CN113498119A - Power control method for non-orthogonal multiple access system - Google Patents

Power control method for non-orthogonal multiple access system Download PDF

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CN113498119A
CN113498119A CN202110480366.3A CN202110480366A CN113498119A CN 113498119 A CN113498119 A CN 113498119A CN 202110480366 A CN202110480366 A CN 202110480366A CN 113498119 A CN113498119 A CN 113498119A
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user
theta
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朱政宇
杨晨一
鲍浩
吕亚培
王家政
李铮
孙钢灿
郝万明
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Zhengzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
    • 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

Abstract

The invention discloses a power control method of a non-orthogonal multiple access system, which jointly optimizes the energy efficiency and the spectrum efficiency of the system and comprises the following steps: s1: constructing a downlink multi-user NOMA system model, and setting system parameters; s2: defining a new system performance measurement function, and constructing a target problem of a combined optimization system EE and SE; s3, converting the original multi-objective optimization problem into a single-objective optimization problem only containing single variables, and providing a power distribution algorithm based on two-layer optimization to solve the objective problem; s4: and the inner layer deduces an expression of the optimal closed form of the user power distribution coefficient according to the KKT condition, and on the basis, the outer layer searches for the optimal system transmitting power which maximizes the joint objective function based on a binary search algorithm. The invention provides a power optimization allocation method based on a multi-user downlink NOMA system, which takes the joint optimization problem of EE and SE into consideration. And a perfect CSI condition is assumed, QoS of all access users is guaranteed, and effective compromise between EE and SE of the system can be realized according to time-varying communication requirements.

Description

Power control method for non-orthogonal multiple access system
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a power control method for non-orthogonal multiple access.
Background
In recent years, the continuous update of wireless communication technology brings about profound changes to various aspects of human social life. Meanwhile, with the rapid development of the internet of things and the fifth generation mobile communication technology, the future wireless communication technology also faces more and more serious challenges. In addition to low latency, mobility, reliability, etc., its demand for large capacity becomes exceptionally significant. In order to better satisfy the development requirements of a 5G communication system for a data communication rate 100-1000 times higher than that of a 4G system, a device access amount 10-100 times higher than that of the 4G system and supporting more diversified QoS, improvement on the prior art and innovation on the prior art are urgent. As an improvement of the conventional Orthogonal Multiple Access (OMA) technology, non-orthogonal multiple access (NOMA) is now proposed. NOMA is a prominent radio technology, which enables a transmitting end to simultaneously transmit information of a plurality of users in a same frequency domain by superposition mainly through power multiplexing, so that in a NOMA transmission system, users are not limited by binary selection of frequency band and time resources, and joint optimization of the system in terms of power domain, code domain and receiver design is realized by relaxing orthogonal wireless resource limitation conditions. Different users can be distinguished by non-orthogonal waveforms on the same time or frequency resource, which means that in the same time-frequency resource, NOMA can support multiple users to share access, and the technical scheme has significant advantages in improving the system spectrum efficiency under the background of shortage of current wireless spectrum resources.
Therefore, in order to ensure the advantages of the NOMA technology, the research on the power control problem in the related system not only conforms to the development trend of the emerging communication technology, but also relieves the problem of lack of wireless frequency band resources to a certain extent, which is very beneficial to improving the performance of the wireless communication system and promoting the development of social economy.
On the other hand, research has shown that today's infrastructure of information and communication technology consumes more than 3% of the energy worldwide, most of which is consumed by base stations. Therefore, while paying attention to data communication demands, green wireless radio, which is mainly studied on energy utilization, has become an unavoidable trend in the academic and industrial fields of today. Currently, the power distribution design problem in the NOMA system is a single-target optimization problem which takes Energy Efficiency (EE) or Spectrum Efficiency (SE) as a target. However, in an actual communication network, effective balancing between EE and SE of the system is often needed according to different communication scenarios to better adapt to communication requirements, and especially under the background that the current communication equipment is high in consumption and the data rate requirement is large, which causes electromagnetic pollution, it is necessary to jointly research the balancing problem between EE and SE.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, establish a network model of a multi-user NOMA downlink system, provide a power control scheme of a joint optimization system EE and SE, and consider the transmission power limit of a base station and the QoS constraint of a user at the same time:
the technical problems to be solved are as follows:
problem 1: performing mathematical modeling on a joint optimization problem based on system EE and SE aiming at a downlink multi-user NOMA system model;
problem 2: according to a specific mathematical optimization problem, converting an original multi-target problem into a single-target problem by using a multi-target problem processing method, and designing and analyzing a power control algorithm.
