CN112770395B - 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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CN112770395B
CN112770395B CN201911065907.5A CN201911065907A CN112770395B CN 112770395 B CN112770395 B CN 112770395B CN 201911065907 A CN201911065907 A CN 201911065907A CN 112770395 B CN112770395 B CN 112770395B
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power distribution
uplink noma
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CN112770395A (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
    • 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
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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, which comprise the following steps: setting 2N users of an uplink NOMA system to be uniformly distributed in a cell, and dividing the users into N clusters in pairwise manner; constructing estimation optimization models of 2N users based on constraint conditions of uplink NOMA; calculating possible upper and lower bounds of the power distribution factors of the users based on the estimated optimization model; and calculating the optimal power distribution factor of the user according to the estimated 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, and in particular, to a method, a system, a storage medium, and a terminal for optimal dynamic power allocation based on non-orthogonal multiple access (Non Orthogonal Multiple Access, NOMA).
Background
The fifth generation mobile communication (5th generation mobile networks,5G) involves 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 upcoming 5G post communication systems. Among these, NOMA technology is one of the most attractive of these. Compared with the traditional orthogonal multiple access technology, the non-orthogonal multiple access technology mainly comprises the following two steps:
(1) At the transmitting end, two or more user signals are multiplied by respective transmitting power and channel gain in the power domain respectively and then are overlapped with each other;
(2) At the receiving end, the respective signals are separated from the superimposed signals using conventional techniques of serial interference cancellation (Successive Interference Cancellation, SIC).
Wherein power allocation is a key technique in non-orthogonal multiple access because reasonable power allocation directly affects the spectral and energy efficiency of non-orthogonal multiple access transmissions. In the prior art, on the one hand, power allocation is divided into uplink power allocation and downlink power allocation. For downlink power allocation, power allocation strategies have already advanced technology. While studies of uplink power allocation have remained elusive so far. On the other hand, the power allocation can be further divided into dynamic power allocation and fixed power allocation. A fixed power allocation can achieve excellent throughput under certain channel conditions but cannot flexibly adapt to dynamically changing channel environments.
Disclosure of Invention
In view of the above-mentioned drawbacks 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 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: setting 2N users of an uplink NOMA system to be uniformly distributed in a cell, and dividing the users into N clusters in pairwise manner; constructing estimation optimization models of 2N users based on constraint conditions of uplink NOMA; calculating possible upper and lower bounds of the power distribution factors of the users based on the estimated optimization model; and calculating the optimal power distribution factor of the user according to the estimated optimization model and the possible upper and lower bounds of the power distribution factor.
In an embodiment of the present invention, the estimated optimization model is:
Figure BDA0002259332760000021
representing alpha when sum throughput of 2N users is maximum b and Ga,b Is a value of (2); and the following constraints need to be satisfied:
Figure BDA0002259332760000022
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，/>
Figure BDA0002259332760000023
0≤α b ≤1,0≤b≤2N，
Figure BDA0002259332760000024
wherein a, b are two users in a cluster, R n (n=a, b) represents the minimum reception rate of user n, P tol Indicating the sharpness required to exercise the SIC,
Figure BDA0002259332760000025
and />
Figure BDA0002259332760000026
Representing the reception rate, alpha, of user a and user b, respectively, in an upstream OMA system b Representing the power allocation factor, h, of user b n (n=a, b) represents the channel gain between the base station and the user, P represents the intra-cluster power, +.>
Figure BDA0002259332760000027
σ 2 Is the variance of additive white gaussian noise, P tol Indicating the sharpness required for the use of serial interference cancellation.
In one embodiment of the present invention, calculating the possible upper and lower bounds of the power distribution factor of the user based on the estimated optimization model includes the steps of:
converting the estimated optimization model into an optimization model of each cluster of users
Figure BDA0002259332760000028
And the following constraints need to be satisfied: />
Figure BDA0002259332760000029
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，0≤α b ≤1；
According to the constraint condition, obtain
Figure BDA00022593327600000210
Figure BDA00022593327600000211
In one embodiment of the present invention, the optimal power allocation factor of the user b is
Figure BDA0002259332760000031
wherein ,/>
Figure BDA0002259332760000032
The optimal power allocation factor for user a is
Figure BDA0002259332760000033
In one embodiment of the present invention, when the users are pairwise allocated into N clusters, user a and user b are paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 A+b=2n+1, h denotes the channel gain.
