CN106788645B - 一种多用户mimo分布式基站系统中能量效率最优化方法 - Google Patents

一种多用户mimo分布式基站系统中能量效率最优化方法 Download PDF

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CN106788645B
CN106788645B CN201710062954.9A CN201710062954A CN106788645B CN 106788645 B CN106788645 B CN 106788645B CN 201710062954 A CN201710062954 A CN 201710062954A CN 106788645 B CN106788645 B CN 106788645B
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刘楠
任红
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White Box Shanghai Microelectronics Technology Co ltd
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Southeast University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0456Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/022Site diversity; Macro-diversity
    • H04B7/024Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0452Multi-user MIMO systems
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04TPC
    • H04W52/18TPC being performed according to specific parameters
    • H04W52/26TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/265TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the quality of service QoS
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04TPC
    • H04W52/18TPC being performed according to specific parameters
    • H04W52/26TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
    • H04W52/267TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service] taking into account the information rate
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04TPC
    • H04W52/30TPC using constraints in the total amount of available transmission power
    • H04W52/34TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W52/00Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04TPC
    • H04W52/38TPC being performed in particular situations
    • H04W52/42TPC being performed in particular situations in systems with time, space, frequency or polarisation diversity

Abstract

本发明提出一种多用户MIMO分布式基站系统中能量效率最优化方法:首先给出了在基站发送功率限制和用户速率要求下系统能够支持的最大用户选择算法,该算法能够找到系统能够支持的最大用户集合;然后提出了一种新型的低复杂度的能效最大化算法,该算法同时考虑了前端链路选择问题,最后得到基站的发送预编码设计方案。本发明能够解决多用户MIMO分布式基站系统中基站发送预编码设计问题,同时合理地进行的前端链路选择,使得系统的能量效率最大,能够快速收敛到最优解。

