Secure Offloading in NOMA-Aided Aerial MEC Systems Based on Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning-based secure offloading scheme for NOMA-aided UAV-MEC systems, optimizing trajectory and power to minimize costs while ensuring data security against eavesdroppers.

Contribution

It introduces a novel joint optimization framework for UAV trajectory, power, and computation frequency with security constraints, using deep RL to handle high-dimensional actions.

Findings

The scheme effectively minimizes network computational costs.

It ensures security against eavesdroppers with uncertain locations.

Simulation results confirm the scheme's effectiveness.

Abstract

Mobile edge computing (MEC) technology can reduce user latency and energy consumption by offloading computationally intensive tasks to the edge servers. Unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) technology enable the MEC networks to provide offloaded computing services for massively accessed terrestrial users conveniently. However, the broadcast nature of signal propagation in NOMA-based UAV-MEC networks makes it vulnerable to eavesdropping by malicious eavesdroppers. In this work, a secure offload scheme is proposed for NOMA-based UAV-MEC systems with the existence of an aerial eavesdropper. The long-term average network computational cost is minimized by jointly designing the UAV's trajectory, the terrestrial users' transmit power, and computational frequency while ensuring the security of users' offloaded data. Due to the eavesdropper's location uncertainty, the worst-case security scenario is considered through the estimated eavesdropping range. Due to the high-dimensional continuous action space, the deep deterministic policy gradient algorithm is utilized to solve the non-convex optimization problem. Simulation results validate the effectiveness of the proposed scheme.