CN115843034A - Power distribution method and system of intelligent reflector assisted non-orthogonal multiple access system - Google Patents
Power distribution method and system of intelligent reflector assisted non-orthogonal multiple access system Download PDFInfo
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Abstract
The invention provides an intelligent reflecting surface auxiliary non-orthogonal multiple access system power distribution method and system facing an ultra-reliable low-delay user aiming at scenes such as an intelligent power grid and the like, wherein the user is easily blocked by a barrier, and the method specifically comprises the steps of establishing a communication system model among a base station, an intelligent reflecting surface and the ultra-reliable low-delay user; according to the current intelligent reflecting surface phase shift vector and the power distribution and decoding sequence, the sum rate of a user is taken as an optimization target, and the optimal reflecting surface phase shift vector and power distribution are obtained; obtaining a reflecting surface phase shift vector meeting rank constraint according to Gaussian randomization; and judging whether the iteration completion condition is met. The method combines the advantage of the expansion coverage of the intelligent reflecting surface with the advantage of improving the system throughput by the non-orthogonal multiple access, improves the reliability of the system, and improves the total rate performance of users.
Description
Technical Field
The invention designs a power distribution method and a system of an orthogonal multiple access system, in particular to the power distribution method of an IRS (intelligent resilient station) assisted orthogonal multiple access system facing an ultra-reliable low-delay user, which is applied to the technical field of mobile communication.
Background
With the rapid increase of internet user access demands and the increase of capacity brought by advanced multimedia applications, the demand of people for data rate is continuously increased, so that the communication system must improve the capability of processing large-scale data. The non-orthogonal multiple access technology can realize the service of a plurality of users on the same resource block, has higher frequency spectrum efficiency compared with the orthogonal access technology, and can effectively improve the throughput of the system. In application scenarios such as medical robots, autonomous vehicles, and factory automation, the system is required to respond within several milliseconds, and thus the real-time performance of the system is highly required. One of the 5G key services, ultra-reliable low latency (URLLC), can implement high-reliability low-latency end-to-end communication, and according to the requirements of 3GPP, the transmission reliability of a single data frame should reach 99.9%, and the end-to-end latency should be less than 1ms.
In a wireless communication link, the quality of a signal may be degraded by reflection, refraction, and reflection scattering of the signal due to path loss, objects such as buildings, and the like. To ensure the quality of the received signal, we can increase the transmission power, but at the same time, reduce the power efficiency. With the development of electromagnetic material technology in recent years, smart reflective surfaces made of artificial electromagnetic material films have appeared. The intelligent reflective surface can be electronically controlled by integrated electronics to steer the signal to any direction, thereby making the propagation environment controllable. At present, many researches on the combined application of the intelligent reflecting surface and the ultra-reliable low-delay technology exist, but related researches on the combination of the intelligent reflecting surface assisted non-orthogonal multiple access system and the ultra-reliable low-delay technology still need to be carried out.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects of the technology, the power distribution method of the non-orthogonal multiple access system, which effectively improves the coverage area and the throughput of the communication system, provides a communication link for an ultra-reliable low-delay user by utilizing an intelligent reflecting surface and is easy to deploy, is provided.
The technical scheme is as follows: in order to achieve the technical purpose, the power distribution method of the intelligent reflector assisted non-orthogonal multiple access system comprises the following specific steps:
establishing a base station, an intelligent reflecting surface and a non-orthogonal multiple access system model of an ultra-reliable low-delay user, wherein the base station sends signals to the user by utilizing superposition coding, a direct link between the base station and the user is blocked, and the intelligent reflecting surface directly reflects the signals from the base station to increase a reliable communication link for the user;
jointly designing a base station transmitting power distribution and intelligent reflecting surface phase shift vector and a decoding sequence according to a non-orthogonal multiple access system model, wherein the sum of all user rates needs to be optimized by the optimal power distribution and the intelligent reflecting surface phase shift vector;
designing a decoding sequence according to the distance between a user and the reflecting surface and the priority, and obtaining an optimal power distribution matrix and an intelligent reflecting surface phase shift matrix by alternately optimizing the power distribution matrix and the intelligent reflecting surface phase shift matrix;
and performing Gaussian randomization on the optimal intelligent reflecting surface phase shift matrix to obtain a corresponding intelligent reflecting surface phase shift vector, judging whether the condition of finishing iteration is met, and if so, finishing the iteration.
