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CN104065403B - Design method of key parameter of downlink chain in high-energy-efficiency large-scale antenna system - Google Patents

Design method of key parameter of downlink chain in high-energy-efficiency large-scale antenna system Download PDF

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CN104065403B
CN104065403B CN201410240352.4A CN201410240352A CN104065403B CN 104065403 B CN104065403 B CN 104065403B CN 201410240352 A CN201410240352 A CN 201410240352A CN 104065403 B CN104065403 B CN 104065403B
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base station
users
parameters
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user distribution
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CN104065403A (en
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钟财军
孔垂丽
张朝阳
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Zhejiang University ZJU
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    • 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
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    • 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

本发明公开了一种高能效大规模天线系统中下行链路关键参数的设计方法，属于通信领域，通过该方法，可获得使大规模天线系统下行链路能量效率最高的系统参数，包括基站天线数目M，小区用户数目K，和基站发射功率PT。本发明提供的参数设计方法分为“用户分布模型参数计算”和“系统能量效率分析及优化计算”两步。“用户分布模型参数计算”包括：统计基站覆盖范围内用户与基站的距离信息，建立实际的用户分布模型；计算相应用户分布模型的关键参数。“系统能量效率分析及优化计算”包括：推导高能效大规模天线系统下行链路的能量效率表达EE＝f(M,K,PT)；根据系统的资源配置条件，分析得到几种不同情况，并进一步得到每种情况下的最佳参数

The invention discloses a method for designing key parameters of the downlink in a large-scale antenna system with high energy efficiency, which belongs to the field of communication. Through the method, the system parameters with the highest energy efficiency of the downlink of the large-scale antenna system can be obtained, including base station antennas The number M, the number K of users in the cell, and the transmit power P T of the base station. The parameter design method provided by the invention is divided into two steps of "parameter calculation of user distribution model" and "system energy efficiency analysis and optimization calculation". "Calculation of user distribution model parameters" includes: calculating the distance information between users and the base station within the coverage of the base station, establishing an actual user distribution model; and calculating the key parameters of the corresponding user distribution model. "System energy efficiency analysis and optimization calculation" includes: deriving the energy efficiency expression EE=f(M,K,PT ) for the downlink of a large-scale antenna system with high energy efficiency; according to the resource allocation conditions of the system, several different situations are obtained through analysis , and further get the best parameters in each case

Description

Design method for key parameters of downlink in high-energy-efficiency large-scale antenna system
Technical Field
The invention relates to the field of communication, in particular to a design method of key parameters of a downlink in an energy-efficient large-scale antenna system.
Background
A large-scale antenna (Massive MIMO) technology is an extension of the conventional MIMO technology, and tens of users are served by configuring hundreds of antennas in a base station, so that the energy efficiency of the system can be improved substantially theoretically. According to the prior literature, the transmission power of the antenna is reported to be in a linear descending trend along with the increase of the number of the antennas. However, this conclusion is based on not considering the power consumption of the base station circuitry. In practical systems, in addition to the antenna transmission power, the associated hardware of each antenna, such as digital-to-analog converters, power amplifiers, filters, etc., consumes energy. As the number of antennas increases, the power consumption of the circuit part increases significantly, and therefore, it is important to find an optimal balance when designing an energy-efficient communication system.
The existing literature is mainly the following methods for improving the downlink energy efficiency of a large-scale antenna system: (1) optimizing a beam former to control the transmitting direction of the beam at the base station end; (2) performing power control at a base station end according to the needs of users in a cell; (3) for a low load environment, the base station adopts a Discontinuous Transmission (DTX) data transmission mode.
After analyzing the existing method, the inventor finds that: the methods mentioned in the literature are all to improve energy efficiency by optimizing power, beam, etc. after the system is built, or are too complex, have some lack of feasibility, or have narrow application range, and have certain limitations. The method (1) needs to obtain all channel information and solve the optimal beam former, and is relatively complex and difficult to implement. The range of power control in the method (2) is limited by the dynamic range of the transceiving hardware, and has certain limitation. The method (3) is only suitable for the low-load environment, and cannot be used for the high-load cell.
