CN112770395A - Optimal dynamic power distribution method, system, medium and terminal based on uplink NOMA - Google Patents

Optimal dynamic power distribution method, system, medium and terminal based on uplink NOMA Download PDF

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CN112770395A
CN112770395A CN201911065907.5A CN201911065907A CN112770395A CN 112770395 A CN112770395 A CN 112770395A CN 201911065907 A CN201911065907 A CN 201911065907A CN 112770395 A CN112770395 A CN 112770395A
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users
uplink noma
power allocation
power distribution
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CN112770395B (en
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王兴位
徐天衡
周婷
封松林
胡宏林
魏凡博
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Shanghai Advanced Research Institute of CAS
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    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • 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
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    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides an optimal dynamic power distribution method, a system, a medium and a terminal based on uplink NOMA, comprising the following steps: 2N users of an uplink NOMA system are set to be uniformly distributed in a cell, and the users are paired pairwise into N clusters; constructing an estimation optimization model of 2N users based on constraint conditions of uplink NOMA; calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model; and calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor. The optimal dynamic power distribution method, the system, the medium and the terminal based on the uplink NOMA can reasonably utilize the power distributed in each cluster in the cell in the uplink NOMA system, thereby reducing the power waste and improving the throughput of the system.

Description

Optimal dynamic power distribution method, system, medium and terminal based on uplink NOMA
Technical Field
The present invention relates to the field of wireless communications technologies, and in particular, to an optimal dynamic power allocation method, system, storage medium, and terminal based on Non-Orthogonal Multiple Access (NOMA).
Background
Fifth generation mobile communications (5G) relates to applications and services in various fields. To address this challenge, a wide variety of technologies are expected to play a significant role in 5G and beyond-5G communication systems. Among them, the NOMA technique is one of the most attractive techniques among these. Compared with the traditional orthogonal multiple access technology, the non-orthogonal multiple access technology mainly adds the following two steps:
(1) at a transmitting end, two or more user signals are respectively multiplied by respective transmitting power and channel gain in a power domain and then are mutually superposed;
(2) at the receiving end, the respective signals are separated from the superimposed signals by using the conventional Successive Interference Cancellation (SIC) technique.
Among other things, power allocation is a key technology in non-orthogonal multiple access, because reasonable power allocation directly affects the spectral efficiency and energy efficiency of non-orthogonal multiple access transmission. In the prior art, on one hand, power allocation is divided into uplink power allocation and downlink power allocation. For downlink power allocation, the power allocation strategy has a mature technology. And the research of uplink power allocation is still lacking so far. On the other hand, the power allocation can be divided into dynamic power allocation and fixed power allocation. A fixed power allocation may achieve excellent throughput under certain channel conditions, but may not be flexible to adapt to dynamically changing channel environments.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide an optimal dynamic power allocation method, system, medium and terminal based on uplink NOMA, which can reasonably utilize the power allocated in each cluster in a cell in the uplink NOMA system, thereby reducing power waste and improving the throughput of the system.
To achieve the above and other related objects, the present invention provides an optimal dynamic power allocation method based on uplink NOMA, including the following steps: 2N users of an uplink NOMA system are set to be uniformly distributed in a cell, and the users are paired pairwise into N clusters; constructing an estimation optimization model of 2N users based on constraint conditions of uplink NOMA; calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model; and calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor.
In an embodiment of the present invention, the estimation optimization model is:
Figure BDA0002259332760000021
represents the sum throughput of 2N usersb and Ga,bA value of (d); and the following constraint conditions are required to be satisfied:
Figure BDA0002259332760000022
Pαb|hb|2-P(1-αb)|ha|2≥Ptol，
Figure BDA0002259332760000023
0≤αb≤1,0≤b≤2N，
Figure BDA0002259332760000024
wherein a and b are two users in a cluster, Rn(n ═ a, b) denotes the minimum reception rate of user n, PtolIndicating the clarity required to exercise SIC,
Figure BDA0002259332760000025
and
Figure BDA0002259332760000026
respectively representing the receiving rates, alpha, of user a and user b in the uplink OMA systembRepresents the power allocation factor, h, of user bn(n ═ a, b) denotes channel gain between the base station and the user, P denotes intra-cluster power,
Figure BDA0002259332760000027
σ2variance of additive white Gaussian noise, PtolIndicating the required resolution to apply successive interference cancellation.
