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CN115460616A - A Genetic Algorithm Based User Grouping Method for Millimeter Wave Massive MIMO System - Google Patents

A Genetic Algorithm Based User Grouping Method for Millimeter Wave Massive MIMO SystemDownload PDF

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CN115460616A
CN115460616ACN202210691654.8ACN202210691654ACN115460616ACN 115460616 ACN115460616 ACN 115460616ACN 202210691654 ACN202210691654 ACN 202210691654ACN 115460616 ACN115460616 ACN 115460616A
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朱鹏程
江鹏
郑钦元
尤肖虎
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Abstract

Translated fromChinese

本发明提供了一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，采用优胜劣汰遗传机制来行用户分组。从遗传的观点来看，动态地调整用户的分组方式来最终实现稳定分组是一个遗传进化的过程，在这个过程中，不同的用户分组构成一代中的不同精英，精英的基因(或子个体)交叉来产生新的子代，对父代和子代同时进行择优处理来完成自然选择机制，最终得到稳定的用户分组。本发明相比于早期的启发式方法，在性能上具有较大的优势；相比于贪婪式方法，在性能相近的同时显著降低了其复杂度。

Figure 202210691654

The invention provides a method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm, and uses a genetic mechanism of survival of the fittest to perform user grouping. From a genetic point of view, it is a process of genetic evolution to dynamically adjust the grouping method of users to finally achieve stable grouping. In this process, different user groups constitute different elites in a generation, and elite genes (or sub-individuals) Cross over to generate new offspring, and perform optimal processing on the parent and offspring at the same time to complete the natural selection mechanism, and finally obtain a stable user group. Compared with the early heuristic method, the present invention has greater advantages in performance; compared with the greedy method, it significantly reduces its complexity while having similar performance.

Figure 202210691654

Description

Translated fromChinese
一种基于遗传算法的毫米波大规模MIMO系统用户分组方法A Genetic Algorithm Based User Grouping Method for Millimeter Wave Massive MIMO System

技术领域technical field

本发明涉及无线通信技术领域，具体涉及一种基于遗传算法的毫米波大规模MIMO系统用户分组方法。The invention relates to the technical field of wireless communication, in particular to a method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm.

背景技术Background technique

随着社会的发展科技的进步，人们对于移动通信流量和连接的需求也日益增长。作为未来移动通信的关键技术之一，毫米波(Millimeter-Wave,mmWave)大规模MIMO(Multiple-Input andMultiple-Output,MIMO)技术正蓬勃发展。其中，虽然毫米波段具有更高的路径损耗，但其较短的波长让大规模MIMO技术更易于实现，同时大规模天线阵列带来的增益也一定程度上弥补了其高路径损耗的缺点，使得毫米波技术与大规模MIMO技术相得益彰。With the development of society and the advancement of technology, people's demand for mobile communication traffic and connections is also increasing. As one of the key technologies for future mobile communications, millimeter-wave (Millimeter-Wave, mmWave) massive MIMO (Multiple-Input and Multiple-Output, MIMO) technology is booming. Among them, although the millimeter wave band has a higher path loss, its shorter wavelength makes the massive MIMO technology easier to implement, and the gain brought by the large-scale antenna array also makes up for its high path loss to a certain extent. Millimeter wave technology complements massive MIMO technology.

考虑到未来物联网对于连接数与带宽的双重需求，单一多址技术已经难以满足庞大连接需求。在此背景下，混合使用多种多址技术以提供更多的连接数量，且针对不同多址技术进行用户分配的优化以提高系统的和速率成为一种现实的选择。一种普遍的做饭是将时分多址 (Time Division Multiple Access,TDMA)与频分多址(Frequency DivisionMultiple Access, FDMA)等正交多址技术与空分多址(Space Division MultipleAccess,ADMA)相结合。然而空分多址技术虽然能带来巨大的多用户速率增益，却容易受到用户信道相关性的影响，相关性强的用户组会严重影响其速率表现。针对此状，很多学者开展了用户分组的研究，其中以半正交用户分组和贪婪分组为代表的算法取得了良好的效果，但仍未达到分组增益的极限。Considering the dual requirements of the Internet of Things for the number of connections and bandwidth in the future, it is already difficult for a single multiple access technology to meet the huge connection requirements. In this context, it is a realistic choice to mix and use multiple multiple access technologies to provide more connections, and to optimize user allocation for different multiple access technologies to improve system performance and speed. A common method is to combine orthogonal multiple access technologies such as Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) with Space Division Multiple Access (ADMA). combined. However, although space division multiple access technology can bring huge multi-user rate gain, it is easily affected by user channel correlation, and user groups with strong correlation will seriously affect its rate performance. In response to this situation, many scholars have carried out research on user grouping, among which algorithms represented by semi-orthogonal user grouping and greedy grouping have achieved good results, but they have not yet reached the limit of grouping gain.