In one embodiment of the present invention, when the users are pairwise allocated into N clusters, user a and user b are paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 And, |a-b|=n, and h represents the channel gain.
In an embodiment of the present invention, when the users are pairwise allocated into N clusters, user a and user b are randomly paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 H represents the 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 dividing the users into N clusters in pairwise pairing;
the construction module is used for constructing estimation optimization models of 2N users based on the constraint conditions of the uplink NOMA;
the first calculation module is used for calculating the possible upper and lower bounds of the power distribution factors of the user based on the estimated optimization model;
the second calculation module is used for calculating the optimal power distribution factor of the user according to the estimated 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 that the terminal executes the optimal dynamic power allocation method based on the uplink NOMA.
As described above, the optimal dynamic power allocation method, system, medium and terminal based on uplink NOMA of the present invention has the following beneficial effects:
(1) 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 one cell, after forming respective clusters, a transmitting end dynamically distributes proper power, so that the maximum correct transmission and reception of signals are realized, the error rate of the signals is reduced, and the throughput of a system is improved;
(3) It is known that a certain number of users are uniformly distributed in one cell and that there are different channel differences between users; when the channel difference of the paired two users is large, through proper power distribution, strong users can share more power in the cluster while ensuring normal transmission of weak users; when the channel difference of the channels in the cluster is smaller, the channel difference between users is increased through power distribution so as to meet the basic condition of SIC application; when the weak user is at the cell edge and the signal to noise ratio is smaller, the power can be distributed to the strong user as much as possible, so that the waste of resources is avoided.
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FIG. 1 is a flow chart of an embodiment of an uplink NOMA-based optimal dynamic power allocation method according to the present invention;
FIG. 2 is a diagram showing the relationship between the power distribution factor of a user and the throughput of the system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an end-to-end pairing model according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a uniform pairing model according to the invention in one embodiment;
FIG. 5 is a schematic diagram of a random pairing model according to the invention in an embodiment;
FIG. 6 is a schematic diagram of an uplink NOMA-based optimal dynamic power distribution system according to the present invention in one embodiment;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the invention.
Description of element reference numerals
61. Pairing module
62. Building modules
63. First computing module
64. Second calculation module
71. Processor and method for controlling the same
72. Memory device
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
The optimal dynamic power distribution method based on the uplink NOMA of the invention sets a cell containing a plurality of users, and obtains the optimal power distribution 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. At the same time, the superiority of this method over the fixed power allocation uplink NOMA and uplink orthogonal multiple access (Orthogonal Multiple Access, OMA) schemes can be verified by using three classical pairing approaches.
As shown in fig. 1, in an embodiment, the method for allocating optimal dynamic power based on uplink NOMA according to the present invention includes the following steps:
step S1, setting 2N users of an uplink NOMA system to be uniformly distributed in a cell, and dividing the users into N clusters in pairwise pairing.
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. In order not to lose generality, 2N users are divided into N clusters and reduced to analyzing the optimal power allocation factor for each cluster in the cell. Setting each cluster to include user a and user b, and using each clusterThe sending signals of the users are respectively:
Figure BDA0002259332760000051
and />
Figure BDA0002259332760000052
wherein Sn (n=a, b) and α n (n=a, b) represents the transmission signal and the power allocation factor of the user n, respectively, and P represents the intra-cluster power. In general, the setting of the power allocation factor is divided into the following two cases:
1) From a conventional perspective, the signal of each user is transmitted at maximum power;
2) From an energy conservation perspective, power allocation factors are used to constrain the power of users within each cluster. In the present invention, the second case is mainly considered, and α is set a +α b ＝1。
Setting signals of receiving end of base station
Figure BDA0002259332760000053
wherein hn (n=a, b) is the channel gain between the base station and the user, its distribution being a rayleigh distribution (Rayleigh Distribution); w is variance sigma 2 Additive white gaussian noise of (c). Wherein when two components of a random two-dimensional vector are in an independent normal distribution with the same variance, the modulus of the vector is in a Rayleigh distribution.