Preferably, the intelligent reflector-assisted non-orthogonal multiple access system model specifically includes:
a base station is configured with a single antenna, K users with ultra-reliable low time delay (URLLC) are all single antennas, and the transmission signal of the base station is as follows:
wherein s is k Representing the transmitted signal of user k, p k Representing the transmit power of user k.
The received signal for user k is:
wherein g and h k Respectively representing the channels between the base station and the intelligent reflecting surface and between the intelligent reflecting surface and the user kStatus information, z k Representing a white gaussian noise signal received at the user side.
Representing a phase shift vector of the intelligent reflecting surface, where n Representing the phase shift of the nth element of the reflective surface and N representing the total number of intelligent reflective surface elements.
Preferably, the optimizing the sum of the rates of all the users comprises the following specific steps:
according to the established intelligent reflecting surface assisted non-orthogonal multiple access system model, the receiving signal-to-interference-and-noise ratio gamma of the user k k Expressed as:
wherein
Represents interference from other users, μ (k) represents the decoding order of user k, and since the user uses successive interference cancellation technique at the receiving end, it can be considered that only the signal decoded after user k will interfere with the signal of user k, σ 2 Representing the thermal noise power received by the user.
According to the SINR, the information rate R of user k k Expressed as:
wherein V (x) =1- (1 + x) -2 ，
m denotes the number of channel realizations and epsilon denotes the decoding error probability.
And (3) establishing an optimization problem by taking the sum of the information rates of all users as an optimization target, and meeting the constraint of the total transmission power of the base station, namely:
wherein
Representing a possible set of all decoding orders, C2 representing that the decoding order is constrained by the case of channel state information, C3 representing that the power allocation is constrained by the decoding order, P max Is the maximum transmit power limit, w, of the base station k Representing the weight or priority of user k.
Preferably, the method for obtaining the optimal power distribution and intelligent reflecting surface phase shift matrix of the optimization problem includes the following steps:
all possibilities of decoding order are K! That is, as the number of users increases, the amount of calculation for traversing each possible decoding order is too large, so a relatively reliable scheme for determining the decoding order according to the priority of the users and the distance between the users and the reflecting surface is proposed. Firstly, decoding is carried out according to the distance between a user and the reflecting surface, the decoding is carried out after the distance is far, the decoding is carried out before the distance is near, if the distances are the same, the decoding is carried out according to the priority, and the decoding is carried out after the priority is high.
Because the optimization problem is non-convex, the optimization problem needs to be converted into a convex problem, and therefore, the power distribution subproblem and the reflector phase shift matrix optimization subproblem are obtained by alternately optimizing two variables of the intelligent reflector phase shift matrix and the power distribution.
First fixed power allocation p k And optimizing the phase shift of the reflecting surface. By adapting the channel gain term by transformation, the order
Then->
Then define >>
And V = vv H ，Q k And V respectively represents the user k, the channel state information between the reflecting surface and the base station and the phase shift information of the intelligent reflecting surface in a hermitian matrix form. V satisfies->
I.e. V is a semi-positive definite matrix with rank 1 and diagonal elements 1, V is a hermitian matrix. The received signal to interference and noise ratio of user k can be reused in the form of the trace Tr (VQ) of the matrix k ) Expressing:
objective function, namely user information rate and
can be expressed as:
wherein
During the l-th iteration, V is adjusted k (V) approximation by a first order Taylor expansion of
Wherein V (l) Representing the feasible point at the ith iteration,
represents V k (V) derivative of (V). The above process converts the sub-problem of optimizing the reflector phase shift V into a convex problem, i.e.:
s.t.C1:Tr(VQ k )≥Tr(VQ j ),ifμ(k)＞μ(j),
C2:rank(V)＝1,
C3:[V] nn ＝1,n＝1,2,...,N,
C4:
C5:
then fixing the reflecting surface phase shift V and optimizing the power distribution { p k }. The expressions of the signal to interference plus noise ratio and the sum of the user information rates are the same as (7) and (8). And introducing a relaxation variable t, constraining the lower bound of the objective function, and converting the objective function into a constraint condition. Then introducing a random variable alpha k ≤γ k Constraining the lower bound of SINR and using alpha k Replacing gamma in user information rate sum k Whereupon the problem turns into
s.t.C1:0≤p k ≤p j ifμ(k)＞μ(j)
C2:
C3:
C4:α k ≤γ k
Wherein C is k (α k )＝log(1+α k )，
/>
During the l-th iteration, V is adjusted k (α k ) Substitution by a first-order Taylor approximation
Wherein
For a feasible point in the ith iteration, <' > H>
Is a V k (α k ) The derivative of (c).