发明内容Contents of the invention

技术问题technical problem

为解决上述问题，本发明提供了一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，通过合理分配用户，极大提高了混合多址系统中的系统和速率。In order to solve the above problems, the present invention provides a method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm. By rationally allocating users, the system and rate in a hybrid multiple access system are greatly improved.

技术方案Technical solutions

为了达到上述目的，本发明提供如下技术方案：In order to achieve the above object, the present invention provides the following technical solutions:

一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，包括以下步骤：A method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm, comprising the following steps:

S1：建立毫米波大规模MIMO系统用户分组的数学模型；S1: Establish a mathematical model for user grouping in millimeter wave massive MIMO systems;

S2：建立分组基础上最大化系统和速率问题；S2: Maximize the system and rate problem based on the establishment of grouping;

S3：利用遗传算法给出所述问题的分组算法。S3: Using a genetic algorithm to give a grouping algorithm for the problem.

进一步的，所述步骤S1具体包括如下子步骤：Further, the step S1 specifically includes the following sub-steps:

S1.1：配置一个服务了K个单天线用户，配备N天线ULA的毫米波全数字时分双工大规模MIMO系统，其中基站单元已通过信道估计得知用户的信道信息；S1.1: Configure a millimeter-wave all-digital time-division duplex massive MIMO system serving K single-antenna users and equipped with N-antenna ULA, in which the base station unit has learned the channel information of the users through channel estimation;

S1.2：建立数据传输模型，设下行信道为H＝[h1T,h2T,...,hKT]T，其中hk为从N天线ULA 到用户k的信道，依据Saleh-Valenzuela毫米波信道模型建模为：S1.2: Establish a data transmission model, set the downlink channel as H=[h1T ,h2T ,...,hKT ]T , where hk is the channel from N antenna ULA to user k, according to Saleh -Valenzuela mmWave channel model modeled as:

Figure RE-GDA0003893136790000021
Figure RE-GDA0003893136790000021

其中

Figure RE-GDA0003893136790000022
为用户k第l径的由路径损耗和阴影衰落组成的复增益，L为多径数量，l＝0代表视距分量，l≥1代表非视距分量，
Figure RE-GDA0003893136790000023
为N天线ULA的阵列响应向量，表示为：in
Figure RE-GDA0003893136790000022
is the complex gain composed of path loss and shadow fading of the lth path of user k, L is the number of multipaths, l=0 represents the line-of-sight component, l≥1 represents the non-line-of-sight component,
Figure RE-GDA0003893136790000023
is the array response vector of N-antenna ULA, expressed as:

Figure RE-GDA0003893136790000024
Figure RE-GDA0003893136790000024

其中，

Figure RE-GDA0003893136790000025
为用户k的l径的到达角，(·)T代表转置；in,
Figure RE-GDA0003893136790000025
is the angle of arrival of user k’s path l, ( )T represents the transpose;

设pk为其预编码向量，则其接受的数据速率为：Let pk be its precoding vector, then its accepted data rate is:

Figure RE-GDA0003893136790000026
Figure RE-GDA0003893136790000026

其中p为发射信号总功率，σ2为噪声方差，log表示对数运算，|·|表示模值运算；Among them, p is the total power of the transmitted signal,σ2 is the noise variance, log means logarithmic operation, and |·| means modulus operation;

S1.3：利用正交多址将K个用户平均分为G＞1组，同组用户中采用SDMA进行服务，设

Figure RE-GDA0003893136790000027
为用户全集，则该分组规则可以描述为：S1.3: Use orthogonal multiple access to divide K users into groups G>1 on average, use SDMA to serve users in the same group, set
Figure RE-GDA0003893136790000027
is the complete set of users, then the grouping rules can be described as:

Figure RE-GDA0003893136790000028
Figure RE-GDA0003893136790000028

Figure RE-GDA0003893136790000029
Figure RE-GDA0003893136790000029

Figure RE-GDA00038931367900000210
Figure RE-GDA00038931367900000210

S1.4：分组

Figure RE-GDA00038931367900000211
中用户k的速率表示为：S1.4: Grouping
Figure RE-GDA00038931367900000211
The rate of user k in is expressed as:

Figure RE-GDA00038931367900000212
Figure RE-GDA00038931367900000212

其中，p为发射信号总功率，hk用户k的下行信道，σ2为噪声方差，则分组

Figure RE-GDA00038931367900000213
的和速率表述为：Among them, p is the total power of the transmitted signal, hk is the downlink channel of user k, σ2 is the noise variance, then the group
Figure RE-GDA00038931367900000213
The sum rate of is expressed as:

Figure RE-GDA0003893136790000031
Figure RE-GDA0003893136790000031

假设分组

Figure RE-GDA0003893136790000032
在正交多址系统中的资源分配系数为Sg＞0,
Figure RE-GDA0003893136790000033
则系统的和速率表述为：hypothetical grouping
Figure RE-GDA0003893136790000032
The resource allocation coefficient in the orthogonal multiple access system is Sg >0,
Figure RE-GDA0003893136790000033
Then the sum rate of the system is expressed as:

Figure RE-GDA0003893136790000034
Figure RE-GDA0003893136790000034

S2：建立系统最大化和速率问题：S2: Build system maximization and rate problems:

Figure RE-GDA0003893136790000035
Figure RE-GDA0003893136790000035

Figure RE-GDA0003893136790000036
Figure RE-GDA0003893136790000036

Figure RE-GDA0003893136790000037
Figure RE-GDA0003893136790000037

Figure RE-GDA0003893136790000038
Figure RE-GDA0003893136790000038

S3.1：设定迭代次数ITER，精英数量E，分组数量G，子代数量S，交叉数量Mut以及门限γ；S3.1: Set the number of iterations ITER, the number of elites E, the number of groups G, the number of offspring S, the number of crossovers Mut and the threshold γ;

S3.2：首先产生初始父代，即对每个精英e，根据分组数量G随机产生分组

Figure RE-GDA0003893136790000039
S3.2: First generate the initial parent generation, that is, for each elite e, randomly generate groups according to the number of groups G
Figure RE-GDA0003893136790000039

S3.3：开始进行迭代，在每次迭代中进行如下操作：S3.3: Start to iterate, and perform the following operations in each iteration:

S3.3.1：首先对每个父代精英e生成S个子代，具体对子代s的操作如下：S3.3.1: First, generate S offspring for each parent elite e, and the specific operation on offspring s is as follows:

S3.3.1.1：首先令子代s为

Figure RE-GDA00038931367900000310
并随机选择交叉数量mut∈{1,2,...,Mut}；S3.3.1.1: First let the child s be
Figure RE-GDA00038931367900000310
And randomly select the number of intersections mut∈{1,2,...,Mut};

S3.3.1.2：在每次交叉中，随机选择组号和需要交叉的两用户进行交叉，从而完成变异；S3.3.1.2: In each crossover, randomly select the group number and the two users who need to crossover to complete the mutation;

S3.3.1.3：依据子代s的分组信息计算其和速率R；S3.3.1.3: Calculate the sum rate R according to the grouping information of the child s;

S3.3.2：对所有父代完成上述操作后，全面考虑父代子代并保留和速率最大的E个精英；S3.3.2: After completing the above operations for all parents, fully consider the parents and children and retain E elites with the highest sum rate;

S3.4：如果连续多次优化程度小于阈值γ，则结束迭代，得到和速率最大的分组方式即为最终分组方式，否则跳至S对上一步中得到的E个精英进行下一次迭代。S3.4: If the degree of optimization is less than the threshold γ for several consecutive times, then the iteration ends, and the grouping method with the highest sum rate is obtained as the final grouping method, otherwise, skip to S and perform the next iteration on the E elites obtained in the previous step.

