低空单层无人机网络覆盖优化控制策略

郭洋; 高原; 程绍驰; 王晓楠

doi:10.3969/j.issn.0258-2724.20230535

低空单层无人机网络覆盖优化控制策略

doi: 10.3969/j.issn.0258-2724.20230535

军事科学院战争研究院，北京 100091

基金项目: 国家自然科学基金（62222121, 62341110）

详细信息

作者简介:
郭洋（1984—），男，副研究员，博士，研究方向为信息与通信工程，E-mail：guoyangnudt@163.com

通讯作者:
王晓楠（1989—），男，副研究员，博士，研究方向为自主无人系统，E-mail：wangxiaonan1989@foxmail.com

中图分类号: TN929.5；TP273；V279
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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-13
- 修回日期: 2023-12-14
- 网络出版日期: 2024-04-29
- 刊出日期: 2023-12-15

Optimization Control Strategy for Low-Altitude and Single-Layer Unmanned Aerial Vehicle Network Coverage

War Studies Institute, Academy of Military Science, Beijing 100091, China

摘要

摘要:
为研究应急通信场景中无人机网络自主控制问题，采用多架旋翼无人机搭载6G一体化基站，通过机间组网形成低空单层无人机网络，对地面任务区域内的用户提供无线网络服务；针对典型场景构建数值模型，利用多智能体强化学习方法求解无人机网络的优化控制策略，并分析无人机基站数量、无人机基站通信距离对于任务地域无线网络覆盖效果的影响. 研究结果表明：利用强化学习方法得到的优化控制策略收敛效果较好；无人机网络覆盖得分和公平覆盖指数的学习曲线变化趋势相似，在第1000～2 000个episode之间快速增长，随后进入平台区；在无人机基站通信覆盖距离1.0 km、飞行高度300 m条件下，将无人机基站数量从3个增加到7个，网络覆盖得分提高53.28%，公平覆盖指数提高43.57%；当无人机基站数5个、飞行高度300 m条件下，基站通信距离从1.0 km增加到2.5 km时，无人机网络覆盖得分提高86.01%，公平覆盖指数提高41.47%.
- 无人机 /
- 无线网络 /
- 强化学习 /
- 通信覆盖
Abstract:
In order to study the autonomous control problem of unmanned aerial vehicle (UAV) networks in emergency communication scenarios, multiple rotary-wing UAVs equipped with 6G base stations were used, and a low-altitude and single-layer UAV network was formed through the interconnection between UAVs, thus providing wireless network services to users in the ground task area. A numerical model was constructed for typical scenarios, and a multi-agent reinforcement learning method was used to solve the optimization control strategy of the UAV network. The effects of the number of UAV base stations and the communication distance of UAV base stations on the wireless network coverage in the task area were investigated. The research results indicate that the optimization control strategy obtained by using reinforcement learning methods can converge well. The learning curves of the UAV network coverage score and fairness coverage index have similar trends. The curves rapidly increase between the 1000th and 2000th episode and then change slowly. Under the conditions of a communication coverage distance of 1 km and a flight altitude of 300 m for UAV base stations, the UAV network coverage score increases by 53.28%, and the fairness coverage index increases by 43.57% when the number of UAV base stations increases from 3 to 7. Under the condition of five UAV base stations and a flight altitude of 300 m, the UAV network coverage score increases by 86.01%, and the fairness coverage index increases by 41.47% when the communication distance of the base stations increases from 1.0 km to 2.5 km.
- unmanned aerial vehicles /
- wireless networks /
- reinforcement learning /
- communication coverage

HTML全文

图 1 低空单层无人机网络架构

Figure 1. Low-altitude and single-layer UAV network architecture

下载: 全尺寸图片幻灯片

图 2 神经网络架构及数据流向示意

Figure 2. Neural network architecture and data flow

下载: 全尺寸图片幻灯片

图 3 无人机网络全局奖励值的典型学习曲线

Figure 3. Typical learning curve of global reward value of UAV network

下载: 全尺寸图片幻灯片

图 4 无人机网络覆盖得分以及公平指数典型变化曲线

Figure 4. Typical curves of UAV network coverage score and fairness index

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图 5 无人机网络覆盖得分、公平覆盖指数与基站数量的关系

Figure 5. Relationships among UAV network coverage score, fairness index, and number of base stations

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图 6 无人机网络覆盖得分、公平覆盖指数与通信覆盖距离的关系

Figure 6. Relationships among UAV network coverage score, fairness index, and communication coverage distance

下载: 全尺寸图片幻灯片

表 1 仿真实验参数数值

Table 1. Parameters for simulation experiment

参数名称	数值
无人机基站数量/个	3
任务地域的长、宽/km	4、4
任务地域划分的方格数量/个	100
重点保障方格数量/个	5
无人机基站覆盖距离/km	1
无人机基站飞行高度/m	300
无人机基站最大飞行速度/（m·s⁻¹）	20
重点方格覆盖得分	10
一般方格覆盖得分	1
无人机基站飞出边界处罚值	1
无人机基站间碰撞处罚值	1

下载: 导出CSV

表 2 强化学习主要参数

Table 2. Main parameters for reinforcement learning

参数名称	数值
神经网络层数/层	2
隐藏网络节点数/个	64
学习率	0.01
折扣因子	0.95
$\tau $	0.99
batch size	1000

下载: 导出CSV

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