The purpose of the invention is realized as follows:
a power control method for a non-orthogonal multiple access system, comprising:
s1, constructing a downlink multi-user NOMA system model and setting system parameters;
s2, defining a new system performance measurement function, and constructing a target problem of the combined optimization system EE and SE;
s3: converting an original multi-objective optimization problem into a single-objective optimization problem only containing single variables, and providing a power distribution algorithm based on two-layer optimization to solve the objective problem;
s4: and the inner layer deduces an optimal closed expression of the user power distribution coefficient according to the KKT condition, and on the basis, the outer layer searches for the optimal system transmitting power which maximizes the joint objective function based on a binary search algorithm.
Drawings
Fig. 1 is a schematic flow chart structure of a power control method of a non-orthogonal multiple access system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a network model of a multi-user downlink NOMA system according to an embodiment of the present invention;
FIG. 3 is a graph of the variation of the transmission power of the system EE and SE according to an embodiment of the invention;
FIG. 4 is an EE comparison of a power control scheme versus a full power allocation scheme implementation of an embodiment of the present invention;
FIG. 5 is a graph of optimal transmit power versus weight factor for an embodiment of the present invention;
FIG. 6 is a graph of the change in iteration number required to search for optimal transmit power based on the dichotomy principle in practicing the present invention;
FIG. 7 is a graph of the variation of the system EE, SE and the joint objective function with the weighting factor according to the embodiment of the present invention;
the invention has the beneficial effects that:
the power control method of the non-orthogonal multiple access system provided by the invention establishes a network model of a downlink multi-user NOMA system, and considers the joint optimization problem of the EE and the SE of the system. A power control algorithm based on two-layer optimization is provided while the QoS of a user and the maximum transmitting power of a base station are guaranteed. The power control algorithm provided by the invention can flexibly realize the selection emphasis of the system between EE and SE so as to better adapt to the real-time service requirements under different communication scenes and ensure the comprehensive performance of the system.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and are not to be construed as limiting the scope of the present invention.
Example 1:
fig. 1 is a flowchart illustrating a power control method of a non-orthogonal multiple access system according to this example, including the following steps:
s1, constructing a downlink multi-user NOMA system model and setting system parameters; the system model is shown in fig. 2, and specifically includes the following:
consider a downlink multi-user NOMA transmission scenario in which 1 central base station serves M single-antenna users simultaneously. The channel coefficient from the base station to the mth user is hm＝gmd-α/2Wherein g ismRepresents rayleigh fading, d represents the distance from the base station to the mth user, and α is the path loss exponent. Assuming that the instantaneous CSI of all users is known at the base station, without loss of generality, we assume that the channel gains of all users can be arranged in ascending order: 0 < | h1|2≤|h2|2…≤|hM|2。
According to the NOMA technical principle, a base station sends superposed information containing M user signals to each user through a power multiplexing technology, and a user receiving end solves the problem of interference among superposed users by executing SIC. For example, the mth user first decodes information of the ith (i-1, 2, …, M-1) user and subtracts it from the received superimposed signal, and then decodes its own signal by regarding the signal of the jth (j-M +1, M +2, …, M) user as interference.
Based on the above analysis, the achievable rate for the mth user can be expressed as:
Figure BDA0003048986530000041
wherein, PmaxRepresenting the maximum available transmit power at the base station,
Figure BDA0003048986530000042
and distributing the coefficient for the power of the mth user.
Thus, the total throughput of the system can be expressed as:
Figure BDA0003048986530000051
assuming that the system has a unit bandwidth, EE and SE of the system can be expressed as follows, respectively:
ηSE＝R, (3)
Figure BDA0003048986530000052
wherein, PcFor the system static circuit power consumption, P is the transmission power actually consumed by the system.
