CN109039498B - Energy efficiency optimization method based on RAU and user distance relationship in multi-cell large-scale DAS - Google Patents

Energy efficiency optimization method based on RAU and user distance relationship in multi-cell large-scale DAS Download PDF

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CN109039498B
CN109039498B CN201810733971.5A CN201810733971A CN109039498B CN 109039498 B CN109039498 B CN 109039498B CN 201810733971 A CN201810733971 A CN 201810733971A CN 109039498 B CN109039498 B CN 109039498B
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李民政
李艳峰
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Shanghai Dianji University
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Abstract

本发明涉及一种多小区大规模DAS中基于RAU和用户距离关系的能效优化方法。本发明首先根据RAU与用户之间的距离，推导出系统吞吐量的解析式。然后根据具有路径损耗、阴影衰落、多径衰落特征的复合信道与多个实际功耗参数间的关系建立了系统下行链路能效的优化模型。最后，在此基础上推导出系统能效与小区RAU个数、用户天线数、及发射功率的变化关系，并通过提出的算法得到了系统能效最优的理论值。本发明具有以较少的迭代次数获得最优系统能效、易于实现的优点。

Figure 201810733971

The invention relates to an energy efficiency optimization method based on the relationship between RAU and user distance in a multi-cell large-scale DAS. The present invention first derives the analytical formula of the system throughput according to the distance between the RAU and the user. Then, according to the relationship between the composite channel with path loss, shadow fading and multipath fading characteristics and multiple actual power consumption parameters, an optimization model of the downlink energy efficiency of the system is established. Finally, on this basis, the relationship between the system energy efficiency and the number of RAUs in the cell, the number of user antennas, and the transmission power is deduced, and the optimal theoretical value of the system energy efficiency is obtained through the proposed algorithm. The present invention has the advantages of obtaining optimal system energy efficiency with less iterations and being easy to implement.

Figure 201810733971

Description

Energy efficiency optimization method based on RAU and user distance relationship in multi-cell large-scale DAS
Technical Field
The invention relates to an energy efficiency optimization method based on a Remote Access Unit (RAU) and user distance relationship in multi-cell large-scale DAS (Distributed Antenna Systems, DAS for short), and belongs to the technical field of wireless communication.
Background
The energy crisis and environmental pollution make green communication become the key of next-generation communication, and a large-scale Multiple-Input Multiple-Output (Massive MIMO) communication system can significantly improve energy efficiency, and has become a research hotspot in the field of mobile communication. But the main problem is that a large number of antennas are deployed on the same base station, and the channel correlation is strong. The combination of Massive MIMO and DAS can effectively improve the energy efficiency and frequency efficiency of the system, obviously reduce the channel correlation and become a key technology of a new generation of communication network. DAS can improve energy efficiency and coverage by shortening the average distance between a transmitter and a user, thereby reducing transmission power, and DAS can significantly improve system energy efficiency compared to a Centralized Antenna System (CAS).
The CAS is to arrange all antennas at the same position in a cell, the mutual distance between the antennas is about several times of the wavelength of the transmitted electromagnetic wave, the channel correlation is strong, and the problem of cell communication blind area is caused because the transmitted antennas are too concentrated. The DAS is mainly characterized in that a plurality of antennas are distributed discretely in a cell, the mutual distance between the antennas is about ten meters or one hundred meters, and each distributed antenna is connected with a baseband processing unit through a special medium, so that the problems of channel correlation and communication blind areas can be effectively solved. Especially in the 5G era, the problem of communication blind area should be avoided as much as possible under the large background of everything interconnection. Currently, there are many studies on CAS energy efficiency problems, but there are few studies on DAS energy efficiency, especially on multi-cell DAS energy efficiency.
Currently, research on the energy efficiency of a single-cell DAS mainly focuses on power allocation and antenna selection, and research is performed under an ideal channel model in which only large-scale fading influence is considered and small-scale fading influence is ignored, and for total power affecting the energy efficiency, only transmission power and static power are considered and RAU dynamic power and backhaul link power are ignored. Few documents simultaneously consider the influence of the number of user antennas, the number of cell RAUs, the maximum transmission power and the like on energy efficiency, and in a more complex multi-cell DAS, the documents for such research are less.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing multi-cell large-scale DAS only considers single-cell single-parameter research energy efficiency.
