CN112153653A - Reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method - Google Patents

Reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method Download PDF

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CN112153653A
CN112153653A CN202011008028.1A CN202011008028A CN112153653A CN 112153653 A CN112153653 A CN 112153653A CN 202011008028 A CN202011008028 A CN 202011008028A CN 112153653 A CN112153653 A CN 112153653A
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optimization problem
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phase shift
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王鸿
张翔宇
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Nanjing University of Posts and Telecommunications
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/14—Spectrum sharing arrangements between different networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/26—TPC being performed according to specific parameters using transmission rate or quality of service QoS [Quality of Service]
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Abstract

The invention discloses a reconfigurable intelligent surface assisted NOMA (non-orthogonal multiple access) downlink low-power transmission method. On the premise of ensuring the signal-to-interference-and-noise ratio requirement of a user, establishing an optimization problem of base station power allocation and RIS phase offset; establishing a conversion relation between a power distribution coefficient and an RIS phase shift factor by using the characteristics of the optimization problem, and converting the original optimization problem into a simple phase shift optimization problem; further simplifying the optimization problem by utilizing a one-by-one phase rotation method, and solving the optimal phase offset of each iteration through a one-dimensional search and penalty function method; and repeating iteration until the objective function is converged to obtain the optimal phase offset matrix of the RIS and the power distribution coefficient of the base station. Compared with the traditional orthogonal multiple access transmission scheme and the RIS-free transmission scheme, the RIS random phase shift and the RIS equal phase shift transmission scheme, the transmission method provided by the invention can obviously reduce the total transmission power consumption of the system.

Description

Reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method
Technical Field
The invention relates to the technical field of reconfigurable intelligent surfaces in wireless communication, in particular to a reconfigurable intelligent surface assisted non-orthogonal multiple access (NOMA) downlink low-power-consumption transmission method.
Background
The traditional wireless communication propagation environment is random and uncontrollable, which may cause a certain damage to information transmission. In recent years, with the development of artificial electromagnetic materials, a Reconfigurable Intelligent Surface (RIS) has been proposed, which can intelligently configure a wireless communication propagation environment, thereby significantly improving the performance of a wireless communication network. The RIS, as a passive device, does not have any signal processing capability, and can adjust the propagation direction and the phase of the wireless signal only by configuring the phase offset according to the channel characteristics. Non-orthogonal multiple access (NOMA) can support higher system throughput and a larger number of concurrent connections than orthogonal multiple access techniques, and thus has received a great deal of attention from both academic and industrial circles.
In a newly-appeared RIS-assisted NOMA downlink system, the advantages of RIS and NOMA in the system cannot be jointly utilized by a traditional orthogonal multiple access transmission scheme and a RIS-assisted-free transmission NOMA scheme, and for a simple RIS phase offset design scheme, such as random phase design and uniform phase design, the time-varying channel state information cannot be fully utilized to intelligently configure a wireless transmission environment, so that the advantages of RIS cannot be exerted to the maximum extent, and the power efficiency of the RIS-assisted system is influenced.
In view of the above, there is a need for improvements to existing RIS assisted NOMA downlink systems to address the above-mentioned problems.
Disclosure of Invention
The invention aims to provide a reconfigurable intelligent surface assisted NOMA downlink low-power-consumption transmission method, which can reduce the total transmission power consumption of a system.
In order to achieve the purpose, the invention provides the following technical scheme:
a reconfigurable intelligent surface assisted NOMA downlink low-power-consumption transmission method mainly comprises the following steps:
step 1: in NOMA transmission mode, defining U users to share same time frequency resource block, obtaining each user receiving signal zuThe expression of (1);
step 2: according to the equivalent channel strength, determining the demodulation order of each user, and obtaining the demodulation received signal-to-interference-and-noise ratio required by each user at each user
Figure BDA0002696628810000021
The expression of (1);
and step 3: establishing a power distribution coefficient w for a base stationuThe optimization problem Q1 of the matrix phi of RIS phase shift, minimize the total transmitted power;
and 4, step 4: according to the characteristics of the optimization problem, a power distribution coefficient { w is constructeduThe relation between the matrix phi and the phase shift matrix phi;
and 5: converting the optimization problem Q1 into an optimization problem Q2 of a pure phase offset vector v by using the relational expression formed in the step 4;
step 6: solving the transformed optimization problem Q2 in an iteration and one-by-one phase rotation mode;
and 7: repeating the step 6 until the objective function is converged to obtain an optimal phase shift matrix phi, and further obtaining an optimal power distribution coefficient { w }u}。
As a further improvement of the invention, step 1 is that in the NOMA transmission mode, the base station transmits the superposed signal as
Figure BDA0002696628810000022
Received signal z of any user uuCan be expressed as:
Figure BDA0002696628810000023
wherein, wkTransmission power coefficient, x, allocated to user k for base stationkFor the transmission of user k, hB,uChannel coefficient h of direct path from base station to user uB,IIs the channel vector of base station to RIS, hI,uIs the channel vector from RIS to user u,
Figure BDA0002696628810000024
for the channel vector hI,uConjugate transpose of (n)uFor the noise signal at user u, obey mean of 0 and variance of
Figure BDA0002696628810000025
Phi is a phase shift matrix.
As a further improvement of the present invention, the phase shift matrix Φ can be expressed as
Figure BDA0002696628810000026
Wherein diag {. is a diagonalization operation, φnIs the phase shift factor of the nth reflection unit of the RIS, phinBelongs to a uniformly quantized set of discrete phases S, namely:
Figure BDA0002696628810000031
as a further improvement of the present invention, step 2 is specifically to demodulate the user signal with the equivalent channel strength weaker than that of the user U before demodulating the self signal according to the NOMA demodulation principle, and delete the demodulated signal by using the serial interference deletion receiver, that is, the user U demodulates and deletes the signals of the users U + 1-U in sequence, and the received signal to interference and noise ratios of the user k signals at the user U
Figure BDA0002696628810000032
Can be expressed as:
Figure BDA0002696628810000033
wherein,
Figure BDA0002696628810000034
is the noise power.
As a further improvement of the invention, the stronger the equivalent channel strength is, the smaller the user serial number is, when the user serial number u is<k, the equivalent channel strength satisfies:
Figure BDA0002696628810000035
as a further improvement of the present invention, the optimization problem Q1 in step 3 is represented as:
Figure BDA0002696628810000036
s.t.C1：
Figure BDA00026966288100000313
C2：
Figure BDA00026966288100000314
C3：φn∈S，1≤n≤N，
wherein,
Figure BDA0002696628810000039
the minimum signal-to-interference-and-noise ratio (SINR) required to be satisfied by the user k, P is the total power of system transmission, the constraint C1 is used to guarantee the service quality of each user, the constraint C2 is used to guarantee the equivalent channel gain order, and the constraint C3 is used to guarantee that the phase offset of each reflection unit belongs to the discrete phase set by the system.
As a further improvement of the present invention, step 4 is to obtain an optimal value of the optimization problem Q1 by a contradiction method, where the optimal value is that the constraint C1 takes an equal sign, and for the user k (k >1), the constraint C1 may be converted into:
Figure BDA00026966288100000310
wherein, when 1 < i < k,
Figure BDA00026966288100000311
if not, then,
Figure BDA00026966288100000312
the total system transmit power in the optimization problem Q1 objective function can be converted into:
Figure BDA0002696628810000041
as a further improvement of the present invention, the optimization problem Q1 in step 5 can be equivalently transformed into the optimization problem Q2, and the optimization problem Q2 is represented as:
Figure BDA0002696628810000042
s.t.C4:
Figure BDA0002696628810000049
C3：φn∈S，1≤n≤N，
wherein,
Figure BDA0002696628810000044
is a vector of RIS phase shift factors,
Figure BDA0002696628810000045
the status information of the cascade channel composed of the base station to RIS channel and the RIS to user channel.
As a further improvement of the present invention, the solving of the optimization problem Q2 in step 6 includes the following three steps:
step 6 a: establishing an optimal solution v for the t-th iteration using a phase-by-phase rotation method(t)And the t-1 th iterative solution v(t-1)The relationship between the first and the second, specifically, the t-1 th iteration solution v(t-1)The phase offsets of the N reflection units are sequentially rotated to obtain the t-th iteration solution v(t)The tth iterative solution v(t)Expressed as:
Figure BDA0002696628810000046
wherein,
Figure BDA0002696628810000047
is a phase shift matrix of the nth reflection unit, thetan，tIs the phase shift amount of the nth reflection unit;
step 6 b: establishing an n-th phase rotated phase offset vector vt,nPhase offset vector v after phase rotation with the n-1 th phaset,n-1Will optimize the problemQ2 is simplified to an optimization problem Q3, specifically:
Figure BDA0002696628810000048
s.t.C5：fu(θn，t)-fk(θn，t)≥0，u＜k，
C6：θn，t∈S，
wherein,
fu(θn，t)＝aucosθn，t+businθn，t+tu，
Figure BDA0002696628810000051
Figure BDA0002696628810000052
Figure BDA0002696628810000053
Figure BDA0002696628810000054
Figure BDA0002696628810000055
Figure BDA0002696628810000056
wherein,
Figure BDA0002696628810000057
are respectively variable Au，CuThe conjugate of (a) to (b),
Figure BDA0002696628810000058
for concatenated channel state information vectors
Figure BDA0002696628810000059
N element of (v)t，nThe phase offset vector after the nth phase rotation can also be expressed as:
Figure BDA00026966288100000510
step 6 c: obtaining the optimal solution of the optimization problem Q3 in the t iteration in a one-dimensional search and penalty function mode
Figure BDA00026966288100000511
The optimal solution to the optimization problem Q3 is represented as:
Figure BDA00026966288100000512
wherein I (-) is a penalty function for violating the constraint C5, defined as
Figure BDA00026966288100000513
The penalty factor L is a large positive number for penalizing variables that do not meet the constraint C5.
As a further development of the invention, step 7 is embodied in the construction of an optimal phase shift matrix using the optimal phase shift factor
Figure BDA00026966288100000514
Substituting phi into the limiting condition C1 converted in the step 4 to obtain the optimal power distribution coefficient { wuAnd (c) the step of (c) in which,
Figure BDA00026966288100000515
the optimal phase shift factor for each reflection unit of the RIS is expressed as:
Figure BDA00026966288100000516
Figure BDA00026966288100000517
the sum of the optimal phase shift factors for all iterations of each reflection unit, the optimal phase shift factor
Figure BDA00026966288100000518
After the objective function converges, T is the number of iterations required for convergence, mod2 pi represents the pair
Figure BDA0002696628810000061
And performing 2 pi modulus extraction.
The invention has the beneficial effects that: on the premise of meeting the service quality of each user, the channel state information is fully utilized, and the purpose of reducing the total transmission power consumption of the system is realized through the combined optimization of base station power distribution and RIS phase deviation.
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FIG. 1 is an overall flow chart of the present invention.
Fig. 2 is a diagram of the RIS-assisted NOMA system model architecture.
Fig. 3 is a graph of the total transmission power consumption of two user NOMA systems under different SINR threshold conditions.
Fig. 4 is a graph of the total transmit power consumption of a three-user NOMA system under different SINR threshold conditions.
Fig. 5 is a graph of the effect of the number of RIS reflector units on the total transmit power consumption of the system.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
In the invention, a cellular downlink single-antenna NOMA transmission scene with the coverage radius of R is considered, a base station is positioned at the center of a coverage area, and U users are uniformly distributed at the outer radius of R and the inner radius of R to avoid near-field effectnWithin the annular footprint. In the normal mode of the NOMA transmission,the base station provides service for U users on the same time-frequency resource block, and one RIS with N reflecting units is deployed in the cell coverage area. The distance between the base station and the RIS, the distance between the base station and the user u, and the distance between the RIS and the user u are dB,I，dB,u，dI,u. The system model structure of the present invention is shown in fig. 2.
The overall flow of the reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method is shown in FIG. 1, and specifically comprises the following steps:
step 1: in NOMA transmission mode, defining U users to share same time frequency resource block, obtaining each user receiving signal zuThe expression of (1);
in NOMA transmission mode, the base station transmits a superimposed signal of
Figure BDA0002696628810000062
Wherein, wkTransmission power coefficient, x, allocated to user k for base stationkFor user k. Any user u receives the signal zuCan be expressed as:
Figure BDA0002696628810000071
wherein h isB,uChannel coefficient h of direct path from base station to user uB,IIs the channel vector of base station to RIS, hI,uIs the channel vector from RIS to user u,
Figure BDA0002696628810000072
for the channel vector hI,uConjugate transpose of (n)uFor the noise signal at user u, obey mean of 0 and variance of
Figure BDA0002696628810000073
Complex gaussian distribution. Φ is a phase shift matrix, which can be expressed as
Figure BDA0002696628810000074
Wherein diag {. is diagonalAnd (5) carrying out conversion operation. Phi is anThe phase shift factor for the nth reflection unit of the RIS, which belongs to a uniformly quantized set S of discrete phases, i.e.
Figure BDA0002696628810000075
Step 2: according to the equivalent channel strength, determining the demodulation order of each user, and obtaining the demodulation received signal-to-interference-and-noise ratio required by each user at each user
Figure BDA0002696628810000076
The expression of (1);
according to the equivalent channel strength, the users are sequentially ordered from strong to weak, that is, the smaller the user serial number is, the greater the equivalent channel strength is, and if the user serial number u < k, the equivalent channel strength satisfies:
Figure BDA0002696628810000077
according to the NOMA demodulation principle, before a user U demodulates a self signal, a user signal with a channel strength weaker than that of the user signal needs to be demodulated first, and the demodulated signal is deleted by adopting a serial interference deletion receiver, that is, the user U needs to demodulate and delete signals of users U + 1-U in sequence, and the received signal to interference and noise ratio of a user k signal at the user U can be expressed as follows:
Figure BDA0002696628810000078
wherein,
Figure BDA0002696628810000079
is the noise power.
For user 1, it first demodulates all other user signals in sequence and deletes them, then when it demodulates its own signal, there is no interference from other users, and the signal-to-interference-and-noise ratio can be expressed as:
Figure BDA00026966288100000710
and step 3: establishing a power distribution coefficient w for a base stationuThe optimization problem Q1 of the matrix phi of RIS phase shift, minimize the total transmitted power;
in the design of a transmission scheme, in order to minimize the total power consumption of system transmission, a base station power distribution coefficient { w ] needs to be jointly optimizeduAnd the RIS phase offset matrix phi. The optimization problem Q1 may be expressed as:
Figure BDA0002696628810000081
s.t.C1：
Figure BDA0002696628810000089
C2：
Figure BDA0002696628810000083
C3：φn∈S，1≤n≤N，
wherein,
Figure BDA0002696628810000084
for the minimum signal-to-interference-and-noise ratio required by user k, the constraint condition C1 is used to guarantee the service quality of each user, the constraint condition C2 is used to guarantee the equivalent channel gain order, and further guarantee the user demodulation order formulated in step 2, and the constraint condition C3 is used to guarantee that the phase offset of each reflection unit belongs to the discrete phase set by the system.
And 4, step 4: according to the characteristics of the optimization problem, a power distribution coefficient { w is constructeduThe relation between the matrix phi and the phase shift matrix phi;
as can be obtained by the contradiction, when the optimization problem Q1 takes the optimal value, for the user 1, the constraint condition C1 takes the equal sign, that is, the following conditions are satisfied:
Figure BDA0002696628810000085
similarly, for user k (k >1), the constraint C1 may translate to:
Figure BDA0002696628810000086
wherein, when l < i < k,
Figure BDA0002696628810000087
if not, then,
Figure BDA0002696628810000088
and 5: converting the optimization problem Q1 into an optimization problem Q2 of a pure phase offset vector v by using the relational expression formed in the step 4;
using the power distribution coefficient w obtained in step 4uThe relation between the RIS phase shift matrix phi and the optimization problem Q1 target function can be converted into:
Figure BDA0002696628810000091
to separate the optimization problem Q1 objective function from the phase shift factor in the constraints, we use
Figure BDA0002696628810000092
A vector representing the components of the RIS phase shift factor,
Figure BDA0002696628810000093
and the state information of the cascade channel composed of the base station-to-RIS channel and the RIS-to-user channel is represented. Then the equivalent transformed optimization problem Q2 of the original optimization problem Q1 can be expressed as:
Figure BDA0002696628810000094
s.t.C4:
Figure BDA0002696628810000098
C3：φn∈S，1≤n≤N，
in the optimization problem Q2, the optimization variables contain only RIS phase offset vectors, and the constraint C4 is transformed from the constraint C2.
Step 6: solving the transformed optimization problem Q2 in an iteration and one-by-one phase rotation mode;
the optimization problem Q2 needs to be solved in three steps, and the specific process is as follows:
step 6 a: establishing the optimal solution v of the t-th iteration in a mode of phase rotation one by one(t)And the t-1 th iterative solution v(t-1)The relationship between them.
Specifically, the t-1 th iteration is solved for v(t-1)The phase offsets of the N reflection units are sequentially rotated to obtain a solution v of the t iteration(t). The tth iterative solution v(t)Can be expressed as:
Figure BDA0002696628810000096
wherein,
Figure BDA0002696628810000097
is a phase shift matrix of the nth reflection unit, a phase shift factor of the nth reflection unit for rotating the RIS, thetan，tIs the phase shift amount of the nth reflection unit.
Step 6 b: establishing an n-th phase rotated phase offset vector vt,nPhase offset vector v after phase rotation with the n-1 th phaset,n-1The relation between the two is further simplified to the optimization problem Q3, namely the optimization problem Q2 which needs to be solved.
The phase offset vector after the nth phase rotation can be expressed as
Figure BDA0002696628810000101
The following relationship can be obtained:
Figure BDA0002696628810000102
further, based on the result v of the previous phase rotationt,n-1The optimization problem Q2 can be simplified to the optimization problem Q3 as follows:
Figure BDA0002696628810000103
s.t.C5：fu(θn，t)-fk(θn，t)≥0，u＜k，
C6：θn，t∈S，
wherein,
fu(θn，t)＝aucosθn，t+businθn，t+tu，
Figure BDA0002696628810000104
Figure BDA0002696628810000105
Figure BDA0002696628810000106
Figure BDA0002696628810000107
Figure BDA0002696628810000108
wherein, the restriction conditions C5 and C6 are obtained by respectively transforming the restriction conditions C4 and C3 in the optimization problem Q2,
Figure BDA0002696628810000109
are respectively variable Au，CuThe conjugate of (a) to (b),
Figure BDA00026966288100001010
for concatenated channel state information vectors
Figure BDA00026966288100001011
The nth element of (1).
Step 6 c: sequentially obtaining the optimal solution of the optimization problem Q3 in the t iteration in a one-dimensional search and penalty function mode
Figure BDA00026966288100001012
For the optimization problem Q3, the optimization variable θnThe method belongs to a discrete phase set S, and an optimal solution can be found out in a one-dimensional searching mode. The optimal solution for the optimization problem Q3 is represented as:
Figure BDA00026966288100001013
wherein I (-) is a penalty function for violating the constraint C5, which is defined as
Figure BDA00026966288100001014
The penalty factor L is a large positive number for penalizing variables that do not meet the constraint C5.
By analogy, the optimal phase offset factor of each RIS reflecting unit in the t iteration is obtained in turn in a phase optimization mode one by one
Figure BDA0002696628810000111
And 7: repeating the step 6 until the objective function is converged to obtain an optimal phase shift matrix phi, and further obtaining an optimal power distribution coefficient { w }u}；
Obtaining the optimal phase shift factor after the convergence of the target function
Figure BDA0002696628810000112
Where T is the number of iterations required for convergence. Adding the phase offset factors obtained by all iterations of each reflection unit, and expressing the phase offset factors as follows:
Figure BDA0002696628810000113
and 2 pi modulus is taken to obtain the optimal phase shift factor of each reflection unit of RIS, namely
Figure BDA0002696628810000114
Constructing an optimal phase shift matrix using optimal phase shift factors
Figure BDA0002696628810000115
Then substituting phi into the relational expression between the power distribution and the phase deviation in the step 4 to obtain the optimal power distribution coefficient { wu}。
The performance of the reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method provided by the invention is explained by Monte Carlo simulation experiments. The system parameters are as follows: cell radius R250 m, near field radius Rn20m, distance d between base station and RISB,I50m, white gaussian noise power σ2The number N of RIS reflection units is 32-256, the number D of RIS discrete phase resolution digit is 10, the number U of NOMA users on the same resource block is 2-3, and the penalty factor L is 1015. The channels between the base station and the RIS, between the RIS and the user and between the base station and the user comprise large-scale fading and small-scale fading, the path loss indexes are respectively 2, 3 and 3.5, the small-scale fading between the base station and the RIS obeys the Laisi distribution, the line-of-sight path accounts for 0.8, and the small-scale fading of the channels between the RIS and the user and between the base station and the user obeys the Rayleigh distribution.
Fig. 3 shows the relationship between the total emission power consumption and the signal-to-interference-and-noise ratio threshold of two-user NOMA systems, the number of RIS reflecting units is 128. Fig. 4 shows the relationship between the total transmit power consumption and the signal to interference plus noise ratio threshold for a three-user NOMA system. As can be seen from fig. 3 and 4, the transmission method proposed by the present invention can greatly reduce the total transmission power consumption of the system, regardless of two-user NOMA systems or three-user NOMA systems. Compared with the traditional orthogonal multiple access transmission scheme, the total emission power consumption of the scheme provided by the invention is reduced by about 5dBm, and the emission power consumption is saved more as the signal-to-interference-and-noise ratio threshold value is increased. The total transmit power consumption of the proposed scheme is reduced by about 10dBm compared to the non-RIS assisted NOMA transmission scheme. Compared with a random phase and equiphase offset RIS assisted NOMA transmission method, the total emission power consumption of the scheme provided by the invention is reduced by about 5 dBm.
Fig. 5 shows the influence of the number of RIS reflector units on the total transmission power consumption of two user NOMA systems, and the user signal-to-interference-and-noise ratio threshold is set to 10. It can be seen from the figure that when the number of RIS reflecting units is increased from 32 to 256, the method of the present invention can significantly reduce the total transmission power consumption of the system. Compared with the traditional orthogonal multiple access transmission scheme, the total emission power consumption of the scheme provided by the invention is reduced by more than 6 dBm; compared with the NOMA transmission scheme without RIS assistance, the total emission power consumption of the scheme provided by the invention is reduced by more than 5 dBm; compared with a random phase offset and equiphase offset RIS assisted NOMA transmission scheme, the total emission power consumption of the scheme provided by the invention is reduced by more than 4dBm, and the total emission power consumption of the scheme provided by the invention is reduced by more along with the increase of the number of reflecting units.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (10)

1. A reconfigurable intelligent surface assisted NOMA downlink low-power-consumption transmission method is characterized by comprising the following steps:
step 1, in NOMA transmission mode, defining U users to share the same time frequency resource block, and obtaining each user receiving signal zuThe expression of (1);
step 2, according to the equivalent channel intensity, the demodulation sequence of each user is determined, and the demodulation receiving signal-to-interference-and-noise ratio required by each user is obtained at each user
Figure FDA0002696628800000011
The expression of (1);
step 3, establishing a power distribution coefficient { w ] of the base stationuAn optimization problem Q1 with the RIS phase shift matrix phi;
step 4, constructing a power distribution coefficient { wuThe relation between the matrix phi and the phase shift matrix phi;
step 5, converting the optimization problem Q1 into an optimization problem Q2 of a pure phase offset vector v by using the relational expression formed in the step 4;
step 6, solving the transformed optimization problem Q2 in an iteration and one-by-one phase rotation mode;
step 7, repeating the step 6 until the objective function is converged to obtain an optimal phase shift matrix phi, and further obtain an optimal power distribution coefficient { w }u}。
2. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 1, wherein the step 1 specifically comprises: in NOMA transmission mode, the base station transmits a superimposed signal of
Figure FDA0002696628800000012
The received signal z of any user uuCan be expressed as:
Figure FDA0002696628800000013
wherein, wkTransmission power coefficient, x, allocated to user k for base stationkFor the transmission of user k, hB,uChannel coefficient h of direct path from base station to user uB,IIs the channel vector of base station to RIS, hI,uIs the channel vector from RIS to user u,
Figure FDA0002696628800000014
for the channel vector hI,uConjugate transpose of (n)uFor the noise signal at user u, obey mean of 0 and variance of
Figure FDA0002696628800000015
Phi is a phase shift matrix.
3. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 2, wherein: the phase shift matrix Φ can be expressed as
Figure FDA0002696628800000021
Wherein diag {. is a diagonalization operation, φnPhase shift factor for the nth reflection unit of RIS, said phinBelonging to a uniformly quantized set S of discrete phases, denoted
Figure FDA0002696628800000022
4. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 1, characterized in that: step 2, according to NOMA demodulation principle, before user U demodulates its own signal, firstly demodulating said user signal whose equivalent channel strength is weaker than that of its own signal, and adopting serial interference deleting receiver to delete the demodulated signal, i.e. user U firstly demodulates and deletes the signal of user U + 1-U in turn, and the received signal-to-interference-plus-noise ratio of user k signal at user U
Figure FDA0002696628800000023
Can be expressed as:
Figure FDA0002696628800000024
wherein,
Figure FDA0002696628800000025
is the noise power.
5. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 4, wherein: the stronger the equivalent channel strength is, the smaller the user serial number is, and when the user serial number u < k, the equivalent channel strength satisfies:
Figure FDA0002696628800000026
6. the reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 1, characterized in that: the optimization problem Q1 in step 3 is represented as:
Figure FDA0002696628800000027
s.t.C1：
Figure FDA0002696628800000028
C2：
Figure FDA0002696628800000029
C3：φn∈S，1≤n≤N，
wherein,
Figure FDA00026966288000000210
the minimum signal-to-interference-and-noise ratio (SINR) required to be satisfied by the user k, P is the total power of system transmission, the constraint C1 is used to guarantee the service quality of each user, the constraint C2 is used to guarantee the equivalent channel gain order, and the constraint C3 is used to guarantee that the phase offset of each reflection unit belongs to the discrete phase set by the system.
7. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 6, wherein: step 4 is specifically to obtain an optimal value of the optimization problem Q1 through a contradiction method, where the optimal value is obtained by taking an equal sign for the constraint condition C1, and for a user k (k >1), the constraint condition C1 may be converted into:
Figure FDA0002696628800000031
wherein, when l < i < k,
Figure FDA0002696628800000032
if not, then,
Figure FDA0002696628800000033
the total system transmit power in the optimization problem Q1 objective function can be converted into:
Figure FDA0002696628800000034
8. the reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 7, wherein: in step 5, the optimization problem Q1 can be equivalently transformed into an optimization problem Q2, and the optimization problem Q2 is represented as:
Figure FDA0002696628800000035
s.t.C4:
Figure FDA0002696628800000036
C3：φn∈S，1≤n≤N，
wherein,
Figure FDA0002696628800000037
is a vector of RIS phase shift factors,
Figure FDA0002696628800000038
the status information of the cascade channel composed of the base station to RIS channel and the RIS to user channel.
9. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 1, characterized in that: when the optimization problem Q2 is solved in step 6, the method includes the following three steps:
step 6 a: establishing an optimal solution v for the t-th iteration using a phase-by-phase rotation method(t)And the t-1 th iterative solution v(t-1)The relationship between the first and the second, specifically, the t-1 th iteration solution v(t-1)The phase offsets of the N reflection units are sequentially rotated to obtain the t-th iteration solution v(t)Expressed as:
Figure FDA0002696628800000041
wherein,
Figure FDA0002696628800000042
Figure FDA0002696628800000043
is a phase shift matrix of the nth reflection unit, thetan，tIs the phase shift amount of the nth reflection unit;
step 6 b: establishing an n-th phase rotated phase offset vector vt,nPhase offset vector v after phase rotation with the n-1 th phaset,n-1The relation between the optimization problems Q2 is simplified into an optimization problem Q3, which specifically comprises the following steps:
Figure FDA0002696628800000044
s.t.C5：fu(θn，t)-fk(θn，t)≥0，u＜k，
C6：θn，t∈S，
wherein,
fu(θn，t)＝au cosθn，t+bu sinθn，t+tu，
Figure FDA0002696628800000045
Figure FDA0002696628800000046
Figure FDA0002696628800000047
Figure FDA0002696628800000048
Figure FDA0002696628800000049
Figure FDA00026966288000000410
wherein,
Figure FDA00026966288000000411
are respectively variable Au，CuThe conjugate of (a) to (b),
Figure FDA00026966288000000412
for concatenated channel state information vectors
Figure FDA00026966288000000413
N element of (v)t，nThe phase offset vector after the nth phase rotation can also be expressed as:
Figure FDA00026966288000000414
step 6 c: obtaining the optimal solution of the optimization problem Q3 in the t iteration in a one-dimensional search and penalty function mode
Figure FDA00026966288000000415
The expression for obtaining the optimal solution of the optimization problem Q3 is:
Figure FDA00026966288000000416
wherein I (-) is a penalty function for violating the constraint C5, defined as
Figure FDA0002696628800000051
The penalty factor L is a large positive number for penalizing variables that do not meet the constraint C5.
10. The reconfigurable intelligent surface-assisted NOMA downlink low-power-consumption transmission method according to claim 9, wherein: step 7 is to construct the optimal phase shift matrix by using the optimal phase shift factor
Figure FDA0002696628800000052
Substituting phi into the limiting condition C1 converted in the step 4 to obtain the optimal power distribution coefficient { wuAnd (c) the step of (c) in which,
Figure FDA0002696628800000053
the optimal phase shift factor for each reflection unit of the RIS is expressed as:
Figure FDA0002696628800000054
Figure FDA0002696628800000055
the sum of the optimal phase shift factors for all iterations of each reflection unit, the optimal phase shift factor
Figure FDA0002696628800000056
Obtained after the convergence of the target function, T is the iteration number required by the convergence, mod2 pi represents the pair
Figure FDA0002696628800000057
And performing 2 pi modulus extraction.
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