超密集网络导频复用干扰避免策略

doi:10.11959/j.issn.1000-436x.2020120

通信学报 ›› 2020, Vol. 41 ›› Issue (7): 165-171.doi: 10.11959/j.issn.1000-436x.2020120

超密集网络导频复用干扰避免策略

黄杰¹,杨凡¹(),高乙文²,张博为³

¹ 重庆理工大学电气与电子工程学院，重庆 400054
² 中国移动通信集团重庆有限公司，重庆 400044
³ 国家电网重庆电力公司信息通信分公司，重庆 400012

修回日期:2020-05-08 出版日期:2020-07-25 发布日期:2020-08-01
作者简介:黄杰（1988- ），男，重庆人，博士，重庆理工大学讲师，主要研究方向为无线通信理论、通信网络、下一代移动通信技术、认知无线电、无线通信资源分配等|杨凡（1983- ），男，湖北广水人，博士，重庆理工大学副教授，主要研究方向为无线宽带自适应传输、无线通信网络、下一代移动通信技术、无线通信中的编码技术等|高乙文（1987- ），女，重庆人，中国移动通信集团重庆有限公司校园中心经理，主要研究方向为移动通信网络、下一代移动、通信系统等|张博为（1988- ），男，重庆人，博士，国家电网重庆电力公司信息通信分公司工程师，主要研究方向为无线通信理论、MIMO系统干扰管理、5G网络通信技术
基金资助:
重庆市基础科学与前沿技术研究专项基金资助项目(cstc2019jcyj-msxmX0233);重庆市教育委员会科学技术研究计划基金资助项目(KJQN201901125);重庆市教育委员会科学技术研究计划基金资助项目(KJQN201901103);重庆理工大学科研启动基金资助项目(2019ZD42);重庆理工大学科研启动基金资助项目(2019ZD63)

Interference avoidance strategy for ultra dense network with pilot reuse

Jie HUANG¹,Fan YANG¹(),Yiwen GAO²,Bowei ZHANG³

¹ School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China
² China Mobile Group Chongqing Co.,Ltd.,Chongqing 400044,China
³ State Grid Chongqing Information and Telecommunication Company,Chongqing 400012,China

Revised:2020-05-08 Online:2020-07-25 Published:2020-08-01
Supported by:
Chongqing Research Program of Basic Research and Frontier Technology(cstc2019jcyj-msxmX0233);Science and Technology Research Program of Chongqing Education Commission of China(KJQN201901125);Science and Technology Research Program of Chongqing Education Commission of China(KJQN201901103);Scientific Research Foundation of Chongqing University of Technology(2019ZD42);Scientific Research Foundation of Chongqing University of Technology(2019ZD63)

摘要/Abstract

摘要：

针对超密集网络中导频复用将产生同一导频的复用干扰，严重影响信道估计的准确性，提出了一种超密集网络导频复用干扰避免策略。通过建立超密集网络子载波的干扰模型，推导子载波的干扰概率，并采用压缩感知理论建立超密集网络非均匀导频位置优化模型，据此提出了超密集网络导频复用干扰避免策略。仿真结果表明，与传统信道估计算法相比，所提策略增加了导频复用干扰避免策略的信道估计，能有效避免导频复用产生的干扰，保证信道估计的准确性。

关键词: 超密集网络, 导频复用, 干扰避免, 信道估计

Abstract:

In the ultra dense network (UDN),the pilot reuse scheme would produce significant interference,which will affect the accuracy of channel estimation.To solve this problem,an interference avoidance strategy for UDN with pilot reuse was proposed.An interference model of subcarriers for UDN was provided and the interference probability of subcarriers was derived.Then,based on the model,a pilot position selection model was proposed and an interference avoidance strategy for UDN with pilot reuse was provided.The simulation results show that compared with the traditional channel estimation algorithm,the channel estimation with proposed interference avoidance strategy can effectively avoid the interference and ensure the accuracy of channel estimation in UDN with pilot reuse.

Key words: ultra dense network, pilot reuse, interference avoidance, channel estimation

中图分类号:

TN929.5

黄杰,杨凡,高乙文,张博为. 超密集网络导频复用干扰避免策略[J]. 通信学报, 2020, 41(7): 165-171.

Jie HUANG,Fan YANG,Yiwen GAO,Bowei ZHANG. Interference avoidance strategy for ultra dense network with pilot reuse[J]. Journal on Communications, 2020, 41(7): 165-171.

