Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time

Area of Science:

• Wireless Communication
• Distributed Computing
• Optimization Theory

Background:

• Intelligent Reflecting Surfaces (IRS) enhance wireless coverage passively by reconfiguring signal paths.
• Mobile Edge Computing (MEC) reduces latency by bringing computation closer to users.
• Integrating IRS and MEC offers potential for improved wireless network performance.

Purpose of the Study:

• To propose a novel optimization scheme for IRS-enhanced MEC systems.
• To minimize the maximum computational time for end-user tasks in such systems.
• To address the non-convexity and complexity of joint optimization variables.

Main Methods:

• Formulating an optimization problem for task segmentation, transmission power, IRS phase shift, and MEC resources.
• Transforming the non-convex problem into a convex one by decoupling into sub-problems.
• Deriving closed-form solutions for task segmentation and edge resources using Karush-Kuhn-Tucker conditions.
• Employing convex optimization techniques for transmission power and IRS phase shift design.

Main Results:

• An efficient optimization scheme for IRS-enhanced MEC is developed.
• The proposed scheme effectively minimizes the maximum task computation time.
• Numerical results validate the superiority of the proposed scheme over benchmark methods.

Conclusions:

• The proposed optimization scheme offers significant performance gains in IRS-enhanced MEC.
• Joint optimization of task segmentation, power, phase shift, and resources is crucial.
• IRS-enhanced MEC presents a promising direction for future wireless networks.