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20242026
most citedRobust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information

1 citations · 3 across the 11 of their papers we have counts for

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9 papers · 1 filter

eess.SP2025

Robust Optimization for Movable Antenna-aided Cell-Free ISAC with Time Synchronization Errors

Yue Xiu, Yang Zhao, Ran Yang +5

The cell-free integrated sensing and communication (CF-ISAC) system, which effectively mitigates intra-cell interference and provides precise sensing accuracy, is a promising techn…

eess.SP2025

Movable Antenna Enhanced Federated Fine-Tuning of Large Language Models via Hybrid Client Selection Optimization

Yang Zhao, Yue Xiu, Chengxiao Dai +2

Federated fine-tuning of large language models (LLMs) over bandwidth-limited 6G links must meet strict round-time and energy budgets. Analog over-the-air (OTA) aggregation reduces…

eess.SP2025

Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection

Yang Zhao, Minrui Xu, Ping Wang +1

Over-the-air (OTA) federated learning (FL) effectively utilizes communication bandwidth, yet it is vulnerable to errors during analog aggregation. While removing users with unfavor…

eess.SP2025

Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI

Yue Xiu, Yang Zhao, Ran Yang +5

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state inform…

eess.SP20241 cited

Latency Minimization for Movable Antennas-Enabled Relay-aided D2D Mobile Edge Computing Communication Systems

Yue Xiu, Yang Zhao, Ran Yang +5

Device-to-device (D2D)-assisted mobile edge computing (MEC) is one of the critical technologies of future sixth generation (6G) networks. The core of D2D-assisted MEC is to reduce…

eess.SP2024

Movable Antenna-Aided Federated Learning with Over-the-Air Aggregation: Joint Optimization of Positioning, Beamforming, and User Selection

Yang Zhao, Yue Xiu, Minrui Xu +1

Federated learning (FL) in wireless computing effectively utilizes communication bandwidth, yet it is vulnerable to errors during the analog aggregation process. While removing use…