User distribution is another key issue in large-scale antenna systems. The system analysis and optimization in the existing literature mostly assume that users are subject to uniform distribution, but in practice, the distribution conditions of the users are limited by physical environmental factors, such as roads, rivers, buildings, parks and the like; on the one hand, the method is influenced by crowd gathering, such as highly concentrated hot spot areas like shopping centers, airports and the like; the non-uniform distribution is often presented in the geographic space, so that the optimization aiming at the more practical non-uniform distribution model has important value.
The inventor sets up a system energy efficiency function by starting from a universal non-uniform user distribution model and taking closed expression of system average region spectral efficiency as an entry point, and proposes to design the number M of base station antennas, the number K of cell users and the transmission power P of a base station on the basis of the functionTThe simple method of the three key parameters improves the energy efficiency of the downlink of the large-scale antenna system.
Disclosure of Invention
The invention aims to provide a design method of key parameters of a downlink in an energy-efficient large-scale antenna system, aiming at the problems that a user distribution model is not practical enough and the energy efficiency of the system is low in the current situation.
The design method of the key parameters of the downlink in the high-energy-efficiency large-scale antenna system comprises the following steps: counting distance information between users and a base station in a coverage area of the base station to obtain an actual user distribution model; calculating key parameters of the corresponding user distribution model; deriving an energy efficiency expression EE ═ f (M, K, P) for a large-scale antenna system downlinkT) (ii) a According to the resource allocation condition of the system, analyzing to obtain several different conditions and further obtaining the optimal parameters under each condition
The actual user distribution model comprises:
(1) if the users are distributed in the center of the cell in a centralized way, a user distribution model in the center is adopted, and the probability density function of the user distribution adopts the following formula
Wherein R represents a cell radius, R0Representing the minimum distance of the user from the base station, a and b are key parameters related to a user distribution model;
(2) if the users are distributed in the edge of the cell in a concentrated way, the user distribution model in the edge concentration is adopted, and the probability density function of the user distribution adopts the following formula
Wherein R represents a cell radius, R0Representing the minimum distance of a user from the base station, a and b are key parameters related to the user distribution model.
The calculation of the key parameters a and b comprises the following steps:
(1) if the model is a user distribution model with centralized center, a controls the degree of the users approaching the center of the cell, the larger a indicates that more users are distributed in the central area of the cell, and vice versa, when a is 0, the users are subjected to uniform distribution, b is a normalization constant of a probability density function, and the normalization constant can be obtained through calculation
The user distribution model of the central set is a large-scale shopping mall and a street;
(2) if the model is a user distribution model with concentrated edges, a controls the degree of the users approaching the edge of the cell, the larger a indicates that more users are distributed in the edge area of the cell, and vice versa, when a is 0, the users are subjected to uniform distribution, b is a normalization constant of a probability density function, and the normalization constant can be obtained through calculation
The resource allocation condition of the system comprises given (M, K, P)T) Any two parameters, any one parameter and zero parameters, thereby creating three different optimization methods: the method comprises a single-variable optimization method, a double-variable joint optimization method and a three-variable joint optimization method.
