inis 22(32): 5

Research Article

Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization

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  • @ARTICLE{10.4108/eetinis.v9i32.1376,
        author={Dac-Binh Ha and Van-Truong Truong and Yoonill Lee},
        title={Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={9},
        number={32},
        publisher={EAI},
        journal_a={INIS},
        year={2022},
        month={8},
        keywords={mobile edge computing, intelligent reflecting surface, radio frequency energy harvesting, non-orthogonal multiple access, successful computation probability},
        doi={10.4108/eetinis.v9i32.1376}
    }
    
  • Dac-Binh Ha
    Van-Truong Truong
    Yoonill Lee
    Year: 2022
    Intelligent Reflecting Surface assisted RF Energy Harvesting Mobile Edge Computing NOMA Networks: Performance Analysis and Optimization
    INIS
    EAI
    DOI: 10.4108/eetinis.v9i32.1376
Dac-Binh Ha1,*, Van-Truong Truong1, Yoonill Lee2
  • 1: Duy Tan University
  • 2: College of Lake County
*Contact email: hadacbinh@duytan.edu.vn

Abstract

In this paper, we focus on the performance analysis and optimization of an RF energy harvesting (EH) mobile edge computing (MEC) network by the assistance of the intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) schemes. Specifically, a pair of users harvest RF energy from a hybrid access point (HAP) and offloads their tasks to the MEC server at HAP through wireless links by employing an IRS-aided and uplink NOMA scheme. To evaluate the performance of this proposed system, the closed-form expressions of successful computation and energy transfer efficiency probabilities are derived. We further formulate a multi-objective optimization problem and propose an algorithm to find the optimal energy harvesting time switching ratio value to achieve the best performance, namely SENSGA-II. Moreover, the impacts of the network parameters are provided to draw helpful insight into the system performance. Finally, the Monte-Carlo simulation results are shown to confirm the correctness of our analysis. The results have shown that the deployment of IRS can improve the performance of this considered RF EH NOMA system by increasing the number of reflecting elements.