In an embodiment of the present invention, calculating the possible upper and lower bounds of the power allocation factor of the user based on the estimation optimization model includes the following steps:
converting the estimation optimization model into an optimization model of each cluster of users
Figure BDA0002259332760000028
And the following constraint conditions are required to be satisfied:
Figure BDA0002259332760000029
Pαb|hb|2-P(1-αb)|ha|2≥Ptol，0≤αb≤1；
according to the constraint condition, obtaining
Figure BDA00022593327600000210
Figure BDA00022593327600000211
In an embodiment of the present invention, the optimal power allocation factor of user b is
Figure BDA0002259332760000031
wherein ,
Figure BDA0002259332760000032
the optimal power allocation factor of user a is
Figure BDA0002259332760000033
In an embodiment of the present invention, when pairwise pairing of the users is divided into N clusters, the user a and the user b are paired, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2And a + b is 2N +1, and h represents a channel gain.
In an embodiment of the present invention, when pairwise pairing of the users is divided into N clusters, the user a and the user b are paired, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2And | a-b | ═ N, and h denotes a channel gain.
In an embodiment of the present invention, when pairwise pairing of the users is divided into N clusters, the user a and the user b are randomly paired, and | h |1|2≥...|hb|2≥|ha|2...≥|h2N|2And h denotes a channel gain.
Correspondingly, the invention provides an optimal dynamic power distribution system based on uplink NOMA, which comprises a pairing module, a construction module, a first calculation module and a second calculation module;
the pairing module is used for setting 2N users of the uplink NOMA system to be uniformly distributed in a cell and pairwise pairing the users into N clusters;
the construction module is used for constructing an estimation optimization model of 2N users based on the constraint condition of uplink NOMA;
the first calculation module is used for calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model;
and the second calculation module is used for calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor.
The present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method for optimal dynamic power allocation based on upstream NOMA.
Finally, the present invention provides a terminal comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program stored in the memory, so as to enable the terminal to execute the above optimal dynamic power allocation method based on uplink NOMA.
As described above, the optimal dynamic power allocation method, system, medium, and terminal based on uplink NOMA according to the present invention have the following beneficial effects:
(1) the dynamic change of the power distribution factor along with the channel state is realized, and different channel conditions can be effectively adapted;
(2) when users with different channel gains exist in a cell, after respective clusters are formed, a sending end dynamically distributes proper power, so that the signals are correctly sent and received to the maximum extent, the error rate of the signals is effectively reduced finally, and the throughput of a system is improved;
(3) knowing that certain users are uniformly distributed in a cell and different channel differences exist among the users; when the channel difference of the two paired users is large, the strong user can share more power in the cluster while ensuring the normal transmission of the weak user through proper power distribution; when the channel difference of the channels in the cluster is small, the channel difference among users is increased through power distribution so as to meet the basic condition of applying SIC; when the weak user is at the edge of the cell and the signal-to-noise ratio is small, as much power as possible can be allocated to the strong user, thereby avoiding the waste of resources.
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FIG. 1 is a flow chart illustrating an exemplary method for uplink NOMA based optimal dynamic power allocation in accordance with the present invention;
FIG. 2 is a diagram illustrating the relationship between the power allocation factor of a user and the system throughput in one embodiment of the present invention;
FIG. 3 is a diagram illustrating a head-to-tail pairing model according to an embodiment of the invention;
FIG. 4 is a diagram illustrating a uniform pairing model according to an embodiment of the invention;
FIG. 5 is a diagram illustrating a random matching model according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an exemplary embodiment of an upstream NOMA based optimized dynamic power allocation system;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the invention.
