Energy Consumption Minimization in Secure Multi-antenna UAV-assisted MEC Networks with Channel Uncertainty

TL;DR

This paper presents a robust energy-efficient scheme for secure task offloading and computation in UAV-assisted MEC networks, optimizing multiple parameters under channel uncertainty to enhance performance and security.

Contribution

It introduces a joint optimization framework for energy minimization in UAV-assisted MEC with channel uncertainty, employing convex reformulation and a novel SCA-based solution approach.

Findings

Proposed scheme outperforms existing benchmarks in energy efficiency.

SCA-based algorithm shows superior convergence and computational performance.

Numerical results validate the effectiveness of the joint optimization approach.

Abstract

This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users' quality of service and information security requirements and the UAV's trajectory's causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmission power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperform existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.