In order to solve the above technical problem, a technical solution of the present invention is to provide an energy efficiency optimization method based on a RAU and a user distance relationship in a multi-cell large-scale DAS, which is characterized by comprising the following steps:
step 1, obtaining a k cell user MS of a multi-cell large-scale DAS cellkEnergy efficiency analysis formula xi of downlinkEE：
Figure BDA0001720559710000021
In the formula, N represents the number of antennas of each user, PFIXRepresenting static power consumption partsM denotes the number of antennas arranged for each RAU, L denotes the number of RAUs, PRAURepresenting the dynamic power requirement, P, of each RAU antennatDenotes the total transmission power required by the user, tau denotes the power amplification factor, PBHDenotes power consumption of a backhaul link, γ denotes a signal-to-noise ratio, K denotes a total number of cells, α denotes a path loss factor, λ denotes a constant satisfying λ ═ 1n (10)/10, and σ denotesshRepresents the standard deviation;
step 2, solving energy efficiency analytic expression xi within the RAU number rangeEEComprises the following steps:
step 201, initializing RAU number l to 1, xiEE(L) 0, and the number of radio frequency chains is SL；
Step 202, if l is less than SLIf l' ═ l +1, xi is calculatedEE(L ═ L'), proceed to step 203; if l is greater than or equal to SLTerminating the algorithm;
step 203, judge xiEE(L＝l′)≤ξEE(L ═ L) is established, and if so, L and ξ at that timeEE(L ═ L) is an optimum value, and is directly output, and if it is not true, L ═ L' is updated, and ξ is calculatedEE(L ═ L), return to step 202;
step 3, solving energy efficiency analytic expression xi within the range of the number of the user antennasEEComprises the following steps:
step 301, initializing the number n of user antennas to 1, ξEE(N-0) and the maximum number of user antennas is SN；
Step 302, if n is less than SNIf n' is n +1, xi is calculatedEE(N ═ N'), proceed to step 203; if n is greater than or equal to SNTerminating the algorithm;
step 303, judge xiEE(N＝n′)≤ξEEWhether (N ═ N) is true or not, and if true, N and ξ at that timeEE(N ═ N) is an optimum value, and is directly output, and if not, N ═ N' is updated, and ξ is calculatedEE(N), return to step 302;
step 4, solving energy efficiency analytic expression xi in the range of emission powerEEIncluding the optimal solution ofThe following steps:
step 401, initializing the transmission power p to 1, ξEE(PtSetting the threshold value of the maximum transmission power of the system as p) to 0
Figure BDA0001720559710000031
And step length lambda;
step 402, if
Figure BDA0001720559710000032
P' ═ p + λ, and ξ is calculatedEE(PtP'), go to step 203; if it is
Figure BDA0001720559710000033
The algorithm is terminated;
step 403, judge ξEE(Pt＝p′)≤ξEE(PtP), if true, p and xi at that timeEE(PtP) is the optimum value, and is directly output, if not, p is updated, and p is p', and xi is calculatedEE(PtP), return to step 402.
According to the invention, an analytic expression of system throughput is deduced according to the distance between the RAU and the user. And then establishing an optimization model of the system downlink energy efficiency according to the relation between a composite channel with the characteristics of path loss, shadow fading and multipath fading and a plurality of actual power consumption parameters. Finally, the change relation between the system energy efficiency and the number of the cell RAUs, the number of the user antennas and the transmitting power is deduced on the basis, and the theoretical value of the optimal system energy efficiency is obtained through the proposed algorithm.
The invention considers various factors (the number of user antennas, the number of cell RAUs, the maximum transmitting power and the like) influencing the energy efficiency of the multi-cell large-scale DAS at the same time, and optimizes the system energy efficiency under an actual channel model and a power consumption model containing a plurality of actual power consumption parameters, so that the invention has the advantages of obtaining the optimal system energy efficiency with less iteration times and being easy to realize.
Aiming at the communication scene of the multi-cell large-scale DAS, the mathematical analytic expression of the multi-cell large-scale DAS throughput is obtained based on the distance between the user and the RAUs, and the mathematical model of the system energy efficiency is established on the basis of considering the real channel and the actual power consumption model. Then, the fact that the energy efficiency of the multi-cell large-scale DAS system has the optimal solution in the value ranges of the number of RAUs, the number of user antennas, the transmitting power and the like is verified, and the optimal solution of the energy efficiency in the value ranges of the factors is solved through a low-complexity algorithm.
The invention has the advantages of low complexity and easy realization.