The univariate optimization method comprises the following steps: given (M, K, P)T) Any two of these three parameters, the other one, for the individual optimization of the number of base station antennas M and the total base station transmitted power PTIn both cases, by closed form solution of MoptAnddirectly, and for the case of individually optimizing the number of users K, it is necessary to obtain the optimal value K by maximizing the energy efficiency of the system in a limited spaceopt。
The bivariable joint optimization method comprises the following steps: given (M, K, P)T) Any one of these three parameters, two other parameters are optimized, including:
(1) given the number of users K, jointly optimizing the number M of base station antennas and the base station transmitting power PT；
S100, when the algorithm starts to operate, setting initial base station transmitting power PT＞0；
S101, updating the number M of base station antennas to be Mopt；
S102, updating the transmitting power PTIs composed of
S103, circularly executing the steps S101 and S102 until the algorithm is converged, and obtaining the optimal number M of the base station antennasoptAnd base station transmit power
Wherein the judgment condition of algorithm convergence is that the integer M is not changed any more;
(2) given the number M of base station antennas, jointly optimizing the number K of users and the transmission power P of the base stationT；
S110, when the algorithm starts to run, setting initial base station transmitting power PT＞0；
S111, updating the transmitting power PTIs composed of
S112, optimizing the number K of users to be Kopt；
S113, circularly executing the steps S111 and S112 until the algorithm converges, and obtaining the optimal user number KoptAnd base station transmit power
Wherein the judgment condition of algorithm convergence is that the integer K is not changed any more;
(3) given the number of base station antennas PTJointly optimizing the number K of users and the transmitting power M of the base station;
s120, setting the initial user number K to be 1 when the algorithm starts to run;
s121, updating the number M of the base station antennas to beMopt；
S122, for MoptRounding off, and optimizing the number of users K to Kopt；
S123, circularly executing the steps S121 and S122 until the algorithm converges, and obtaining the optimal user number KoptAnd base station transmission power Mopt；
The judgment condition of algorithm convergence is that the integers K and M are not changed any more.
The three-variable joint optimization method comprises the following steps: to (M, K, P)T) Performing joint optimization on the three parameters;
s20, when the algorithm starts to run, setting the initial base station transmitting power PTThe number K of users is more than 0, the number M of base station antennas is more than 3;
s21, updating the number K of users to Kopt；
S22, updating the number M of base station antennas to Mopt；
S23, updating the transmitting power PTIs composed of
S24, circularly executing the steps S21 to S23 until the algorithm converges and obtaining the optimal three important system parameters
The judgment condition of algorithm convergence is that the integers K and M are not changed any more.
The invention has the beneficial effects that:
(1) the invention considers that the user distribution is limited by physical environment and is influenced by crowd aggregation, establishes a more practical user distribution model, comprises two modes of central concentration and edge concentration, and can adjust the relevant parameters of the model to adapt to different user distribution scenes. Avoiding the impracticality of the conventional uniformly distributed model.
(2) The invention optimizes (M, K, P) according to different system requirementsT) The three important system parameters enable the system to have the highest energy efficiency, and compared with the traditional power control or beam forming method, the method has the advantage of low algorithm complexity. By means of the optimization design, partial resources and expenses are saved, the system energy efficiency is improved to the maximum extent, and the green communication concept is met.
Drawings
FIG. 1 is a flow chart of a method for designing important parameters of a large-scale antenna system;
FIG. 2(a) is a user profile in a central set of the present invention;
FIG. 2(b) is a user profile for edge concentration of the present invention;
FIG. 3 is the variation of energy efficiency with the number of users for different user distributions and antenna numbers according to the present invention;
fig. 4 is a curve obtained by jointly optimizing the number of base station antennas and the transmission power for a given number of users according to the present invention.
Detailed Description
The method for designing the key parameters of the downlink in the high-energy-efficiency large-scale antenna system comprises the following steps:
counting distance information between users and a base station in a coverage area of the base station to obtain an actual user distribution model; calculating key parameters of the corresponding user distribution model; deriving an energy efficiency expression EE-f (M, K, P) for a large-scale antenna system downlinkT) (ii) a According to the resource allocation condition of the system, analyzing to obtain several different conditions and further obtaining the optimal parameters under each condition
The actual user distribution model comprises:
(1) if the users are distributed in the center of the cell in a centralized way, a user distribution model in the center is adopted, and the probability density function of the user distribution adopts the following formula
Wherein R represents a cell radius, R0Representing the minimum distance of the user from the base station, a and b are key parameters related to a user distribution model;
(2) if the users are distributed in the edge of the cell in a concentrated way, the user distribution model in the edge concentration is adopted, and the probability density function of the user distribution adopts the following formula
Wherein R represents a cell radius, R0Representing the minimum distance of a user from the base station, a and b are key parameters related to the user distribution model.
