PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming
Zhaoming Hu, Ruikang Zhong, Xidong Mu, Dengao Li, Yuanwei Liu

TL;DR
This paper proposes a PASS-enhanced MEC architecture that jointly optimizes task offloading and uplink beamforming using deep reinforcement learning, significantly improving latency and convergence in dynamic wireless environments.
Contribution
It introduces a novel PASS-based MEC system with a load balancing-aware DRL algorithm for joint optimization, addressing instability issues in the objective function.
Findings
Enhanced convergence compared to fixed-PA and MIMO baselines.
Improved latency performance in high-density UE scenarios.
Effective mitigation of path loss and signal blockage in high-frequency MEC.
Abstract
A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm.…
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