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Fig. 1 is a block model diagram of a multi-cell large-scale DAS system according to an embodiment;
FIG. 2 is a graph showing a variation of energy efficiency values for different RAU numbers in the example;
FIG. 3 is a graph showing a variation of energy efficiency values for different user antenna numbers in the embodiment;
FIG. 4 shows different transmission powers P in the embodimenttA change curve of a value of lower energy efficiency;
FIG. 5 is a three-dimensional graph of energy efficiency and multi-parameters of the system in the example.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The invention provides an energy efficiency optimization method based on the RAU and user distance relationship in a multi-cell large-scale DAS, which is based on the following argument:
establishing a multi-cell large-scale DAS model based on the distance between the RAU and the user:
Figure BDA0001720559710000041
in the formula (1), YkA received signal representing a k cell user; k represents the total number of cells；HikIndicating all RAUs to MSs in cell ikOf the channel response matrix, MSkRepresents a k cell user; dikIndicating all RAUs to MSs in cell ikA distance vector of (d); xiIndicates all RAUs in cell ilkFor cell user MSkThe transmission signal of (1); n represents additive white gaussian noise.
During downlink communication, the l-th RAU in cell i is relative to the k-th cell user MSkA distance d betweenlikComprises the following steps:
Figure BDA0001720559710000042
when i ═ k:
Figure BDA0001720559710000043
when i ≠ k:
Figure BDA0001720559710000051
in the formulae (2), (3) and (4), (a)lik，θlik) Indicates the first RAU in the cell i relative to the k cell user MSkIn polar coordinate form; (rho, theta) is the k cell user MSkIn polar coordinate form; p (rho) and p (theta) are k cell users MSkA probability density function of the polar coordinates (p, θ) with respect to the center of the coverage area; r represents a cell radius; l represents the number of RAUs deployed per cell.
Kth cell user MSkAverage information amount of IkComprises the following steps:
Figure BDA0001720559710000052
in formula (5):
IMan identity matrix representing the M dimensions; gamma represents the signal-to-noise ratio; n represents eachNumber of antennas of individual users, HWRepresenting a small-scale fading matrix; i isNAn identity matrix representing N dimensions;
Figure BDA0001720559710000055
represents an intermediate variable, wherein:
Figure BDA0001720559710000053
in the above formula, R ═ R; d ═ 2R; sLikRepresents logarithmic shadow fading; α represents a path loss factor.
Figure BDA0001720559710000054
In the above formula, HW，LDenotes an intermediate variable, HW，LikL-th to k-th cell user MS representing ith cellkThe small-scale fading matrix.
To IkK cell user MS obtained by averagingkAverage information amount C ofkComprises the following steps:
Ck＝E[Ik] (6)。
according to the definition of energy efficiency, the user MS of the kth cell of the large-scale DAS system with multiple cells can be realizedkEnergy efficiency analysis formula xi of downlinkEE：
Figure BDA0001720559710000061
In the formula, N represents the number of user antennas, PFIXDenotes a static power consumption part, M denotes the number of antennas per RAU configuration, L denotes the number of RAUs, and P denotesRAURepresenting the dynamic power requirement, P, of each RAU antennatDenotes the total transmission power required by the user, tau denotes the power amplification factor, PBHDenotes power consumption of a backhaul link, γ denotes a signal-to-noise ratio, K denotes a total number of cells, α denotes a path loss factor, λ denotes a constant satisfying λ ═ 1n (10)/10,σshthe standard deviation is indicated.
Energy efficiency analytic formula xiEEThe optimal solution exists in the respective value ranges of the number of RAUs, the number of user antennas, the transmitting power and the like:
(1) energy efficiency analytic expression xi in the value range of RAU number LEEThere is an optimal solution.
Energy efficiency analysis formula xiEEThen, there are:
Figure BDA0001720559710000062
in the formula (7), PtotalRepresents the total power consumption of the system, CkIndicates the k cell user MSkAverage information amount of (2).
Since the denominator of the formula (7) is a positive number, the positive and negative of the formula (7) are completely determined by numerator. Order to
Figure BDA0001720559710000063
And taking a derivative of ζ (L) can result in:
Figure BDA0001720559710000064
this indicates that the zeta (L) value decreases with increasing L and satisfies
Figure BDA0001720559710000065
That is, there is l ∈ [0, ∞) ] so that ζ (l) becomes 0. At the same time satisfy
Figure BDA0001720559710000071
In [0, l]Greater than zero, and less than zero in [ l, ∞). So xiEEIn [0, l]Increment, decrement at [ l, ∞). Presence of an appropriate l on [0, ∞) ] makes ξEEAnd max.
