Method and apparatus for determining maximum sum rate of cooperative rate-splitting multiple access system

Abstract

The present application provides a method and an apparatus for determining the maximum sum rate of a cooperative rate splitting multiple access system. The method includes: optimizing a preliminary optimization vector by using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizing a preliminary sum rate by using the Newton steepest descent gradient method to obtain a target sum rate; when the iteration end condition is not satisfied, replacing the preliminary sum rate with the target sum rate and replacing the preliminary optimization vector with the target optimization vector, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; when the iteration end condition is satisfied, the obtained target sum rate is the maximum sum rate, and the iteration end condition is that the target difference is less than a preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition. This method solves the problem of poor communication quality of long-distance users in the rate splitting system in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of power line communication, and in particular, to a method for determining the maximum sum rate of a cooperative rate splitting multiple access system, a determining device, a computer-readable storage medium, a processor, and a determining system. Background Art

[0002] Power Line Communication (PLC) is a promising technology that can provide efficient and robust solutions for many home network and smart grid applications. In the power Internet of Things, the number of users in the data acquisition system is large and the access is uncertain. There are many users in some time periods, and relatively few users in some other time periods. To cope with this situation, an adaptive multiple access - rate splitting multiple access can be adopted at the physical layer. Compared with non-orthogonal multiple access, rate splitting multiple access is applicable to various network loads (underloaded and overloaded states) and user deployments (with different channel strengths and channel directions). Rate splitting multiple access can be regarded as a disguised unification of non-orthogonal multiple access and orthogonal multiple access. Considering the access mode of rate splitting multiple access, it is equivalent to non-orthogonal multiple access in the time periods with a large number of users, and equivalent to orthogonal multiple access in the time periods with a small number of users. To improve the communication quality and reliability of edge users, a cooperative method is used to take users with better channel quality as relays to help edge users transmit signals. Therefore, combining rate splitting multiple access with cooperative technology under the power line communication channel can better cope with the future power Internet of Things and improve the communication quality of end users.

[0003] Existing research has proved that applying non-orthogonal multiple access technology, or even cooperative non-orthogonal multiple access technology, to PLC can obtain better performance than orthogonal multiple access technology and improve the communication service quality of device terminals. However, the existing technologies that apply non-orthogonal multiple access technology, or even cooperative non-orthogonal multiple access technology, to PLC cannot flexibly cope with the problems of large-scale user access and uncertain access in the data acquisition system, and can only achieve good performance in scenarios where the user channel differences are large and in overloaded networks. Therefore, how to cope with large-scale user access and uncertain access in the data acquisition system, improve the communication quality and reliability of edge users, and achieve reliable data transmission is an urgent problem to be solved by those skilled in the art.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background art of the technology described in this article. Therefore, the background art may contain certain information that is not prior art known to those skilled in the art in this country. Summary of the Invention

[0005] The main objective of this application is to provide a method, a device, a computer-readable storage medium, a processor, and a determination system for determining the maximum sum rate of a cooperative rate-splitting multiple access system, so as to solve the problem of poor communication quality of users at long distances in the existing cooperative rate-splitting multiple access system.

[0006] According to one aspect of an embodiment of the present invention, the cooperative rate-splitting multiple access system includes a transmitting end, a first user, and a second user. The distance between the first user and the transmitting end is less than the distance between the second user and the transmitting end. The method includes: an initialization step of initializing an optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; an optimization step of optimizing the preliminary optimization vector by using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizing a preliminary sum rate by using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; a judgment step of judging whether a target difference is less than a preset value, and judging whether the target optimization vector simultaneously satisfies a sum rate constraint condition, a common rate constraint condition, and a total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; an update step of, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replacing the preliminary sum rate with the target sum rate and replacing the preliminary optimization vector with the target optimization vector; sequentially repeating the optimization step, the judgment step, and the update step at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; when the target difference is less than the preset value, outputting a first optimal optimization vector and outputting a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputting a second optimal optimization vector and outputting a second maximum sum rate, where the first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process.