Description of the element reference numerals
61 mating module
62 building block
63 first calculation Module
64 second computing Module
71 processor
72 memory
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The optimal dynamic power allocation method based on the uplink NOMA sets a cell containing a plurality of users, and obtains the optimal power allocation factor through an estimation model which can optimize a plurality of constraint conditions of the users, thereby reducing the power waste and improving the throughput of the system. Meanwhile, the superiority of the method compared with the uplink NOMA and uplink Orthogonal Multiple Access (OMA) scheme with fixed power distribution can be verified by using three classical pairing modes.
As shown in fig. 1, in an embodiment, the method for allocating optimal dynamic power based on uplink NOMA of the present invention includes the following steps:
and step S1, setting 2N users of the uplink NOMA system to be uniformly distributed in a cell, and pairwise pairing the users into N clusters.
Specifically, a cell with 2N users is set, and the base station is located in the center of the cell. The base station and each user are set to a single antenna form. Without loss of generality, the 2N users are divided into N clusters and simplified to analyze the optimal power allocation factor for each cluster in the cell. Setting that each cluster comprises a user a and a user b, wherein the transmission signals of the users in each cluster are respectively as follows:
Figure BDA0002259332760000051
and
Figure BDA0002259332760000052
wherein Sn(n ═ a, b) and αn(n ═ a, b) respectively denote a transmission signal and a power allocation factor of user n, and P denotes intra-cluster power. Generally, the setting of the power allocation factor is divided into the following two cases:
1) conventionally, the signal of each user is transmitted with the maximum power;
2) the power allocation factor is used to constrain the power of users within each cluster from an energy conservation perspective. In the present invention, the second case is mainly considered, and alpha is seta+αb＝1。
Setting signals of base station receiving end
Figure BDA0002259332760000053
wherein hn(n ═ a, b) is the channel gain between the base station and the user, whose Distribution is Rayleigh Distribution (Rayleigh Distribution); w is variance σ2White additive gaussian noise. Wherein, when two components of a random two-dimensional vector are in independent normal distribution with the same variance, the mode of the vector is in Rayleigh distribution.
To avoid loss of generality, the channel gains h for 2N users are set in descending order, e.g. | h1|2≥|h2|2≥|h3|2...≥|h2N|2. Wherein user a and user b belong to any one of N clusters having different distances from the base station, and | ha|2≤|hb|2. Because the channel gain of the user b is relatively large, the user b firstly treats the user a as noise, then uses Minimum Mean Square Error (MMSE) algorithm to separate the user b from the superposed signal, then uses SIC technology to subtract the signal of the user b from the superposed signal, and finally uses MMSE algorithm to separate the signal of the user a.
Assuming that the signal bandwidth in each cluster is set to 1HZ, the reception rate of user a and user b in the uplink NOMA system
Figure BDA0002259332760000061
And
Figure BDA0002259332760000062
the shannon formula can be expressed as:
Figure BDA0002259332760000063
wherein ,
Figure BDA0002259332760000064
and step S2, constructing an estimation optimization model of 2N users based on the constraint conditions of the uplink NOMA.
Specifically, the estimation optimization model constructs an estimation model of power distribution by performing optimization processing on a plurality of constraint conditions of a user.
Setting an intermediate variable G for describing the relationship between the paired users in the 2N usersa,b, wherein
Figure BDA0002259332760000065
The uplink NOMA system itself includes the following three constraints:
firstly, the receiving rate of a user is not less than the minimum receiving rate of the user;
(II) the receiving rate of the uplink NOMA is not less than that of the uplink OMA;
and (III) the condition that serial interference elimination is carried out by the uplink NOMA is met, namely the minimum power difference required for distinguishing the signal to be decoded from the residual undecoded signal.
Therefore, according to the above constraint conditions, the estimation optimization model is constructed as follows:
Figure BDA0002259332760000066
the model represents the sum throughput maximum time alpha of 2N usersb and Ga,bAnd the following constraints need to be satisfied:
Figure BDA0002259332760000067
Pαb|hb|2-P(1-αb)|ha|2≥Ptol，
Figure BDA0002259332760000068
0≤αb≤1,0≤b≤2N，
Figure BDA0002259332760000071
wherein ,Rn(n ═ a, b) denotes the minimum reception rate of user n, PtolIndicating the clarity required to exercise SIC.