(2) Energy efficiency analytic expression xi in value range of user antenna number NEEThere is an optimal solution.
Paxi xiEEDerived with respect to N
Figure BDA0001720559710000072
t represents an intermediate variable in the form of an integral of the gamma function.
Order to
Figure BDA0001720559710000073
Then there is
Figure BDA0001720559710000074
Visible energy efficiency analytical formula xiEEIs a convex function with respect to N. So that there must be one n at [0, ∞) so that
Figure BDA0001720559710000075
Namely, it is
Figure BDA0001720559710000076
Greater than zero at [0, n) and less than zero at [ n, ∞). So xiEEN is gradually increased at [0, N) and gradually decreased at [ N, ∞), and N is energy efficiency analysis formula xiEEThe number of antennas at the optimum time.
(3) Energy efficiency analytic formula xiEEAt a transmission power PtThere is an optimal solution within the value range of (a).
Passing through xiEEIn respect of PtIs derived by
Figure BDA0001720559710000077
Order to
Figure BDA0001720559710000078
To PtThe derivation can be:
Figure BDA0001720559710000079
visible Ψ (P)t) With respect to variable PtDecrease progressively and satisfy
Figure BDA00017205597100000710
I.e. the presence of a p at [0, ∞)tSuch that Ψ (p)t) Is equal to 0, and
Figure BDA00017205597100000711
at [0, pt]Is greater than zero at [ p ]tAnd ∞) is less than zero. Visible emission power of pt(0≤pt≤Pt) Time-energy efficiency analytic formula xiEEAnd taking an optimal value.
Specifically, the method comprises the following steps:
in this example, considering a large-scale DAS including K uncooperative cells, L RAUs uniformly distributed along a circular optical fiber backhaul are deployed in each circular cell with a radius R, where the first RAU of the K-th cell is denoted as RAUlkL ∈ {1, 2., L }, K ∈ {1, 2., K }, each RAUlkAnd configuring M antennas. Each cell has only one user (MS) uniformly distributed in the cell, N antennas are configured, and a plurality of RAUs can be received simultaneouslylkIs transmitted. Considering the RAU of a non-own cellli(i ≠ k) for MSkIn case of interference of (3), L RAUsli(i ≠ k) in a circle of radius 2R (in MS)kThe center of the cell is the center of the circle). The parameter settings are shown in table 1 below:
TABLE 1
Figure BDA0001720559710000081
The energy efficiency is optimized by the embodiment, and the algorithm uses the condition energy efficiency analytic expression xiEEThe condition that the optimal solution exists in the respective value ranges of the RAU number, the user antenna number, the transmitting power and the like is met, and the evidence is obtained. Therefore, the following three algorithms can be set to respectively solve the optimal values of energy efficiency in the ranges of the number of RAUs, the number of user antennas, the transmission power and the like.
Algorithm 1: and solving the optimal value of the energy efficiency within the RAU number range. Since the number of RAUs cannot exceed the number of available radio frequency chains, the number of radio frequency chains can be used as a termination condition for seeking the number of RAUs. Meanwhile, since the number of RAUs can only be an integer, the step size can be set to 1 when the iterative step size is set.
Step 201, initializing the RAU number lIs 1, xiEE(L) 0, and the number of radio frequency chains is SL；
Step 202, if l is less than SLIf l' ═ l +1, xi is calculatedEE(L ═ L'), proceed to step 203; if l is greater than or equal to SLTerminating the algorithm;
step 203, judge xiEE(L＝l′)≤ξEE(L ═ L) is established, and if so, L and ξ at that timeEE(L ═ L) is an optimum value, and is directly output, and if it is not true, L ═ L' is updated, and ξ is calculatedEE(L ═ L), return to step 202.
And 2, algorithm: and solving the optimal value of the energy efficiency within the range of the number of the user antennas. The maximum number of subscriber antennas to which the system can connect is taken as a termination condition and the step size is set to 1.
Step 301, initializing the number n of user antennas to 1, ξEE(N-0) and the maximum number of user antennas is SN；
Step 302, if n is less than SNIf n' is n +1, xi is calculatedEE(N ═ N'), proceed to step 203; if n is greater than or equal to SNTerminating the algorithm;
step 303, judge xiEE(N＝n′)≤ξEEWhether (N ═ N) is true or not, and if true, N and ξ at that timeEE(N ═ N) is an optimum value, and is directly output, and if not, N ═ N' is updated, and ξ is calculatedEE(N), return to step 302.