To avoid loss of generality, the channel gains h of 2N users are set in descending order, e.g., |h 1 | 2 ≥|h 2 | 2 ≥|h 3 | 2 ...≥|h 2N | 2 . Wherein, the user a and the user b belong to any one of N clusters with different distances from the base station, and |h a | 2 ≤|h b | 2 . Since the channel gain of user b is relatively large, user b first treats user a as noise, then separates user b from the superimposed signal using a minimum mean square error (Minimum Mean Square Error, MMSE) algorithm, then subtracts the signal of user b from the superimposed signal using SIC technique, and finally separates the signal of user a using MMSE algorithmAnd (5) discharging.
Assuming that the signal bandwidth within each cluster is set to 1HZ, the reception rates of user a and user b in the upstream NOMA system
Figure BDA0002259332760000061
and />
Figure BDA0002259332760000062
The shannon formula can be expressed as:
Figure BDA0002259332760000063
wherein ,
Figure BDA0002259332760000064
and S2, constructing estimation optimization models 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 optimizing a plurality of constraint conditions of a user.
To describe the relationship between paired users of 2N users, an intermediate variable G is set a,b, wherein
Figure BDA0002259332760000065
The upstream NOMA system itself includes the following three constraints:
the receiving rate of the user is not less than the minimum receiving rate of the user;
(II) the receiving rate of the uplink NOMA is not less than the receiving rate of the uplink OMA;
and (III) meeting the condition that the uplink NOMA performs serial interference deletion, namely, the minimum power difference required for distinguishing the signal to be decoded from the residual non-decoded signal.
Therefore, according to the constraint conditions, the constructed estimated optimization model is:
Figure BDA0002259332760000066
the model represents alpha when the sum throughput of 2N users is maximum b and Ga,b And the following constraints are satisfied:
Figure BDA0002259332760000067
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，
Figure BDA0002259332760000068
0≤α b ≤1,0≤b≤2N，/>
Figure BDA0002259332760000071
wherein ,Rn (n=a, b) represents the minimum reception rate of user n, P tol Indicating the sharpness required to exercise SIC.
Figure BDA0002259332760000072
and />
Figure BDA0002259332760000073
The reception rates of user a and user b in the upstream OMA system are indicated, respectively.
Figure BDA0002259332760000074
Meaning that the user is not allowed to pair with himself, nor is the user allowed to repeat pairing.
And step S3, calculating possible upper and lower bounds of the power distribution factors of the user based on the estimated optimization model.
Specifically, the estimated optimization model belongs to an optimization problem for 2N users. From analysis, the optimization problem can be simplified into an optimization problem of each cluster.
Firstly, converting the estimated optimization model into an optimization model of each cluster of users
Figure BDA0002259332760000075
And the following constraints need to be satisfied:
(1)
Figure BDA0002259332760000076
(2)
Figure BDA0002259332760000077
(3)Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol
(4)0≤α b ≤1
then, solving the constraint conditions can obtain:
(i)
Figure BDA0002259332760000078
(ii)
Figure BDA0002259332760000079
(iii)
Figure BDA0002259332760000081
(iiv)
Figure BDA0002259332760000082
(v)
Figure BDA0002259332760000083
the above constraints (i) - (v) must all satisfy 0.ltoreq.α b And.ltoreq.1, wherein the constraints (i) - (iv) must satisfy the constraint (v). Thus, P in constraint (v) is available tol The value range of (2) is P tol ≤|h b | 2 ρ。
To facilitate handling of constraints (i) - (v), the upper bounds of constraints (i) and (iii) are set separatelyIs defined as
Figure BDA0002259332760000084
And
Figure BDA0002259332760000085
the lower bounds of constraints (ii), (iiv) and (v) are set to +.>
Figure BDA0002259332760000086
and />
Figure BDA0002259332760000087
I.e.