S2, defining a new system performance measurement function, and constructing a target problem of the combined optimization system EE and SE; the method specifically comprises the following steps:
first, fig. 3 shows the variation of the system EE with SE for different static circuit powers with the number of users M being 2, 3, 4, respectively. It was first discovered that as the power of the static circuit increases, the maximum EE value that the system can achieve decreases, while SE is unaffected. This is determined by the definition of EE and SE, and when the static circuit power is larger, the total power consumption of the system becomes larger, but the spectral efficiency remains unchanged, and then the power utilization rate is reduced, so EE is reduced. Secondly, the transmission power is continuously increased, the EE is firstly increased along with the increase of the power, when the EE reaches a certain peak point, the transmission power is continuously increased to improve the SE, the utilization rate of the transmission power is reduced, the EE begins to be reduced, namely, the change trends of the EE and the SE begin to be contradictory, which means that the EE and the SE have compromise.
Because the EE and SE units are different, the weighted summation operation directly performed on the EE and SE units easily causes large error on the result, so that eta needs to be subjected to the operation firstlySEAnd ηEECarrying out normalization to make the two have additivity and comparability, and then defining a new system metric function based on a weighted summation method:
W(λEE,λSE)＝ω·λEE+(1-ω)λEE, (5)
wherein the content of the first and second substances,
Figure BDA0003048986530000053
and is
Figure BDA0003048986530000054
And
Figure BDA0003048986530000055
respectively representing the maximum system EE and SE achievable within a given base station transmit power range; ω ═ { ω |0 ≦ ω ≦ 1} represents the selection weight factors for EE and SE. Generally, communication systems often face different communication requirements in practical scenarios. When the communication data volume of the system is large, the system uses as much transmission power as possible to improve the utilization rate of the frequency spectrum, and the utilization rate of the energy resources of the system is reduced; when the power of the system is limited, the system aims to ensure the basic communication quality, and the energy utilization rate needs to be improved to the maximum extent. As can be seen from the system metric function defined by equation (5), the system can be weighted between EE and SE by adjusting the weighting factor ω. Specifically, when ω is 1, the system metric is equivalent to EE; when ω is 0, the metric of the system becomes SE.
By substituting formulae (3) and (4) into formula (5), further obtained is:
Figure BDA0003048986530000061
then, under the constraint of the maximum transmission power of the base station, considering the QoS requirements of all users at the same time, the multi-objective joint optimization problem of the multi-user NOMA system EE and SE can be further expressed as follows:
max λEE-SE, (7a)
Figure BDA0003048986530000062
Pmin≤P≤Pmaxand is
Figure BDA0003048986530000063
The constraints of the mathematical problem include:
constraint of total transmission power of system: pmin≤P≤PmaxAnd is
Figure BDA0003048986530000064
QoS requirements of each user:
Figure BDA0003048986530000065
further define the
Figure BDA0003048986530000066
The minimum transmit power required to satisfy the user QoS in all systems. Wherein
Figure BDA0003048986530000067
Minimum transmit power required for user m, and
Figure BDA0003048986530000068
the following can be calculated sequentially according to formula (8) in the order of M ═ M, M-1, …, 1:
Figure BDA0003048986530000069
wherein the content of the first and second substances,
Figure BDA00030489865300000610
s3: converting an original multi-objective optimization problem into a single-objective optimization problem only containing single variables, and providing a power distribution algorithm based on two-layer optimization to solve the objective problem; the method specifically comprises the following steps:
defining theta (theta is larger than { theta |0 ≦ theta ≦ 1}) as the transmission power P actually consumed and the total transmission power P of the base stationmaxThe following definitions are given for the ratio of (c):
Figure BDA0003048986530000071
Figure BDA0003048986530000072
Figure BDA0003048986530000073
Figure BDA0003048986530000074
based on the above definitions, R can be rearranged as:
Figure BDA0003048986530000075
at this time, the final solution of the target problem (7) is a function with respect to the unique variable θ.