The calculation of the key parameters a and b comprises the following steps:
(1) if the model is a user distribution model with centralized center, a controls the degree of the users approaching the center of the cell, the larger a indicates that more users are distributed in the central area of the cell, and vice versa, when a is 0, the users are subjected to uniform distribution, b is a normalization constant of a probability density function, and the normalization constant can be obtained through calculation
The user distribution model of the central set is a large-scale shopping mall and a street;
(2) if the model is a user distribution model with concentrated edges, a controls the degree of the users approaching the edge of the cell, the larger a indicates that more users are distributed in the edge area of the cell, and vice versa, when a is 0, the users are subjected to uniform distribution, b is a normalization constant of a probability density function, and the normalization constant can be obtained through calculation
The resource allocation condition of the system comprises given (M, K, P)T) Any two parameters, any one parameter and zero parameters, thereby creating three different optimization methods: the method comprises a single-variable optimization method, a double-variable joint optimization method and a three-variable joint optimization method.
The univariate optimization method comprises the following steps: given (M, K, P)T) Any two of these three parameters, the other one, for the individual optimization of the number of base station antennas M and the total base station transmitted power PTIn both cases, by closed form solution of MoptAnddirectly, and for the case of individually optimizing the number of users K, it is necessary to obtain the optimal value K by maximizing the energy efficiency of the system in a limited spaceopt。
The bivariable joint optimization method comprises the following steps: given (M, K, P)T) Any one of these three parameters, two other parameters are optimized, including:
(1) given the number of users K, jointly optimizing the number M of base station antennas and the base station transmitting power PT；
S100, when the algorithm starts to operate, setting initial base station transmitting power PT＞0；
S101Updating the number M of base station antennas to Mopt；
S102, updating the transmitting power PTIs composed of
S103, circularly executing the steps S101 and S102 until the algorithm is converged, and obtaining the optimal number M of the base station antennasoptAnd base station transmit power
Wherein the judgment condition of algorithm convergence is that the integer M is not changed any more;
(2) given the number M of base station antennas, jointly optimizing the number K of users and the transmission power P of the base stationT；
S110, when the algorithm starts to run, setting initial base station transmitting power PT＞0；
S111, updating the transmitting power PTIs composed of
S112, optimizing the number K of users to be Kopt；
S113, circularly executing the steps S111 and S112 until the algorithm converges, and obtaining the optimal user number KoptAnd base station transmit power
Wherein the judgment condition of algorithm convergence is that the integer K is not changed any more;
(3) given the number of base station antennas PTJointly optimizing the number K of users and the transmitting power M of the base station;
s120, setting the initial user number K to be 1 when the algorithm starts to run;
s121, updating the number M of the base station antennas to be Mopt；
S122, for MoptRounding off, and optimizing the number of users K to Kopt；
S123, circularly executing the steps S121 and S122 until the algorithm converges, and obtaining the optimal user number KoptAnd base station transmission power Mopt；
The judgment condition of algorithm convergence is that the integers K and M are not changed any more.
The three-variable joint optimization method comprises the following steps: to (M, K, P)T) Performing joint optimization on the three parameters;
s20, when the algorithm starts to run, setting the initial base station transmitting power PTThe number K of users is more than 0, the number M of base station antennas is more than 3;
s21, updating the number K of users to Kopt；
S22, updating the number M of base station antennas to Mopt；
S23, updating the transmitting power PTIs composed of
S24, circularly executing the steps S21 to S23 until the algorithm converges and obtaining the optimal three important system parameters
The judgment condition of algorithm convergence is that the integers K and M are not changed any more.
The invention is further described with reference to the following figures and specific embodiments.