[0007] Optionally, the sending end sends a superimposed signal to the first user and the sending end sends the superimposed signal to the second user. The superimposed signal includes a common signal, a first private signal, and a second private signal. The first user sends a relay signal to the second user. Before the initialization step, the method includes: decoding the common signal received by the first user to obtain a first common interference value, a first common signal-to-interference-plus-noise ratio, and a first common rate; decoding the common signal received by the second user to obtain a second common interference value and a second common signal-to-interference-plus-noise ratio; decoding the first private signal received by the first user to obtain a first private interference value, a first private signal-to-interference-plus-noise ratio, and a first private rate; decoding the second private signal received by the second user to obtain a second private interference value, a second private signal-to-interference-plus-noise ratio, and a second private rate; jointly decoding the common signal and the relay signal received by the second user to obtain the second common rate. The first common interference value is the sum of the noise signal and the interference signal in the common signal received by the first user. The second common interference value is the sum of the noise signal and the interference signal in the common signal received by the second user. The first private interference value is the sum of the noise signal and the interference signal in the first private signal received by the first user. The second private interference value is the sum of the noise signal and the interference signal in the second private signal received by the second user; calculating the sum of the sum rate of the first user and the sum rate of the second user according to the first common rate, the second common rate, the first private rate, and the second private rate to obtain the preliminary sum rate. The sum rate of the first user is the sum of the first common rate and the first private rate. The sum rate of the second user is the sum of the second common rate and the second private rate; using the precoding matrix, the time allocation parameter, the first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private rate, and the second private rate as elements to form the optimization vector. The time allocation parameter is the ratio of the first transmission time to the second transmission time. The first transmission time is the sum of the time required to transmit the common signal and the private signal transmission time. The private signal transmission time is the sum of the time required to transmit the first private signal and the time required to transmit the second private signal. The second transmission time is the sum of the first transmission time and the time required to transmit the relay signal.

[0008] Optionally, the initialization step includes initializing a precoding matrix, a time allocation parameter, the first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first private rate, and the second private rate.

[0009] Optionally, the precoding matrix includes a common precoding vector, a first private precoding vector, and a second private precoding vector. The initialization step further includes initializing the common precoding vector, the first private precoding vector, and the second private precoding vector. The common precoding vector is used for encoding the common signal, the first private precoding vector is used for encoding the first private signal, and the second private precoding vector is used for encoding the second private signal.

[0010] Optionally, determining whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition includes: when the target optimization vector simultaneously satisfies the first sum rate constraint condition and the second sum rate constraint condition, determining that the target optimization vector satisfies the sum rate constraint condition; when the target optimization vector simultaneously satisfies the first common rate constraint condition and the second common rate constraint condition, determining that the target optimization vector satisfies the common rate constraint condition; and when the target optimization vector satisfies the constraint condition of the total power at the transmitter end, determining that the target optimization vector satisfies the total power constraint condition.

[0011] Optionally, the optimization step includes: calculating the product of the first preliminary vector in the previous optimization process and a preset coefficient to obtain a second preliminary vector, where the first preliminary vector includes the preliminary optimization vector and the preliminary sum rate; calculating the sum of the first preliminary vector and the second preliminary vector in the previous optimization process to obtain a target vector; and outputting the target optimization vector and the target sum rate according to the target vector.

[0012] According to another aspect of the embodiments of the present invention, there is also provided an apparatus for determining the maximum sum rate of a cooperative rate-splitting multiple access system. The cooperative rate-splitting multiple access system includes a transmitting end, a first user, and a second user. The distance between the first user and the transmitting end is less than the distance between the second user and the transmitting end. The apparatus includes: an initialization unit that initializes an optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; an optimization unit that optimizes the preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes a preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; a judgment unit that judges whether a target difference is less than a preset value, and judges whether the target optimization vector simultaneously satisfies a sum rate constraint condition, a common rate constraint condition, and a total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; an update unit that, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replaces the preliminary sum rate with the target sum rate and replaces the preliminary optimization vector with the target optimization vector; an iteration unit that sequentially repeats the optimization unit, the judgment unit, and the update unit at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; an output unit that, when the target difference is less than the preset value, outputs a first optimal optimization vector and outputs a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputs a second optimal optimization vector and outputs a second maximum sum rate, where the first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process.