Algorithm 3: and solving the optimal value of the energy efficiency in the transmission power range. The transmission power is set to be too large, which not only can not improve the system energy efficiency, but also causes resource waste, resulting in worse system performance, so the system usually sets a threshold of the maximum transmission power, and can use the threshold as an energy efficiency analytic expression xiEEWhen the optimal transmission power is optimal, seeking an optimal transmission power termination condition, setting a proper step length, and specifically implementing the following steps:
step 401, initializing the transmission power p to 1, ξEE(PtSetting the threshold value of the maximum transmission power of the system as p) to 0
Figure BDA0001720559710000091
And step length lambda;
step 402, if
Figure BDA0001720559710000092
P' ═ p + λ, and ξ is calculatedEE(PtP'), go to step 203; if it is
Figure BDA0001720559710000093
The algorithm is terminated;
step 403, judge ξEE(Pt＝p′)≤ξEE(PtP), if true, p and xi at that timeEE(PtP) is the optimum value, and is directly output, if not, p is updated, and p is p', and xi is calculatedEE(PtP), return to step 402.
As shown in fig. 2, the energy efficiency EE is a value for different RAUs. As can be seen from fig. 2, in the present invention, the energy efficiency EE increases gradually and then decreases gradually as the number L of RAUs increases, that is, the energy efficiency EE has an optimal solution within the value range of the number L of RAUs. It can also be seen from the figure that: firstly, when the RAU antenna number L and the user antenna number N are determined, the energy efficiency EE is gradually increased along with the increase of the cell number K; and secondly, when the number L of the RAU antennas is constant with the number K of the cells, the energy efficiency EE is improved along with the increase of the number N of the user antennas.
As shown in fig. 3, energy efficiency EE is a value for the number of different user antennas. As can be seen from the legend 3, in the present invention, the energy efficiency EE increases and then decreases along with the overall variation trend of the user antenna number N, that is, the energy efficiency EE has an optimal solution within the value range of the user antenna number N. It can also be seen from the figure that: when the number of cells K and the transmission power PtWhen the number of RAUs is determined, the energy efficiency EE is increased along with the increase of the number L of the RAUs.
As shown in fig. 4, energy efficiency EE is a value at different transmission power. As can be seen from legend 4, in the present invention, the energy efficiency EE is a function of the transmitted power PtIncreasing and then decreasing, i.e. energy efficiency EE at transmission power PtThere is an optimal solution within the value range of (a). It can also be seen from the figure that: when the number of RAUs is fixed (L is 6), the energy efficiency EE is increased along with the increment of the number N of the user antennas; when the user is on dayWhen the number of lines is constant (N is 4), the energy efficiency EE decreases as the number L of RAUs increases.
As shown in fig. 5, the energy efficiency EE is a value when the transmission power and the RAU number are different. As can be seen from the figure, the energy efficiency is maximized when the number of RAUs is 4 and the transmission power is 6. As can be seen from the graph, energy efficiency can be optimized by simultaneously and appropriately setting the values of a plurality of parameters.
In the above embodiment, the step length is set to 1 in the algorithms (1) and (2), and the number of RAUs and the number of user antennas are not infinitely increased, so the algorithms (1) and (2) can find the optimal result with a small number of iterations. The algorithm (3) can also find the optimal solution quickly by reasonably setting the step length lambda. Therefore, the algorithm does not additionally increase excessive overhead in practical application and is easy to implement.
In conclusion, the method can obtain the optimal energy efficiency value with lower iteration times and is easy to realize in practice.