The method for designing the key parameters of the downlink in the high-energy-efficiency large-scale antenna system is suitable for a Multi-user Multi-antenna (Multi-user MIMO) system of a single cell. The method specifically comprises the following steps:
a base station is positioned in the center of a circular cell, M antennas are configured on the base station, users of K single antennas are randomly distributed in the cell, the noise power is-114 dbm, shadow fading follows lognormal distribution, the mean value is 0, and the variance is 8 dB. Defining the energy efficiency of the system asWherein R isdlIs the sum rate (bits/channel use), P, of all users in the systemtotalRepresenting the total energy loss (Joule/channel use) of the system.
The energy loss of the single-cell system comprises three parts:
(1) energy loss P of power amplifierPA。
Wherein, PTRepresenting the total transmit power of the base station, 0 < η ≦ 1 is the energy efficiency of the amplifier, determined by the performance of the amplifier.
(2) Energy loss P of circuitC。
The circuit energy loss is an important component of the total energy consumption of the system, and the hardware facilities of each antenna of the base station comprise a digital-to-analog converter, a mixer, a filter and the like, and the energy loss is PBSThe hardware facilities of each user include low noise amplifier, direct frequency amplifier, filter and analog-digital converter, etc., and the energy loss is PUE。PsynFor the energy loss of the frequency synthesizer, P0Including energy losses in refrigeration systems and the like. The energy loss of this part can be expressed as:
PC＝MPBS+2Psyn+KPUE+P0
(3) letterNumber processing energy consumption PSP。
As the number of antennas increases, the power consumption of signal processing cannot be ignored, and before analyzing the power consumption of signal processing, a quantity L (flops/Watt) characterizing the efficiency of calculating signal processing is defined and the channel state is considered to be constant in the T period. According to the sequence of signal processing, first, the base station encodes and modulates the K information sequences sent to the user, consuming KP each time using the channelcodThe energy of the joule. Secondly, the base station performs precoding once in each coherence time T, where zero forcing precoding method with low complexity is selected, so it costs each time the channel is utilizedThe energy of the joule. Then, in the signal transmission stage, the precoding matrix needs to be multiplied by the information sequence, which consumesThe energy of the joule. Finally, the user end decodes the received signal and consumes KPdecThe energy of the joule. So PSPCan be expressed as:
since the zero-forcing precoding algorithm is used at the base station end in the present embodiment, K < M. The flow chart is shown in fig. 1.
S30, in the coverage area of the base station, counting the distance information between the user and the base station in the coverage area of the base station to obtain an actual user distribution model;
s31, calculating key parameters a and b;
s32, calculating to obtain an energy efficiency expression EE ═ f (M, K, P) of the large-scale antenna systemT)
Wherein,representing the signal-to-noise ratio, PTThe total transmit power of the base station is obtained β based on the user distribution, and for the user distribution model in the central set,for a user distribution model that is edge-concentrated,α is a large scale fading factor.
S33, resource demand analysis and corresponding algorithm. The method comprises the following steps:
s330, setting key parameters (M, K, P) according to the requirements of the systemT) The number of (2) is divided into three cases.
Case 1: given (M, K, P)T) Any two parameters of (1), optimizing the remaining one parameter;
case 2: given (M, K, P)T) Jointly optimizing the remaining two parameters according to any one parameter of the two parameters;
case 3: given (M, K, P)T) Zero parameters of (2), jointly optimizing the three parameters;
s331, algorithm of case 1.
If the situation is 1, executing a univariate optimization method, comprising the following steps:
(1) given (M, K, P)T) Of these three parameters, (M, K), P is optimizedT；
In the case of this kind of situation,can be directly obtained by closed-type solution
(2) Given (M, K, P)T) Of these three parameters (M, P)T) Optimizing K;
for this case, K needs to be obtained by maximizing the energy efficiency of the system in a limited spaceopt。
(3) Given (M, K, P)T) Of these three parameters (K, P)T) Optimizing M;
for this case, MoptCan be directly obtained by closed-type solution
S332, algorithm of the 2 nd case.