有益效果Beneficial effect

本发明公开了一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，采用优胜劣汰遗传机制来行用户分组。从遗传的观点来看，动态地调整用户的分组方式来最终实现稳定分组是一个遗传进化的过程，在这个过程中，不同的用户分组构成一代中的不同精英，精英的基因(或子个体)交叉来产生新的子代，对父代和子代同时进行择优处理来完成自然选择机制，最终得到稳定的用户分组。本发明相比于早期的启发式方法，在性能上具有较大的优势；相比于贪婪式方法，在性能相近的同时显著降低了其复杂度。The invention discloses a method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm, and adopts a genetic mechanism of survival of the fittest to perform user grouping. From a genetic point of view, it is a process of genetic evolution to dynamically adjust the grouping method of users to finally achieve stable grouping. In this process, different user groups constitute different elites in a generation, and elite genes (or sub-individuals) Crossover to generate new offspring, the parent and offspring are selected at the same time to complete the natural selection mechanism, and finally a stable user grouping is obtained. Compared with the earlier heuristic method, the present invention has greater advantages in performance; compared with the greedy method, it significantly reduces its complexity while having similar performance.

附图说明Description of drawings

图1为本发明提供的基于遗传算法的毫米波大规模MIMO系统用户分组方法流程图。FIG. 1 is a flowchart of a method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm provided by the present invention.

图2为本发明实施例在ZF预编码下的和速率仿真对比图。FIG. 2 is a comparison diagram of sum rate simulation under ZF precoding according to an embodiment of the present invention.

图3为本发明实施例在MMSE预编码下的和速率仿真对比图。FIG. 3 is a comparison diagram of sum rate simulation under MMSE precoding according to an embodiment of the present invention.

具体实施方式detailed description

以下将结合具体实施例对本发明提供的技术方案进行详细说明，应理解下述具体实施方式仅用于说明本发明而不用于限制本发明的范围。The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

为了达到上述目的，本发明提供如下技术方案：In order to achieve the above object, the present invention provides the following technical solutions:

一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，包括：A method for grouping users of a millimeter wave massive MIMO system based on a genetic algorithm, comprising:

S1：建立毫米波大规模MIMO系统用户分组的数学模型；S1: Establish a mathematical model for user grouping in millimeter wave massive MIMO systems;

S2：建立分组基础上最大化系统和速率问题；S2: Maximize the system and rate problem based on the establishment of grouping;

S3：利用遗传算法给出所述问题的分组算法。S3: Using a genetic algorithm to give a grouping algorithm for the problem.

进一步的，所述步骤S1具体包括如下子步骤：Further, the step S1 specifically includes the following sub-steps:

S1.1：配置一个服务了K个单天线用户，配备N＝128天线ULA的毫米波全数字时分双工大规模MIMO系统，数字预编码方式考虑ZF(Zero-forcingBeamforming,ZF)和MMSE(MinimumMeanSquareError,MMSE)分组数G＝4。考虑到实际基站三扇区布局，即每个阵列只服务正面约120°的用户，将用户生成的范围限定在[1/6π,5/6π]之间；S1.1: Configure a millimeter-wave all-digital time-division duplex massive MIMO system serving K single-antenna users and equipped with N=128 antenna ULAs. The digital precoding method considers ZF (Zero-forcing Beamforming, ZF) and MMSE (Minimum Mean Square Error , MMSE) The number of groups G=4. Considering the three-sector layout of the actual base station, that is, each array only serves users with a frontal angle of about 120°, the range of user generation is limited to [1/6π, 5/6π];

S1.2：建立数据传输模型，设下行信道为H＝[h1T,h2T,...,hKT]T，其中hk为从N天线ULA 到用户k的信道，依据Saleh-Valenzuela毫米波信道模型建模为：S1.2: Establish a data transmission model, set the downlink channel as H=[h1T ,h2T ,...,hKT ]T , where hk is the channel from N antenna ULA to user k, according to Saleh -Valenzuela mmWave channel model modeled as:

Figure RE-GDA0003893136790000041
Figure RE-GDA0003893136790000041

其中

Figure RE-GDA0003893136790000042
为用户k第l径的由路径损耗和阴影衰落组成的复增益，L为多径数量，l＝0代表视距分量，l≥1代表非视距分量，其中路径损耗为：in
Figure RE-GDA0003893136790000042
is the complex gain composed of path loss and shadow fading of the user's k-th path, L is the number of multipaths, l=0 represents the line-of-sight component, and l≥1 represents the non-line-of-sight component, where the path loss is:

PL(dB)＝-35.4+26log10(d)+20log10(fc)PL(dB)＝-35.4+26log10 (d)+20log10 (fc )

其中，d为用户到基站的距离，单位为米，fc＝30GHz为载波频率。阴影衰落具有3dB标准差。

Figure RE-GDA0003893136790000043
为N天线ULA的阵列响应向量，表示为：Wherein, d is the distance from the user to the base station in meters, and fc =30 GHz is the carrier frequency. Shadow fading has a 3dB standard deviation.
Figure RE-GDA0003893136790000043
is the array response vector of N-antenna ULA, expressed as:

Figure RE-GDA0003893136790000044
Figure RE-GDA0003893136790000044

其中，

Figure RE-GDA0003893136790000045
为用户k的l径的到达角，(·)T代表转置。in,
Figure RE-GDA0003893136790000045
is the angle of arrival of user k's path l, (·)T stands for transpose.

设pk为其预编码向量，则其接受的数据速率为：Let pk be its precoding vector, then its accepted data rate is:

Figure RE-GDA0003893136790000051
Figure RE-GDA0003893136790000051

其中p为发射信号总功率设置为p＝50dBm，σ2为噪声方差设置为σ2＝-174dBm/Hz，log 表示对数运算，|·|表示模值运算；Among them, p is set to p=50dBm for the total power of the transmitted signal, σ2 is set to σ2 =-174dBm/Hz for the noise variance, log means logarithmic operation, and |·| means modulus operation;

S1.3：本发明考虑SDMA(如ADMA)混合理想TDMA，不同用户分组服务于不同的时域或频域资源上以消除干扰，在同组用户中采用SDMA技术，因此需要合理进行用户分组以实现同组用户间信道的弱相关性从而提高资源利用效率。考虑该用户分组方法将K个用户平均分为G＞1组，设

Figure RE-GDA0003893136790000052
为用户全集，则该分组规则可以描述为：S1.3: The present invention considers that SDMA (as ADMA) mixes ideal TDMA, and different user groups serve on different time domain or frequency domain resources to eliminate interference, adopt SDMA technology in the same group of users, therefore need to carry out user grouping reasonably to Realize the weak correlation of channels among users in the same group to improve resource utilization efficiency. Consider this user grouping method to divide K users into groups G>1 on average, set
Figure RE-GDA0003893136790000052
is the complete set of users, then the grouping rules can be described as:

Figure RE-GDA0003893136790000053
Figure RE-GDA0003893136790000053

Figure RE-GDA0003893136790000054
Figure RE-GDA0003893136790000054

Figure RE-GDA0003893136790000055
Figure RE-GDA0003893136790000055

S1.4：分组

Figure RE-GDA0003893136790000056
中用户k的速率表示为：S1.4: Grouping
Figure RE-GDA0003893136790000056
The rate of user k in is expressed as:

Figure RE-GDA0003893136790000057
Figure RE-GDA0003893136790000057

其中，p为发射信号总功率，hk用户k的下行信道，σ2为噪声方差，则分组

Figure RE-GDA0003893136790000058
的和速率表述为：Among them, p is the total power of the transmitted signal, hk is the downlink channel of user k, σ2 is the noise variance, then the group
Figure RE-GDA0003893136790000058
The sum rate of is expressed as:

Figure RE-GDA0003893136790000059
Figure RE-GDA0003893136790000059

假设分组

Figure RE-GDA00038931367900000510
在正交多址系统中的资源分配系数为Sg＞0,
Figure RE-GDA00038931367900000511
则系统的和速率表述为：hypothetical grouping
Figure RE-GDA00038931367900000510
The resource allocation coefficient in the orthogonal multiple access system is Sg >0,
Figure RE-GDA00038931367900000511
Then the sum rate of the system is expressed as:

Figure RE-GDA00038931367900000512
Figure RE-GDA00038931367900000512

S2：建立系统最大化和速率问题：S2: Build system maximization and rate problems:

Figure RE-GDA00038931367900000513
Figure RE-GDA00038931367900000513

Figure RE-GDA00038931367900000514
Figure RE-GDA00038931367900000514

Figure RE-GDA00038931367900000515
Figure RE-GDA00038931367900000515

Figure RE-GDA00038931367900000516
Figure RE-GDA00038931367900000516

S3包括如下步骤：S3 includes the following steps:

