无人机辅助无蜂窝大规模MIMO中的空地协同调度

doi:10.11959/j.issn.1000-0801.2022242

电信科学 ›› 2022, Vol. 38 ›› Issue (8): 37-44.doi: 10.11959/j.issn.1000-0801.2022242

• 专题：无人机空地无线通信技术 • 上一篇下一篇

无人机辅助无蜂窝大规模MIMO中的空地协同调度

邓丹昊¹, 王朝炜¹, 江帆², 王卫东¹

¹ 北京邮电大学电子工程学院，北京 100876
² 西安邮电大学通信与信息工程学院，陕西西安 710121

修回日期:2022-07-18 出版日期:2022-08-20 发布日期:2022-08-01
作者简介:邓丹昊（1996- ），女，北京邮电大学电子工程学院博士生，主要研究方向为无线资源管理技术
王朝炜（1982- ），男，博士，北京邮电大学电子工程学院副教授，主要研究方向为下一代移动通信技术、无线传感器与IoT技术等
江帆（1982- ），女，博士，西安邮电大学通信与信息工程学院教授，主要研究方向为基于人工智能的边缘计算及缓存技术、D2D通信技术、5G超密集异构网络中的无线资源管理等
王卫东（1967- ），男，博士，北京邮电大学电子工程学院教授，主要研究方向为卫星移动通信、下一代移动通信技术、IoT技术等
基金资助:
国家重点研发计划项目(2020YFB1807204)

Coordinated air-ground scheduling for UAV-assisted cell-free massive MIMO

Danhao DENG¹, Chaowei WANG¹, Fan JIANG², Weidong WANG¹

¹ School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
² School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Revised:2022-07-18 Online:2022-08-20 Published:2022-08-01
Supported by:
The National Key Research and Development Program of China(2020YFB1807204)

摘要/Abstract

摘要：

摘要：无蜂窝大规模多输入多输出（multiple-input multiple-output，MIMO）技术采用大量接入点（access point， AP）为地面用户提供高效的通信服务，但在有高速移动用户的场景中，会加剧对信道状态信息的依赖。为了减少导频资源的消耗，提出了一种综合无人机（unmanned aerial vehicle，UAV）辅助通信和无蜂窝大规模MIMO通信的双系统架构，该架构能够预测高速移动用户的轨迹，利用无人机为其提供可靠通信；进一步提出了基于深度强化学习（deep reinforcement learning，DRL）的无人机轨迹设计和地面用户调度方案，在满足各类约束的前提下实现系统总和速率最大化。仿真结果表明，与现有方案相比，所提方案能够有效提升系统容量。

关键词: 无蜂窝大规模MIMO, 无人机辅助通信, 联合空地调度, 深度强化学习

Abstract:

Cell-free massive multiple-input multiple-output (MIMO) technology uses a large number of access points (AP) to provide efficient communication services for terrestrial users.However, massive APs and users increase the demand for channel state information detection, especially for users with high moving speeds.In order to reduce the consumption of pilot resources, a dual system architecture that integrates unmanned aerial vehicle-assisted (UAV-assisted) communications and cell-free massive MIMO communications was proposed.The architecture was able to predict the movement trajectories of high-speed users and use UAVs to provide them with reliable communications.A UAV trajectory design and user scheduling scheme based on deep reinforcement learning (DRL) was further proposed, which maximized the system sum rate under the premise of satisfying various constraints.Simulation results demonstrate that the proposed scheme is able to predict user trajectories and improve system sum rate compared with existing schemes.

Key words: cell-free massive MIMO, UAV-assisted communication, coordinated air-ground scheduling, DRL

中图分类号:

TN915

邓丹昊, 王朝炜, 江帆, 王卫东. 无人机辅助无蜂窝大规模MIMO中的空地协同调度[J]. 电信科学, 2022, 38(8): 37-44.

Danhao DENG, Chaowei WANG, Fan JIANG, Weidong WANG. Coordinated air-ground scheduling for UAV-assisted cell-free massive MIMO[J]. Telecommunications Science, 2022, 38(8): 37-44.

图/表 5

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无人机辅助无蜂窝大规模MIMO中的空地协同调度

Coordinated air-ground scheduling for UAV-assisted cell-free massive MIMO

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