PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming

2025-10-26

AI Summary

To address high task offloading latency and unstable wireless links in mobile edge computing (MEC) under high-frequency dynamic environments, this paper proposes a joint optimization framework for uplink PASS beamforming and task offloading. It innovatively employs a reconfigurable squeezed-antenna system (PASS) to establish short-range line-of-sight (LoS) links and formulates the problem as a Markov decision process (MDP). A load-balancing-aware proximal policy optimization (LBPPO) algorithm is designed, embedding both node-level and waveguide-level load information into the policy network to mitigate training instability caused by the max operator in conventional deep reinforcement learning (DRL). Experimental results demonstrate that, compared with fixed-antenna and MIMO-assisted MEC baselines, the proposed approach achieves significantly lower end-to-end latency—especially under multi-user and high transmit-power conditions—while exhibiting faster convergence and higher transmission reliability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Mechanism Design
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 $max$ operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

Problem

• Optimizing task offloading and uplink beamforming in MEC systems
• Minimizing network latency in dynamic wireless environments
• Addressing signal path loss and blockage with pinching-antenna technology

Innovation

• PASS establishes short-distance LoS links via dielectric waveguides
• Joint optimization of uplink beamforming and task offloading
• LBPPO algorithm incorporates load balancing into policy design