S3.1:设定迭代次数ITER，精英数量E，分组数量G，子代数量S，交叉数量Mut以及门限γ；S3.1: Set the number of iterations ITER, the number of elites E, the number of groups G, the number of offspring S, the number of crossovers Mut and the threshold γ;

S3.2：首先产生初始父代，即对每个精英e，根据分组数量G随机产生分组

Figure RE-GDA0003893136790000061
S3.2: First generate the initial parent generation, that is, for each elite e, randomly generate groups according to the number of groups G
Figure RE-GDA0003893136790000061

S3.3：开始进行迭代，在每次迭代中进行如下操作：S3.3: Start to iterate, and perform the following operations in each iteration:

S3.3.1：首先对每个父代精英e生成S个子代，具体对子代s的操作如下：S3.3.1: First, generate S offspring for each parent elite e, and the specific operation on offspring s is as follows:

S3.3.1.1：首先令子代s为

Figure RE-GDA0003893136790000062
并随机选择交叉数量mut∈{1,2,...,Mut}；S3.3.1.1: First let the child s be
Figure RE-GDA0003893136790000062
And randomly select the number of intersections mut∈{1,2,...,Mut};

S3.3.1.2：在每次交叉中，随机选择组号和需要交叉的两用户进行交叉，从而完成变异；S3.3.1.2: In each crossover, randomly select the group number and the two users who need to crossover to complete the mutation;

S3.3.1.3：依据子代s的分组信息计算其和速率R；S3.3.1.3: Calculate the sum rate R according to the grouping information of the child s;

S3.3.2：对所有父代完成上述操作后，全面考虑父代子代并保留和速率最大的E个精英；S3.3.2: After completing the above operations for all parents, fully consider the parents and children and retain E elites with the highest sum rate;

S3.4：如果连续多次优化程度小于阈值γ，则结束迭代，得到和速率最大的分组方式即为最终分组方式，否则跳至S对上一步中得到的E个精英进行下一次迭代。S3.4: If the degree of optimization is less than the threshold γ for several consecutive times, then the iteration ends, and the grouping method with the highest sum rate is obtained as the final grouping method, otherwise, skip to S and perform the next iteration on the E elites obtained in the previous step.

图2、3(分别采用ZF和MMSE预编码)为本具体实施方法的仿真结果与常见算法结果对比图，图中随机分组算法作为分组算法对比的基线，半正交分组算法曲线为应用半正交分组算法的第一组的速率，也是半正交方法的上界，各类基于半正交分组算法改进的分组算法速率均无法超过该界限。Figures 2 and 3 (using ZF and MMSE precoding respectively) are the comparison charts between the simulation results of this specific implementation method and the results of common algorithms. The rate of the first group of the orthogonal grouping algorithm is also the upper bound of the semi-orthogonal method, and the rate of various improved grouping algorithms based on the semi-orthogonal grouping algorithm cannot exceed this limit.

仿真实验表明，本算法在ZF及MMSE场景下均有良好的表现。在ZF中，用户数较低时略低于半正交分组算法，但由于其仅为第一组成绩，因此略微的优势不具备参考价值。随着用户数的增长，本算法的性能逐渐反超，并对其他算法产生了性能上的压制，直至达到峰值后，贪婪分组算法有了部分反超，且三种算法明显好于随机分组算法，且本算法与贪婪分组算法取得了极大的性能改善；在MMSE中，与ZF的结论类似，在前半段被半正交分组算法略微超过，峰值附近对贪婪分组算法有略微优势，对半正交分组算法与随机分组算法则是压制状态。在超过峰值后逐渐被贪婪分组算法反超。Simulation experiments show that this algorithm has good performance in both ZF and MMSE scenarios. In ZF, when the number of users is low, it is slightly lower than the semi-orthogonal grouping algorithm, but because it is only the first group of results, the slight advantage has no reference value. With the increase of the number of users, the performance of this algorithm gradually overtakes, and suppresses the performance of other algorithms. After reaching the peak value, the greedy grouping algorithm partially overtakes, and the three algorithms are obviously better than the random grouping algorithm, and This algorithm and the greedy grouping algorithm have achieved great performance improvement; in MMSE, similar to the conclusion of ZF, it is slightly surpassed by the semi-orthogonal grouping algorithm in the first half, and there is a slight advantage to the greedy grouping algorithm near the peak, and to the semi-orthogonal grouping algorithm. The grouping algorithm and the random grouping algorithm are in a suppressed state. After exceeding the peak value, it is gradually overtaken by the greedy grouping algorithm.

