CN112564746B - Optimal GEE-based power distribution method in CF mmWave mMIMO system - Google Patents

Optimal GEE-based power distribution method in CF mmWave mMIMO system Download PDF

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CN112564746B
CN112564746B CN202011443980.4A CN202011443980A CN112564746B CN 112564746 B CN112564746 B CN 112564746B CN 202011443980 A CN202011443980 A CN 202011443980A CN 112564746 B CN112564746 B CN 112564746B
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何云
申敏
郑建宏
郑焕平
陈吕洋
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H04B7/0426—Power distribution
    • H04B7/043—Power distribution using best eigenmode, e.g. beam forming or beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to an optimal GEE-based power distribution method in a CF mmWave mMIMO system, belonging to the technical field of wireless communication. The method comprises the following steps: s1: building CF mmWave mMIMO; s2: calculating the reachable rate of the user; s3: constructing a global energy efficiency optimization problem P1; s4: and solving the problem P1 by adopting an iteration method based on Gauss-Seidel-type to obtain the transmission power of each access point to each user. The invention adopts CF mMIMO technology to reduce the interference between users, especially the interference at the edge of the cell to improve the user experience; and the achievable rate and the power distribution strategy performance of a high system are improved by combining the CF mMIMO technology and the millimeter wave technology.

Description

Optimal GEE-based power distribution method in CF mmWave mMIMO system
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a power distribution method based on optimal GEE in a CF mmWave mMIMO system.
Background
In the area covered by the wireless communication base station, the large-scale MIMO can be configured with more than 10 antennas, compared with a 4G system (4 or 8 antennas can be configured), the number of the antennas is increased to more than two levels, the antennas adopt a large-scale centralized placement mode and are applied to users in the covered area, in addition, on the same time-frequency resource, the spatial freedom degree of the base station is fully exerted, and when the large-scale MIMO is communicated with the base station, the frequency spectrum efficiency is greatly improved, so that the capacity of preventing cell interference is realized. The power between the base station and the user can be increased by using the diversity or array gain of the large-scale antenna configuration of the base station.
Characteristics of massive MIMO communication: 1) random variation: in the conventional MIMO, since the number of antennas is small, the channels formed by the transmitting end and the receiving end have their individuality and uniqueness, and have small correlation with each other. However, when the number of antennas increases to infinity, the antennas originally belong to a random channel matrix, and at this time, each element has certain certainty, so that the matrix can be decomposed in some ways to reduce the overall operation complexity. Furthermore, the larger the aperture of the antenna array, the higher its accuracy will become. 2) Reducing inter-user interference: as the number of base station side antennas increases, the channels between users tend to be orthogonal, and when the number of base station antennas tends to be infinite, thermal noise and irrelevant inter-cell interference, which seriously affect the performance of the communication system, are usually negligible. The method of coordinating different base stations for cooperative transmission is not needed to be adopted to reduce interference, but the power of a useful signal is increased through a plurality of antennas, so that the signal-to-interference ratio is increased, and the influence of the interference is reduced. 3) System performance is limited by pilot pollution: system performance will be limited only by pilot pollution from reusing the same pilot sequence between adjacent cells. In order to reduce pilot pollution, researchers have proposed methods to shift the pilot sequence. Although the same pilot sequence is used between different cells, the pilot sequences between adjacent cells are located in the frame at positions that avoid overlapping with each other. Thus, even if all users are simultaneously transmitting in the uplink, the pilot pollution caused by pilot multiplexing can not occur.
In order to reduce the adverse effect of pilot pollution on the performance of a massive MIMO system, with the TDD mode, multiple users can use different orthogonal pilot signals to perform their respective channel estimation, but considering that the symbol period is less than the coherence time, the number of pilot information cannot be infinite, which results in that users in neighboring cells may use the same pilot information, and thus the pilot information received by the base station is not from the user in the cell, thereby causing the phenomenon of pilot pollution. At present, no mature solution exists, and most researches solve the problem from the aspects of special design of pilot frequency, cooperative work among cells and the like.