(i)
Figure BDA0002259332760000088
(ii)
Figure BDA0002259332760000089
(iii)
Figure BDA00022593327600000810
(iiv)
Figure BDA00022593327600000811
(v)
Figure BDA00022593327600000812
And S4, calculating the optimal power distribution factor of the user according to the estimated optimization model and the possible upper and lower bounds of the power distribution factor.
Specifically, the sum throughput of users in each cluster is set
Figure BDA00022593327600000813
Then
Figure BDA00022593327600000814
According to |h a | 2 ≤|h b | 2 Availability and throughput R sum Is related to the power distribution factor alpha b As shown in fig. 2.
Therefore, in order to calculate the optimal throughput, an optimal and efficient α is first obtained b . The upper bound in constraints (one) and (two) must be no less than the lower bound. First for (i) and (ii) in constraint (one), if
Figure BDA0002259332760000091
The minimum reception rate of weak user a will be met; if->
Figure BDA0002259332760000092
Then the minimum reception rate for strong user b will be met; if->
Figure BDA0002259332760000093
Then both the weak and strong users will satisfy conditions (i) and (ii) and the solution is available:
Figure BDA0002259332760000094
to simplify the process, the upper bound is set as
Figure BDA0002259332760000095
Similarly, constraint (II) must also be satisfied in which (iii) and (iv) are not smaller than the upper bound, i.e.
Figure BDA0002259332760000096
According to |h a | 2 ≤|h b | 2 Is available in the form of
Figure BDA0002259332760000097
The upper and lower bounds on constraint (one) can be further divided into the following two cases:
1. when (when)
Figure BDA0002259332760000098
In this case, the derivation of the optimal power allocation factor can be divided into 2 cases according to the channel conditions of the users in the cluster:
(1) When the signal-to-noise ratio (Signal Noise Ratio, SNR) is relatively small, the following may occur:
Figure BDA0002259332760000099
in this case, the upper bound constraint (iii) fails, so the upper bound +.>
Figure BDA00022593327600000910
And (5) distributing factors for optimal power.
(2) As the signal-to-noise ratio increases,
Figure BDA00022593327600000911
still being the optimal power allocation factor. Because according to
Figure BDA00022593327600000912
Figure BDA00022593327600000913
Alpha is alpha b Is defined by the maximum upper bound of (2). Therefore, in this case, the optimal power allocation factor +.>
Figure BDA00022593327600000914
Known->
Figure BDA00022593327600000915
Thereby get->
Figure BDA00022593327600000916
According to the above situation->
Figure BDA00022593327600000917
There are two definition domains->
Figure BDA00022593327600000918
And
Figure BDA00022593327600000919
the comparison results show that:
Figure BDA00022593327600000920
is known to be
Figure BDA00022593327600000921
To increase the function, let x 1 ＝[2|h a | 2 ρ(|h a | 2 ρ+1)+(|h b | 2 ρ-|h a | 2 ρ)] 2 ，x 2 ＝(|h b | 2 ρ-|h a | 2 ρ) 2 +4|h b | 2 ρ|h a | 2 ρ(|h a | 2 ρ+1), x is calculated 1 -x 2 ＝(|h a | 2 ρ) 2 Not less than 0, and can be further verified to be (|h) a | 2 ρ+1) -W is not less than 0. Thus->
Figure BDA00022593327600000922
The definition domain is->
Figure BDA00022593327600000923
2. In addition, another case needs to be considered, namely
Figure BDA00022593327600000924
Namely +.>
Figure BDA00022593327600000925
(know->
Figure BDA00022593327600000926
The ∈10 is already calculated>
Figure BDA00022593327600000927
) Also, itThat is, the weak user is far from the cell base station and the signal-to-noise ratio (SNR) is small<5 dB), the following cases are possible>
Figure BDA0002259332760000101
In this case, all upper bounds (i) and (iii) in the constraint are exacerbated by the harsh conditions in which the weak user is located. Thus the optimal power distribution factor is meeting
Figure BDA0002259332760000102
In the case of (a), a maximum value is selected from the lower bound: />
Figure BDA0002259332760000103
To ensure->
Figure BDA0002259332760000104
Need to verify->
Figure BDA0002259332760000105
and />
Figure BDA0002259332760000106
Obtaining the value range. Known->
Figure BDA0002259332760000107
And also (b)
Figure BDA0002259332760000108
And->
Figure BDA0002259332760000109
Thus->
Figure BDA00022593327600001010
For->
Figure BDA00022593327600001011
Is available in the form of
Figure BDA00022593327600001012
The optimal power allocation factor obtained from the above two cases is:
Figure BDA00022593327600001013
according to the above relation, the derivation of the optimal power distribution factor of the simplified model of the estimated optimization model is also applicable to the general case. This can be achieved by:
Figure BDA00022593327600001014
the embodiments of the present invention are described in detail below. 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 process 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 first divided into different clusters using a pairing method to provide conditions for subsequent power allocation. In particular, the main embodiments can be classified into 3 types.