Introducing random variables
Constraining the lower bound, z, of the SINR expression numerator k And (3) constraining the upper bound of the denominator to obtain a new constraint condition:
here C4C is still a non-convex constraint, with a convex constraint obtained using a first order Taylor approximation
Wherein
Is the approximate point in the previous iteration.
The above process converts the sub-problem of optimizing power allocation into a convex problem, i.e.:
solving the problems (12) and (19) separately for each iteration with a tool box of cvx to obtain optimal solutions for power and reflecting surfaces using { p } p, respectively k } * And V * And (4) showing.
Preferably, the method for obtaining the optimal intelligent reflecting surface phase shift vector by performing gaussian randomization on the optimal intelligent reflecting surface phase shift matrix comprises:
to V * By performing singular value decomposition, i.e. V * = ASB, generating L random vectors r l And construct a vector
Order to
Find x that maximizes users and rates l Namely the optimal intelligent reflecting surface vector. And finally outputting the optimal solution meeting the iteration completion condition. />
The present application further provides a power distribution system for an intelligent reflector assisted non-orthogonal multiple access system, comprising:
a system model construction module: according to the number and the position of users and the number of elements of an intelligent reflecting surface, establishing a non-orthogonal multiple access system model of a base station, the intelligent reflecting surface and an ultra-reliable low-delay user, wherein the base station sends signals to the users by utilizing superposition coding, a direct link between the base station and the users is blocked, and the intelligent reflecting surface directly reflects the signals from the base station to increase a reliable communication link for the users;
a power allocation calculation module: jointly designing a base station transmitting power distribution and intelligent reflecting surface phase shift vector and a decoding sequence according to a non-orthogonal multiple access system model, wherein the sum of all user rates needs to be optimized by the optimal power distribution and the intelligent reflecting surface phase shift vector;
the power allocation calculation module includes: designing a decoding sequence design submodule of a decoding sequence according to the distance between a user and the reflecting surface and the priority, a power optimization submodule for optimizing power distribution and an intelligent reflecting surface optimization submodule for optimizing an intelligent reflecting surface phase shift matrix; acquiring optimal power distribution and an intelligent reflecting surface phase shift matrix by alternately optimizing the power distribution and the intelligent reflecting surface phase shift matrix;
an output module: and performing Gaussian randomization on the optimal intelligent reflecting surface phase shift matrix to obtain a corresponding intelligent reflecting surface phase shift vector, judging whether the condition of finishing iteration is met, and if so, finishing the iteration.
The present application further provides a terminal device, including: a memory; one or more processors coupled with the memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the intelligent reflector assisted non-orthogonal multiple access system power allocation method.
The present application further provides a computer readable storage medium having program code stored therein that is invoked by a processor to perform a method for intelligent reflector assisted non-orthogonal multiple access system power allocation.
Has the advantages that:
1) The method combines the advantage of the enhanced coverage of the intelligent reflecting surface with the advantage of the non-orthogonal multiple access, improves the total rate performance and the reliability of the user, and meets the requirements of the user on ultra-reliability and low time delay;
2) The power distribution method of the non-orthogonal multiple access system designed by the invention can adjust the transmitting power of the base station according to the channel state information and the weight of the user, can improve the fairness of the system and ensure the information rate performance of the user with poor channel condition;
3) The power allocation method of the non-orthogonal multiple access system designed by the invention allows a plurality of users to use the same physical resource, thereby having high bandwidth efficiency.