Claims (7)

Translated fromChinese
1.一种基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于，包括以下步骤：1. a method for grouping users of a millimeter wave massive MIMO system based on genetic algorithm, is characterized in that, comprises the following steps:S1：建立毫米波大规模MIMO系统用户分组的数学模型；S1: Establish a mathematical model for user grouping in millimeter wave massive MIMO systems;S2：建立分组基础上最大化系统和速率问题；S2: Maximize the system and rate problem based on the establishment of grouping;S3：利用遗传算法给出所述问题的分组算法。S3: Using a genetic algorithm to give a grouping algorithm for the problem.2.根据权利要求1所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于，所述步骤S1具体包括如下子步骤：2. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 1, wherein said step S1 specifically includes the following sub-steps:S1.1：配置一个服务K个单天线用户，配备N天线ULA的毫米波全数字时分双工大规模MIMO系统，其中基站单元已通过信道估计得知用户的信道信息；S1.1: Configure a millimeter-wave full-digital time-division duplex massive MIMO system serving K single-antenna users and equipped with N-antenna ULA, in which the base station unit has learned the channel information of the users through channel estimation;S1.2：建立数据传输模型，设下行信道为H＝[h1T,h2T,...,hKT]T，其中hk为从N天线ULA到用户k的信道，依据Saleh-Valenzuela毫米波信道模型建模为：S1.2: Establish a data transmission model, set the downlink channel as H=[h1T ,h2T ,...,hKT ]T , where hk is the channel from N antenna ULA to user k, according to Saleh -Valenzuela mmWave channel model modeled as:
Figure FDA0003700109880000011
Figure FDA0003700109880000011
其中
Figure FDA0003700109880000012
为用户k第l径的由路径损耗和阴影衰落组成的复增益，L为多径数量，l＝0代表视距分量，l≥1代表非视距分量，
Figure FDA0003700109880000013
为N天线ULA的阵列响应向量，表示为：in
Figure FDA0003700109880000012
is the complex gain composed of path loss and shadow fading of the lth path of user k, L is the number of multipaths, l=0 represents the line-of-sight component, l≥1 represents the non-line-of-sight component,
Figure FDA0003700109880000013
is the array response vector of N-antenna ULA, expressed as:
Figure FDA0003700109880000014
Figure FDA0003700109880000014
其中，
Figure FDA0003700109880000015
为用户k的l径的到达角，(·)T代表转置；in,
Figure FDA0003700109880000015
is the angle of arrival of user k’s path l, ( )T represents the transpose;设pk为其预编码向量，则其接受的数据速率为：Let pk be its precoding vector, then its accepted data rate is:
Figure FDA0003700109880000016
Figure FDA0003700109880000016
其中p为发射信号总功率，σ2为噪声方差，log表示对数运算，|·|表示模值运算；Among them, p is the total power of the transmitted signal,σ2 is the noise variance, log means logarithmic operation, and |·| means modulus operation;S1.3：利用正交多址将K个用户平均分为G＞1组，同组用户中采用SDMA进行服务，设U为用户全集，则该分组规则描述为：S1.3: Use orthogonal multiple access to divide K users into groups G>1 on average, and use SDMA to serve users in the same group. Let U be the complete set of users, then the grouping rule is described as:U＝{1,2,...,K}U={1,2,...,K}U＝U1∪U2∪...∪UGU＝U1 ∪U2 ∪...∪UG
Figure FDA0003700109880000021
Figure FDA0003700109880000021
S1.4：分组Ug中用户k的速率表示为：S1.4: The rate of user k in group Ug is expressed as:
Figure FDA0003700109880000022
Figure FDA0003700109880000022
其中，p为发射信号总功率，hk用户k的下行信道，σ2为噪声方差，则分组Ug的和速率表述为：Among them, p is the total power of the transmitted signal, hk is the downlink channel of user k, and σ2 is the noise variance, then the sum rate of the group Ug is expressed as:
Figure FDA0003700109880000023
Figure FDA0003700109880000023
假设分组Ug在正交多址系统中的资源分配系数为Sg＞0,
Figure FDA0003700109880000024
则系统的和速率表述为：Suppose the resource allocation coefficient of group Ug in the orthogonal multiple access system is Sg >0,
Figure FDA0003700109880000024
Then the sum rate of the system is expressed as:
Figure FDA0003700109880000025
Figure FDA0003700109880000025
