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基于甲虫搜索的改进粒子群无人机辅助网络部署优化算法

陈佳美 李世昂 李玉峰 王宇鹏 别玉霞

陈佳美, 李世昂, 李玉峰, 王宇鹏, 别玉霞. 基于甲虫搜索的改进粒子群无人机辅助网络部署优化算法[J]. 电子与信息学报, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404
引用本文: 陈佳美, 李世昂, 李玉峰, 王宇鹏, 别玉霞. 基于甲虫搜索的改进粒子群无人机辅助网络部署优化算法[J]. 电子与信息学报, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404
CHEN Jiamei, LI Shiang, LI Yufeng, WANG Yupeng, BIE Yuxia. Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404
Citation: CHEN Jiamei, LI Shiang, LI Yufeng, WANG Yupeng, BIE Yuxia. Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1697-1705. doi: 10.11999/JEIT220404

基于甲虫搜索的改进粒子群无人机辅助网络部署优化算法

doi: 10.11999/JEIT220404
基金项目: 国家自然科学基金(61901284),辽宁省自然科学基金(2019-ZD-0220),航空科学基金(201926054001)
详细信息
    作者简介:

    陈佳美:女,讲师,研究方向为空地无线网络资源管理

    李世昂:男,硕士生,研究方向为空地无线网络资源管理

    李玉峰:男,教授,研究方向为图像处理

    王宇鹏:男,教授,研究方向为自组织网络与车联网

    别玉霞:女,副教授,研究方向为卫星网络

    通讯作者:

    陈佳美 chenjiamei5870@163.com

  • 中图分类号: TN919.72

Improved Particle Swarm Optimization Unmanned Aerial Vehicle-assisted Network Deployment Optimization Algorithm Based on Beetle Antennae Search

Funds: The National Natural Science Foundation of China (61901284), The Natural Science Foundation of Liaoning Province (2019-ZD-0220), The Aeronautical Science Foundation of China (201926054001)
  • 摘要: 在体育赛场等用户大规模聚集或者突发灾难的情况下,地面基站经常面临过载甚至瘫痪的问题。此时,多无人机(UAV)辅助网络系统可以很好地为地面基站提供信号补偿,有效地增强局部地区的通信质量。然而,无人机的机动性和网络流动引起的拓扑结构变化,会导致频繁的间歇性连接甚至出现传输故障。因此,UAV基站的有效部署以及网络性能的优化成为亟待解决的问题。该文提出一种基于甲虫搜索的改进粒子群UAV辅助网络部署优化算法—智能高效算法(IEA),利用甲虫搜索算法(BAS)的个体寻优优势,对粒子群算法(PSO)进行改进,并首次采用双门限约束保证用户通信质量,使得多UAV系统下的网络性能得到了改善。仿真结果表明,相对于传统算法,该文提出的IEA算法在系统吞吐量、用户平均吞吐量以及频谱效率等方面都获得了较大提升。
  • 图  1  基于LAP的大型室外活动场景的UAV辅助通信

    图  2  BAS仿生原理图

    图  3  系统吞吐量对比

    图  4  CDF系统吞吐量曲线

    图  5  系统GU平均吞吐量对比

    图  6  多UAV系统下的频谱效率

    图  7  3种算法下UAV与覆盖GU

    算法1 IEA算法
     (1) 在允许范围内随机初始化${{\rm{UAV}}_j}$的位置$t_j^u$和速度 $v_j^u$
     (2) 令迭代$u = 1$
     (3) DO
     (4)  For ${{\rm{UAV}}_j}$
     (5)   计算适应度函数值
     (6)     If适应度函数值比$p_{{\rm{sbest}}}^u$大
     (7)     设置当前的适应度函数值为$p_{{\rm{sbest}}}^u$
     (8)     结束如果
     (9)   结束循环
     (10) 选择UAV的最优适应度函数值$p_{{\rm{gbest}}}^u$
     (11) For ${{\rm{UAV}}_j}$
     (12)  For每个维度$k$
     (13)     根据如下公式计算速度
     (14)     $v_j^{u + 1} = \omega v_j^u + {c_1}{r_1}\left( {p_{{\rm{sbest}}}^u - t_j^i} \right)$
               $+ {c_2}{r_2}\left( {p_{{\rm{gbest}}}^u - t_j^u} \right)$
     (15)     根据公式更新的${{\rm{UAV}}_j}$位置
     (16) ${t_{ {\rm{right} } } } = t_j^u + v_j^u \times {\bf{dir} } \times l$
     (17) ${t_{ {\rm{left} } } } = x_j^u - v_j^u \times {\bf{dir} } \times l$
     (18) $t_j^{u + 1} = t_j^u + \left( {1 - \lambda } \right) \times {\rm{step} } \times {\bf{dir} } \times {\rm{sign} }$
        $\left( {f\left( {t_{\rm{r}}^u} \right) - f\left( {t_{\rm{l}}^u} \right)} \right) \times v_j^u+ \lambda \times v_j^u$
     (19)  结束循环
     (20) 结束循环
     (21) $u = u + 1$
     (22) 迭代完毕,结束循环
    下载: 导出CSV

    表  1  仿真参数

    参数符号设定值
    环境参数1$a$9.61
    环境参数2$b$0.16
    波长(m/s)$c$300 000 000
    载波频率(MHz)${f_c}$2 000
    GU的数量(个)$ N $5~15
    UAV的数量(个)$ M $1~3
    UAV的发射功率(dBm)$ {P_t} $10
    噪声功率(dBm)$ {N_0} $–102
    信噪比阈值(dB)${{\rm{SNR}}_{{\rm{th}}} }$10
    UAV覆盖半径阈值(m)${R_{{\rm{th}}} }$300
    带宽(MHz)$ B $0.2
    边界长度(m)$ {R_L} $1 000
    UAV的最大飞行高度(m)${h_{\min } }$20
    UAV的最小飞行高度(m)$ {h_{\max }} $500
    视距传播额外路径损耗${\eta _{{\rm{Los}}} }$1
    非视距传播额外路径损耗${\eta _{{\rm{nLos}}} }$20
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-06
  • 修回日期:  2022-05-27
  • 网络出版日期:  2022-05-30
  • 刊出日期:  2023-05-10

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