Example 1: pairing user a and user b in 2N users by adopting a head-tail pairing model, as shown in fig. 3, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 Where a+b=2n+1, i.e
Figure BDA00022593327600001015
Example 2: pairing user a and user b in 2N users by adopting uniform pairing model as shown in fig. 4, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 Where |a-b|=n, i.e
Figure BDA0002259332760000111
Example 3: the random pairing model is adopted to randomly pair the user a and the user b in 2N users, as shown in fig. 5, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 In this case G a,b And the temporary unknown can be determined according to the actual situation.
Specifically, the above embodiment is realized by the steps of:
and step 1, distributing proper power based on the deduced estimation optimization model.
Minimum receiving rate of user a and user b, channel gain |h n | 2 (n=1, 2..2n), intra-cluster power P, and sharpness P tol (P tol ≤|h b | 2 ρ) into the derived optimal power allocation factor, resulting in:
Figure BDA0002259332760000112
the power allocated by the users in each cluster can be derived from this:
Figure BDA0002259332760000113
and />
Figure BDA0002259332760000114
And step 2, adding the optimal power distribution factor into a transmitting signal of the user side to form a superposition signal.
The transmission signals of the user a and the user b are respectively set as follows:
Figure BDA0002259332760000115
and />
Figure BDA0002259332760000116
And then sendThe terminal is overlapped, and the signal of the receiving terminal of the base station is: />
Figure BDA0002259332760000117
And step 3, separating out an original signal in the superimposed signal based on a serial interference deleting technology.
First of all will receive the signal in
Figure BDA0002259332760000118
Part is regarded as noise, and the signal S of the strong user b is solved b Then let the
Figure BDA0002259332760000119
Finally, the weak user signal S is solved a 。
And 4, verifying the credibility of the scheme based on an uplink orthogonal multiple access scheme and an uplink non-orthogonal multiple access scheme of fixed power allocation.
41 Orthogonal multiple access technical scheme
In contrast, the cells of 2N users based on the orthogonal multiple access scheme are also divided into N clusters in the same pairing manner, the bandwidth in each cluster is set to be 1/2HZ, and then the receiving rate of each user is
Figure BDA00022593327600001110
42 Non-orthogonal multiple access (ofdma) scheme for fixed power allocation
Let alpha b =m, m is a constant and 0.ltoreq.m.ltoreq.1, from which it can be derived: p (P) b ＝α bP and Pa ＝(1-α b )P。
Therefore, compared with the orthogonal multiple access technical scheme and the non-orthogonal multiple access technical scheme with fixed power distribution, the invention can realize the maximum correct transmission and reception of the signal through the power distribution in the uplink NOMA, and finally effectively reduce the error rate of the signal and improve the throughput of the system.
As shown in fig. 6, in one embodiment, the optimal dynamic power allocation system based on upstream NOMA 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 that 2N users of the uplink NOMA system are uniformly distributed in a cell, and divide the users into N clusters by pairwise pairing.
The construction module 62 is connected to the pairing module 61, and is configured to construct an estimated optimization model of 2N users based on the constraint conditions of uplink NOMA.
The first calculation module 63 is connected to the construction module 62 for calculating the possible upper and lower bounds of the power allocation factor of the user based on the estimated optimization model.
The second calculation module 64 is connected to the first calculation module 63, and is configured to calculate an optimal power distribution factor of the user according to the estimated optimization model and the possible upper and lower bounds of the power distribution factor.
The structures and principles of the pairing module 61, the construction module 62, the first calculation module 63 and the second calculation module 64 are in one-to-one correspondence with the steps in the above-mentioned optimal dynamic power allocation method based on uplink NOMA, so that the details are not repeated here.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. The modules can be realized in a form of calling the processing element through software, can be realized in a form of hardware, can be realized in a form of calling the processing element through part of the modules, and can be realized in a form of hardware. For example: the x module may be a processing element which is independently set up, or may be implemented in a chip integrated in the device. The x module may be stored in the memory of the above device in the form of program codes, and the functions of the x module may be called and executed by a certain processing element of the above device. The implementation of the other modules is similar. All or part of the modules can be integrated together or can be implemented independently. 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 a software form. The above modules may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), one or more microprocessors (Digital Singnal Processor, DSP for short), one or more field programmable gate arrays (Field Programmable Gate Array, FPGA for short), 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 (Central Processing Unit, CPU) or other processor that may invoke the program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC) for short.
The storage medium of the present invention stores a computer program which, when executed by a processor, implements the above-described optimal dynamic power allocation method based on upstream NOMA. Preferably, the storage medium includes: various media capable of storing 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 a computer program.
The memory 72 includes: various media capable of storing 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 a computer program stored in the memory 72, so that the terminal performs the above-mentioned optimal dynamic power allocation method based on upstream NOMA.
Preferably, the processor 71 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; but also digital signal processors (Digital Signal Processor, DSP for short), application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), field programmable gate arrays (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
In summary, the optimal dynamic power distribution method, system, medium and terminal based on uplink NOMA realize dynamic change of the power distribution factor along with the channel state, and can effectively adapt to different channel conditions; when users with different channel gains exist in one cell, after forming respective clusters, a transmitting end dynamically distributes proper power, so that the maximum correct transmission and reception of signals are realized, the error rate of the signals is reduced, and the throughput of a system is improved; it is known that a certain number of users are uniformly distributed in one cell and that there are different channel differences between users; when the channel difference of the paired two users is large, through proper power distribution, strong users can share more power in the cluster while ensuring normal transmission of weak users; when the channel difference of the channels in the cluster is smaller, the channel difference between users is increased through power distribution so as to meet the basic condition of SIC application; when the weak user is at the cell edge and the signal to noise ratio is smaller, the power can be distributed to the strong user as much as possible, so that the waste of resources is avoided. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.

Claims (9)

1. The optimal dynamic power distribution method based on the uplink NOMA is characterized by comprising the following steps of:
setting 2N users of an uplink NOMA system to be uniformly distributed in a cell, and dividing the users into N clusters in pairwise manner;
constructing estimation optimization models of 2N users based on constraint conditions of uplink NOMA;
calculating the upper and lower bounds of the power distribution factors of the users based on the estimated optimization model;
calculating the optimal power distribution factor of the user according to the estimated optimization model and the upper and lower bounds of the power distribution factor;
the estimated optimization model is as follows:
Figure FDA0004170551100000011
representing alpha when sum throughput of 2N users is maximum b and Ga,b Is a value of (2); and the following constraints need to be satisfied:
Figure FDA0004170551100000012
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，/>
Figure FDA0004170551100000013
0≤α b ≤1,0≤b≤2N，/>
Figure FDA0004170551100000014
wherein a, b are two users in a cluster, R n (n=a, b) represents the minimum reception rate of user n, P tol Indicating the sharpness required to exercise the SIC,
Figure FDA0004170551100000015
and />
Figure FDA0004170551100000016
Representing the reception rate, alpha, of user a and user b, respectively, in an upstream OMA system b Representing the power allocation factor, h, of user b n (n=a, b) represents a base station and a userChannel gain between, P represents intra-cluster power,/->
Figure FDA0004170551100000017
σ 2 Is the variance of additive white gaussian noise, P tol Indicating the sharpness required for the deletion using serial interference;
Figure FDA0004170551100000018
and />
Figure FDA0004170551100000019
The reception rates of user a and user b in the upstream NOMA system are shown, respectively.
