Overview on Intelligent Wireless Resource Management of Millimeter Wave Communications under High-speed Railway
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摘要: 为满足高速铁路智能化发展对铁路移动通信系统提出的新需求，基于第5代(5G)无线通信技术的高铁移动网络将采用宽带毫米波频段以提高传输容量。基于此，该文首先结合高铁传输需求及场景特殊性，分析了定向毫米波通信在网络覆盖鲁棒性、移动支持能力及链路稳定性与管理方面的问题。然后，探讨了通过融合传统6 GHz以下频段(简称sub-6 GHz)与毫米波频段以兼顾网络覆盖与传输容量的新一代高铁无线接入网络架构，其中全向覆盖的sub-6GHz频段提供鲁棒覆盖，定向毫米波通信提升传输速率。最后，在该网络架构基础上，研究了如何利用深度学习算法进行业务特征与传输环境的预测，并智能决策sub-6 GHz与毫米波双频段的无线资源分配、波束对齐及切换优化，最终实现高可靠、低时延、大容量新一代高铁移动通信系统。Abstract: To satisfy the new requirements brought by the intelligent development of high-speed railways, future railway mobile networks based on the Fifth Generation (5G) wireless technologies will apply broadband millimeter wave bands to enhance the transmission capability. Therefore, in this paper, considering the transmission requirements and scenario characteristics of high-speed railways, the problems of millimeter wave communications in network coverage robustness, mobility support capability, link stability and management are analyzed. Then, to guarantee the network coverage while improving the transmission capacity, future high-speed railway wireless network architecture based on the integration of conventional sub-6 GHz and millimeter wave bands is discussed, where the omni-directional sub-6 GHz bands provide robust coverage, and the directional millimeter wave communications improve transmission rate. Finally, under this network architecture, this paper investigates how to employ deep learning algorithms to predict the service characteristics and propagation environments, and make decisions for radio resource allocation, beam alignment, and handover optimization for sub-6 GHz and millimeter wave bands, to realize eventually the high reliability, low latency, and large capacity for the future high-speed railway mobile systems.
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表 1 面向高铁毫米波通信的相关研究
研究内容 关键技术与智能算法 达成目标 参考文献 通信网络架构 网络切片
控制面与数据面解耦
sub-6 GHz与毫米波融合
云接入网络架构
车车通信实现定制化服务
大幅度提升容量
保障移动性能
满足应急通信需求[12,13,15,16] 业务与信道特征 业务预测，长短期记忆网络(Long Short
Term Memory, LSTM)及变种等
信道预测，LSTM及变种等
智能反射面，强化学习等预测传输需求
预测传输环境
改变传输环境[22,23,27–32] 无线资源分配 区分业务的资源调度，强化学习等
网络切片资源预留，强化学习等保障资源可用性
保障资源隔离性[40–47] 切换优化 切换参数自适应优化，深度神经网络等
快速波束对齐、跟踪，强化学习等提高切换成功率
提高链路稳定性[51–55] 
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表 2 业务预测时间尺度与预测算法需求及网络资源配置
业务预测时间尺度 预测算法需求 网络资源配置 大尺度 (周、月) 指数平滑、线性回归、
支持向量机等网络功能单元配置、工作频段及
带宽配置、RRU资源配置等中尺度 (分、时) LSTM算法及其变种等 为不同切片预留网络功能模块、
为不同切片预留传输带宽等小尺度 (毫秒、秒) LSTM算法及其变种等 时隙分配、子载波分配、空间流分配、
功率分配、调制编码方式等
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表 3 业务预测参数设置
切片
类型业务参数(模式1/模式2/模式3) URLLC切片 数据包个数:均匀分布 (4000, 4500)/ (1500, 2000)/(500,600)
包内字节数(B): 均匀分布(500, 600)/ (100, 200)/(20,30)
移动速度(m/s): 均匀分布(100, 138)/ (10, 30)/ (1, 3)
用户量: 均匀分布(2, 5)/ (2, 8)/ (6, 16)eMBB切片 数据包个数:泊松分布(密度) 6000/ 2400/240
包内字节数(B): 均匀分布(3000, 3500)/ (300, 400)/(100,200)
移动速度(m/s): 均匀分布(100, 138)/ (10, 30)/ (1, 3)
用户量: 均匀分布(2, 5)/ (2, 8)/ (6, 16)mMTC切片 数据包个数:均匀分布 (200, 500)/ (1000, 1100)/(10,100)
包内字节数(B): 均匀分布(100, 110)/ (200, 210)/(50,70)
移动速度(m/s): 均匀分布(100, 138)/ (10, 20)/ 0
用户量: 均匀分布(4, 10)/ (1, 10)/ (1, 20)
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