基于改进遗传算法的D2D资源分配策略
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昆明理工大学信息工程与自动化学院,昆明 650500

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国家自然科学基金(61761025)资助项目。


D2D Resource Allocation Strategy Based on Improved Genetic Algorithm
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Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

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    摘要:

    针对D2D(Device to device, D2D)通信技术在蜂窝系统中的资源分配与干扰问题,提出一种基于改进遗传算法的D2D资源分配策略。首先,确定保证蜂窝用户和D2D用户通信质量的功率范围,然后提出一种改进的遗传算法来确定D2D的最佳发射功率,最大化系统吞吐量。该算法在保证蜂窝系统服务质量(Quality-of-service, QoS)的同时,让交叉算子和变异算子随进化代数进行自适应变化,从而达到全局最优。仿真结果表明,本文所提算法可有效提升系统吞吐量并提高D2D用户的信道利用率。

    Abstract:

    Aiming at the problem of resource allocation and interference of D2D (Device to device) communication technology in cellular system, a D2D resource allocation strategy based on the improved genetic algorithm is proposed. Firstly, the power range to ensure the communication quality between cellular users and D2D users is determined, and then an improved genetic algorithm is proposed to determine the optimal transmit power of D2D to maximize the system throughput. The algorithm guarantees the quality-of-service-e (QoS) of cellular system, and makes crossover operator and mutation operator change adaptively with evolution algebra, so as to achieve the global optimization. Simulation results show that the proposed algorithm can effectively improve system throughput and channel utilization of D2D users.

    表 1 主要仿真参数Table 1 Main simulation parameters
    图1 系统模型Fig.1 System model
    图2 自适应交叉率和变异率Fig.2 Adaptive crossover rate and mutation rate
    图3 自适应遗传算法流程图Fig.3 Flow chart of adaptive genetic algorithm
    图4 信道利用率变化曲线Fig.4 Channel utilization curves
    图5 系统吞吐量变化曲线Fig.5 System throughput change curves
    图6 系统平均吞吐量的累积分布函数Fig.6 Cumulative distribution function of the average throughput of the system
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安浩杰,彭艺,刘煜恒,付晓霞.基于改进遗传算法的D2D资源分配策略[J].数据采集与处理,2021,36(2):357-364

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  • 收稿日期:2019-12-28
  • 最后修改日期:2020-10-20
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  • 在线发布日期: 2021-04-15