图/表 7

图1

图2

图3

图4

图5

图6

图7

参考文献 17

[1]	KAMEL M , HAMOUDA W , YOUSSEF A . Ultra-dense networks:a survey[J]. IEEE Communications Surveys ＆ Tutorials, 2016,18(4): 2522-2545.
[2]	PAN C , MEHRPOUYAN H , LIU Y ,et al. Joint pilot allocation and robust transmission design for ultra-dense user-centric TDD C-RAN with imperfect CSI[J]. IEEE Transactions on Wireless Communications, 2018,17(3): 2038-2053.
[3]	PAN C , REN H , ELKASHLAN M ,et al. Robust beamforming design for ultra-dense user-centric C-RAN in the face of realistic pilot contamination and limited feedback[J]. IEEE Transactions on Wireless Communications, 2018,18(2): 780-795.
[4]	TUGNAIT J K . On detection and mitigation of reused pilots in massive MIMO systems[J]. IEEE Transactions on Communications, 2017,66(2): 688-699.
[5]	何毅 . Massive MIMO导频设计与信道估计[D]. 成都:西南交通大学, 2017.
	HE Y . Massive MIMO pilot design and channel estimation[D]. Chengdu:Southwest Jiaotong University, 2017.
[6]	PARK E , BAE J , JU H ,et al. Resource allocation for full-duplex systems with imperfect co-channel interference estimation[J]. IEEE Transactions on Wireless Communications, 2019,18(4): 65-75.
[7]	SRINIVAS B , MAWATWAL K , SEN D ,et al. An iterative semi-blind channel estimation scheme and uplink spectral efficiency of pilot contaminated one-bit massive MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2019,68(8): 7854-7868.
[8]	RAVITEJA P , PHAN K T , HONG Y . Embedded pilot-aided channel estimation for OTFS in delay–doppler channels[J]. IEEE Transactions on Vehicular Technology, 2019,68(5): 4906-4917.
[9]	GONG Z , LI C , JIANG F . Channel estimation for sparse massive MIMO channels in low SNR regime[J]. IEEE Transactions on Cognitive Communications and Networking, 2018,4(4): 883-893.
[10]	MUPPIRISETTY L S , CHARALAMBOUS T , KAROUT J ,et al. Location-aided pilot contamination avoidance for massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2018,17(4): 2662-2674.
[11]	TAN Y , ZHOU J , QIN J . Novel channel estimation for non-orthogonal multiple access systems[J]. IEEE Signal Processing Letters, 2016,23(12): 1781-1785.
[12]	YANG H H , GERACI G , QUEK T Q S ,et al. Cell-edge-aware precoding for downlink massive MIMO cellular networks[J]. IEEE Transactions on Signal Processing, 2017,65(13): 3344-3358.
[13]	ALI S , CHEN Z , YIN F . Eradication of pilot contamination and zero forcing precoding in the multi-cell TDD massive MIMO systems[J]. IET Communications, 2017,11(13): 2027-2034.
[14]	PAN C , REN H , ELKASHLAN M ,et al. Weighted sum-rate maximization for the ultra-dense user-centric TDD C-RAN downlink relying on imperfect CSI[J]. IEEE Transactions on Wireless Communications, 2019,18(2): 1182-1198.
[15]	LIN Y , ZHANG R , YANG L ,et al. Modularity-based user-centric clustering and resource allocation for ultra dense networks[J]. IEEE Transactions on Vehicular Technology, 2018,67(12): 12457-12461.
[16]	WU J , ZHANG Z , HONG Y ,et al. Cloud radio access network (C-RAN):a primer[J]. IEEE Network, 2015,29(1): 35-41.
[17]	ALIMI I A , TEIXEIRA A L , MONTEIRO P P . Toward an efficient C-RAN optical fronthaul for the future networks:a tutorial on technologies,requirements,challenges,and solutions[J]. IEEE Communications Surveys ＆ Tutorials, 2018,63(12): 132-156.

超密集网络导频复用干扰避免策略

Interference avoidance strategy for ultra dense network with pilot reuse

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 17

相关文章 15

Metrics

推荐阅读 0

[1]	崔伟, 于颖, 于海霞, 陈超, 李云鹏. 基于IOC-CSMP的OFDM系统稀疏信道快速重构算法[J]. 通信学报, 2023, 44(2): 52-58.
[2]	曾嵘, 杭潇. 车联网环境下可重构智能反射面辅助无线信道估计算法[J]. 通信学报, 2022, 43(8): 142-150.
[3]	蒋占军, 刘欢, 张华卫, 李翠然. OTFS接收端信道估计辅助的无记忆功放非线性失真补偿算法[J]. 通信学报, 2022, 43(12): 134-145.
[4]	邢旺, 唐晓刚, 周一青, 张冲, 潘振岗. 面向OTFS的时延-多普勒域信道估计方法综述[J]. 通信学报, 2022, 43(12): 188-201.
[5]	梅锴, 赵海涛, 刘潇然, 刘军, 熊俊, 任保全, 魏急波. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022, 43(1): 59-70.
[6]	黄源, 何怡刚, 吴裕庭, 程彤彤, 隋永波, 宁暑光. 基于深度学习的压缩感知FDD大规模MIMO系统稀疏信道估计算法[J]. 通信学报, 2021, 42(8): 61-69.
[7]	廖勇, 蔡志镕. 基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计[J]. 通信学报, 2021, 42(4): 177-184.
[8]	吕新荣, 李有明, 国强. MIMO-OFDM系统的信道与脉冲噪声联合估计方法[J]. 通信学报, 2021, 42(12): 54-64.
[9]	傅友华, 陈栋. 混合智能反射表面结构辅助的毫米波通信信道估计[J]. 通信学报, 2021, 42(10): 189-196.
[10]	王莹, 任军, 史可, 林彬. 基于深度学习的广义频分复用系统时频双选择信道估计[J]. 通信学报, 2021, 42(10): 233-242.
[11]	刘婷婷,罗义南,杨晨阳. 基于多智能体深度强化学习的分布式干扰协调[J]. 通信学报, 2020, 41(7): 38-48.
[12]	王海荣, 董健, 王玉辉. 大规模MIMO系统中基于谱分离的导频去污染[J]. 通信学报, 2020, 41(4): 197-205.
[13]	李晓娜,王中方,程谦,付婧雯,张顺亮. 基于迭代联合传输的超密集网络动态干扰协调方案[J]. 通信学报, 2020, 41(2): 176-186.
[14]	吴宣利,谢子怡,吴玮. 室内超密集网络中基于干扰图的自适应干扰协调方法[J]. 通信学报, 2019, 40(9): 15-23.
[15]	吴宣利,陈旭. 干扰门限与回程容量限制下UDN的能效与谱效联合优化算法[J]. 通信学报, 2019, 40(12): 86-97.