Claims (1)

1.一种多小区大规模DAS中基于RAU和用户距离关系的能效优化方法，其特征在于，包括以下步骤：1. the energy efficiency optimization method based on RAU and user distance relation in a kind of multi-cell large-scale DAS, is characterized in that, comprises the following steps: 步骤1、获得多小区大规模DAS系统小区的第k个小区用户MSk下行链路的能效解析式ξEE：Step 1. Obtain the energy efficiency analytical formula ξ EE of the downlink of the kth cell user MS k of the multi-cell large-scale DAS system cell:
Figure FDA0001720559700000011
Figure FDA0001720559700000011
 式中，N表示用户天线个数，PFIX表示静态功率消耗部分，M表示每个RAU配置的天线数，L表示RAU个数，PRAU表示每个RAU天线的动态功率要求，Pt表示用户要求的总的传输功率，τ表示功放因子，PBH表示回程链路的功率消耗，γ表示信噪比，K表示小区的总数，α表示路径损耗因子，λ表示满足λ＝ln(10)/10的常数，σsh表示标准差；In the formula, N represents the number of user antennas, P FIX represents the static power consumption part, M represents the number of antennas configured in each RAU, L represents the number of RAUs, P RAU represents the dynamic power requirement of each RAU antenna, and P t represents the user The total required transmission power, τ represents the power amplifier factor, P BH represents the power consumption of the backhaul link, γ represents the signal-to-noise ratio, K represents the total number of cells, α represents the path loss factor, and λ represents the satisfaction of λ=ln(10)/ A constant of 10, σ sh represents the standard deviation; 步骤2、求解在RAU个数范围内的能效解析式ξEE的最优解，包括以下步骤：Step 2. Solve the optimal solution of the energy efficiency analytical formula ξ EE within the range of the number of RAUs, including the following steps: 步骤201、将RAU个数l初始化为1，ξEE(L＝l)＝0，设射频链的个数为SL；Step 201, initialize the number 1 of RAUs to 1, ξ EE (L=1)=0, and set the number of radio frequency chains to be S L ; 步骤202、若l＜SL，则l′＝l+1，计算ξEE(L＝l′)，进入步骤203；若l≥SL，则终止算法；Step 202, if l< SL , then l'=l+1, calculate ξ EE (L=l'), and go to step 203; if l≥SL , terminate the algorithm; 步骤203、判断ξEE(L＝l′)≤ξEE(L＝l)是否成立，若成立，则此时的l和ξEE(L＝l)是最优值，直接输出，若不成立，更新l，l＝l′，计算ξEE(L＝l)，返回步骤202；Step 203: Determine whether ξ EE (L=l′)≤ξ EE (L=1) is established, if so, then l and ξ EE (L=1) at this time are optimal values, and output directly, if not, Update l, l=l', calculate ξ EE (L=l), and return to step 202; 步骤3、求解在用户天线数范围内的能效解析式ξEE的最优解，包括以下步骤：Step 3. Solve the optimal solution of the energy efficiency analytical formula ξ EE within the range of the number of user antennas, including the following steps: 步骤301、将用户天线数n初始化为1，ξEE(N＝n)＝0，设最大用户天线个数为SN；Step 301: Initialize the number n of user antennas to 1, ξ EE (N=n)=0, and set the maximum number of user antennas to be S N ; 步骤302、若n＜SN，则n′＝n+1，计算ξEE(N＝n′)，进入步骤203；若n≥SN，则终止算法；Step 302, if n<S N , then n'=n+1, calculate ξ EE (N=n'), and go to step 203; if n≥S N , terminate the algorithm; 步骤303、判断ξEE(N＝n′)≤ξEE(N＝n)是否成立，若成立，则此时的n和ξEE(N＝n)是最优值，直接输出，若不成立，更新n，n＝n′，计算ξEE(N＝n)，返回步骤302；Step 303: Determine whether ξ EE (N=n′)≤ξ EE (N=n) is established, if so, then n and ξ EE (N=n) at this time are optimal values, and output directly, if not established, Update n, n=n', calculate ξ EE (N=n), and return to step 302; 步骤4、求解在发射功率范围内的能效解析式ξEE的最优解，包括以下步骤：Step 4. Solve the optimal solution of the energy efficiency analytical formula ξ EE within the transmit power range, including the following steps: 步骤401、将发射功率p初始化为1，ξEE(Pt＝p)＝0，设系统最大发射功率的阈值为
Figure FDA0001720559700000021
及步长λ；Step 401: Initialize the transmit power p to 1, ξ EE (P t =p)=0, and set the threshold of the maximum transmit power of the system to be
Figure FDA0001720559700000021
and step size λ; 步骤402、若
Figure FDA0001720559700000022
则p′＝p+λ，计算ξEE(Pt＝p′)，进入步骤203；若
Figure FDA0001720559700000023
则终止算法；Step 402, if
Figure FDA0001720559700000022
Then p'=p+λ, calculate ξ EE (P t =p'), go to step 203; if
Figure FDA0001720559700000023
then terminate the algorithm; 步骤403、判断ξEE(Pt＝p′)≤ξEE(Pt＝p)是否成立，若成立，则此时的p和ξEE(Pt＝p)是最优值，直接输出，若不成立，更新p，p＝p′，计算ξEE(Pt＝p)，返回步骤402。Step 403: Determine whether ξ EE (P t =p')≤ξ EE (P t =p) is established, if so, then p and ξ EE (P t =p) at this time are optimal values, and output directly, If not, update p, p=p', calculate ξ EE (P t =p), and return to step 402 .
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