S4: the inner layer deduces an optimal closed expression of the user power distribution coefficient according to the KKT condition, and on the basis, the outer layer searches for optimal system transmitting power which enables a combined objective function to be maximized based on a binary search algorithm; the method specifically comprises the following steps:
the power distribution of the inner layer is to the parameter by regarding theta as a constant
Figure BDA0003048986530000076
An assignment is made whose solution is a function of θ. It can be found that for any given θ, the first term to the right of the medium sign in equation (13) is a constant, where the optimization problem for R is equivalent to:
Figure BDA0003048986530000077
Figure BDA0003048986530000078
Figure BDA0003048986530000079
equation (14b) is a mathematical transformation of the constraint (7 b). Further, the objective function described in equation (14a) is the sum of M-1 subfunctions having the same form, and the optimal solution thereof can be given by proposition 1.
Proposition 1: optimal user power distribution coefficient for maximizing an objective function (6a)
Figure BDA00030489865300000710
Can be given by equation (15):
Figure BDA0003048986530000081
the outer layer, as such, can be described as a univariate optimization problem with respect to θ:
Figure BDA0003048986530000082
s.t.Pmin/Pmax≤θ≤1； (16b)
according to theorem 1, obtaining the optimal system transmitting power which maximizes the target problem (16) by adopting a binary search algorithm;
theorem 1: lambda'EE-SE(θ) is a strict pseudo-concave function with respect to θ.
Target function λ'EE-SE(theta) has been shown to be a strict pseudo-concave function with respect to theta, which guarantees the existence and uniqueness of a globally optimal solution to the problem sought. Meanwhile, the optimal solution is equation d λ'EE-SERoot of (θ)/d θ ═ 0. Wherein for d λ'EE-SEThe expression of (θ)/d θ is shown in formula (17). Further, after determining a suitable weight factor ω according to system requirements, an optimal power consumption factor θ that maximizes the objective function can be found in the outer layer algorithm by a binary search principle*(ii) a The method specifically comprises the following steps:
the base station firstly calculates the required minimum system transmitting power P according to the QoS requirement of the userminAnd judging whether or not the conditions are satisfied
Figure BDA0003048986530000083
Then, in the feasible region range [ P ] of thetamin/Pmax,1]In accordance with
Figure BDA0003048986530000084
Finding the optimal power dissipation factor theta*(ii) a Wherein the content of the first and second substances,
Figure BDA0003048986530000085
the size of (c) can be summarized into the following three cases:
(1) if it is not
Figure BDA0003048986530000086
This is shown at θ*∈[Pmin/Pmax,1]In range of λ'EE-SEIncreased and then decreased, when the optimum power consumption factor is theta*∈[Pmin/Pmax,1]The method can be obtained by dichotomy searching;
(2) if it is not
Figure BDA0003048986530000087
This is shown at θ*∈[Pmin/Pmax,1]In range of λ'EE-SEThe value of (c) is increasing. The optimal power consumption factor is θ ═ 1;
(3) if it is not
Figure BDA0003048986530000091
This is shown at θ*∈[Pmin/Pmax,1]In range of λ'EE-SEIs decreasing, the optimum power dissipation factor is θ ═ Pmin/Pmax；
Finally obtaining a joint objective function lambda'EE-SE(theta) maximized optimal system transmit power theta*PmaxAnd simultaneously recording the iteration times t required by performing binary search.