Description

一种多用户MIMO分布式基站系统中能量效率最优化方法
技术领域
本发明涉及移动通信系统中的网络技术领域,尤其是一种多用户MIMO分布式基站系统中能量效率最优化方法。
背景技术
由于分布式基站系统可以大大提高系统频谱效率,拓宽无线网络的覆盖范围以及提高链路的可靠性,分布式系统已经被认为是下一代移动网络的主体架构。目前还没有文献对多用户MIMO分布式系统中的能效问题进行研究,主要因为优化问题比较难以求解。
发明内容
发明目的:本发明的目的在于针对现有技术存在的不足,提供一种多用户MIMO分布式天线系统中能量效率最优化的方法,该方法一方面解决了系统资源有限下用户的选择问题,也就是剔除部分用户让系统能够工作;另一方面,在给定选择用户条件下,给出了联合求解预编码矩阵以及链路选择的低复杂度算法。
技术方案:为实现上述发明目的,本发明采取的技术方案为:
一种多用户MIMO分布式基站系统中能量效率最优化方法,包括步骤:
(1)以最大化系统中的用户接入数为目标建立模型A:
Figure BDA0001219870360000011
Figure BDA0001219870360000012
Figure BDA0001219870360000013
其中,
Figure BDA0001219870360000014
表示系统中所有用户的集合,
Figure BDA0001219870360000015
K表示系统中的用户数量;
Figure BDA0001219870360000016
为基站的集合,
Figure BDA0001219870360000017
I为基站总数;
Figure BDA0001219870360000018
表示选出的用户集合;V表示
Figure BDA0001219870360000019
由所有用户预编码矩阵的集合,Vi,k为第i个基站到第k个用户的预编码矩阵;Rk(V)为用户k在预编码矩阵V下的信道容量,Rk,min为用户k的最小速率需求;Pi,max为第i个基站的功率上限;
求解模型I,得到在基站发送功率限制和用户速率要求下系统能够支持的最大用户集合
Figure BDA00012198703600000110
Figure BDA00012198703600000111
对应的预编码矩阵V*
(2)以系统能效最大化为目标问题构建模型B:
Figure BDA0001219870360000021
Figure BDA0001219870360000022
Figure BDA0001219870360000023
求解模型B,得到的预编码矩阵即为最终的发送预编码方案。
进一步的,所述步骤(1)中求解模型A的步骤包括:
(2-1)首先引入辅助变量
Figure BDA0001219870360000024
定义问题模型P1:
Figure BDA0001219870360000025
Figure BDA0001219870360000026
Figure BDA0001219870360000027
(2-2)将问题模型P1转化为凸优化问题模型P2:
Figure BDA0001219870360000028
Figure BDA0001219870360000029
Figure BDA00012198703600000210
式中,Hk表示系统中所有基站到用户k的信道矩阵;Vk表示系统中所有基站到用户k的预编码矩阵,
Figure BDA00012198703600000211
Wk为辅助矩阵;Uk为用户k的检测矩阵;tk为预设的阈值;
(2-3)初始化用户集合
Figure BDA00012198703600000212
迭代次数n=1,最大迭代次数nmax,可行预编码矩阵V(0)
(2-4)计算:
Figure BDA0001219870360000031
Figure BDA0001219870360000032
式中,σk为给定的常数,
Figure BDA0001219870360000033
的计算公式为:
Figure BDA0001219870360000034
(2-5)根据
Figure BDA0001219870360000035
求解问题模型P2,得到的解为
Figure BDA0001219870360000036
(2-6)根据
Figure BDA0001219870360000037
计算:
Figure BDA0001219870360000038
Figure BDA0001219870360000039
Figure BDA00012198703600000310
(2-7)判断是否满足n<nmax;若满足,则计算n=n+1,返回步骤(2-4);否则,输出
Figure BDA00012198703600000311
(2-8)将
Figure BDA00012198703600000312
代入问题模型P2,求得αk
(2-9)判断是否满足αk=1,
Figure BDA00012198703600000313
若满足,则根据
Figure BDA00012198703600000314
计算V*,计算
Figure BDA00012198703600000315
输出V*
Figure BDA00012198703600000316
若不满足,则找出
Figure BDA00012198703600000317
计算
Figure BDA00012198703600000318
返回步骤(2-4)。
进一步的,所述步骤(2)中求解模型B的步骤包括:
(3-1)将问题模型B转化为问题模型P3:
Figure BDA00012198703600000319
其中,η为系统能效;
Figure BDA00012198703600000320
Ek为MSE矩阵,Ek的计算公式为:
Figure BDA0001219870360000041
sk为基站发送给用户k的数据信息;
