Secure Computation Offloading for IRS-Assisted Mobile Edge Computing Networks

Mengru Wu, Weijin Chen, Kexin Li, Liping Qian
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Abstract

This paper investigates secure computation offloading for an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) network. Specifically, wireless devices offload partial computation tasks to an access point integrated with a MEC server. During this process, an eavesdropper tries to overhear the private information of devices. We propose an IRS-assisted secure offloading scheme that adjusts the phase of the IRS to improve the security of computation offloading. We aim to maximize the minimum secrecy rate of devices by jointly optimizing the transmit power of devices for computation offloading, the computation task partitions, and the passive beamforming of the IRS while satisfying the offloading rate constraint and the energy budget constraint. To deal with the formulated highly non-convex problem, we develop a block coordinate descent-based algorithm that decouples the problem into two subproblems of optimizing resource allocation and passive beamforming. Besides, successive convex approximation and semidefinite relaxation are exploited to solve the two subproblems. Simulation results demonstrate that our designed scheme yields a performance enhancement compared to benchmarks.
irs辅助移动边缘计算网络的安全计算卸载
研究了智能反射面(IRS)辅助移动边缘计算(MEC)网络的安全计算卸载问题。具体地说,无线设备将部分计算任务卸载到与MEC服务器集成的接入点。在这个过程中,窃听者试图偷听设备的私有信息。我们提出了一种IRS辅助安全卸载方案,该方案通过调整IRS的相位来提高计算卸载的安全性。在满足卸载率约束和能量预算约束的前提下,通过联合优化计算卸载设备的发射功率、计算任务划分和IRS的无源波束形成,使设备的最小保密率最大化。为了解决高度非凸问题,我们开发了一种基于块坐标下降的算法,将该问题解耦为优化资源分配和被动波束形成两个子问题。此外,利用连续凸逼近和半定松弛来求解这两个子问题。仿真结果表明,与基准测试相比,我们设计的方案产生了性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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