sas 15(2): e4

Research Article

Using Joint Particle Swarm Optimization and Genetic Algorithm for Resource Allocation in TD-LTE Systems

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  • @ARTICLE{10.4108/eai.19-8-2015.2260168,
        author={Jianbo Du and Liqiang Zhao and Jie Xin and Jen-Ming Wu and Jie Zeng},
        title={Using Joint Particle Swarm Optimization and Genetic Algorithm for Resource Allocation in TD-LTE Systems},
        journal={EAI Endorsed Transactions on Self-Adaptive Systems},
        volume={1},
        number={2},
        publisher={EAI},
        journal_a={SAS},
        year={2015},
        month={9},
        keywords={lte, radio resource management (rrm), particle swarm optimization (pso), genetic algorithm (ga)},
        doi={10.4108/eai.19-8-2015.2260168}
    }
    
  • Jianbo Du
    Liqiang Zhao
    Jie Xin
    Jen-Ming Wu
    Jie Zeng
    Year: 2015
    Using Joint Particle Swarm Optimization and Genetic Algorithm for Resource Allocation in TD-LTE Systems
    SAS
    EAI
    DOI: 10.4108/eai.19-8-2015.2260168
Jianbo Du1,*, Liqiang Zhao1, Jie Xin1, Jen-Ming Wu2, Jie Zeng3
  • 1: Xidian University, Xi'an,China
  • 2: National Tsing Hua University, Hsinchu, Taiwan
  • 3: Research Institute of Information Technology, Tsinghua University, Beijing, China
*Contact email: dujianboo@163.com

Abstract

This paper presents a joint radio resource allocation scheme in LTE/LTE-A systems. In order to maximize system throughput while satisfying the minimum user rate requirement, the resource allocation is modeled as a convex optimization with constraints in this paper, which is proved to be NP-hard. Hence, a heuristic approach based on joint Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed. The proposed method exploits the benefits of GA and PSO so that it could avoid the low speed problem of genetic algorithm and the local optimum trap concern in particle swarm optimization algorithm. Simulation results show that the proposed algorithm can overcome the disadvantages of genetic algorithm and particle swarm optimization algorithm, and achieve better performance, e.g., a faster convergence and global optimum.