[0013] According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and the program executes any one of the methods described above.

[0014] According to yet another aspect of the embodiments of the present invention, there is also provided a processor, where the processor is used to run a program, and the program executes any one of the methods described above when running.

[0015] According to one aspect of an embodiment of the present invention, there is also provided a system for determining the maximum sum rate of a cooperative rate splitting multiple access system, including: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include those for executing any one of the methods described above.

[0016] In an embodiment of the present invention, in the method for determining the maximum sum rate of the above collaborative rate-splitting multiple access system, first, the optimization vector is initialized through an initialization step to obtain a preliminary optimization vector, and the above optimization vector includes multiple communication parameters; then, through an optimization step, the Newton steepest descent gradient method is used to optimize the above preliminary optimization vector to obtain a target optimization vector, and the Newton steepest descent gradient method is used to optimize the preliminary sum rate to obtain a target sum rate, where the above preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; then, through a judgment step, it is judged whether the target difference is less than a preset value, and it is judged whether the above target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the above target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; then, through an update step, when the above target difference is equal to or greater than the above preset value and the above target optimization vector simultaneously satisfies the above sum rate constraint condition, the above common rate constraint condition, and the above total power constraint condition, the above target sum rate is used to replace the above preliminary sum rate, and the above target optimization vector is used to replace the above preliminary optimization vector; then, the above optimization step, the above judgment step, and the above update step are sequentially repeated at least once until the above target difference is less than the above preset value or the above target optimization vector at most satisfies any one of the above rate constraint conditions, the above common rate constraint condition, and the above total power constraint condition; finally, when the above target difference is less than the above preset value, the first optimal optimization vector is output, and the first maximum sum rate is output, and when the above target optimization vector at most satisfies any one of the above sum rate constraint conditions, the above common rate constraint condition, and the above total power constraint condition, the second optimal optimization vector is output, and the second maximum sum rate is output, where the above first optimal optimization vector is the above target optimization vector of the current optimization process, the above first maximum sum rate is the above target sum rate of the current optimization process, the above second optimal optimization vector is the above target optimization vector of the previous optimization process, and the above second maximum sum rate is the above target sum rate of the previous optimization process. This method optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constrained quadratic programming optimization problem with the constraint conditions including the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition and the objective function being the preliminary sum rate. When the target difference is less than the preset value, the first maximum sum rate is output, and when the target optimization vector at most satisfies any one of the sum rate constraint conditions, the common rate constraint condition, and the total power constraint condition, the second maximum sum rate is output, that is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constrained quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This method solves the problem of poor communication quality of long-distance users in the existing collaborative rate-splitting multiple access system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings of the specification, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0018] Figure 1 A flowchart of a method for determining the maximum sum rate of a cooperative rate-splitting multiple access system according to an embodiment of this application is shown;

[0019] Figure 2 A flowchart of a method for determining the maximum sum rate of a cooperative rate-splitting multiple access system according to a specific embodiment of this application is shown;

[0020] Figure 3 A schematic diagram of a device for determining the maximum sum rate of a cooperative rate-splitting multiple access system according to an embodiment of this application is shown. Detailed implementation manners

[0021] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. This application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned accompanying drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances so as to describe the embodiments of this application here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these process, method, product or device.

[0024] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element can be directly on the other element, or there can also be intermediate elements. Moreover, in the specification and claims, when an element is described as being "connected" to another element, the element can be "directly connected" to the other element, or "connected" to the other element through a third element.

[0025] As described in the background art, in the prior art, the communication quality of long-distance users in a cooperative rate-splitting multiple access system is poor. To solve the above problems, in a typical embodiment of the present application, a method for determining the maximum sum rate of a cooperative rate-splitting multiple access system, a determining device, a computer-readable storage medium, a processor, and a determining system are provided.

[0026] According to an embodiment of the present application, a method for determining the maximum sum rate of a cooperative rate-splitting multiple access system is provided.

