通信学报 ›› 2019, Vol. 40 ›› Issue (11): 19-29.doi: 10.11959/j.issn.1000-436x.2019217

• 学术论文 • 上一篇    下一篇

宏基站和微基站群之间的预测性信道调度算法

谢映海1,2,姚若河2,吴斌1   

  1. 1 珠海中慧微电子博士后工作站,广东 珠海 519085
    2 华南理工大学电子与信息学院,广东 广州 510640
  • 修回日期:2019-09-24 出版日期:2019-11-25 发布日期:2019-12-06
  • 作者简介:谢映海(1983- ),男,福建仙游人,博士,珠海中慧微电子博士后工作站在站博士后,主要研究方向为数字宽带通信。|姚若河(1961- ),男,博士,华南理工大学教授、博士生导师,主要研究方向为电子器件及应用。|吴斌(1971- ),男,广东深圳人,博士,珠海中慧微电子博士后工作站总工程师,主要研究方向为智能物联网技术。
  • 基金资助:
    广东省科技计划基金资助项目(2016B010123004)

Predictive channel scheduling algorithm between macro base station and micro base station group

Yinghai XIE1,2,Ruohe YAO2,Bin WU1   

  1. 1 Post-doctoral Workstation of Zhuhai Zhonghui Microelectronics,Zhuhai 519085,China
    2 School of Electronics and Information,South China University of Technology,Guangzhou 510640,China
  • Revised:2019-09-24 Online:2019-11-25 Published:2019-12-06
  • Supported by:
    The Guangdong Science and Technology Project(2016B010123004)

摘要:

针对 5G超蜂窝网络的宏基站和微基站群之间的非实时业务传输,提出了一种新型预测性信道调度算法。首先,利用静止通信体之间无线信道的随机平稳过程特征,从古典概率论的角度为调度过程建立了一个离散信道状态概率空间并对其进行事件域分割;然后,通过各事件域的概率数值计算实现对多用户多非实时业务的高效调度。理论分析和仿真结果表明,所提算法计算复杂度低,和其他一些经典调度算法相比,所提算法可在更长时间维度上进行业务优化传输,在吞吐量性能上逼近最大信噪比算法,重负荷情况下系统吞吐量提升了约14%;同时通过精确量化计算实现了业务期望速率和实际调度速率的自适应匹配。

关键词: 超蜂窝网络, 平稳随机过程, 离散信道状态概率空间, 预测性信道调度, 量化调度

Abstract:

A novel predictive channel scheduling algorithm was proposed for non-real-time traffic transmission between macro-base stations and micro-base stations in 5G ultra-cellular networks.First,based on the stochastic stationary process characteristics of wireless channels between stationary communication agents,a discrete channel state probability space was established for the scheduling process from the perspective of classical probability theory,and the event domain was segmented.Then,the efficient scheduling of multi-user,multi-non-real-time services was realized by probability numerical calculation of each event domain.The theoretical analysis and simulation results show that the algorithm has low computational complexity.Compared with other classical scheduling algorithms,the new algorithm can optimize traffic transmission in a longer time dimension,approximate the maximum signal-to-noise ratio algorithm in throughput performance,and increase system throughput by about 14% under heavy load.At the same time,the new algorithm is accurate.Quantitative computation achieves a self-adaption match between the expected traffic rate and the actual scheduling rate.

Key words: super cellular network, stationary stochastic process, discrete channel state probability space, predictive channel scheduling, quantitative scheduling

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