Figure BDA0002259332760000072
And
Figure BDA0002259332760000073
respectively representing the reception rates of user a and user b in the upstream OMA system.
Figure BDA0002259332760000074
Meaning that the user is not allowed to pair with itself, nor is it allowed to repeat the pairing.
And step S3, calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model.
Specifically, the estimation optimization model belongs to an optimization problem for 2N users. By analysis, the optimization problem can be simplified into an optimization problem of each cluster.
Firstly, the estimation optimization model is converted into an optimization model of each cluster of users
Figure BDA0002259332760000075
And the following constraint conditions are required to be satisfied:
(1)
Figure BDA0002259332760000076
(2)
Figure BDA0002259332760000077
(3)Pαb|hb|2-P(1-αb)|ha|2≥Ptol
(4)0≤αb≤1
then, solving the constraint conditions can obtain:
(i)
Figure BDA0002259332760000078
(ii)
Figure BDA0002259332760000079
(iii)
Figure BDA0002259332760000081
(iiv)
Figure BDA0002259332760000082
(v)
Figure BDA0002259332760000083
all of the above constraints (i) - (v) must satisfy 0 ≦ α b1, where the constraints (i) - (iv) in turn all have to satisfy the constraint (v). Thus, P in the constraint (v) is availabletolHas a value range of Ptol≤|hb|2ρ。
In order to easily handle the constraints (i) to (v), the upper limits of the constraints (i) and (iii) are set to
Figure BDA0002259332760000084
And
Figure BDA0002259332760000085
the lower bounds of the constraints (ii), (iiv), and (v) are set to
Figure BDA0002259332760000086
And
Figure BDA0002259332760000087
namely, it is
(i)
Figure BDA0002259332760000088
(ii)
Figure BDA0002259332760000089
(iii)
Figure BDA00022593327600000810
(iiv)
Figure BDA00022593327600000811
(v)
Figure BDA00022593327600000812
And step S4, calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor.
Specifically, the sum throughput of users per cluster is set
Figure BDA00022593327600000813
Then
Figure BDA00022593327600000814
According to | ha|2≤|hb|2Availability, and throughput RsumIs about the power distribution factor alphabAs shown in fig. 2.
Therefore, to calculate the optimal throughput, the optimal and efficient α should be obtained firstb. The upper bound in constraints (one) and (two) must not be less than the lower bound. First for (i) and (ii) in constraint (one), if
Figure BDA0002259332760000091
The minimum reception rate of the weak user a will be satisfied; if it is not
Figure BDA0002259332760000092
Then the minimum reception rate for strong user b will be met; if it is not
Figure BDA0002259332760000093
Then both weak and strong users will satisfy conditions (i) and (ii), and the solution can be:
Figure BDA0002259332760000094
to simplify the processing, the upper bound of the above equation is set to
Figure BDA0002259332760000095
Similarly, in constraint (II)Also (iii) and (iv) of (iv) must satisfy the upper bound of not less than the lower bound, i.e.
Figure BDA0002259332760000096
According to | ha|2≤|hb|2Can obtain the product
Figure BDA0002259332760000097
The upper and lower bounds in constraint (one) can be divided into the following two cases:
1. when in use
Figure BDA0002259332760000098
In time, according to the channel condition of the users in the cluster, the derivation of the optimal power allocation factor can be divided into 2 cases:
(1) when the Signal-to-Noise Ratio (SNR) is relatively small, the following may occur:
Figure BDA0002259332760000099
in this case, the upper bound constraint (iii) fails, and thus the upper bound
Figure BDA00022593327600000910
And allocating factors for the optimal power.