Drawings
Fig. 1 is a diagram of a model of an intelligent reflector-assisted non-orthogonal multiple access system constructed in accordance with the present invention.
FIG. 2 is a flow chart of the present invention.
FIG. 3 is a graph illustrating the convergence curves of the present invention compared to other methods in the examples.
Fig. 4 is a graphical illustration of the number-and rate-of different intelligent reflective surface elements of the present invention compared to other methods in an example.
Fig. 5 is a diagram of different total transmit power-and rate-diagrams comparing the present invention with other methods in an example.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
the invention discloses a power distribution method of an intelligent reflector assisted non-orthogonal multiple access system, which specifically comprises the following steps:
step 1: a non-orthogonal multiple access system model of a base station, an intelligent reflecting surface and an ultra-reliable low-delay user shown in figure 1 is established, the base station is assumed to be located at the origin of a coordinate axis, the user is randomly distributed in a circular area with the distance of 200m from the base station as the radius of the circle center of 20m, and the intelligent reflecting surface is deployed (150m, 30m). The base station transmits signals to users by using superposition coding, a direct link between the base station and the users is blocked, the signals from the base station are directly reflected by the intelligent reflecting surface to increase a reliable communication link for the users, the base station carries out superposition coding on the signals of the users in a non-orthogonal multiple access mode, the signals are transmitted by using the same physical resource, and the bandwidth efficiency is improved. The user eliminates the interference between users by using continuous interference, and the information rate of the user is improved;
if a base station is configured with a single antenna, K users with ultra-reliable low time delay (URLLC) are all single antennas, and the transmission signal of the base station is as follows:
wherein s is k Representing the transmitted signal of user k, p k Representing the transmit power of user k.
The received signal for user k is:
wherein g is a group of
Respectively representing the channel state information between the base station and the intelligent reflecting surface, between the intelligent reflecting surface and the user k, z k Representing a white gaussian noise signal received at the user side. />
Representing the phase shift vector of the intelligent reflective surface.
Step 2: according to the intelligent reflector assisted non-orthogonal multiple access system model in the step 1, in order to improve the sum rate of system users and meet the transmission power constraint of a base station, base station transmission power distribution, an intelligent reflector phase shift vector and a decoding sequence, optimal power distribution and an intelligent reflector phase shift vector are designed in a combined mode;
the specific steps for optimizing the sum of the user rates are as follows:
step 2.1: according to the intelligent reflector assisted non-orthogonal multiple access system model established in the step 1, the receiving signal-to-interference-and-noise ratio of a user is as follows:
wherein
Represents interference from other users, μ (k) represents the decoding order of user k, and since the user uses successive interference cancellation technique at the receiving end, it can be considered that only the signal decoded after user k will interfere with the signal of user k, σ 2 Representing the thermal noise power received by the user.
Based on SINR, usersk information rate R k Expressed as:
wherein V (x) =1- (1 + x) -2 ，
m denotes the number of channel realizations, epsilon denotes the decoding error probability, the number of channel realizations m =200 in the experiment, and the decoding error probability is set to 10e-6.
Step 2.2: and establishing an optimization problem by taking the sum of the information rates of all users as an optimization target, and meeting the constraint of the total transmission power of the base station, namely:
wherein
Representing a possible set of all decoding orders, C2 representing that the decoding order is constrained by the case of channel state information, C3 representing that the power allocation is constrained by the decoding order, P max Is the maximum transmit power limit, w, of the base station k Representing the weight or priority of user k.
And step 3: converting the power distribution problem of optimizing the sum of the rates of all users and eliminating mutual interference among the users into a convex problem easy to solve, specifically designing a decoding sequence according to the distance and priority between the users and the reflecting surface, acquiring optimal power distribution and an intelligent reflecting surface phase shift matrix by alternately optimizing the power distribution and the intelligent reflecting surface phase shift matrix, and judging whether the conditions of finishing iteration are met;
the specific method for obtaining the optimal power distribution and the intelligent reflecting surface vector comprises the following steps:
step 3.1: all possibilities of decoding order are K! That is, as the number of users increases, the amount of calculation for traversing each possible decoding order is too large, so a relatively reliable scheme for determining the decoding order according to the priority of the users and the distance between the users and the reflecting surface is proposed. Firstly, decoding is carried out according to the distance between a user and the reflecting surface, the decoding is carried out after the distance is far, the decoding is carried out before the distance is near, if the distances are the same, the decoding is carried out according to the priority, and the decoding is carried out after the priority is high.
