超密度异构蜂窝网络能效优化

doi:10.13190/j.jbupt.2020-052

北京邮电大学学报 ›› 2021, Vol. 44 ›› Issue (1): 20-25,58.doi: 10.13190/j.jbupt.2020-052

超密度异构蜂窝网络能效优化

潘子宇, 杨洁

南京工程学院信息与通信工程学院, 南京 211167

收稿日期:2020-05-26 出版日期:2021-02-28 发布日期:2021-09-30
作者简介:潘子宇(1984-),男,副教授,E-mail:panziyu@njit.edu.cn.
基金资助:
国家自然科学基金项目（61901211，61701221）；南京工程学院科研基金项目（ZKJ201801）

Energy Efficiency Optimization for Ultra Dense Heterogeneous Cellular Networks

PAN Zi-yu, YANG Jie

School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Received:2020-05-26 Online:2021-02-28 Published:2021-09-30

摘要/Abstract

摘要： 利用泊松点过程理论研究了超密度异构蜂窝网络中的基站部署问题，特别是小基站密度对网络能量效率的影响，提出了一种通过优化小基站密度来实现网络能效最大化的方法.首先，借助泊松点过程理论对超密度异构蜂窝网络进行建模，推导了各层基站的覆盖概率以及基于业务荷载的最低可达速率，得出整个网络的最低可达吞吐量；其次，根据网络能效的定义，结合基站能耗模型，推导了与基站密度相关的网络能效的闭合表达式；最后，就小基站密度对网络能效的影响进行分析，提出了一种基于牛顿搜索法的小基站密度优化算法，实现网络能效的最大化.仿真结果验证了理论分析的正确性，同时也表明通过优化小基站部署密度完全能够实现网络能效的最大化.

关键词: 能量效率, 超密度异构网络, 随机几何, 牛顿搜索法

Abstract: Based on Poisson point process(PPP) theory in stochastic geometry,the base station(BS) deployment problem in ultra-dense heterogeneous cellular networks,especially the influence of the distribution density of small cells on network energy efficiency is studied,and a method to maximize network energy efficiency(EE) through optimizing the density of small cells is proposed. Firstly,the coverage probability of each tier BS and the BS load oriented minimum achievable rate are derived through the PPP theory. Secondly,according to the definition of network EE and base station energy consumption model,the closed expression of network EE related to BS density is derived. Finally,the influence of the distribution density of the small cells on the network EE is analyzed,and an optimization algorithm of the small cell density based on Newton search method is proposed to maximize the network EE. Simulations verify the correctness of the theoretical analysis;and show that the network EE can be maximized through optimizing the deployment density of small cells.

Key words: energy efficiency, ultra-dense heterogeneous networks, stochastic geometry, Newton iteration method

中图分类号:

TN911

潘子宇, 杨洁. 超密度异构蜂窝网络能效优化[J]. 北京邮电大学学报, 2021, 44(1): 20-25,58.

PAN Zi-yu, YANG Jie. Energy Efficiency Optimization for Ultra Dense Heterogeneous Cellular Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 20-25,58.