If the situation is 2, executing a bivariate joint optimization method, comprising the following steps:
(1) given the number of users K, jointly optimizing the number M of base station antennas and the base station transmitting power PT；
Executing steps S100-S103;
(2) given the number M of base station antennas, jointly optimizing the number K of users and the transmission power P of the base stationT；
Executing steps S110-S113;
(3) given the number of base station antennas PTJointly optimizing the number K of users and the transmitting power M of the base station;
executing steps S120-S123;
s333, algorithm of case 3.
If the case 3 is true, the three-variable joint optimization method is executed, i.e., the steps S20-S24 are executed.
The principle of the method and the achieved technical effect can be as follows:
the invention provides a method with guiding significance for the design of key parameters of a down link in a high-energy-efficiency large-scale antenna system, namely, a user distribution model is actually and accurately obtained according to the number M of base station antennas, the number K of cell users and transmitting power PTThe three key parameters are used as entry points, and (M, K, P) is optimized according to different system requirementsT) These three key system parameters make the system energy efficient. Compared with the traditional power control or beam forming method, the method has the advantage of low algorithm complexity, saves partial resources and expenses, improves the energy efficiency of the system to the maximum extent, and accords with the concept of green communication.
Fig. 2(a) is a central user distribution model in this embodiment, where a is set to 1, and 1000 users are randomly generated in (R, R)0) In a cell of (600,50) m, the dots in blue represent users.
Fig. 2(b) shows a distribution model of users with edge concentration in this embodiment, where a is set to 1, and 1000 users are randomly generated in (R, R)0) In a cell of (600,50) m, the dots in blue represent users.
FIG. 3 is a diagram illustrating the setting of the total transmission power P of the base station for different user models in this embodimentTWhen the number of base station antennas is 0.001W, the system energy efficiency varies with the number of users in the cell. It can be seen that the larger the conventional number M of base station antennas, the higher the system energy efficiency is, and it does not apply here, but there is an optimum value for the number of base station antennas. In addition, the impact of user distribution on system energy efficiency can also be seen by fig. 3.
Fig. 4 shows the optimal number M of base station antennas obtained by performing the bivariate joint optimization method under the condition that the radius of the cell is 600M and different user numbers K are given in this embodimentoptAnd base station transmit powerThe above sub-diagram describes the optimal number of base station antennas M corresponding to different numbers of users KoptThe following sub-diagram describes the optimal base station transmit power for different numbers of users KFor convenience of expression, the ordinate represents the optimum base station transmission powerCorresponding maximum signal-to-noise ratio ρopt。
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be covered by the present invention.

Claims (5)

