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

TL;DR

This work addresses latency-critical MEC in dynamic wireless environments by integrating PASS with MEC to provide short-distance LoS links. A Markov decision process is constructed to jointly optimize uplink PASS beamforming and task offloading, and a load balancing-aware PPO (LBPPO) algorithm is proposed to stabilize training in the presence of max-based objectives. The approach introduces in-network node and waveguide load balancing in the state and action spaces, along with a tailored reward and neural architectures for policy and value estimation. Numerical results show that PASS-enhanced MEC with adaptive uplink beamforming outperforms fixed-PA and traditional MIMO baselines, particularly as the number of UEs grows or transmit power increases, highlighting the practical potential of PASS-enabled, DRL-based MEC systems. The method offers a scalable pathway to achieve low-latency, high-reliability edge computing in future B5G/6G networks.

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.

Figures (5)

• Figure 1: System model of PASS-enabled MEC system.
• Figure 2: Flow diagram of the LBPPO algorithm.
• Figure 3: Average reward of different models.
• Figure 4: UEs versus average latency.
• Figure 5: Transmit power versus average latency.