Step 3.2: since the optimization problem in step 2.2 is non-convex, we need to convert it into a convex problem, and for this reason, we obtain the power distribution sub-problem and the reflection surface phase shift matrix optimization sub-problem respectively by alternately optimizing two variables of the intelligent reflection surface phase shift matrix and the power distribution.
First fixed power allocation p k And optimizing the phase shift of the reflecting surface. By adapting the channel gain term by transformation, the order
Then->
Then define >>
And V = vv H ，Q k And V respectively represents the user k, the channel state information between the reflecting surface and the base station and the phase shift information of the intelligent reflecting surface in a hermitian matrix form. V satisfies->
I.e. V is a semi-positive definite matrix with rank 1 and diagonal elements 1, V is a hermitian matrix. The received signal to interference and noise ratio of user k can be reused in the form of the trace Tr (VQ) of the matrix k ) Expression:
objective function, namely user information rate and
can be expressed as:
wherein
During the l-th iteration, V is adjusted k (V) approximation by a first order Taylor expansion of
Wherein V (l) Representing the feasible point at the ith iteration,
represents V k (V) derivative of (V). The above process converts the sub-problem of optimizing the reflector phase shift V into a convex problem, i.e.:
s.t.C1:Tr(VQ k )≥Tr(VQ j ),ifμ(k)＞μ(j),
C2:rank(V)＝1,
C3:[V] nn ＝1,n＝1,2,...,N,
C4:
C5:
then fixing the reflecting surface phase shift V and optimizing the power distribution { p k }. The expressions of the sum of the signal to interference and noise ratio and the user information rate are the same as (7) and (8). And introducing a relaxation variable t, constraining the lower bound of the objective function, and converting the objective function into a constraint condition. Then introducing a random variable alpha k ≤γ k Constraining the lower bound of SINR and using alpha k Replacing gamma in user information rate sum k Whereupon the problem turns into
s.t.C1:0≤p k ≤p j ifμ(k)＞μ(j)
C2:
C3:
C4:α k ≤γ k
Wherein C is k (α k )＝log(1+α k )，
During the l-th iteration, V is adjusted k (α k ) Substitution by a first-order Taylor approximation
Wherein
For a feasible point in the ith iteration, <' > H>
Is a V k (α k ) The derivative of (c).
Introducing random variables
Constraining the lower bound, z, of the SINR expression numerator k And (3) constraining the upper bound of the denominator to obtain a new constraint condition:
here C4C is still a non-convex constraint, with a convex constraint obtained using a first order Taylor approximation
Wherein
Is the approximate point in the previous iteration.
The above process converts the sub-problem of optimizing power allocation into a convex problem, i.e.:
step 3.3: solving the problems (12) and (19) respectively by using the tool box with cvx for each iteration, and finally obtaining the optimal solution of the power and the reflecting surface by using { p } p respectively k } * And V * And (4) showing.
And 4, step 4: and (3) utilizing Gaussian randomization to enable the optimal intelligent reflecting surface phase shift matrix obtained in the step (3) to meet the constraint that the matrix rank is 1, obtaining a corresponding intelligent reflecting surface phase shift vector, judging whether the conditions for completing iteration are met, and if the conditions are met, ending the iteration.
To V * By performing singular value decomposition, i.e. V * = ASB, generating L random vectors r l And construct the vector
Order to
Find x that maximizes users and rates l Namely the optimal intelligent reflecting surface vector. And finally outputting the optimal solution meeting the iteration completion condition.
In the experiment, in order to verify the performance of the set calculation method, the intelligent reflecting surface auxiliary channel link is assumed to obey Rice distribution, the Rice factor is set to be 10, the noise power spectral density is-174 dBm/Hz, the transmission bandwidth is 180KHz, the error probability is set to be 10e-6, the channel realization number is set to be 200, and finally the algorithm designed by the invention is compared with other reference algorithms, wherein the algorithm comprises a scheme (Shannon) that the information rate is calculated by adopting a Shannon formula, a scheme (Random phase) that Random phase distribution and independent optimization power distribution are adopted, a scheme (IRS) that Random power distribution and independent optimization intelligent reflecting surface phase shift are adopted, and a scheme (FDMA) that frequency division multiple access optimization power and intelligent reflecting surface phase shift are adopted. Through verification, the algorithm of the invention can be converged more quickly, and higher summation rate is achieved.
The application also provides an intelligent reflector assisted non-orthogonal multiple access system power distribution system, which mainly comprises: a system model construction module: according to the number and the position of users and the number of elements of an intelligent reflecting surface, establishing a non-orthogonal multiple access system model of a base station, the intelligent reflecting surface and an ultra-reliable low-delay user, wherein the base station sends signals to the users by utilizing superposition coding, a direct link between the base station and the users is blocked, and the intelligent reflecting surface directly reflects the signals from the base station to increase a reliable communication link for the users;
a power allocation calculation module: jointly designing a base station transmitting power distribution, an intelligent reflecting surface phase shift vector and a decoding sequence according to a non-orthogonal multiple access system model, wherein the sum of all user rates needs to be optimized by the optimal power distribution and the intelligent reflecting surface phase shift vector;
the power allocation calculation module includes: designing a decoding sequence design submodule of a decoding sequence according to the distance between a user and the reflecting surface and the priority, a power optimization submodule for optimizing power distribution and an intelligent reflecting surface optimization submodule for optimizing an intelligent reflecting surface phase shift matrix; acquiring optimal power distribution and an intelligent reflecting surface phase shift matrix by alternately optimizing the power distribution and the intelligent reflecting surface phase shift matrix;
an output module: and performing Gaussian randomization on the optimal intelligent reflecting surface phase shift matrix to obtain a corresponding intelligent reflecting surface phase shift vector, judging whether the condition of finishing iteration is met, and if so, finishing the iteration.
For specific limitations of the power allocation system of the non-orthogonal multiple access system, reference may be made to the above limitations of the power allocation method of the intelligent reflector-assisted non-orthogonal multiple access system, and details thereof are not repeated herein. The various modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent of a processor in the terminal device, and can also be stored in a memory in the terminal device in a software form, so that the processor can call and execute operations corresponding to the modules.
The application also provides a terminal device, which can be a computer device. The terminal device in the present application may comprise one or more of the following components: a processor, a memory, and one or more applications, wherein the one or more applications can be stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the methods described in the above method embodiments applied to a terminal device, and also configured to perform the above intelligent reflector assisted non-orthogonal multiple access system power allocation method.
A processor may include one or more processing cores. The processor connects various parts within the overall terminal device using various interfaces and lines, performs various functions of the terminal device and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory, and calling data stored in the memory. Alternatively, the processor may be implemented in hardware using at least one of Digital Signal Processing (DSP), field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor can integrate one or a combination of a Central Processing Unit (CPU), a Graphic Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is to be understood that the modem may be implemented by a communication chip without being integrated into the processor.
The Memory may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The storage data area may also store data created by the terminal device in use, and the like.
The application also provides a computer readable storage medium. The computer readable storage medium has program code stored therein that is invoked by a processor to perform the above-described intelligent reflector assisted non-orthogonal multiple access system power allocation method, and that is also invoked by a processor to perform the above-described intelligent reflector assisted non-orthogonal multiple access system power allocation method.
The computer-readable storage medium may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer readable storage medium has storage space for program code for performing any of the method steps described above. The program code can be read from or written to one or more computer program products. The program code may be compressed, for example, in a suitable form.
It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be construed as the protection scope of the present invention. All the components not specified in this embodiment can be implemented by the prior art.