Disclosure of Invention
In view of this, the present invention provides a power allocation method based on optimal total energy efficiency (GEE) in a cellular-millimeter wave MIMO (CF mmWave MIMO) system, which is used to reduce inter-user interference in the MIMO system, especially interference at the edge of a cell, thereby improving user experience; and meanwhile, the achievable rate and the power distribution strategy performance of the system are improved.
In order to achieve the purpose, the invention provides the following technical scheme:
a power allocation method based on optimal overall energy efficiency (GEE) in a CF mmWave mMIMO system specifically comprises the following steps:
s1: building a CF mmWave mMIMO system;
s2: calculating the reachable rate of the user;
s3: constructing a global energy efficiency optimization problem P1;
s4: and solving the problem P1 by adopting an iteration method based on Gauss-Seidel-type to obtain the transmission power of each access point to each user.
Further, in step S1, the CF mmWave mimo system is built as follows: suppose that the system is configured with M Access Points (APs) and K users, each of which is configured with NrA receiving antenna, each AP is configured with NtA plurality of transmitting antennas; suppose that
Figure BDA0002823555520000021
Representing the relationship between the AP and the users, wherein when D (m, k) is 1, the mth AP serves the kth user, and when D (m, k) is 0, the mth AP does not serve the kth user; the mth AP serves as a set of users
Figure BDA0002823555520000022
The AP of the kth user is clustered as
Figure BDA0002823555520000023
In order to achieve network scalability, a user-centric (UC) power control strategy is adopted, assuming that the maximum number of users that can be served by each AP is NUE,maxEach UE can be N at mostAP,maxAn AP service; in order to make this
Figure BDA0002823555520000024
And
Figure BDA0002823555520000025
constructing D such that D is independent of the number of users K
Figure BDA0002823555520000026
Further, D is implemented such that
Figure BDA0002823555520000027
The method comprises the following specific steps: first, select NUE,maxRows of the maximum channel gain activation matrix D; second step according to M-NAP,maxThe minimum channel gains are cleared on each column of D.
Further, in step S2, the reachable rate of the kth user is represented as:
Figure BDA0002823555520000028
Figure BDA0002823555520000029
wherein the content of the first and second substances,
Figure BDA00028235555200000210
the correlation matrix representing the kth user plus effective noise, B0Denotes the subcarrier bandwidth, I denotes the identity matrix, Hk,mRepresents the channel gain between the kth user and the mth AP;
Figure BDA0002823555520000031
beamformer representing the kth user, IPAn identity matrix of size P is represented,
Figure BDA0002823555520000032
is expressed as size NrA full 1 matrix of/P, P representing the number of groups of receive antenna groups; fm,kA precoding matrix used by the mth AP for the kth user is represented; the transmission power of the mth AP to the kth user is etam,kThe transmission power of other users is eta/m,k。
Further, in step S3, the global energy efficiency optimization problem P1 is constructed as:
Figure BDA0002823555520000033
Figure BDA0002823555520000034
Figure BDA0002823555520000035
wherein, PTRepresenting the total power, Pc,mRepresents the power consumption of the circuit of the mth AP, Pbh,mRepresents backhaul link power, P, of the mth APmax,mRepresents the maximum transmission power of the mth AP, and δ is the transmission efficiency.
Further, step S4 specifically includes: adopting an iteration method based on Gauss-Seidel-type to convert the problem P1 into a problem P2:
Figure BDA0002823555520000036
Figure BDA0002823555520000037
wherein t represents an iteration index; while in each iteration to solve the P2 problem, the power of other users
Figure BDA0002823555520000038
Is assumed to be known, due to the presence
Figure BDA0002823555520000039
The mth AP serves a user, so the P2 problem needs to be solved
Figure BDA00028235555200000310
Sub-problems, each sub-problem may employ the Dinkelbach algorithmSolving the problem; converting the limit of problem P2 into a power limit for a single user, i.e. a power limit for a single user
Figure BDA00028235555200000311
Wherein
Figure BDA00028235555200000312
Is a normalization factor that is a function of,
Figure BDA00028235555200000313
representing the power value calculated in the t-1 th iteration.
The invention has the beneficial effects that: the invention adopts CF mMIMO technology to reduce the interference between users, especially the interference at the edge of the cell to improve the user experience; the invention combines CF mMIMO and millimeter wave technologies, and adopts a power distribution strategy to improve the total energy efficiency performance of the system.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of the optimal GEE-based power allocation method of the present invention;