1.一种高能效大规模天线系统中下行链路关键参数的设计方法，其特征在于，统计基站覆盖范围内用户与基站的距离信息，获得实际的用户分布模型；计算相应用户分布模型的关键参数；推导大规模天线系统下行链路的能量效率表达EE＝f(M,K,PT)，其中M为基站天线数目；K为用户数目；PT为基站发射功率；根据系统的资源配置条件，包括给定(M,K,PT)的任意两个参数，任意一个参数和零个参数三种情况，由此产生三种不同的优化方法：包括单变量优化方法、双变量联合优化方法和三变量联合优化方法；并进一步得到每种情况下的最佳参数其中Mopt为优化的基站天线数目；Kopt为优化的用户数目；为优化的基站发射功率；1. A method for designing key parameters of the downlink in a high-energy-efficiency large-scale antenna system is characterized in that, the distance information between users and base stations in the coverage of the statistics base station is obtained to obtain the actual user distribution model; the key to calculate the corresponding user distribution model Parameters; deduce the energy efficiency expression EE=f(M,K,PT ) of the downlink of the large-scale antenna system, where M is the number of base station antennas; K is the number of users; PT is the transmit power of the base station; according to the resource configuration of the system Conditions, including any two parameters given (M, K, P T ), three situations of any one parameter and zero parameters, resulting in three different optimization methods: including single variable optimization method, double variable joint optimization method and three-variable joint optimization method; and further get the best parameters in each case Wherein M opt is the number of optimized base station antennas; K opt is the number of optimized users; For the optimized base station transmit power; 所述的实际的用户分布模型包括：The actual user distribution model described includes: (1)如果用户在小区中心集中分布，采用中心集中的用户分布模型，用户分布的概率密度函数采用如下公式(1) If the users are concentrated in the center of the cell, the center-centralized user distribution model is adopted, and the probability density function of the user distribution adopts the following formula 其中，R表示小区半径，r0表示用户距离基站的最小距离，a和b是与用户分布模型有关的关键参数；Among them, R represents the radius of the cell, r0 represents the minimum distance between the user and the base station, and a and b are key parameters related to the user distribution model; (2)如果用户在小区边缘集中分布，采用边缘集中的用户分布模型，用户分布的概率密度函数采用如下公式(2) If the users are distributed concentratedly at the edge of the cell, the user distribution model concentrated at the edge is adopted, and the probability density function of the user distribution adopts the following formula 其中，R表示小区半径，r0表示用户距离基站的最小距离，a和b是与用户分布模型有关的关键参数。Among them, R represents the radius of the cell, r 0 represents the minimum distance between the user and the base station, and a and b are key parameters related to the user distribution model. 2.根据权利要求1所述的高能效大规模天线系统中下行链路关键参数的设计方法，其特征在于，所述的关键参数a和b的计算包括：2. the method for designing key parameters of the downlink in the high energy efficiency large-scale antenna system according to claim 1, is characterized in that, the calculation of described key parameters a and b comprises: (1)如果是中心集中的用户分布模型，a控制用户趋近小区中心的程度，a越大，表明更多的用户分布在小区的中心区域，反之亦然，a＝0时，用户服从均匀分布，b是概率密度函数的归一化常数，通过计算(1) If it is a center-centralized user distribution model, a controls the degree to which users approach the center of the cell. The larger a is, the more users are distributed in the center of the cell, and vice versa. When a=0, the users obey the uniform distribution, b is the normalization constant of the probability density function, calculated by 所述中心集中的用户分布模型为大型商场，街道；The centralized user distribution model in the center is a large shopping mall and a street; (2)如果是边缘集中的用户分布模型，a控制用户趋近小区边缘的程度，a越大，表明更多的用户分布在小区的边缘区域，反之亦然，a＝0时，用户服从均匀分布，b是概率密度函数的归一化常数，通过计算(2) If it is a user distribution model concentrated on the edge, a controls the degree to which users approach the edge of the cell. The larger a is, it indicates that more users are distributed in the edge area of the cell, and vice versa. When a=0, the users obey the uniform distribution, b is the normalization constant of the probability density function, calculated by 。 . 3.根据权利要求1所述的高能效大规模天线系统中下行链路关键参数的设计方法，其特征在于，所述的单变量优化方法为：给定(M,K,PT)这三个参数中的任意两个，优化另一个参数，对于单独优化基站天线数目M和基站发射总功率PT这两种情况，通过闭式解Mopt和直接求出，而对于单独优化用户数目K的情况，需要借助在有限空间里使系统的能量效率最高，获得最优值Kopt。3. the design method of downlink key parameter in the large-scale antenna system of high energy efficiency according to claim 1, it is characterized in that, described univariate optimization method is: given (M, K, P T ) these three For any two of the two parameters, optimize another parameter. For the two cases of separately optimizing the number of base station antennas M and the total power of base station transmission P T , through the closed-form solution M opt and It can be obtained directly, and for the case of optimizing the number of users K alone, it is necessary to obtain the optimal value K opt by maximizing the energy efficiency of the system in a limited space. 