Minimization of Latency in D2D-Assisted MEC Collaborative Offloading Based on Intelligent Reflecting Surface
Jun Zhou,
Chenwei Feng,
Yawei Sun and
Jiaxing Guo
With the rapid development of various intelligent scenarios, the demand for low latency, efficient processing, and energy optimization is increasing. In smart communities, intelligent transportation, industrial environments, and other scenarios, a large amount of data is generated that needs to be processed in a short time. Traditional cloud computing models are difficult to meet the requirements for real-time and computing efficiency due to the long data transmission distance and high latency. Therefore, this paper introduces Intelligent Reflecting Surfaces (IRS) into the optimization model of Device-to-Device (D2D) communication and Mobile Edge Computing (MEC) collaborative offloading to enhance system performance and minimize total latency. This paper proposes a latency minimization problem for joint offloading mode selection, computing resource allocation, and IRS phase beamforming. The original problem is decoupled into three subproblems using the Block Coordinate Descent (BCD) algorithm. Through precise potential game theory, the Nash equilibrium (NE) is achieved, and multi-objective optimization is realized using the Lagrangian multiplier method and KKT conditions. Finally, a phase shift optimization problem is solved using the gradient descent algorithm. Simulation results show that the proposed algorithm outperforms other benchmark schemes in terms of performance.
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