FIG. 2 is a graph comparing the performance of the optimal GEE and average power of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and the specific meaning of the terms described above will be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 to fig. 2, fig. 1 is a power allocation method based on an optimal GEE in a CF mmWave mimo system designed in the present invention, which specifically includes the following steps:
s1: building a CF mmWave mMIMO system;
suppose that the system is configured with M Access Points (APs) and K users, each of which is configured with NrA receiving antenna, each AP is configured with NtA plurality of transmitting antennas; suppose that
Figure BDA0002823555520000041
Represents the relationship between the AP and the user, and represents that the mth AP serves the kth AP when D (m, k) is 1A user, when D (m, k) is 0, indicating that the mth AP does not serve the kth user; the mth AP serves as the set of users
Figure BDA0002823555520000051
The AP of the kth user is clustered as
Figure BDA0002823555520000052
To achieve network scalability, a user-centric (UC) power control strategy is adopted, assuming that the maximum number of users that can be served by each AP is NUE,maxEach UE can be N at mostAP,maxAn AP service; in order to make this
Figure BDA0002823555520000053
And
Figure BDA0002823555520000054
constructing D such that D is independent of the number of users K
Figure BDA0002823555520000055
The implementation steps are as follows: first, select NUE,maxRows of the maximum channel gain activation matrix D; second step according to M-NAP,maxThe minimum channel gains are cleared on each column of D.
S2: calculating the reachable rate of the user;
the achievable rate for the kth user is expressed as:
Figure BDA0002823555520000056
Figure BDA0002823555520000057
wherein the content of the first and second substances,
Figure BDA0002823555520000058
the correlation matrix representing the kth user plus effective noise, B0Denotes the subcarrier bandwidth, I denotes the identity matrix, Hk,mRepresents the channel gain between the kth user and the mth AP;
Figure BDA0002823555520000059
beamformer representing the kth user, IPAn identity matrix of size P is represented,
Figure BDA00028235555200000510
is expressed as size NrA full 1 matrix of/P, P representing the number of groups of receive antenna groups; fm,kA precoding matrix used by the mth AP for the kth user is represented; the transmission power of the mth AP to the kth user is etam,kThe transmission power of other users is eta/m,k。
S3: constructing a global energy efficiency optimization problem P1;
the constructed global energy efficiency optimization problem P1 is as follows:
Figure BDA00028235555200000511
Figure BDA00028235555200000512
Figure BDA00028235555200000513
wherein, PTRepresenting the total power, Pc,mRepresents the power consumption of the circuit of the mth AP, Pbh,mDenotes backhaul link power of the mth AP, Pmax, m denotes maximum transmission power of the mth AP, and δ denotes transmission efficiency.
According to the nature of the logarithmic function, there are:
Figure BDA0002823555520000061
wherein the content of the first and second substances,
Figure BDA0002823555520000062
when eta/m,kWhen known, due to log2If | is a convex function, then
Figure BDA0002823555520000063
About etam,kAnd therefore can be solved using standard tools for fractional programming problems.
S4: solving a problem P1 by adopting an iteration method based on Gauss-Seidel-type to obtain the transmission power of each access point to each user; the method specifically comprises the following steps: problem P1 is turned into problem P2:
Figure BDA0002823555520000064
Figure BDA0002823555520000065
wherein t represents an iteration index; while in each iteration to solve the P2 problem, the power of other users
Figure BDA0002823555520000066
Is assumed to be known, due to the presence
Figure BDA0002823555520000067
The mth AP serves a user, so the P2 problem needs to be solved
Figure BDA0002823555520000068
Sub-problems, each sub-problem can be solved by adopting Dinkelbach algorithm; converting the limit condition of (6.2) into a power limit condition of a single user, i.e.
Figure BDA0002823555520000069
Wherein
Figure BDA00028235555200000610
Is normalizationThe factor(s) is (are),
Figure BDA00028235555200000611
representing the power value calculated in the t-1 th iteration.
The specific iterative process for solving the P2 problem, namely the method for calculating the optimal GEE power, is as follows:
Figure BDA00028235555200000612
in this simulation, the carrier frequency is considered to be f073GHz with subcarrier bandwidth B0At 200MHz, an UMi open square scenario was used, the maximum distance between the AP and the user was 50m, 25 clusters were randomly used, the power spectral density was-174 dBm/Hz, the receiver noise figure was F at 6dB, and each AP used MF precoding. Table 1 describes the simulation parameters.
TABLE 1
Figure BDA0002823555520000071
Fig. 2 depicts the GEE performance of the optimal ge power distribution strategy and the average power distribution strategy, and simulation results show that the optimal ge power distribution strategy is superior to the average power distribution strategy.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (4)