4.根据权利要求1所述的高能效大规模天线系统中下行链路关键参数的设计方法，其特征在于，所述的双变量联合优化方法为：给定(M,K,PT)这三个参数中的任意一个，优化另外两个参数，包括：4. the design method of the downlink key parameter in the large-scale antenna system with high energy efficiency according to claim 1, is characterized in that, described bivariate joint optimization method is: given (M, K, P T ) this Any one of the three parameters, optimize the other two parameters, including: (1)给定用户数目K，联合优化基站天线数目M和基站发射功率PT；(1) Given the number of users K, jointly optimize the number of base station antennas M and base station transmit power P T ; S100、算法开始运行时，设置初始基站发射功率PT>0；S100. When the algorithm starts to run, set the initial base station transmit power P T >0; S101、更新基站天线数目M为Mopt；S101. Update the number M of base station antennas to M opt ; S102、更新发射功率PT为 S102 . Update the transmit power PT as S103、循环执行步骤S101和S102，直到算法收敛，获得最优的基站天线数目Mopt和基站发射功率 S103, steps S101 and S102 are executed in a loop until the algorithm converges to obtain the optimal number of base station antennas M opt and base station transmission power 其中算法收敛的判断条件为整数M不再变化；The judging condition for the convergence of the algorithm is that the integer M does not change any more; (2)给定基站天线数目M，联合优化用户数目K和基站发射功率PT；(2) Given the number of base station antennas M, jointly optimize the number of users K and base station transmit power P T ; S110、算法开始运行时，设置初始基站发射功率PT>0；S110. When the algorithm starts to run, set the initial base station transmit power P T >0; S111、更新发射功率PT为 S111 . Update the transmit power PT as S112、优化用户数目K为Kopt；S112. Optimizing the number of users K is K opt ; S113、循环执行步骤S111和S112，直到算法收敛，获得最优的用户数目Kopt和基站发射功率 S113, cyclically execute steps S111 and S112 until the algorithm converges, and obtain the optimal number of users Kopt and base station transmission power 其中算法收敛的判断条件为整数K不再变化；The judging condition for the convergence of the algorithm is that the integer K does not change anymore; (3)给定基站天线数目PT，联合优化用户数目K和基站发射功率M；(3) Given the number of base station antennas P T , jointly optimize the number of users K and the transmit power M of the base station; S120、算法开始运行时，设置初始用户数目K＝1；S120. When the algorithm starts running, set the initial number of users K=1; S121、更新基站天线数目M为Mopt；S121. Update the number M of base station antennas to M opt ; S122、对Mopt取四舍五入，优化用户数目K为Kopt；S122. Round up M opt , and optimize the number of users K to K opt ; S123、循环执行步骤S121和S122，直到算法收敛，获得最优的用户数目Kopt和基站发射功率Mopt；S123. Steps S121 and S122 are cyclically executed until the algorithm converges, and the optimal number of users K opt and base station transmission power M opt are obtained; 其中算法收敛的判断条件为整数K和M不再变化。The judging condition for the convergence of the algorithm is that the integers K and M do not change. 5.根据权利要求1所述的高能效大规模天线系统中下行链路关键参数的设计方法，其特征在于，所述的三变量联合优化方法为：对(M,K,PT)这三个参数进行联合优化；5. The method for designing key parameters of the downlink in the high-energy-efficiency large-scale antenna system according to claim 1, wherein the three-variable joint optimization method is: for (M, K, P T ) these three parameters are jointly optimized; S20、算法开始运行时，设置初始基站发射功率PT>0，用户数目K＝1，基站天线数目M＝3；S20. When the algorithm starts to run, set the initial base station transmit power P T >0, the number of users K=1, and the number of base station antennas M=3; S21、更新用户数目K为Kopt；S21. Update the number of users K to K opt ; S22、更新基站天线数目M为Mopt；S22. Update the number M of base station antennas to M opt ; S23、更新发射功率PT为 S23 . Update the transmission power PT as S24、循环执行步骤S21到S23，直到算法收敛，获得最优的三个重要系统参数 S24, cyclically execute steps S21 to S23 until the algorithm converges, and obtain the optimal three important system parameters 其中算法收敛的判断条件为整数K和M不再变化。The judging condition for the convergence of the algorithm is that the integers K and M do not change.
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