参考文献

[1] Gao Zhen, Dai Linglong, Mi D, et al. MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network[J]. IEEE Wireless Communications, 2015, 22(5):13-21.
[2] Hu R Q, Qian Y. An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems[J]. IEEE Communications Magazine, 2014, 52(5):94-101.
[3] Lin P C, Casanova L F, Lin Y C. Analytical framework for power saving evaluation in two-tier heterogeneous mobile networks[J]. Wireless Networks, 2017, 23(4):985-999.
[4] Yunas S F, Valkama M, Niemelä J. Spectral and energy efficiency of ultra-dense networks under different deployment strategies[J]. IEEE Communications Magazine, 2015, 53(1):90-100.
[5] Dhillon H S, Ganti R K, Baccelli F, et al. Modeling and analysis of K-Tier downlink heterogeneous cellular networks[J]. IEEE Journal on Selected Areas in Communications, 2012, 30(3):550-560.
[6] Singh S, Dhillon H S, Andrews J G. Offloading in heterogeneous networks:modeling, analysis, and design insights[J]. IEEE Transactions on Wireless Communications, 2013, 12(5):2484-2497.
[7] Patcharamaneepakorn P, Wang Chengxiang, Yu Fu, et al. Quadrature space-frequency index modulation for energy-efficient 5G wireless communication systems[J]. IEEE Transactions on Communications, 2018, 66(7):1345-1355.
[8] Yu Nuo, Miao Yuting, Mu Lan, et al. Minimizing energy cost by dynamic switching ON/OFF base stations in cellular networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(11):7457-7469.
[9] Celebi H, Güvenç I·smail. Load analysis and sleep mode optimization for energy-efficient 5G small cell networks[C]//Proceedings of IEEE International Conference on Communications Workshops. Paris:IEEE, 2017:1159-1164.
[10] Chang Peiliang, Miao Guowang. Energy and spectral efficiency of cellular networks with discontinuous Transmission[J]. IEEE Transactions on Wireless Communications, 2017, 16(5):2991-3002.
[11] 李云, 王俊伟, 赵为粮, 等. 基于基站密度和业务负载的异构蜂窝网络能效优化[J]. 电子与信息学报, 2017, 39(4):854-859. Li Yu, Wang Junwei, Zhao Weiliang, et al. Optimizing the energy efficiency of heterogeneous cellular networks based on the base station density and traffic load[J]. Journal of Electronics & Information Technology, 2017, 39(4):854-859.
[12] Haenggi M. Stochastic geometry for wireless networks[M]. London:Cambridge University Press, 2012:208-212.

超密度异构蜂窝网络能效优化

Energy Efficiency Optimization for Ultra Dense Heterogeneous Cellular Networks

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

[1]	田心记, 蒋清丽. 下行NOMA系统中最大化能量效率的功率分配方案[J]. 北京邮电大学学报, 2021, 44(1): 38-44.
[2]	焦铭晗, 彭木根, 刘晨熙. 无人机网络的覆盖及切换性能研究[J]. 北京邮电大学学报, 2020, 43(6): 74-81.
[3]	胡长俊, 袁树杰. 矿井WSN自适应能量有效及能耗均衡的数据收集方法[J]. 北京邮电大学学报, 2018, 41(2): 86-91.
[4]	范琮珊, 张天魁, 曾志民. 基于业务量突发性的异构蜂窝网络最优基站密度[J]. 北京邮电大学学报, 2017, 40(6): 30-36.
[5]	谢显中, 王闯, 张森林, 葛振涛. 基于干扰对齐的多用户全双工通信中干扰消除方法[J]. 北京邮电大学学报, 2015, 38(6): 87-92.
[6]	覃团发, 李亮亮, 闫龙, 邢杰, 蒙云番. 基于网络编码和占空比的无线传感器网络节能方案[J]. 北京邮电大学学报, 2014, 37(4): 83-87.
[7]	张鹤立, 纪红, 王永斌. 自组织异构网络中的资源优化配置[J]. 北京邮电大学学报, 2014, 37(2): 5-8,47.
[8]	王圣森, 冯春燕, 郭彩丽. 上行MU-SIMO中高能效联合波束赋形与功率控制[J]. 北京邮电大学学报, 2013, 36(5): 66-70.
[9]	江甲沫王文博彭木根. 无线通信分布式天线系统的高能效组网方案[J]. 北京邮电大学学报, 2012, 35(5): 1-5.
[10]	李蓉冯志勇田辉王莹. 延长无线传感器网络生存周期的跨层算法[J]. 北京邮电大学学报, 2012, 35(2): 50-53.
[11]	刘凯许成谦. 由平方剩余差集与差集偶构造二元ZCZ序列偶集[J]. 北京邮电大学学报, 2009, 32(4): 50-53.