1. A power distribution method based on optimal total energy efficiency in a CF mmWave mMIMO system is characterized by specifically comprising the following steps:
s1: building a cellular-free millimeter wave large-scale MIMO (cell-free millimeter wave massive MIMO, CF mmWave mMIMO) system;
s2: calculating the reachable rate of the user;
s3: the global energy efficiency optimization problem P1 is constructed as follows:
P1：
Figure FDA0003558497020000011
Figure FDA0003558497020000012
Figure FDA0003558497020000013
Figure FDA0003558497020000014
wherein eta ism,kRepresents the transmission power of the mth Access Point (AP) to the kth user, and the transmission power of other users is eta/m,k，
Figure FDA0003558497020000015
Indicates the achievable rate, P, of the k-th userTRepresenting the total power, Pmax,mRepresents the maximum transmission power, P, of the mth APc,mRepresents the power consumption of the circuit of the mth AP, Pbh,mThe backhaul link power of the mth AP is represented, and δ is the transmission efficiency; K. m respectively represents the number of APs and the number of users;
Figure FDA0003558497020000016
represents the set of users served by the mth AP,
s4: solving a problem P1 by adopting an iteration method based on Gauss-Seidel-type to obtain the transmission power of each access point to each user;
adopting an iteration method based on Gauss-Seidel-type to convert the problem P1 into a problem P2:
P2：
Figure FDA0003558497020000017
Figure FDA0003558497020000018
Figure FDA0003558497020000019
wherein t represents an iteration index; while in each iteration to solve the P2 problem, the power of other users
Figure FDA00035584970200000110
Is assumed to be known, due to the presence
Figure FDA00035584970200000111
The mth AP serves one user, so the P2 problem needs to be solved
Figure FDA00035584970200000112
Sub-problems, each sub-problem can be solved by adopting a Dinkelbach algorithm; converting the limit of the problem P2 into a power limit for a single user, i.e.
Figure FDA0003558497020000021
Wherein
Figure FDA0003558497020000022
Is a normalization factor that is a function of,
Figure FDA0003558497020000023
representing the power value calculated in the t-1 th iteration.
2. The power allocation method of claim 1,in step S1, the set up CF mmWave mimo system is: suppose that the system is configured with M APs and K users, each user configuring NrA receiving antenna, each AP is configured with NtA transmitting antenna; suppose that
Figure FDA0003558497020000024
Representing the relationship between the AP and the users, wherein when D (m, k) is 1, the mth AP serves the kth user, and when D (m, k) is 0, the mth AP does not serve the kth user; the mth AP serves as a set of users
Figure FDA0003558497020000025
The AP of the kth user is clustered as
Figure FDA0003558497020000026
Adopting a power control strategy taking users as a center, and assuming that the maximum number of users that each AP can serve is NUE,maxEach UE can be N at mostAP,maxAn AP service; construction D of
Figure FDA0003558497020000027
3. The power allocation method of claim 2, wherein D is implemented such that
Figure FDA0003558497020000028
The method comprises the following specific steps: first, select NUE,maxRows of the maximum channel gain activation matrix D; second step according to M-NAP,maxThe minimum channel gains are cleared on each column of D.
4. The power allocation method according to claim 2 or 3, wherein in step S2, the reachable rate of the kth user is represented as:
Figure FDA0003558497020000029
Figure FDA00035584970200000210
wherein the content of the first and second substances,
Figure FDA00035584970200000211
the correlation matrix representing the kth user plus effective noise, B0Denotes the subcarrier bandwidth, I denotes the identity matrix, Hk,mRepresents the channel gain between the kth user and the mth AP;
Figure FDA00035584970200000212
beamformer representing the kth user, IPDenotes an identity matrix of size P, 1Nr/PIs expressed as size NrA full 1 matrix of/P, P representing the number of groups of receive antenna groups; fm,kIndicating the precoding matrix used by the mth AP for the kth user.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111147112A (en) * 2019-12-30 2020-05-12 中国传媒大学 Energy maximization collection method based on MIMO-NOMA system

Family Cites Families (7)

* Cited by examiner, † Cited by third party
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US10236952B1 (en) * 2018-01-17 2019-03-19 B. G. Negev Technologies And Applications Ltd., At Ben-Gurion University Energy-efficient power and offset allocation of spatial multiplexing in multimode fiber
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US10644804B1 (en) * 2019-03-11 2020-05-05 King Fahd University Of Petroleum And Minerals Cell clustering and power allocation for energy-efficient VLC networks
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CN111405584B (en) * 2020-03-17 2022-03-11 重庆邮电大学 Energy efficiency power distribution method based on non-orthogonal multiple access in MIMO system
CN112039563B (en) * 2020-09-09 2021-06-11 东南大学 Large-scale MIMO safe multicast transmission power distribution method with optimal energy efficiency

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111147112A (en) * 2019-12-30 2020-05-12 中国传媒大学 Energy maximization collection method based on MIMO-NOMA system

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* Cited by examiner, † Cited by third party
Title
Global Energy Efficiency Optimization for Wireless-Powered Massive MIMO Aided Multiway AF Relay Networks;Fangqing Tan等;《IEEE Transactions on Signal Processing》;20180312;第66卷(第9期);2384-2398 *

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