# Joint Model Partitioning and Bandwidth Allocation for UAV-Assisted Space–Air–Ground–Sea Integrated Network: A Hybrid A3C-PPO Approach

## TL;DR

This paper proposes a new method combining A3C and PPO to optimize UAV-assisted networks for efficient task processing and resource allocation.

## Contribution

A hybrid A3C-PPO framework for joint model partitioning and bandwidth allocation in UAV-assisted SAGSIN.

## Key findings

- The proposed algorithm improves task completion rates in dynamic SAGSIN scenarios.
- It reduces task processing latency and enhances resource utilization compared to existing methods.
- The weighted priority scheduling mechanism ensures fairness and prevents resource monopolization.

## Abstract

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios.