CN109327894A - Multiple cell MIMO-NOMA optimal power allocation method based on AF panel - Google Patents
Multiple cell MIMO-NOMA optimal power allocation method based on AF panel Download PDFInfo
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Abstract
The invention belongs to wireless communication technology fields, disclose a kind of multiple cell MIMO-NOMA optimal power allocation method based on AF panel；The multiple cell MIMO-NOMA optimal power allocation method based on AF panel constructs multiple cell MIMO-NOMA system model, subscriber channel fading model；Inter-cell interference is eliminated by interference alignment techniques, obtains the mathematical model of power distribution problems；Tight lower bound coefficient and corresponding replacement are constructed, converts convex optimization problem for former power distribution problems；Iterative algorithm finds out optimal power allocation scheme.The invention proposes multiple cell MIMO-NOMA to interfere dispelling tactics, and optimal multi-plot joint power distribution problems are derived based on the inhibition to interference, make system and rate reach maximization under the premise of ensureing user fairness, obtains the optimal transmission scheme under optimal multiple cell NIMO-NOMA scene.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a multi-cell MIMO-NOMA optimal power allocation method based on interference suppression.
Background
Currently, the current state of the art commonly used in the industry is such that: as Non-orthogonal multiple access (NOMA) technology can effectively cope with the challenges of high throughput, low delay, large connection and the like in future wireless networks, the technology has attracted extensive attention and research and has become one of the alternatives of 5G networks. The NOMA technique allows multiple users to share the same power domain channel resource and perform differentiated scheduling on the same. So that the NOMA technology can serve more users than Orthogonal Multiple Access (OMA). In addition, the system performance can be further improved by combining the NOMA technology and the MIMO technology. In recent years, single cell NOMA systems for system and rate, user fairness, and energy efficiency have been extensively studied by researchers. With the demand for higher spectrum utilization, the NOMA network of multiple cells receives more and more attention and attention. In the multi-cell NOMA network scenario, the interference between cells will be very serious, resulting in a serious performance degradation for cell edge users. According to the power distribution principle of the NOMA technology, more power is occupied by cell edge users; therefore, the inter-cell interference in a multi-cell NOMA network will be more severe than in a multi-cell OMA network, making the performance of cell-edge users worse. Furthermore, joint power allocation in multi-cell NOMA networks is more complex than in single-cell NOMA networks. Several existing NOMA networks are studied as follows: in the prior art, a cooperative beam forming scheme is proposed for a two-cell MIMO-NOMA system, and an effective precoding matrix and a decoding matrix are obtained. In the second prior art, a base station side precoding matrix design principle is proposed for a multi-cell MIMO-NOMA system from the perspective of throughput maximization. In the third prior art, for a multi-cell MIMO-NOMA system, an optimal power allocation strategy is found with the goals of minimizing total transmit power and maximizing system and rate as optimization targets, respectively. Precoding and decoding matrices in prior art multi-cell NOMA networks to avoid inter-cell interference, but do not consider efficient power allocation strategies. The second prior art does not consider inter-cell interference cancellation and power allocation. The power allocation problem in the prior art three does not consider inter-cell interference. Due to the fact that various problems are comprehensively considered and the difficulty is high, in the method, the solution is carried out under specific conditions aiming at a specific problem in a multi-cell MIMO-NOMA scene. However, in an actual multi-cell MIMO-NOMA scenario, not only an optimal power allocation strategy needs to be considered to improve network capacity, but also the influence of severe inter-cell interference existing in a multi-cell on edge users needs to be considered, so that an optimal transmission strategy combining inter-cell interference suppression and power allocation needs to be sought.
In summary, the problems of the prior art are as follows:
(1) in the prior art, a precoding matrix and a decoding matrix in a multi-cell NOMA network are used for avoiding inter-cell interference, but an efficient power allocation strategy is not considered, the allocation efficiency is not high, a large number of users are difficult to serve, the improvement of network capacity is limited, and the method is not suitable for actual deployment.
(2) In the second prior art, inter-cell interference cancellation and power allocation are not considered, and the second prior art is an optimal power allocation strategy under an ideal condition and cannot be directly used in an actual scene.
(3) In the third prior art, the power allocation problem does not consider inter-cell interference, thereby causing severe degradation of cell edge user performance.
The difficulty and significance for solving the technical problems are as follows:
the difficulty of the multi-cell MIMO-NOMA optimal power distribution method based on interference suppression is that the optimal power distribution problem and the problem of interference among multiple cells in the actual multi-cell NOMA scene are comprehensively considered. The method has the advantages that the optimal power distribution strategy is obtained to improve the network capacity, meanwhile, the influence of the interference among multiple cells on edge users is reduced, and the performance of all users is guaranteed. The design of the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method is an optimal transmission strategy combining inter-cell interference suppression and power allocation. The method effectively improves the resource utilization rate, ensures the performance and fairness of users, and provides a solution for the deployment of the future multi-cell MIMO-NOMA network.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a multi-cell MIMO-NOMA optimal power allocation method based on interference suppression.
The invention is realized in this way, a multi-cell MIMO-NOMA optimal power distribution method based on interference suppression, the multi-cell MIMO-NOMA optimal power distribution method based on interference suppression constructs a multi-cell MIMO-NOMA system model, a user channel fading model; eliminating inter-cell interference through an interference alignment technology to obtain a mathematical model of a power distribution problem; constructing a tight lower bound coefficient and corresponding substitution, and converting the original power distribution problem into a convex optimization problem; and (4) solving an optimal power distribution scheme by an iterative algorithm.
Further, the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method comprises the following steps:
step one, obtaining a user channel fading matrix based on a multi-cell MIMO-NOMA system model;
step two, obtaining a user received signal model and constructing an optimal power distribution optimization problem;
step three, converting the original power distribution problem into a convex optimization problem by constructing a tight lower bound of the original objective function;
solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient;
and step five, solving the optimal power distribution scheme through an iterative algorithm.
Further, the first step specifically includes: there are I base stations with Nt antennas, and J users with Nr antennas in each base station; using U [ i,1]And U [ i,2]Respectively representing a central user and an edge user in the same frequency band of a cell; matrix arrayIs the channel fading coefficient from the ith BS to the jth user of the mth cell (m e I), α represents the large scale fading factor, and all elements of the matrix G are independently and identically distributed and follow the CN (0,1) distribution.
Further, the second step specifically includes: using IA method for cell edge users according to channel fading matrix, then Ui, 1]And U [ i,2]The received signals of (a) may be represented as:wherein,in order to be able to receive the signal,represents other base station pairs U [ i,1]]Interference of ni,1(n ═ 1,2) is gaussian white noise; u [ i,1]]And U [ i,2]The rate of (d) is expressed as:wherein,the three terms are channel gain; the system and rate are:wherein p ═ p1,1,p1,2,...,pI，2]The optimal power allocation is modeled as:
Problem1：maxpR(p)
s.t.
C2:pi,1-pi,2＜0,i＝1,2…I
C3:Ri,1≥Ci,1,Ri,2≥Ci,2,i＝1,2…I；
wherein C1 is a total power constraint condition, C2 is a power constraint condition required by the NOMA network, and C3 is a minimum rate requirement of a multi-cell MIMO-NOMA network user.
Further, the third step specifically includes: a tight lower bound of the original objective function is constructed,the lower bound tightness factor αi,jAnd βi,jAre respectively asAnd isIs an arbitrary non-negative number; when in useTime, lower boundWill be provided withCarrying out the original formula:
the optimal power allocation problem translates into:
s.t.
further, the fourth step specifically includes: converting the optimal power allocation problem into a dual problem, converting the problem into:
wherein, mui,υi,ωiAnd ξ is Lagrange multiplier, and takes initial tight lower bound coefficient αi,j0 and βi,jSolving for L (P2) according to the KKT condition, resulting in a set of power allocation schemes P ═ P1,1,p1,2,...,pI，2]。
Further, the fifth step specifically includes: judging whether the obtained power distribution scheme is converged or not, if not, according to the obtained power distribution scheme p ═ p1,1,p1,2,...,pI，2]ComputingDeriving a new tight lower bound factor αi,jAnd βi,jAnd repeatedly solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient until iteration converges to obtain the optimal power distribution scheme of each base station of the multi-cell MIMO-NOMA system.
Another object of the present invention is to provide a wireless communication system applying the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method.
Another object of the present invention is to provide a non-orthogonal multiple access system applying the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method.
Another object of the present invention is to provide an information data processing terminal applying the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method.
In summary, the advantages and positive effects of the invention are: the invention is based on a multi-cell MIMO-NOMA system model, takes the interference among cells into consideration, and introduces a new optimal power distribution calculation method; compared with the prior art I, the optimal power allocation calculation method is considered, and compared with the prior art II and the prior art III, the interference among multiple cells is considered, so that the fairness of users is guaranteed. The invention provides a multi-cell MIMO-NOMA interference elimination strategy, deduces an optimal multi-cell joint power distribution problem based on interference suppression, maximizes the system and the speed on the premise of ensuring user fairness, obtains an optimal transmission scheme under an optimal multi-cell NIMO-NOMA scene, and provides a solution for the deployment of the future multi-cell MIMO-NOMA scene.
Drawings
Fig. 1 is a flowchart of a multi-cell MIMO-NOMA optimal power allocation method based on interference suppression according to an embodiment of the present invention.
Fig. 2 is a schematic view of an application scenario of the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Efficient power allocation strategies are not considered for the prior art; inter-cell interference cancellation and power allocation are not considered; the problem of inter-cell interference is not considered. The invention particularly relates to a multi-cell transmission method combining interference suppression and power allocation, which is applied to a multi-cell MIMO-NOMA wireless network and can eliminate inter-cell interference, improve the performance of cell edge users and improve the overall performance of a system.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the method for allocating optimal power for multi-cell MIMO-NOMA based on interference suppression according to the embodiment of the present invention includes the following steps:
s101: obtaining a user channel fading matrix based on a multi-cell MIMO-NOMA system model;
s102: obtaining a user received signal model and constructing an optimal power distribution optimization problem;
s103: converting the original power distribution problem into a convex optimization problem by constructing a tight lower bound of an original objective function;
s104: solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient;
s105: and solving an optimal power distribution scheme according to an iterative algorithm.
The method for allocating the power of the multi-cell MIMO-NOMA system provided by the embodiment of the invention specifically comprises the following steps:
(1) in a multi-cell MIMO-NOMA system, there are I base stations with Nt antennas, and J users with Nr antennas in each base station. Using U [ i,1]And U [ i,2]Respectively representing the central user and the edge user in the same frequency band of the cell. Matrix arrayIs the channel fading coefficient from the ith BS to the jth user of the mth cell (m e I), where α represents the large scale fading factor, and all elements of the matrix G are independently and identically distributed and follow the CN (0,1) distribution.
(2) Using IA method for cell edge users according to channel fading matrix, then Ui, 1]And U [ i,2]The received signals of (a) may be represented as:wherein,in order to be able to receive the signal,represents other base station pairs U [ i,1]]Interference of ni,1(n is 1,2) is white gaussian noise. U [ i,1]]And U [ i,2]The rate of (d) can be expressed as:wherein,the three terms are channel gain. From this it follows that the system and rate are:wherein p ═ p1,1,p1,2,...,pI，2]Based on this, the optimal power allocation can be modeled as an optimization problem as follows:
Problem1：maxpR(p)
s.t.
C2:pi,1-pi,2＜0,i＝1,2…I
C3:Ri,1≥Ci,1,Ri,2≥Ci,2,i＝1,2…I；
wherein C1 is a total power constraint condition, C2 is a power constraint condition required by the NOMA network, and C3 is a minimum rate requirement of a multi-cell MIMO-NOMA network user.
(3) A tight lower bound of the original objective function is constructed,the lower bound tightness factor αi,jAnd βi,jAre respectively asAnd isIs an arbitrary non-negative number. When in useTime, lower boundWill be provided withCarrying out the original formula:
the optimal power allocation problem can translate into:
s.t.
(4) translating the above optimal power allocation problem into its dual problem, translating the problem into:
wherein, mui,υi,ωiAnd ξ is Lagrange multiplier, and takes initial tight lower bound coefficient αi,j0 and βi,jSolving for L (P2) according to the KKT condition, resulting in a set of power allocation schemes P ═ P1,1,p1,2,...,pI，2]。
(5) Judging whether the obtained power distribution scheme is converged or not, if not, according to the obtained power distribution scheme p ═ p1,1,p1,2,...,pI，2]Calculate a new tight lower bound coefficient αi,jAnd βi,jAnd repeating the fourth step until iteration converges to obtain the optimal power allocation scheme of each base station of the multi-cell MIMO-NOMA system.
The application of the principles of the present invention will be further described with reference to the accompanying drawings
As shown in fig. 2, there are three cells in the scenario, BS1, BS2, and BS3, each cell includes one BS, and one cell center user and one cell edge user, and these two users use NOMA for downlink transmission.
Step 1: and obtaining a user channel fading matrix based on a multi-cell MIMO-NOMA system model.
As can be seen from the scenario of fig. 2, there are 3 base stations with Nt antennas, and there are J users with Nr antennas in each base station. Using U [ i,1]And U [ i,2]Respectively representing the central user and the edge user in the same frequency band of the cell (i belongs to {1, 2, 3 }). And carrying out power distribution on the central user and the edge user in the same frequency band. Matrix arrayRepresenting the channel fading coefficients of the ith BS to the jth user of the mth cell (m e I ═ 1,2, 3), where α represents the large scale fading factor, all elements of the matrix G are independently identically distributed and follow the CN (0,1) distribution.
Step 2: obtaining a user received signal model and constructing an optimal power distribution optimization problem;
(2a) the received signals of U [ i,1] and U [ i,2] can be represented as:
wherein,indicating all base stations except the ith base station; y isi,1And Yi,2The second term on the right hand side of (1) represents the term for U [ i,1, respectively]And U [ i,2]Inter-cell interference of (1);is Additive White Gaussian Noise (AWGN). Furthermore, xiIs NtA superimposed signal of x 1, expressed as:wherein,is a precoding vector of the ith BS side,is thatThe number of symbols required for the symbol to be transmitted,representing the power distribution coefficient of the superimposed signal.
(2b) To eliminate severe inter-cell interference, IA method is used for cell edge users and v is usediAll inter-cell interference is concentrated into the same signal space, making the other signals the desired signals. Accordingly, possible conditions for IA are:
wherein d isi,2Is defined as a degree of freedom (DoF),is the unit interference suppression vector at the receiver.
(2c) According to (2a), (2b), U [ i,1]]And U [ i,2]Converts the received signal into:wherein, yi,1＝Yi,1， Then U [ i,1]And U [ i,2]The rate of (d) can be expressed as:wherein, from this, the total rate of the system can be derived as:wherein p ═ p1,1,p1,2,...,pI，2]The optimal power allocation problem can be described as:
Problem1：maxpR(p)
s.t.
C2:pi,1-pi,2＜0,i＝1,2…I
C3:Ri,1≥Ci,1,Ri,2≥Ci,2,i＝1,2…I；
where C1 is the total power constraint, C2 is the power constraint required by the NOMA network, and C3 is the current condition that the multi-cell MIMO-NOMA network user requires the lowest rate.
And step 3: constructing an original objective function tight lower bound and a logarithm change method, and converting an original power distribution problem into a convex optimization problem;
lower bound tightness α of constructioni,jAnd βi,jTo makeThe lower bound tightness factor αi,jAnd βi,jAre respectively asAnd isIs an arbitrary non-negative number. When in useTime, lower boundWill be provided withCarrying out the original formula:
the original problem can be translated into:
s.t.
and 4, step 4: and solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient.
(4a) Translating the above-described optimal power into its dual problem:
minμ,υ,ω,ξDual(P2)
s.t.μ＞0,υ＞0,ω＞0,ξ＞0；
the problem is converted into:
wherein, mui,υi,ωiAnd ξ are lagrange multipliers.
(4b) The relaxation factor is initialized to αi,j0 and βi,j＝0；
(4c) Solving L (P2) according to the KKT condition to obtain a group of power distribution schemes P ═ P1,1,p1,2,...,pI，2]
And 5: and solving an optimal power distribution scheme according to an iterative algorithm.
Judging whether the obtained power distribution scheme is converged or not, if not, according to the obtained power distribution scheme p ═ p1,1,p1,2,...,pI，2]Calculate a new tight lower bound coefficient αi,jAnd βi,jAnd if so, repeating the fourth step until the iterative algorithm is converged to obtain the optimal power allocation scheme of each base station of the multi-cell MIMO-NOMA system.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A multi-cell MIMO-NOMA optimal power distribution method based on interference suppression is characterized in that a multi-cell MIMO-NOMA system model and a user channel fading model are constructed by the multi-cell MIMO-NOMA optimal power distribution method based on interference suppression; eliminating inter-cell interference through an interference alignment technology to obtain a mathematical model of a power distribution problem; constructing a tight lower bound coefficient and corresponding substitution, and converting the original power distribution problem into a convex optimization problem; and (4) solving an optimal power distribution scheme by an iterative algorithm.
2. The interference mitigation based multi-cell MIMO-NOMA optimal power allocation method of claim 1, wherein said interference mitigation based multi-cell MIMO-NOMA optimal power allocation method comprises the steps of:
step one, obtaining a user channel fading matrix based on a multi-cell MIMO-NOMA system model;
step two, obtaining a user received signal model and constructing an optimal power distribution optimization problem;
step three, converting the original power distribution problem into a convex optimization problem by constructing a tight lower bound of the original objective function;
solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient;
and step five, solving the optimal power distribution scheme through an iterative algorithm.
3. The method for allocating optimal power for multi-cell MIMO-NOMA of claim 1, wherein the first step specifically comprises: there are I base stations with Nt antennas, and J users with Nr antennas in each base station; using U [ i,1]And U [ i,2]Respectively representing a central user and an edge user in the same frequency band of a cell; matrix arrayIs the channel fading coefficient from the ith BS to the jth user of the mth cell (m e I), α represents the large scale fading factor, and all elements of the matrix G are independently and identically distributed and follow the CN (0,1) distribution.
4. The interference mitigation based multi-cell MIMO-NOMA optimal power allocation method of claim 1, wherein said second step specifically comprises: using IA method for cell edge users according to channel fading matrix, then Ui, 1]And U [ i,2]The received signals of (a) may be represented as:wherein,in order to be able to receive the signal,represents other base station pairs U [ i,1]]Interference of ni,1(n ═ 1,2) is gaussian white noise; u [ i,1]]And U [ i,2]The rate of (d) is expressed as:wherein,the three terms are channel gain; the system and rate are:wherein p ═ p1,1,p1,2,...,pI，2]The optimal power allocation is modeled as:
Problem1：maxpR(p)
s.t.
C1:
C2:pi,1-pi,2＜0,i＝1,2…I
C3:Ri,1≥Ci,1,Ri,2≥Ci,2,i＝1,2…I；
wherein C1 is a total power constraint condition, C2 is a power constraint condition required by the NOMA network, and C3 is a minimum rate requirement of a multi-cell MIMO-NOMA network user.
5. The interference mitigation based multi-cell MIMO-NOMA optimal power allocation method of claim 1, wherein said step three specifically comprises: a tight lower bound of the original objective function is constructed,the lower bound tightness factor αi,jAnd βi,jAre respectively asAnd isIs an arbitrary non-negative number; when in useTime, lower boundWill be provided withCarrying out the original formula:
the optimal power allocation problem translates into:
s.t.
C4:
C5:
C6:
6. the interference mitigation based multi-cell MIMO-NOMA optimal power allocation method of claim 1, wherein said step four specifically comprises: converting the optimal power allocation problem into a dual problem, converting the problem into:
wherein, mui,υi,ωiAnd ξ is Lagrange multiplier, and takes initial tight lower bound coefficient αi,j0 and βi,jSolving for L (P2) according to the KKT condition, resulting in a set of power allocation schemes P ═ P1,1,p1,2,...,pI，2]。
7. The interference mitigation based multi-cell MIMO-NOMA optimal power allocation method of claim 1, wherein said step five specifically comprises: judging whether the obtained power distribution scheme is converged or not, if not, according to the obtained power distribution scheme p ═ p1,1,p1,2,...,pI，2]Calculate a new tight lower bound coefficient αi,jAnd βi,jAnd repeatedly solving to obtain a group of power distribution schemes according to the initial tight lower bound coefficient until iteration converges to obtain the optimal power distribution scheme of each base station of the multi-cell MIMO-NOMA system.
8. A wireless communication system applying the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method of any one of claims 1 to 7.
9. A non-orthogonal multiple access system applying the interference suppression based multi-cell MIMO-NOMA optimal power allocation method of any one of claims 1-7.
10. An information data processing terminal applying the interference suppression-based multi-cell MIMO-NOMA optimal power allocation method of any one of claims 1 to 7.
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