[0027] Figure 1 is a flowchart of a method for determining the maximum sum rate of a cooperative rate-splitting multiple access system according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0028] Step S101, an initialization step, initializes the optimization vector to obtain a preliminary optimization vector, and the above optimization vector includes multiple communication parameters;

[0029] Step S102, an optimization step, optimizes the above preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes the preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate. The above preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user;

[0030] Step S103, a judgment step, judges whether the target difference is less than a preset value, and judges whether the above target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition. The above target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process;

[0031] Step S104, an update step, when the above target difference is equal to or greater than the above preset value and the above target optimization vector simultaneously satisfies the above sum rate constraint condition, the above common rate constraint condition, and the above total power constraint condition, replaces the above preliminary sum rate with the above target sum rate, and replaces the above preliminary optimization vector with the above target optimization vector;

[0032] Step S105, repeat the above optimization step, the above judgment step, and the above update step in sequence at least once until the above target difference is less than the above preset value or the above target optimization vector satisfies at most any one of the above rate constraint condition, the above common rate constraint condition, and the above total power constraint condition;

[0033] Step S106, when the above target difference is less than the above preset value, output the first optimal optimization vector and output the first maximum sum rate. When the above target optimization vector satisfies at most any one of the above sum rate constraint condition, the above common rate constraint condition, and the above total power constraint condition, output the second optimal optimization vector and output the second maximum sum rate. The above first optimal optimization vector is the above target optimization vector of the current optimization process, the above first maximum sum rate is the above target sum rate of the current above optimization process, the above second optimal optimization vector is the above target optimization vector of the previous above optimization process, and the above second maximum sum rate is the above target sum rate of the previous above optimization process.

[0034] In the method for determining the maximum sum rate of the above collaborative rate splitting multiple access system, first, through an initialization step, the optimization vector is initialized to obtain a preliminary optimization vector, where the above optimization vector includes multiple communication parameters; then, through an optimization step, the Newton steepest descent gradient method is used to optimize the above preliminary optimization vector to obtain a target optimization vector, and the Newton steepest descent gradient method is used to optimize the preliminary sum rate to obtain a target sum rate, where the above preliminary sum rate is the sum of the sum rate of the first user and the above sum rate of the second user; then, through a judgment step, it is judged whether the target difference is less than a preset value, and it is judged whether the above target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the above target difference is the difference between the above target sum rate of the current optimization process and the above target sum rate of the previous above optimization process; then, through an update step, when the above target difference is equal to or greater than the above preset value and the above target optimization vector simultaneously satisfies the above sum rate constraint condition, the above common rate constraint condition, and the above total power constraint condition, the above target sum rate is used to replace the above preliminary sum rate, and the above target optimization vector is used to replace the above preliminary optimization vector; then, the above optimization step, the above judgment step, and the above update step are sequentially repeated at least once until the above target difference is less than the above preset value or the above target optimization vector satisfies at most any one of the above rate constraint condition, the above common rate constraint condition, and the above total power constraint condition; finally, when the above target difference is less than the above preset value, the first optimal optimization vector is output, and the first maximum sum rate is output, and when the above target optimization vector satisfies at most any one of the above sum rate constraint condition, the above common rate constraint condition, and the above total power constraint condition, the second optimal optimization vector is output, and the second maximum sum rate is output, where the above first optimal optimization vector is the above target optimization vector of the current optimization process, the above first maximum sum rate is the above target sum rate of the current above optimization process, the above second optimal optimization vector is the above target optimization vector of the previous above optimization process, and the above second maximum sum rate is the above target sum rate of the previous above optimization process. This method optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constrained quadratic programming optimization problem whose constraint conditions include the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition and the objective function is the preliminary sum rate. When the target difference is less than the preset value, the first maximum sum rate is output, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, the second maximum sum rate is output, that is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constrained quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This method solves the problem of poor communication quality of long-distance users in the existing collaborative rate splitting multiple access system.

[0035] It should also be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0036] In an embodiment of the present application, the sending end sends a superimposed signal to the first user and the sending end sends the superimposed signal to the second user. The superimposed signal includes a common signal, a first private signal, and a second private signal. The first user sends a relay signal to the second user. Before the initialization step, the method includes: decoding the common signal received by the first user to obtain a first common interference value, a first common signal-to-interference-plus-noise ratio, and a first common rate; decoding the common signal received by the second user to obtain a second common interference value and a second common signal-to-interference-plus-noise ratio; decoding the first private signal received by the first user to obtain a first private interference value, a first private signal-to-interference-plus-noise ratio, and a first private rate; decoding the second private signal received by the second user to obtain a second private interference value, a second private signal-to-interference-plus-noise ratio, and a second private rate; jointly decoding the common signal and the relay signal received by the second user to obtain the second common rate. The first common interference value is the sum of the noise signal and the interference signal in the common signal received by the first user. The second common interference value is the sum of the noise signal and the interference signal in the common signal received by the second user. The first private interference value is the sum of the noise signal and the interference signal in the first private signal received by the first user. The second private interference value is the sum of the noise signal and the interference signal in the second private signal received by the second user. According to the first common rate, the second common rate, the first private rate, and the second private rate, calculate the sum of the sum rate of the first user and the sum rate of the second user to obtain the preliminary sum rate. The sum rate of the first user is the sum of the first common rate and the first private rate. The sum rate of the second user is the sum of the second common rate and the second private rate. Use a precoding matrix and a time allocation parameter. The first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private rate, and the second private rate are used as elements to form the optimization vector. The time allocation parameter is the ratio of the first transmission time to the second transmission time. The first transmission time is the sum of the time required to transmit the common signal and the private signal transmission time. The private signal transmission time is the sum of the time required to transmit the first private signal and the time required to transmit the second private signal. The second transmission time is the sum of the first transmission time and the time required to transmit the relay signal. In this embodiment, first, in the direct transmission stage, according to the signal received by the first user Decode the common signal received by the first user to obtain the first common interference value The first common signal-to-interference-plus-noise ratio and the first common rate After the first user successfully decodes the common signal, use SIC to remove the common signal from , decode the first private signal received by the first user, and obtain the first private interference value The first private signal-to-interference-plus-noise ratio and the first private rate R 1,p , according to the signal received by the second user decode the common signal received by the second user, and obtain the second common interference value and the second common signal-to-interference-plus-noise ratio After the second user successfully decodes the common signal, use SIC to remove the common signal from , decode the second private signal received by the second user, and obtain the first private interference value The first private signal-to-interference-plus-noise ratio and the first private rate R 2,p , in the cooperative transmission stage, at the end of the cooperative transmission stage, according to the signal received by the second user and the relay signal Use maximum ratio combining to jointly decode the common signal and the relay signal received by the second user, and obtain the second common rate Among them, Then, calculate R 1,c , R 1,p , R 2,p , R 2,c The sum of, to obtain the preliminary sum rate r, that is, obtain the objective function of the convex quadratic constraint quadratic programming optimization problem, and use P, θ, α, α c , ρ, ρ c , β, β c As elements to form an optimization vector, where β includes and β c includes and ρ includes and ρ c includes and α includes R 1,p and R 2,p , α c includes R 1,c and R 2,c .

[0037] It should be noted that

[0038]

[0039] It should also be noted that the total power P of the sending end t is equal to the total power P of the first user R , that is, P t = P R .

[0040] It should also be noted that the cooperative rate-splitting multiple access system of this application consists of a sending end, a first user, and a second user. The first user closer to the sending end acts as a relay and uses the decode-and-forward protocol to help the second user transmit the common signal. At the sending end, according to the rate-splitting principle, the signals sent to the first user and the second user are divided into a common part and a private part. The common part is encoded together into the common signal, and the private parts are respectively encoded into a first private signal and a second private signal. The superimposed signal s = [s c , s1, s2] T sent by the sending end reaches the first user and the second user through the linear precoding matrix P = [p c , p1, p2]. The signals received by the first user and the second user are x = Ps = p c s c + p1s1 + p2s2, where s c is the common signal, s1 is the first private signal, and s2 is the second private signal.