(2) As the signal-to-noise ratio increases,
Figure BDA00022593327600000911
the factor is still allocated for the optimal power. Because according to
Figure BDA00022593327600000912
Figure BDA00022593327600000913
Is alphabThe maximum upper bound of. Therefore, in this case, the optimal power allocation factor
Figure BDA00022593327600000914
It is known that
Figure BDA00022593327600000915
Thus, can obtain
Figure BDA00022593327600000916
According to the above-mentioned situation,
Figure BDA00022593327600000917
there are two kinds of definitional domains in a time
Figure BDA00022593327600000918
And
Figure BDA00022593327600000919
comparing to obtain:
Figure BDA00022593327600000920
it is known that
Figure BDA00022593327600000921
To increase the function, let x1＝[2|ha|2ρ(|ha|2ρ+1)+(|hb|2ρ-|ha|2ρ)]2，x2＝(|hb|2ρ-|ha|2ρ)2+4|hb|2ρ|ha|2ρ(|ha|2ρ +1), then x is calculated1-x2＝(|ha|2ρ)2Greater than or equal to 0, and can be obtained (| h)a|2Rho +1) -W is more than or equal to 0. Thus, it is possible to provide
Figure BDA00022593327600000922
The definition domain of time is
Figure BDA00022593327600000923
2. In addition, another situation needs to be considered, namely
Figure BDA00022593327600000924
That is to say
Figure BDA00022593327600000925
(known to be
Figure BDA00022593327600000926
Has obtained when
Figure BDA00022593327600000927
) I.e. weak users are relatively far from the cell base station and the signal-to-noise ratio (SNR) is small<5dB), the following may occur
Figure BDA0002259332760000101
In this case, all upper bounds (i) and (iii) in the constraint deteriorate due to the harsh conditions in which the weak user is located. Therefore, the optimal power distribution factor is satisfied
Figure BDA0002259332760000102
In the case of (2), a maximum value is selected from the lower bound:
Figure BDA0002259332760000103
to ensure
Figure BDA0002259332760000104
Need to be verified
Figure BDA0002259332760000105
And
Figure BDA0002259332760000106
obtaining the value range. It is known that
Figure BDA0002259332760000107
And also
Figure BDA0002259332760000108
And is
Figure BDA0002259332760000109
Thus, it is possible to provide
Figure BDA00022593327600001010
For the
Figure BDA00022593327600001011
Can obtain the product
Figure BDA00022593327600001012
The optimal power allocation factor obtained from the above two cases is:
Figure BDA00022593327600001013
according to the above relationship, the derivation of the optimal power allocation factor of the simplified model of the estimation optimization model is also applicable to the general case. This gives:
Figure BDA00022593327600001014
the following provides a detailed description of embodiments of the invention. It should be noted that the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation procedure are given, but the protection scope of the present invention is not limited to the following embodiments.
The 2N users in the transmission scene based on the uplink non-orthogonal multiple access are uniformly distributed in the cell. The users are firstly divided into different clusters by using a pairing method, so that conditions are provided for subsequent power allocation. Specifically, the main embodiments can be classified into 3 types.
Example 1: pairing a user a and a user b in the 2N users by adopting a head-to-tail pairing model, as shown in FIG. 3, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2Wherein a + b is 2N +1, i.e.
Figure BDA00022593327600001015
Example 2: the user a and the user b in the 2N users are paired by adopting a uniform pairing model, as shown in FIG. 4, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2Where | a-b | ═ N, i.e.
Figure BDA0002259332760000111
Example 3: randomly pairing a user a and a user b in the 2N users by adopting a random pairing model, as shown in FIG. 5, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2In this case Ga,bIs temporarily unknown and can be determined according to actual conditions.
Specifically, the above embodiment is implemented by the following steps:
and step 1, distributing proper power based on the derived estimation optimization model.
Minimum receiving rate and channel gain h of user a and user bn|2(N ═ 1,2.. 2N), intra-cluster power P, and sharpness Ptol(Ptol≤|hb|2ρ) is substituted into the derived optimal power allocation factor, resulting in:
Figure BDA0002259332760000112
from this, the power allocated to each user in the cluster can be derived:
Figure BDA0002259332760000113
and
Figure BDA0002259332760000114
and 2, adding the obtained optimal power distribution factor into a sending signal of the user side to form a superposed signal.
The sending signals of the user a and the user b are respectively set as follows:
Figure BDA0002259332760000115
and
Figure BDA0002259332760000116
and then, overlapping at a sending end, wherein the signals of the receiving end of the base station are as follows:
Figure BDA0002259332760000117
and 3, separating the original signal in the superposed signal based on the serial interference deletion technology.
