Fan, Qiang, Zhu, Huiling, Letaief, Khaled B.

Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial f

Externí odkaz: http://arxiv.org/abs/2401.09886

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Two-Timescale Design for RIS-aided Cell-free Massive MIMO Systems with Imperfect CSI

Autor: Eskandari, Mahdi, Zhi, Kangda, Zhu, Huiling, Pan, Cunhua, Wang, Jiangzhou

The objective of this paper is to evaluate the effectiveness of a two-timescale transmission design in cell-free massive multi-input multiple-output (MIMO) systems incorporating reconfigurable intelligent surfaces (RISs) under the assumption of imper

Externí odkaz: http://arxiv.org/abs/2304.02606

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Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning

Autor: Eskandari, Mahdi, Zhu, Huiling, Shojaeifard, Arman, Wang, Jiangzhou

The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless communications. We used S-CSI, which is a long-term average of the cascaded

Externí odkaz: http://arxiv.org/abs/2209.09856

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Smart Interference Management xApp using Deep Reinforcement Learning

Autor: Eskandari, Mahdi, Kapoor, Shipra, Briggs, Keith, Shojaeifard, Arman, Zhu, Huiling, Mourad, Alain

Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutions are essential for tackling interference, and thus achieving the des

Externí odkaz: http://arxiv.org/abs/2204.09707

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