Joint Flexible Beamforming, Resource Allocation, and Task Buffering for Movable-Antenna Enabled MEC Systems
Abstract
The proliferation of computation-intensive applications, such as generative artificial intelligence and low-altitude economy services, demands mobile edge computing (MEC) systems that are both energy-efficient and adaptable to highly dynamic workloads. However, conventional MEC architectures rely on fixed-position antennas with limited spatial flexibility and rigid single-slot task execution models, which fail to accommodate bursty traffic and time-varying channels. To address these challenges, this paper proposes a novel MEC framework empowered by movable antennas (MAs) and task buffering. Unlike traditional approaches, our system enables continuous antenna repositioning to maximize array gains and decouples task generation from immediate execution via buffer queues, allowing for flexible multi-slot scheduling. We formulate a stochastic optimization problem to minimize long-term energy consumption by jointly optimizing receive beamforming, MA positions, and computing resources. Leveraging Lyapunov optimization, we transform the long-term problem into tractable per-slot subproblems and develop an efficient alternating optimization algorithm. Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes in terms of energy consumption, while maintaining stable task queues under unpredictable traffic conditions.
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BibTeX
@misc{wang2026joint,
title = {{Joint Flexible Beamforming, Resource Allocation, and Task Buffering for Movable-Antenna Enabled MEC Systems}},
author = {Hao Wang and Chunjie Wang and Lingtong Ma and Wenchao Liu and Kan Yu and Jinke Ren and Yanyan Shen and Xuhui Zhang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07656},
eprint = {2611.07656},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07656},
}
RIS
TY - UNPB
TI - Joint Flexible Beamforming, Resource Allocation, and Task Buffering for Movable-Antenna Enabled MEC Systems
AU - Hao Wang
AU - Chunjie Wang
AU - Lingtong Ma
AU - Wenchao Liu
AU - Kan Yu
AU - Jinke Ren
AU - Yanyan Shen
AU - Xuhui Zhang
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.07656
UR - https://arxiv.org/abs/2611.07656
AB - The proliferation of computation-intensive applications, such as generative artificial intelligence and low-altitude economy services, demands mobile edge computing (MEC) systems that are both energy-efficient and adaptable to highly dynamic workloads. However, conventional MEC architectures rely on fixed-position antennas with limited spatial flexibility and rigid single-slot task execution models, which fail to accommodate bursty traffic and time-varying channels. To address these challenges, this paper proposes a novel MEC framework empowered by movable antennas (MAs) and task buffering. Unlike traditional approaches, our system enables continuous antenna repositioning to maximize array gains and decouples task generation from immediate execution via buffer queues, allowing for flexible multi-slot scheduling. We formulate a stochastic optimization problem to minimize long-term energy consumption by jointly optimizing receive beamforming, MA positions, and computing resources. Leveraging Lyapunov optimization, we transform the long-term problem into tractable per-slot subproblems and develop an efficient alternating optimization algorithm. Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes in terms of energy consumption, while maintaining stable task queues under unpredictable traffic conditions.
ER -