First of all, in the received signal
Figure BDA0002259332760000118
Partially regarded as noise, to solve the signal S of the strong user bbThen order
Figure BDA0002259332760000119
Finally, weak user signal S is solveda。
And 4, verifying the reliability of the scheme based on the uplink orthogonal multiple access scheme and the uplink non-orthogonal multiple access scheme with fixed power distribution.
41) Orthogonal multiple access technical scheme
In contrast, the cell of 2N users based on the orthogonal multiple access scheme is also divided into N clusters according to the same pairing mode, the bandwidth in each cluster is set to 1/2HZ, and then the receiving rate of each user is
Figure BDA00022593327600001110
42) Non-orthogonal multiple access technical scheme for fixed power distribution
Let alphabM is a constant and 0 ≦ m ≦ 1, from which it can be derived: pb＝αbP and Pa＝(1-αb)P。
Therefore, as can be seen from comparison, compared with the orthogonal multiple access technical scheme and the non-orthogonal multiple access technical scheme with fixed power allocation, the method and the device can achieve maximum correct sending and receiving of signals in uplink NOMA through power allocation, finally effectively reduce the error rate of the signals, and improve the throughput of the system.
As shown in fig. 6, in an embodiment, the uplink NOMA-based optimal dynamic power allocation system of the present invention includes a pairing module 61, a construction module 62, a first calculation module 63, and a second calculation module 64.
The pairing module 61 is configured to set 2N users of the uplink NOMA system to be uniformly distributed in a cell, and pair-wise users are divided into N clusters.
The building module 62 is connected to the pairing module 61, and is configured to build an estimation optimization model for 2N users based on constraints of uplink NOMA.
The first calculating module 63 is connected to the constructing module 62, and is configured to calculate, based on the estimation optimization model, upper and lower bounds of the power allocation factor of the user.
The second calculation module 64 is connected to the first calculation module 63, and is configured to calculate an optimal power allocation factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power allocation factor.
The structures and principles of the pairing module 61, the constructing module 62, the first calculating module 63, and the second calculating module 64 correspond to the steps in the above optimal dynamic power allocation method based on uplink NOMA one to one, and therefore, the details are not repeated herein.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And the modules can be realized in a form that all software is called by the processing element, or in a form that all the modules are realized in a form that all the modules are called by the processing element, or in a form that part of the modules are called by the hardware. For example: the x module can be a separately established processing element, and can also be integrated in a certain chip of the device. In addition, the x-module may be stored in the memory of the apparatus in the form of program codes, and may be called by a certain processing element of the apparatus to execute the functions of the x-module. Other modules are implemented similarly. All or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software. These above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), and the like. When a module is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
The storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the above-described method for optimal dynamic power allocation based on uplink NOMA. Preferably, the storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
As shown in fig. 7, in an embodiment, the terminal of the present invention includes: a processor 71 and a memory 72.
The memory 72 is used for storing computer programs.
The memory 72 includes: various media that can store program codes, such as ROM, RAM, magnetic disk, U-disk, memory card, or optical disk.
The processor 71 is connected to the memory 72, and is configured to execute the computer program stored in the memory 72, so as to enable the terminal to execute the above-mentioned optimal dynamic power allocation method based on uplink NOMA.
Preferably, the Processor 71 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
In summary, the optimal dynamic power allocation method, system, medium and terminal based on uplink NOMA of the present invention realize dynamic change of power allocation factor along with channel state, and can effectively adapt to different channel conditions; when users with different channel gains exist in a cell, after respective clusters are formed, a sending end dynamically distributes proper power, so that the signals are correctly sent and received to the maximum extent, the error rate of the signals is effectively reduced finally, and the throughput of a system is improved; knowing that certain users are uniformly distributed in a cell and different channel differences exist among the users; when the channel difference of the two paired users is large, the strong user can share more power in the cluster while ensuring the normal transmission of the weak user through proper power distribution; when the channel difference of the channels in the cluster is small, the channel difference among users is increased through power distribution so as to meet the basic condition of applying SIC; when the weak user is at the edge of the cell and the signal-to-noise ratio is small, as much power as possible can be allocated to the strong user, thereby avoiding the waste of resources. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (10)

1. An optimal dynamic power allocation method based on uplink NOMA is characterized by comprising the following steps:
2N users of an uplink NOMA system are set to be uniformly distributed in a cell, and the users are paired pairwise into N clusters;
constructing an estimation optimization model of 2N users based on constraint conditions of uplink NOMA;
calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model;
and calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor.