Claims (10)
1. An intelligent reflector-assisted power allocation method for a non-orthogonal multiple access system is characterized by comprising the following specific steps:
establishing a base station, an intelligent reflecting surface and a non-orthogonal multiple access system model of an ultra-reliable low-delay user, wherein the base station sends a signal to the user by using superposition coding, a direct link between the base station and the user is blocked, and the intelligent reflecting surface directly reflects the signal from the base station to increase a reliable communication link for the user;
jointly designing a base station transmitting power distribution, an intelligent reflecting surface phase shift vector and a decoding sequence according to a non-orthogonal multiple access system model, wherein the sum of all user rates needs to be optimized by the optimal power distribution and the intelligent reflecting surface phase shift vector;
designing a decoding sequence according to the distance between a user and the reflecting surface and the priority, and alternately optimizing the power distribution and the intelligent reflecting surface phase shift matrix to obtain the optimal power distribution and intelligent reflecting surface phase shift matrix;
and performing Gaussian randomization on the optimal intelligent reflecting surface phase shift matrix to obtain a corresponding intelligent reflecting surface phase shift vector, judging whether the condition of finishing iteration is met, and if so, finishing the iteration.
2. The method of claim 1, wherein the model of the non-orthogonal multiple access system is specifically:
if a base station is configured with a single antenna, K users with ultra-reliability and low time delay are all single antennas, and the transmission signal of the base station is as follows:
wherein s is k Representing the transmitted signal of user k, p k Represents the transmit power of user k;
the received signal for user k is:
wherein g and
respectively representing the channel state information between the base station and the intelligent reflecting surface, and between the intelligent reflecting surface and the user k, z k Representing a white gaussian noise signal received at the user side,
representing a phase shift vector of the intelligent reflecting surface, where n Representing the phase shift of the nth element of the reflective surface and N representing the total number of intelligent reflective surface elements.
3. The method for power allocation of an intelligent reflector-assisted non-orthogonal multiple access system according to claim 2, wherein the step of optimizing the sum of all user rates comprises the following steps:
according to the established intelligent reflector assisted non-orthogonal multiple access system model, the received signal to interference plus noise ratio gamma of the user k k Expressed as:
wherein
Represents interference from other users, μ (k) represents the decoding order of user k, σ 2 Represents the thermal noise power received by the user;
according to the SINR, the information rate R of user k k Expressed as:
wherein V (x) =1- (1 + x) -2 ，
m represents the number of channel realizations, and epsilon represents the decoding error probability;
and establishing an optimization problem by taking the sum of the information rates of all users as an optimization target, and meeting the constraint of the total transmission power of the base station, namely:
wherein
Representing a possible set of all decoding orders, C2 representing that the decoding order is constrained by the case of channel state information, C3 representing that the power allocation is constrained by the decoding order, P max Is the maximum transmit power limit, w, of the base station k Representing the weight or priority of user k.
4. The method for power allocation of an intelligent reflector assisted non-orthogonal multiple access system according to claim 3, wherein the method for obtaining the optimal power allocation and intelligent reflector phase shift matrix comprises the following specific steps:
decoding after the distance is long and decoding before the distance is short according to the distance between the user and the reflecting surface, and decoding after the distance is high according to the priority when the distance is the same;
respectively obtaining a power distribution subproblem and a subproblem of reflecting surface phase shift matrix optimization by alternately optimizing two variables of an intelligent reflecting surface phase shift matrix and power distribution, and respectively converting the power distribution subproblem and the subproblem of reflecting surface phase shift matrix optimization into convex problems;
solving two convex problems by using a tool box with cvx for each iteration, and respectively using { p } to obtain the optimal solution of power and reflecting surface k } * And V.
