面向密集场景的空天地网络资源分配算法

张鸿; 廖彧歆; 王汝言; 吴大鹏; 杜慧敏

doi:10.11999/JEIT231086

面向密集场景的空天地网络资源分配算法

doi: 10.11999/JEIT231086

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
先进网络与智能互联技术重庆市高校重点实验室重庆 400065
3.
泛在感知与互联重庆市重点实验室重庆 400065

基金项目: 国家自然科学基金(62271096, U20A20157)；重庆市教委科学技术研究项目(KJQN202000626)；重庆市高校创新研究群体(CXQT20017)；重邮信通青创团队支持计划(SCIE-QN-2022-04)；重庆市博士后研究项目(2021XM3058)；重庆市自然科学基金创新发展联合基金(CSTB2023NSCQ-LZX0134)

详细信息

作者简介:
张鸿：男，讲师，博士，研究方向为泛在无线网络、物联网、空天地一体化网络

廖彧歆：女，硕士，研究方向为空天地一体化网络

王汝言：男，教授，博士，研究方向为泛在无线网络、空天地一体化网络

吴大鹏：男，教授，博士，研究方向为泛在无线网络、空天地一体化网络

杜慧敏：女，硕士，研究方向为空天地一体化网络

通讯作者:
吴大鹏　wudp@cqupt.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 修回日期: 2024-04-08
- 网络出版日期: 2024-05-01
- 刊出日期: 2024-05-30

Resource Allocation Algorithm of Space-Air-Ground Integrated Network for Dense Scenarios

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62271096, U20A20157), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202000626), The University Innovation Research Group of Chongqing (CXQT20017), The Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04), Chongqing Postdoctoral Science Special Foundation (2021XM3058), The Natural Science Foundation of Chongqing, China (CSTB2023NSCQ-LZX0134)

摘要

摘要: 空天地网络具有覆盖范围大、吞吐量高、弹性强等优点。该文针对大量用户并发接入、网络负载不均衡所引发的网络拥塞、服务质量恶化等问题，提出一种面向密集场景的资源分配算法。首先以用户需求为中心，根据不同类型用户任务的偏好来构建用户效用函数，然后基于匹配博弈的网络选择算法和结合对偶上升法的功率控制算法来实现负载均衡，优化资源分配方案。实验表明，相较于传统策略，所提策略整体用户接入率至少提高35%，时延和吞吐量方面性能提升超过50%；在密集场景下，能更有效地均衡负载，提升网络性能。
- 空天地一体化网络 /
- 资源分配 /
- 博弈论 /
- 密集场景
Abstract: Space-air-ground integrated network has the advantages of extensive coverage, high throughput, and strong elasticity. A resource allocation algorithm for dense scenarios is proposed to solve the problems of network congestion and deterioration of service quality caused by concurrent access of many users and network load imbalance. Firstly, the user utility function is constructed based on the user demand and the preferences of different types of user tasks. Then, load balancing is realized based on the matching game network selection algorithm and the power control algorithm combined with the dual ascending method, and the resource allocation scheme is optimized. Experimental results show that compared with the traditional strategy, the proposed strategy increases the overall user access rate by at least 35%, and improves the performance of delay and throughput by more than 50%. Load balancing is more effective in dense scenarios and network performance is improved.
- Space-Air-Ground Integrated Network (SAGIN) /
- Resource allocation /
- Game theory /
- Dense scenarios