Figure BDA0001219870360000042
Figure BDA0001219870360000043
为给定的预编码矩阵;
Figure BDA0001219870360000044
为预编码矩阵的可行域,定义为:
Figure BDA0001219870360000045
Figure BDA0001219870360000046
Figure BDA0001219870360000047
Figure BDA0001219870360000048
Figure BDA0001219870360000049
(3-2)定义迭代次数为m,误差门限为ε,初始化m=1,V(0)=V*
Figure BDA00012198703600000410
给定常数δ;
(3-3)在
Figure BDA00012198703600000411
条件下,计算:
Figure BDA00012198703600000412
α=1/log(1+δ-1)>0
Figure BDA00012198703600000413
Figure BDA00012198703600000414
Figure BDA00012198703600000415
Figure BDA00012198703600000416
Figure BDA00012198703600000417
式中,Pfh为每条链路上支持一个用户速率需求所需要的功耗;
(3-4)根据η(m-1)
Figure BDA00012198703600000418
求解问题模型P3,得到解即为
Figure BDA00012198703600000419
(3-5)根据
Figure BDA0001219870360000051
计算:
Figure BDA0001219870360000052
Figure BDA0001219870360000053
Figure BDA0001219870360000054
Figure BDA0001219870360000055
(3-6)判断是否满足|η(n)(n-1)|/η(n-1)≤ε,若满足,则输出
Figure BDA0001219870360000056
并根据
Figure BDA0001219870360000057
得到最终的发送预编码矩阵;若不满足,则计算m=m+1,返回步骤(3-3)。
有益效果:与现有技术相比,本发明具有以下优势:
与现有技术相比,本发明求解了多用户分布式基站系统在各个用户速率约束和分布式接入点功率限制条件下的能效最优化问题,并提出一种新型的用户选择算法以及低复杂度预编码方案,方法简单,结果准确。
附图说明
图1是多用户MIMO分布式基站系统的网络结构图
具体实施方式
下面结合附图对本发明作更进一步的说明。
我们考虑一个多用户MIMO分布式基站系统,该系统如图1所示,系统下行的多用户分布式基站中有I个远程接入点,每个远程接入点配置有M个天线,每个用户配置有N个天线,i=1,2,…,I。当分布式接入点数目小于6时,第j个接入点的位置为(r cos(2π(j-1)/I)),r sin(2π(j-1)/I)),j=1,2,…,I,其中r=2R sin(π/I)/(3πI),否则第一个接入点位于小区中心(0,0),其他I-1个接入点位于(r cos(2π(j-1)/(I-1))),r sin(2π(j-1)/(I-1))),j=2,…,I。
该方法包括如下步骤:
步骤一、首先考虑用户接入问题。具体得是,给定每个分布式接入点的功率约束,系统最大化接入用户数,且这些接入用户满足各自速率需求。本专利给出了一种低复杂度的用户选择算法,选择的用户集合即为最终的接入用户集合。
步骤二、给定第一步中选择的用户集合下,本专利给出了一种低复杂度用户预编码优化算法,同时保证用户速率需求以及接入点功率约束需求。该算法所得的预编码矩阵即为最终的发送预编码方案。
第一阶段:
记基站和用户的集合分别记为
Figure BDA0001219870360000061
Figure BDA0001219870360000062
Figure BDA0001219870360000063
为第i个分布式接入点到第k个用户的预编码矩阵,其中d为发送数据流个数。记
Figure BDA0001219870360000064
为系统中所有基站到用户k的信道矩阵。因此用户k接收到的信号为
Figure BDA0001219870360000065
其中
Figure BDA00012198703600000616
为基站发送给用户k的数据信息,满足
Figure BDA0001219870360000066
nk为接收端的噪声向量,服从分布
Figure BDA0001219870360000067
于是用户的信道容量(nat/s/Hz)可以记为
Figure BDA0001219870360000068
其中V表示所有用户预编码矩阵的集合,
Figure BDA0001219870360000069
系统总功耗建模为Ptotal(V)=PTr(V)+PC+PF(V),其中
Figure BDA00012198703600000610
表示系统总的发送功率,PC为分布式系统的固定功耗,包括分布式节点中的电路功耗以及CPU中的信号处理功耗,PF(V)表示所有前向链路总的功耗,建模为
Figure BDA00012198703600000611
其中ε(·)表示指示函数,Pfh为每条链路上支持一个用户速率需求所需要的功耗。
第一步,本发明第一个目标是最大化用户接入数,同时保证用户的速率需求以及每个分布式接入点的功率约束,该优化问题表示为
Figure BDA00012198703600000612
Figure BDA00012198703600000613
Figure BDA00012198703600000614
其中Rk,min为用户k的最小速率需求,Pi,max为第i个分布式接入点的功率上限。