Figure BDA0003048986530000092
Wherein
Figure BDA0003048986530000093
Comprises the following steps:
Figure BDA0003048986530000094
and is
Figure BDA0003048986530000095
Fig. 4 shows the optimal joint transmit power θ in this embodiment*PmaxAs a function of the weighting factor omega. Wherein the maximum available power P at the base stationmaxAnd 30 dBm. As can be seen from the figure, θ*PmaxInitially it is held constant and equal to Pmax(ii) a As ω increases gradually, θ*PmaxAnd begins to decrease continuously. This is due to the fact that when ω is small, the systemIs equivalent to SE, when the base station uses as much transmit power as possible to maximize the SE of the system. As omega is continuously increased, the measurement index of the system is gradually biased to EE, and theta is at the moment*PmaxSlowly approaching the optimum transmit power P that can only maximize the system EE*。
FIG. 5 shows that the optimal system transmit power θ for maximizing the joint optimization problem is found in the present embodiment under different user numbers and weighting factors*PmaxThe power control scheme provided by the embodiment needs a binary search time variation curve. It can be seen that the actual power consumption of the system increases gradually as the number of users increases. This is because the base station needs more power to satisfy the QoS of the increased users, and it can be found from the convergence rate that the computational complexity of the algorithm does not increase with the increase of the number of users. Second, the optimal transmission power θ of the system when ω is 0.8*PmaxAnd significantly less than 0.2. This is because when ω is 0.2, the main metric of the system is SE, where the system always tends to use the full transmit power to maximize the system capacity. Conversely, when ω is 0.8, the main metric of the system is EE, where the system always looks for an available power value that can keep EE at an optimum value, and this value is always less than the power required to maximize SE. In addition, for the different parameters in the figure to be compared, the proposed method always obtains a more stable theta at about the 7 th search cycle*PmaxThis indicates that the convergence rate is preferable.
Fig. 6 shows the variation of EE and SE with the weighting factor ω in the present embodiment. FIG. 7 shows the joint objective function λE'E-SENormalized energy efficiency lambdaEESum spectrum effect lambdaSECurve with ω. From both figures, it can be seen that system EE gradually increases with increasing ω, whereas SE is opposite. This is determined by the defined joint objective function, and as ω slowly increases and continuously approaches 1, the main optimization index of the system gradually changes from SE to EE, and thus SE gradually decreases. In addition, EE and S approach 0The E values are all kept constant, which is equal to theta in FIG. 4*PmaxThe variations in (c) tend to be uniform. Additionally, the maximum SE of the system can be reached when ω is 0 and the system EE is optimal when ω is 1.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention, and those skilled in the art can make various modifications and variations. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. A power control method for a non-orthogonal multiple access system, comprising:
s1, constructing a downlink multi-user NOMA system model and setting system parameters;
s2, defining a new system performance measurement function, and constructing a target problem of the combined optimization system EE and SE;
s3, converting the original multi-objective optimization problem into a single-objective optimization problem only containing single variables, and providing a power distribution algorithm based on two-layer optimization to solve the objective problem;
and S4, the inner layer deduces the optimal closed expression of the user power distribution coefficient according to the KKT condition, and on the basis, the outer layer searches the optimal system transmitting power which maximizes the combined objective function based on a binary search algorithm.
2. The method as claimed in claim 1, wherein the step S1 specifically includes:
considering a downlink multi-user NOMA transmission scene; wherein 1 central base station serves M single antenna users simultaneously. The channel coefficient from the base station to the mth user is hm＝gmd-α/2Wherein g ismExpressing Rayleigh fading, d expressing the distance from a base station to an m user, and alpha expressing a path loss index; assuming that the instantaneous CSI of all users is known at the base station, without loss of generality, we assume that the channel gains of all users can be arranged in ascending order: 0 < | h1|2≤|h2|2…≤|hM|2；
Thus, the achievable rate for the mth user can be expressed as:
Figure FDA0003048986520000011
wherein, PmaxRepresenting the maximum available transmit power at the base station,
Figure FDA0003048986520000012
distributing the power coefficient for the mth user;
thus, the total throughput of the system can be expressed as:
Figure FDA0003048986520000013
assuming that the total bandwidth of the system is a unit bandwidth, EE and SE of the system can be expressed as follows, respectively:
ηSE＝R， (3)
Figure FDA0003048986520000021
wherein, PcFor the system static circuit power consumption, P is the transmission power actually consumed by the system.