HTML全文

图 1 基于SDN的空天地一体化网络框架

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图 2 能耗控制因子对用户净效用的影响

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图 3 不同用户数下接入率的变化

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图 4 不同用户数下数据平面设备每单位能耗收益的变化

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图 5 不同用户数下用户平均时延和网络吞吐量的变化

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图 6 功率控制后不同用户数下用户成本的变化

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1 基于匹配博弈的网络选择算法

输入：用户效用值集合$ {U^{{\text{user}}}} $，偏好列表$ {{\mathrm{PL}}^{{\text{user}}}} $和$ {{\mathrm{PL}}^{{\text{dev}}}} $，数据　平面设备子信道数
输出：网络选择结果矩阵$ {\text{\{}}{\mathbf{x}}{\text{\} }} $
(1) 初始化：未匹配的用户集合$ {\mathrm{PU}} $
(2) while $ {\mathrm{PU}} \notin \varnothing $ 或设备$ j $容量未满 do
(3) 　用户$ i $对$ {\mathrm{PL}}_i^{{\text{user}}} $中的设备进行评分，并向其中排列第一的　　　设备发起接入申请；
(4) 　设备$ j $根据$ {\mathrm{PL}}_j^{{\text{dev}}} $对发出请求的用户进行排序和评分；
(5) 　if 设备$ j $容量未满 do
(6) 　　根据剩余容量大小，让排名靠前的用户进入候补列表；
(7) 　end if
(8) 　if 用户$ i $已接入 do
(9) 　　将用户$ i $从$ {{\mathrm{PL}}^{{\text{user}}}} $, $ {{\mathrm{PL}}^{{\text{dev}}}} $和$ {\mathrm{PU}} $中删除；
(10) else if用户$ i $被设备$ j $拒绝
(11) 　将设备$ j $从$ {\mathrm{PL}}_i^{{\text{user}}} $中删除，用户$ i $进入下一轮匹配；
(12) end
(13) if 设备$ j $容量已满 do
(14) 　设备$ j $只接收偏好顺位大于候补列表中最低偏好顺位的　　　　用户，并拒绝该用户；
(15) end

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2 基于对偶上升法的功率控制算法

输入：网络选择结果矩阵$ {\text{\{ }}{\mathbf{x}}{\text{\} }} $
输出：功率分配矩阵$ {{\{ }}{\mathbf{p}}{\text{\} }} $
(1) 初始化：${p_{i,j}} = \ln \left( {p_{\text{G}}^{\max }/{I_{\text{G}}}} \right)$, $ {\kappa _1},{\kappa _2},{\kappa _3} = 1 $, $ \varepsilon = 0.0001 $, 　　 $ {\varepsilon _1} = 0.001 $，收敛阈值$\varDelta $，最大迭代次数$ {\mathrm{MIT}} $, $ t = 0 $；
(2) for $ t{\text{ = }}1:{\mathrm{MIT}} $ do
(3) for $ j = 1:N $ do
(4) for $ i = 1:{\mathrm{IN}} $ do
(5) 　计算式(30)，再通过式(33)计算出拉格朗日函数的部分　　　子式；
(6) 　通过式(35)迭代更新${p_{i,j}}$；
(7) 　end for
(8) end for
(9) 结合上面算出的子式，通过式(33)计算出拉格朗日函数值；
(10) for $ i = 1:{\mathrm{IN}} $ do
(11) if 用户成功接入基站 do
(12) 　通过式(36)来迭代更新$ {\kappa _1},{\kappa _2} $；
(13) end if
(14) end for
(15) for $ j = 1:N $ do
(16) 通过式(36)来更新$ {\kappa _3} $；
(17) end for
(18) if $ \left\| {F\left( {{p_{i,j}}} \right) - L\left( {{p_{i,j}},{\kappa _1},{\kappa _2},{\kappa _3}} \right)} \right\| < \varDelta $ do
(19) 结束迭代；
(20) end if
(21)$ t{\text{ = }}t + 1 $；
(22)end for

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3 面向密集场景的资源分配算法(RAA-DS)

(1) 初始化：用户集合$ \mathcal{I} $，微基站集合$ \mathcal{N} $，无人机集合$ \mathcal{M} $，卫星　　集合$ \mathcal{S} $。
(2) 通过层次分析法得到用户任务的偏好权重$ {a_i} $, $ {b_i} $, $ {c_i} $
(3) 通过用户效用函数(24)求得用户效用值，并将其从小到大排　　序得到用户偏好列表$ {{\mathrm{PL}}^{{\text{user}}}} $
(4) 通过设备效用函数(25)求得设备效用值，并将其从大到小排　　序得到设备偏好列表$ {{\mathrm{PL}}^{{\text{dev}}}} $
(5) 根据算法1进行网络选择，得到网络选择结果矩阵
(6) 将网络选择结果矩阵输入进算法2，得到功率分配结果
(7) 输出网络选择结果矩阵$ {{\{ }}{\mathbf{x}}{{\} }} $，功率分配矩阵$ {\text{\{}}{\mathbf{p}}{\text{\} }} $

下载: 导出CSV

表 1 仿真参数设置

参数设定	参考数值	参数设定	参考数值
微基站子信道数量(个)	120	无人机飞行高度(m)	500
微基站发射总功率(W)	60	卫星子信道数量(个)	200
微基站总带宽 (MHz)	20	卫星发射总功率(W)	600
无人机子信道数量(个)	300	卫星总带宽(MHz)	150
无人机发射总功率(W)	450	卫星天线增益(dB)	40
无人机总带宽(MHz)	200	用户接收天线增益(dB)	3
无人机天线增益(dB)	53	附加损耗因子(dB)	1.5

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