5. The method of claim 4, wherein the step of obtaining the sub-problem of reflector phase shift matrix optimization and transforming the sub-problem into a convex problem comprises:
fixed power allocation { p k And (5) optimizing the phase shift of the reflecting surface; by adapting the channel gain term by transformation, the order
Then
Then define
And V = vv H ，Q k V represents the channel state information between the user k and the reflecting surface as well as the phase shift information of the intelligent reflecting surface in a hermitian matrix form; v satisfies V ≥ 0, rank (V) =1, [ V ≥ V] nn ＝1,n＝1,2,...,N,
That is, V is a semi-positive definite matrix, the rank is 1, the diagonal element is 1, and V is a hermitian matrix; receiving SINR reuse matrix trace for user kForm Tr (VQ) k ) Expressing:
objective function, namely user information rate and
expressed as:
wherein
During the l-th iteration, V is adjusted k (V) is approximated by a first order Taylor expansion of
Wherein V (l) Representing the feasible point at the ith iteration,
represents V k (V) derivative of (V); the above process converts the sub-problem of optimizing the reflector phase shift V into a convex problem, i.e.:
s.t.C1:Tr(VQ k )≥Tr(VQ j ),ifμ(k)＞μ(j),
C2:rank(V)＝1,
C3:[V] nn ＝1,n＝1,2,...,N,
6. the method of claim 5, wherein the step of obtaining the sub-problem of power allocation and converting the sub-problem into a convex problem comprises:
fixing the reflecting surface phase shift V, optimizing the power distribution { p k }; the expressions of the signal-to-interference-and-noise ratio and the sum of the user information rates are the same as those in (7) and (8); introducing a relaxation variable t, constraining the lower bound of the objective function, and converting the objective function into a constraint condition; then introducing a random variable alpha k ≤γ k Constraining the lower bound of SINR and using alpha k Replacing gamma in user information rate sum k Whereupon the problem turns into
s.t.C1:0≤p k ≤p j ifμ(k)＞μ(j)
C4:α k ≤γ k
Wherein C is k (α k )＝log(1+α k )，
During the l-th iteration, V is adjusted k (α k ) Substitution by a first-order Taylor approximation
Wherein
As a feasible point in the l-th iteration,
is a V k (α k ) A derivative of (a);
introducing random variables
Constraining the lower bound, z, of the SINR expression numerator k And constraining the upper bound of the denominator to obtain a new constraint condition:
C4C is still a non-convex constraint, and a convex constraint is obtained by using a first-order Taylor approximation
Wherein
As an approximation point in the previous iteration;
the above process transforms the sub-problem of optimizing power allocation into a convex problem, i.e.:
7. the method for allocating power to an intelligent reflector assisted non-orthogonal multiple access system according to claim 4, wherein the method for performing Gaussian randomization on the optimal intelligent reflector phase shift matrix to obtain the corresponding intelligent reflector phase shift vector comprises:
to V * By performing singular value decomposition, i.e. V * = ASB, generate L random vectors r l And construct a vector
Order to
Find x that maximizes users and rates l Namely the corresponding intelligent reflecting surface phase shift vector, and finally outputting the optimal solution meeting the iteration completion condition.
8. An intelligent reflector assisted non-orthogonal multiple access system power distribution system, comprising:
a system model construction module: according to the number and the position of users and the number of elements of an intelligent reflecting surface, establishing a non-orthogonal multiple access system model of a base station, the intelligent reflecting surface and an ultra-reliable low-delay user, wherein the base station sends signals to the users by utilizing superposition coding, a direct link between the base station and the users is blocked, and the intelligent reflecting surface directly reflects the signals from the base station to increase a reliable communication link for the users;
a power allocation calculation module: jointly designing a base station transmitting power distribution, an intelligent reflecting surface phase shift vector and a decoding sequence according to a non-orthogonal multiple access system model, wherein the sum of all user rates needs to be optimized by the optimal power distribution and the intelligent reflecting surface phase shift vector;
the power allocation calculation module includes: a decoding sequence design submodule of a decoding sequence is designed according to the distance between a user and a reflecting surface and the priority, a power optimization submodule for optimizing power distribution and an intelligent reflecting surface optimization submodule for optimizing an intelligent reflecting surface phase shift matrix are designed; acquiring optimal power distribution and an intelligent reflecting surface phase shift matrix by alternately optimizing the power distribution and the intelligent reflecting surface phase shift matrix;
an output module: and performing Gaussian randomization on the optimal intelligent reflecting surface phase shift matrix to obtain a corresponding intelligent reflecting surface phase shift vector, judging whether the condition of finishing iteration is met, and if so, finishing the iteration.
9. A terminal device, comprising: a memory; one or more processors coupled with the memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon program code that can be invoked by a processor to perform the method according to any one of claims 1 to 7.
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