3.根据权利要求2所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于：所述步骤S1.2中的预编码向量pk由线性预编码方法MRT、ZF或MMSE生成。3. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 2, wherein the precoding vector pk in the step S1.2 is determined by the linear precoding method MRT, ZF or MMSE generate.4.根据权利要求2所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于：所述步骤S2具体表述为：4. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 2, wherein: said step S2 is specifically expressed as:将资源在各组中平均分配，此时设
Figure FDA0003700109880000026
最大化系统和速率问题表述为：Allocate the resources equally among the groups, at this time set the
Figure FDA0003700109880000026
The maximization system and rate problem is formulated as:
Figure FDA0003700109880000027
Figure FDA0003700109880000027
5.根据权利要求1所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于：所述步骤S3具体包括如下子步骤：5. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 1, wherein the step S3 specifically includes the following sub-steps:S3.1:设定迭代次数ITER，精英数量E，分组数量G，子代数量S，交叉数量Mut以及门限γ；S3.1: Set the number of iterations ITER, the number of elites E, the number of groups G, the number of offspring S, the number of crossovers Mut and the threshold γ;S3.2：首先产生初始父代，即对每个精英e，根据分组数量G随机产生分组U:,e；S3.2: First generate the initial parent generation, that is, for each elite e, randomly generate a group U:, e according to the group number G;S3.3：开始进行迭代，在每次迭代中进行如下操作：S3.3: Start to iterate, and perform the following operations in each iteration:S3.3.1：首先对每个父代精英e生成S个子代，具体对子代s的操作如下：S3.3.1: First, generate S offspring for each parent elite e, and the specific operation on offspring s is as follows:S3.3.1.1：首先令子代s为U:,E+S(e-1)+s＝U:,e并随机选择交叉数量mut∈{1,2,...,Mut}；S3.3.1.1: First let the offspring s be U:, E+S(e-1)+s = U:, e and randomly select the number of intersections mut∈{1,2,...,Mut};S3.3.1.2：在每次交叉中，随机选择组号和需要交叉的两用户进行交叉，从而完成变异；S3.3.1.2: In each crossover, randomly select the group number and the two users who need to crossover to complete the mutation;S3.3.1.3：依据子代s的分组信息计算其和速率R；S3.3.1.3: Calculate the sum rate R according to the grouping information of the child s;S3.3.2：对所有父代完成上述操作后，全面考虑父代子代并保留和速率最大的E个精英；S3.3.2: After completing the above operations for all parents, fully consider the parents and children and retain E elites with the highest sum rate;S3.4：如果连续多次优化程度小于阈值γ，则结束迭代，得到和速率最大的分组方式即为最终分组方式，否则跳至S对上一步中得到的E个精英进行下一次迭代。S3.4: If the degree of optimization is less than the threshold γ for several consecutive times, then the iteration ends, and the grouping method with the highest sum rate is obtained as the final grouping method, otherwise, skip to S and perform the next iteration on the E elites obtained in the previous step.6.根据权利要求5所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于：精英在基因内部进行交叉，即产生个体的方式是让一个基因内的不同组相互交换成员。6. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 5, characterized in that: elites cross within genes, that is, the way to generate individuals is to allow different groups within a gene to exchange members .7.根据权利要求5所述的基于遗传算法的毫米波大规模MIMO系统用户分组方法，其特征在于：在完成变异、计算和速率进行选择时，为了保证遗传算法中父辈的优秀基因能够保留而不被淘汰，在择优处理中让父辈与子辈同时参与选择，即将父辈与子辈的和速率统一进行排序，并保留和速率最大的前E个精英。7. The method for grouping users of a millimeter-wave massive MIMO system based on a genetic algorithm according to claim 5, characterized in that: when completing mutation, calculation and rate selection, in order to ensure that the excellent genes of the parents in the genetic algorithm can be retained Not to be eliminated, let the parent generation and the child generation participate in the selection at the same time in the selection process, that is, the sum rate of the parent generation and the child generation will be sorted uniformly, and the top E elites with the highest sum rate will be retained.
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