2. The uplink NOMA-based optimal dynamic power allocation method according to claim 1, wherein: the estimation optimization model is as follows:
Figure FDA0002259332750000011
represents the sum throughput of 2N usersb and Ga,bA value of (d); and the following constraint conditions are required to be satisfied:
Figure FDA0002259332750000012
Pαb|hb|2-P(1-αb)|ha|2≥Ptol，
Figure FDA0002259332750000013
0≤αb≤1,0≤b≤2N，
Figure FDA0002259332750000014
wherein a and b are in a clusterTwo users of Rn(n ═ a, b) denotes the minimum reception rate of user n, PtolIndicating the clarity required to exercise SIC,
Figure FDA0002259332750000015
and
Figure FDA0002259332750000016
respectively representing the receiving rates, alpha, of user a and user b in the uplink OMA systembRepresents the power allocation factor, h, of user bn(n ═ a, b) denotes channel gain between the base station and the user, P denotes intra-cluster power,
Figure FDA0002259332750000017
σ2variance of additive white Gaussian noise, PtolIndicating the required resolution to apply successive interference cancellation.
3. The uplink NOMA-based optimal dynamic power allocation method according to claim 2, wherein: calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model comprises the following steps:
converting the estimation optimization model into an optimization model of each cluster of users
Figure FDA0002259332750000021
And the following constraint conditions are required to be satisfied:
Figure FDA0002259332750000022
Pαb|hb|2-P(1-αb)|ha|2≥Ptol，0≤αb≤1；
according to the constraint condition, obtaining
Figure FDA0002259332750000023
Figure FDA0002259332750000024
4. The uplink NOMA-based optimal dynamic power allocation method according to claim 3, wherein:
the optimal power allocation factor of user b is
Figure FDA0002259332750000025
wherein ,
Figure FDA0002259332750000026
Figure FDA0002259332750000027
the optimal power allocation factor of user a is
Figure FDA0002259332750000028
5. The uplink NOMA-based optimal dynamic power allocation method according to claim 1, wherein: when the pairwise pairing of the users is divided into N clusters, pairing the user a and the user b, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2And a + b is 2N +1, and h represents a channel gain.
6. The uplink NOMA-based optimal dynamic power allocation method according to claim 1, wherein: when the pairwise pairing of the users is divided into N clusters, pairing the user a and the user b, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2And | a-b | ═ N, and h denotes a channel gain.
7. The uplink NOMA-based optimal dynamic power allocation method according to claim 1, wherein: will be provided withWhen the pairwise pairing of the users is divided into N clusters, randomly pairing the user a and the user b, and | h1|2≥...|hb|2≥|ha|2...≥|h2N|2And h denotes a channel gain.
8. An optimal dynamic power distribution system based on uplink NOMA is characterized by comprising a pairing module, a construction module, a first calculation module and a second calculation module;
the pairing module is used for setting 2N users of the uplink NOMA system to be uniformly distributed in a cell and pairwise pairing the users into N clusters;
the construction module is used for constructing an estimation optimization model of 2N users based on the constraint condition of uplink NOMA;
the first calculation module is used for calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimation optimization model;
and the second calculation module is used for calculating the optimal power distribution factor of the user according to the estimation optimization model and the possible upper and lower bounds of the power distribution factor.
9. A storage medium having stored thereon a computer program, which when executed by a processor implements the upstream NOMA-based optimal dynamic power allocation method of any one of claims 1 to 7.
10. A terminal, comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the memory-stored computer program to cause the terminal to perform the uplink NOMA-based optimal dynamic power allocation method of any one of claims 1 to 7.
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