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D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.

Xin Guan1, Tiejun Lv1, Zhipeng Lin2

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.

Sensors (Basel, Switzerland)
|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces a cooperative framework combining mobile edge computing (MEC) and device-to-device (D2D) communication to enhance mobile device capabilities. A deep reinforcement learning approach maximizes task computation under delay and resource constraints.

Keywords:
D2D communicationQ learningdeep Q-networkmobile edge computingpartial offloading

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communications

Background:

  • Mobile devices face limitations in computing power and battery life.
  • Mobile Edge Computing (MEC) and Device-to-Device (D2D) communication offer solutions by offloading tasks and enabling local resource sharing.
  • Existing frameworks often struggle with efficient resource allocation and task offloading in multi-user scenarios.

Purpose of the Study:

  • To develop a novel D2D-MEC framework for cooperative partial offloading and computing resource allocation.
  • To maximize the number of devices served within application delay constraints and limited edge computing resources.
  • To address the NP-hard optimization problem of resource allocation in a D2D-MEC system.

Main Methods:

  • Formulation of the multi-user cooperative partial offloading and resource allocation problem.
  • Decoupling the NP-hard problem into two subproblems.
  • Application of convex optimization for the first subproblem.
  • Modeling the second subproblem as a Markov Decision Process (MDP).
  • Development of a Deep Q Network (DQN) algorithm for task computation maximization.

Main Results:

  • The proposed D2D-MEC framework effectively manages task offloading and resource allocation.
  • The integrated approach significantly enhances the system's ability to compute tasks under strict delay and resource limitations.
  • Simulation results validate the superiority and effectiveness of the developed deep reinforcement learning-based scheme compared to existing methods.

Conclusions:

  • The cooperative D2D-MEC framework provides a robust solution for mobile device resource constraints.
  • Deep reinforcement learning, specifically DQN, is highly effective in optimizing task offloading and resource allocation in complex MEC environments.
  • The proposed scheme demonstrates significant performance gains in maximizing computational capacity while adhering to critical application deadlines.