[0041] It should also be noted that the entire communication process of the cooperative rate-splitting multiple access system includes a direct transmission stage and a cooperative transmission stage. In the direct transmission stage, the sending end simultaneously sends the superimposed signal to the first user and the second user. The signal received by U k , k ∈ {1, 2} is In the cooperative transmission stage, the first user re-encodes the decoded common signal and then forwards the re-encoded common signal to the second user with power P R . The relay signal received by the second user is U1 is the first user and U2 is the second user.

[0042] It should also be noted that the frequency- and distance-dependent attenuation A i (d i , f) = exp(-(b0 + b1f m )d i ) in the PLC channel, i ∈ {S1, S2, 12}, where d S1 represents the distance between the sending end and the first user, d S2 represents the distance between the sending end and the second user, and d S1Indicates the distance between the first user and the second user, f is the communication frequency, m is the exponent of the attenuation factor, and b0 and b1 are the attenuation constants obtained from the measurement data.

[0043] It should also be noted that the time allocation parameter θ is the time ratio allocated to the direct transmission stage when the relay operates in the half-duplex mode, and the remaining part (1 - θ) is the time ratio allocated to the cooperative transmission stage.

[0044] In an embodiment of the present application, the initialization step includes: initializing the above-mentioned precoding matrix, the above-mentioned time allocation parameter, the above-mentioned first common interference value, the above-mentioned second common interference value, the above-mentioned first private interference value, the above-mentioned second private interference value, the above-mentioned first common signal-to-interference-plus-noise ratio, the above-mentioned second common signal-to-interference-plus-noise ratio, the above-mentioned first common rate, the above-mentioned second common rate, the above-mentioned first private signal-to-interference-plus-noise ratio, the above-mentioned second private signal-to-interference-plus-noise ratio, the above-mentioned first private rate, and the above-mentioned second private rate. In this embodiment, as Figure 2 shown, the optimization vector, that is, the optimization variable, is initialized. First, the precoding matrix P = [p c , p1, p2] is initialized by using maximum ratio transmission and singular value decomposition. Then, the time allocation parameter θ is initialized to 0.8. Finally, the first common interference value (when k = 1) and the second common interference value (when k = 2) are sequentially initialized to The first private interference value (when k = 1) and the second private interference value (when k = 2) are initialized to The first common signal-to-interference-plus-noise ratio (when k = 1) and the second common signal-to-interference-plus-noise ratio (when k = 2) are initialized to The first common rate (when k = 1) and the second common rate (when k = 2) are initialized to The first private signal-to-interference-plus-noise ratio (when k = 1) and the second private signal-to-interference-plus-noise ratio (when k = 2) are initialized to The first private rate (when k = 1) and the second private rate (when k = 2) are initialized to

[0045] It should be noted that the above is the above The above is the above The above is the above R k,c The above is the above R k,p .

[0046] In an embodiment of the present application, the above precoding matrix includes a common precoding vector, a first private precoding vector, and a second private precoding vector. The initialization step further includes: initializing the above common precoding vector, the above first private precoding vector, and the above second private precoding vector. The above common precoding vector is used to encode the above common signal, the above first private precoding vector is used to encode the above first private signal, and the above second private precoding vector is used to encode the above second private signal. In this embodiment, the first private precoding vector p1 and the second private precoding vector p2 are initialized by maximum ratio transmission as where 0 ≤ λ ≤ 1, and the common precoding vector p is initialized by singular value decomposition as c where p where p c =(1 - λ)P t , u c is the eigenvector corresponding to the largest left singular value of the channel matrix H = [h s1 A S1 , h s2 A S2 , and is calculated by u c = U(:,1), where H = USV H .