3. The method as claimed in claim 1, wherein the step S2 specifically includes:
introducing a weight factor, and obtaining a combined objective function of combined EE and SE based on linear weighted summation:
Figure FDA0003048986520000022
under the constraint of the maximum transmitting power of a base station, the QoS requirements of all users are considered simultaneously, and the multi-objective joint optimization problem of the multi-user NOMA system EE and SE can be constructed as follows:
maxλEE-SE, (6a)
Figure FDA0003048986520000023
Figure FDA0003048986520000024
the constraints of the mathematical problem include:
constraint of total transmission power of system: pmin≤P≤PmaxAnd is
Figure FDA0003048986520000025
(ii) minimum quality of service requirement for each user:
Figure FDA0003048986520000026
obviously, since problem (6) is non-convex, it is difficult to obtain a globally optimal solution in polynomial time. The invention decouples the problem into two layers by proving the pseudo-concave characteristic of the objective function, and solves the two layers respectively.
4. The method as claimed in claim 1, wherein the step S3 specifically includes:
the following definitions are first given:
Figure FDA0003048986520000031
Figure FDA0003048986520000032
Figure FDA0003048986520000033
Figure FDA0003048986520000034
based on the above definitions, R can be rearranged as:
Figure FDA0003048986520000035
defining theta (theta is larger than { theta |0 ≦ theta ≦ 1}) as the transmission power P actually consumed and the total transmission power P of the base stationmaxThe ratio of (a) to (b). At this time, the final solution of the target problem (6) is a function with respect to θ.
5. The method as claimed in claim 1, wherein the step S4 specifically includes:
and the inner layer obtains an optimal user power distribution coefficient closed solution by proposition 1:
proposition 1: optimal user power distribution coefficient for maximizing an objective function (6a)
Figure FDA0003048986520000036
Can be given by equation (12):
Figure FDA0003048986520000037
the outer layer, which can be described as a univariate optimization problem with respect to θ:
Figure FDA0003048986520000038
s.t.Pmin/Pmax≤θ≤1； (13b)
according to theorem 1, a binary search algorithm is adopted to obtain the optimal system transmitting power for maximizing the problem (13);
theorem 1: lambda'EE-SE(θ) is a strict pseudo-concave function with respect to θ.
Target function λ'EE-SE(theta) has been shown to be a strict pseudo-concave function with respect to theta, which guarantees the existence and uniqueness of a globally optimal solution to the problem sought. Meanwhile, the optimal solution is equation d λ'EE-SERoot of (θ)/d θ ═ 0. Wherein for d λ'EE-SEThe expression of (θ)/d θ is shown in formula (14). Further, after determining a suitable weight factor ω according to system requirements, an optimal power consumption factor θ that maximizes the objective function can be found in the outer layer algorithm by a binary search principle*。
Figure FDA0003048986520000041
Wherein
Figure FDA0003048986520000042
Comprises the following steps:
Figure FDA0003048986520000043
and is
Figure FDA0003048986520000044
6. The power control method of a non-orthogonal multiple access system according to claim 1 or claim 5, wherein the binary search algorithm specifically comprises:
the base station firstly calculates the required minimum system transmitting power P according to the QoS requirement of the userminAnd judging whether or not the conditions are satisfied
Figure FDA0003048986520000045
Then, in the feasible region range [ P ] of thetamin/Pmax,1]In accordance with
Figure FDA0003048986520000046
Finding the optimal power dissipation factor theta*(ii) a Wherein the content of the first and second substances,
Figure FDA0003048986520000047
the size of (c) can be summarized into the following three cases:
(1) if it is not
Figure FDA0003048986520000048
This indicates that at θ ∈ [ P ]min/Pmax,1]In range of λ'EE-SEIncreased and then decreased, when the optimum power consumption factor is theta*∈[Pmin/Pmax,1]The method can be obtained by dichotomy searching;
(2) if it is not
Figure FDA0003048986520000049
This is shown at θ*∈[Pmin/Pmax,1]In range of λ'EE-SEThe value of (c) is increasing. The optimal power consumption factor is θ ═ 1;
(3) if it is not
Figure FDA00030489865200000410
This is shown at θ*∈[Pmin/Pmax,1]In range of λ'EE-SEIs always decreasing, the optimum power consumption factor is theta*＝Pmin/Pmax；
Finally obtaining a joint objective function lambda'EE-SE(theta) maxOptimized system transmit power
Figure FDA0003048986520000051
And simultaneously recording the iteration times t required by performing binary search.
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