第二步,首先引入辅助变量
Figure BDA00012198703600000615
定义如下问题
Figure BDA0001219870360000071
Figure BDA0001219870360000072
Figure BDA0001219870360000073
第三步,初始化用户集合
Figure BDA0001219870360000074
其中K为系统中所有需要服务的用户数;
第四步,给定用户集合
Figure BDA0001219870360000075
求解问题(P1)得到
Figure BDA0001219870360000076
和预编码矩阵集合V;
求解问题P1的方法为:
1)将问题模型P1转化为凸优化问题模型P2:
Figure BDA0001219870360000077
Figure BDA0001219870360000078
Figure BDA0001219870360000079
2)初始化迭代次数n=1,最大迭代次数nmax,可行预编码矩阵V(0)。给定V(0),计算
Figure BDA00012198703600000710
其中,
Figure BDA00012198703600000711
的表达式为:
Figure BDA00012198703600000712
3)给定
Figure BDA00012198703600000713
求解问题(P2),求出的解即为
Figure BDA00012198703600000714
4)给定
Figure BDA00012198703600000715
计算
Figure BDA00012198703600000716
Figure BDA00012198703600000717
Figure BDA00012198703600000718
5)判断是否满足n<nmax,若满足,计算n=n+1并跳转步骤3);否则输出
Figure BDA0001219870360000081
第五步,根据
Figure BDA0001219870360000082
计算αk,如果αk=1,
Figure BDA0001219870360000083
输出预编码矩阵V*以及最终的用户集合
Figure BDA0001219870360000084
否则,找出用户
Figure BDA0001219870360000085
剔除用户k*,更新
Figure BDA0001219870360000086
返回第四步。
在第二阶段,在给定第一阶段用户选择集合下,系统优化预编码矩阵来最大化系统能效,具体数学表达为
Figure BDA0001219870360000087
Figure BDA0001219870360000088
Figure BDA0001219870360000089
其中
Figure BDA00012198703600000810
表示第一阶段输出的最优用户集合。
将问题模型B转化为问题模型P3:
Figure BDA00012198703600000811
其中,η为系统能效;
Figure BDA00012198703600000812
Ek为MSE矩阵,Ek的计算公式为:
Figure BDA00012198703600000813
sk为基站发送给用户k的数据信息;
Figure BDA00012198703600000814
Figure BDA00012198703600000815
为给定的预编码矩阵;
Figure BDA00012198703600000816
为预编码矩阵的可行域,定义为:
Figure BDA00012198703600000817
Figure BDA00012198703600000818
Figure BDA00012198703600000819
Figure BDA0001219870360000091
Figure BDA0001219870360000092
步骤一:定义迭代次数为m,误差门限为ε,初始化m=1,V(0)=V*
Figure BDA0001219870360000093
给定常数δ;
步骤二:在
Figure BDA00012198703600000918
条件下,计算:
Figure BDA0001219870360000094
α=1/log(1+δ-1)>0
Figure BDA0001219870360000095
Figure BDA0001219870360000096
Figure BDA00012198703600000919
Figure BDA0001219870360000097
Figure BDA0001219870360000098
式中,Pfh为每条链路上支持一个用户速率需求所需要的功耗;
步骤三:根据η(m-1)
Figure BDA0001219870360000099
求解问题模型P3,得到解即为
Figure BDA00012198703600000910
步骤四:根据
Figure BDA00012198703600000911
计算:
Figure BDA00012198703600000912
Figure BDA00012198703600000913
Figure BDA00012198703600000914
Figure BDA00012198703600000915
步骤五:判断是否满足|η(n)(n-1)|/η(n-1)≤ε,若满足,则输出
Figure BDA00012198703600000916
并根据
Figure BDA00012198703600000917
得到最终的发送预编码矩阵;若不满足,则计算m=m+1,返回步骤二。
以上所述仅是本发明的优选实施方式,应当指出:对于本技术领域的普通技术人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。