2. The optimal dynamic power allocation method based on uplink NOMA according to claim 1, wherein: calculating the upper and lower bounds of the power allocation factor of the user based on the estimated optimization model comprises the steps of:
converting the estimated optimization model into an optimization model of each cluster of users
Figure FDA00041705511000000110
And the following constraints need to be satisfied: />
Figure FDA00041705511000000111
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，0≤α b ≤1；
According to the constraint condition, obtain
Figure FDA0004170551100000021
Figure FDA0004170551100000022
/>
3. The optimal dynamic power allocation method based on uplink NOMA according to claim 2, wherein:
the optimal power allocation factor for user b is
Figure FDA0004170551100000023
wherein ,
Figure FDA0004170551100000024
Figure FDA0004170551100000025
the optimal power allocation factor for user a is
Figure FDA0004170551100000026
4. The optimal dynamic power allocation method based on uplink NOMA according to claim 1, wherein: when the users are pairwise divided into N clusters, user a and user b are paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 A+b=2n+1, h denotes the channel gain.
5. The optimal dynamic power allocation method based on uplink NOMA according to claim 1, wherein: when the users are pairwise divided into N clusters, user a and user b are paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 And, |a-b|=n, and h represents the channel gain.
6. The optimal dynamic power allocation method based on uplink NOMA according to claim 1, wherein: when the users are pairwise paired into N clusters, the users a and b are randomly paired, and |h 1 | 2 ≥...|h b | 2 ≥|h a | 2 ...≥|h 2N | 2 List of hShowing the channel gain.
7. The optimal dynamic power distribution system based on the 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 dividing the users into N clusters in pairwise pairing;
the construction module is used for constructing estimation optimization models of 2N users based on the constraint conditions of the uplink NOMA;
the first calculation module is used for calculating the upper and lower bounds of the power distribution factors of the user based on the estimated optimization model;
the second calculation module is used for calculating the optimal power distribution factor of the user according to the estimated optimization model and the possible upper and lower bounds of the power distribution factor;
the estimated optimization model is as follows:
Figure FDA0004170551100000031
representing alpha when sum throughput of 2N users is maximum b and Ga,b Is a value of (2); and the following constraints need to be satisfied:
Figure FDA0004170551100000032
Pα b |h b | 2 -P(1-α b )|h a | 2 ≥P tol ，/>
Figure FDA0004170551100000033
0≤α b ≤1,0≤b≤2N，/>
Figure FDA0004170551100000034
wherein a, b are two users in a cluster, R n (n=a, b) represents the minimum reception rate of user n, P tol Indicating the sharpness required to exercise the SIC,
Figure FDA0004170551100000035
and />
Figure FDA0004170551100000036
Representing the reception rate, alpha, of user a and user b, respectively, in an upstream OMA system b Representing the power allocation factor, h, of user b n (n=a, b) represents the channel gain between the base station and the user, P represents the intra-cluster power, +.>
Figure FDA0004170551100000037
σ 2 Is the variance of additive white gaussian noise, P tol Indicating the sharpness required for the deletion using serial interference;
Figure FDA0004170551100000038
and />
Figure FDA0004170551100000039
The reception rates of user a and user b in the upstream NOMA system are shown, respectively.
8. A storage medium having stored thereon a computer program, which when executed by a processor implements the optimal dynamic power allocation method based on upstream NOMA of any one of claims 1 to 6.
9. 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 that the terminal performs the optimal dynamic power allocation method based on uplink NOMA according to any one of claims 1 to 6.
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