Claims (1)

1.一种多用户MIMO分布式基站系统中能量效率最优化方法,其特征在于,包括步骤:
(1)以最大化系统中的用户接入数为目标建立模型A:
Figure FDA0002454549290000011
s.t.
Figure FDA0002454549290000012
Figure FDA0002454549290000013
其中,
Figure FDA0002454549290000014
表示系统中所有用户的集合,
Figure FDA0002454549290000015
K表示系统中的用户数量;
Figure FDA0002454549290000016
为基站的集合,
Figure FDA0002454549290000017
I为基站总数;
Figure FDA0002454549290000018
表示选出的用户集合;V表示
Figure FDA0002454549290000019
由所有用户预编码矩阵的集合,Vi,k为第i个基站到第k个用户的预编码矩阵;Rk(V)为用户k在预编码矩阵V下的信道容量,Rk,min为用户k的最小速率需求;Pi,max为第i个基站的功率上限,F表示前向链路;
求解模型A,得到在基站发送功率限制和用户速率要求下系统能够支持的最大用户集合S*和S*对应的预编码矩阵V*;求解模型A的步骤包括:
(1-1)首先引入辅助变量{αk}k∈S,定义问题模型P1:
Figure FDA00024545492900000110
s.t.
Figure FDA00024545492900000111
Figure FDA00024545492900000112
(1-2)将问题模型P1转化为凸优化问题模型P2:
Figure FDA00024545492900000113
s.t.
Figure FDA00024545492900000114
Figure FDA00024545492900000115
Figure FDA00024545492900000116
式中,Hk表示系统中所有基站到用户k的信道矩阵;Vk表示系统中所有基站到用户k的预编码矩阵,
Figure FDA0002454549290000021
Vj表示系统中所有基站到用户j的预编码矩阵,
Figure FDA0002454549290000022
Wk为辅助矩阵;Uk为用户k的检测矩阵;tk为预设的阈值;
(1-3)初始化用户集合
Figure FDA0002454549290000023
迭代次数n=1,最大迭代次数nmax,可行预编码矩阵V(0);;
(1-4)计算:
Figure FDA0002454549290000024
Figure FDA0002454549290000025
式中,σk为给定的常数,
Figure FDA0002454549290000026
的计算公式为:
Figure FDA0002454549290000027
其中,
Figure FDA0002454549290000028
sk为基站发送给用户k的数据信息;
(1-5)根据
Figure FDA0002454549290000029
求解问题模型P2,得到的解为
Figure FDA00024545492900000210
(1-6)根据
Figure FDA00024545492900000211
计算:
Figure FDA00024545492900000212
Figure FDA00024545492900000213
Figure FDA00024545492900000214
(1-7)判断是否满足n<nmax;若满足,则计算n=n+1,返回步骤(1-4);否则,输出
Figure FDA00024545492900000215
(1-8)将
Figure FDA00024545492900000216
代入问题模型P2,求得αk
(1-9)判断是否满足αk=1,
Figure FDA0002454549290000031
若满足,则根据
Figure FDA0002454549290000032
计算V*,计算
Figure FDA0002454549290000033
输出V*
Figure FDA0002454549290000034
若不满足,则找出
Figure FDA0002454549290000035
计算
Figure FDA0002454549290000036
Figure FDA0002454549290000037
表示从集合
Figure FDA0002454549290000038
中剔除k*,返回步骤(1-4);
(2)以系统能效最大化为目标问题构建模型B:
Figure FDA0002454549290000039
s.t.
Figure FDA00024545492900000310
Figure FDA00024545492900000311
其中,Ptotal(V)表示系统总功耗;求解模型B,得到的预编码矩阵即为最终的发送预编码方案;求解模型B的步骤包括:
(2-1)将问题模型B转化为问题模型P3:
(P3):
Figure FDA00024545492900000312
其中,η为系统能效;hk(V)=log|Wk|-Tr(WkEk)+d,
Figure FDA00024545492900000313
d为发送数据流个数,Ek为MSE矩阵,Ek的计算公式为:
Figure FDA00024545492900000314
sk为基站发送给用户k的数据信息;
Figure FDA00024545492900000315
Figure FDA00024545492900000316
为给定的预编码矩阵;
Figure FDA00024545492900000317
为预编码矩阵的可行域,定义为:
Figure FDA00024545492900000318
Figure FDA00024545492900000319
Figure FDA00024545492900000320
Figure FDA0002454549290000041
Figure FDA0002454549290000042
(2-2)定义迭代次数为m,误差门限为ε,初始化m=1,V(0)=V*
Figure FDA0002454549290000043
给定常数δ;
(2-3)在
Figure FDA0002454549290000044
条件下,计算:
Figure FDA0002454549290000045
α=1/log(1+δ-1)>0
Figure FDA0002454549290000046
Figure FDA0002454549290000047
β=-α|S|IPfh logδ+PC
Figure FDA0002454549290000048
Figure FDA0002454549290000049
式中,Pfh为每条链路上支持一个用户速率需求所需要的功耗;PC表示分布式系统的固定功耗;
(2-4)根据η(m-1)
Figure FDA00024545492900000410
求解问题模型P3,得到解即为
Figure FDA00024545492900000411
(2-5)根据
Figure FDA00024545492900000412
计算:
Figure FDA00024545492900000413
Figure FDA00024545492900000414
Figure FDA00024545492900000415
Figure FDA00024545492900000416
(2-6)判断是否满足|η(R)(n-1)|/η(n-1)≤ε,若满足,则输出
Figure FDA00024545492900000417
并根据
Figure FDA00024545492900000418
得到最终的发送预编码矩阵;若不满足,则计算m=m+1,返回步骤(2-3)。
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103997775A (zh) * 2014-06-03 2014-08-20 东南大学 频分复用多用户mimo能效优化方法
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CN105933971A (zh) * 2016-05-16 2016-09-07 重庆邮电大学 一种适用于大规模多输入多输出系统的能效优化方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"Energy Effcient Transmission for Multicast Services in MISO Distributed Antenna Systems";Hong Ren等;《IEEE COMMUNICATIONS TETTERS》;20160430;全文 *

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