[0047] In an embodiment of the present application, determining whether the above target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition includes: when the above target optimization vector simultaneously satisfies the first sum rate constraint condition and the second sum rate constraint condition, determining that the above target optimization vector satisfies the above sum rate constraint condition; when the above target optimization vector simultaneously satisfies the first common rate constraint condition and the second common rate constraint condition, determining that the above target optimization vector satisfies the above common rate constraint condition; when the above target optimization vector satisfies the constraint condition of the total power at the transmitter, determining that the above target optimization vector satisfies the above total power constraint condition. In this embodiment, the first sum rate constraint condition (when k = 1) and the second sum rate constraint condition (when k = 2) are Ω [n] (p k , h Sk , β k ) ≥ ρ k , k ∈ {1, 2}, where The first common rate constraint condition (when k = 1) and the second common rate constraint condition (when k = 2) are c1 + c2 ≤ Z [n] (θ, α 1,c ), Ω[n] (p c ,h Sk ,β k,c )≥ρ k,c ,k∈{1,2}, where c k ≥0, the total transmit power constraint is tr(pp H )≤P t .

[0048] In an embodiment of the present application, the optimization steps include: calculating the product of the first preliminary vector in the previous optimization process and a preset coefficient to obtain a second preliminary vector, where the first preliminary vector includes the preliminary optimization vector and the preliminary sum rate; calculating the sum of the first preliminary vector and the second preliminary vector in the previous optimization process to obtain a target vector; and outputting the target optimization vector and the target sum rate according to the target vector. In this embodiment, the Newton steepest descent gradient method is used to optimize the preliminary optimization vector and the preliminary sum rate, that is, to solve the convex quadratic constrained quadratic programming optimization problem with the preliminary sum rate r as the objective function. Its iterative process is η(m)=(r(m),P(m),θ,α(m),α c (m),ρ(m),ρ c (m),β(m),β c (m)), where β = 0.39, is the gradient of η(m). After the m-th iteration ends, the obtained target sum rate r * and the target optimization vector If the condition |r [m] -r [m-1] |<ε does not hold, update the preliminary sum rate in the (m + 1)-th iteration to r * , and update the preliminary optimization vector in the (m + 1)-th iteration to When the condition |r [m] -r [m-1] |<ε holds, the iteration ends, and the output target sum rate is the optimal objective function and the optimal optimization vector That is, the maximum sum rate of the cooperative rate splitting multiple access system is obtained.

[0049] The embodiment of the present application also provides a device for determining the maximum sum rate of a cooperative rate splitting multiple access system. It should be noted that the device for determining the maximum sum rate of the cooperative rate splitting multiple access system in the embodiment of the present application can be used to execute the method for determining the maximum sum rate of the cooperative rate splitting multiple access system provided by the embodiment of the present application. The following introduces the device for determining the maximum sum rate of the cooperative rate splitting multiple access system provided by the embodiment of the present application.

[0050] Figure 3Schematic diagram of a device for determining the maximum sum rate of a cooperative rate division multiple access system according to an embodiment of the present application. As Figure 3 shown, the device includes:

[0051] Initialization unit 10, which initializes the optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters;

[0052] Optimization unit 20, which optimizes the preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes the preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user;

[0053] Judgment unit 30, which judges whether the target difference is less than a preset value, and judges whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the target difference is the difference between the target sum rate in the current optimization process and the target sum rate in the previous optimization process;

[0054] Update unit 40, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replaces the preliminary sum rate with the target sum rate and replaces the preliminary optimization vector with the target optimization vector;

[0055] Iteration unit 50, which sequentially repeats the optimization unit, the judgment unit, and the update unit at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition;

[0056] Output unit 60, when the target difference is less than the preset value, outputs a first optimal optimization vector and outputs a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputs a second optimal optimization vector and outputs a second maximum sum rate, where the first optimal optimization vector is the target optimization vector in the current optimization process, the first maximum sum rate is the target sum rate in the current optimization process, the second optimal optimization vector is the target optimization vector in the previous optimization process, and the second maximum sum rate is the target sum rate in the previous optimization process.

[0057] In the device for determining the maximum sum rate of the above collaborative rate-splitting multiple access system, the initialization unit initializes the optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; the optimization unit optimizes the preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes the preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; the judgment unit judges whether the target difference is less than a preset value, and judges whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; the update unit, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replaces the preliminary sum rate with the target sum rate and replaces the preliminary optimization vector with the target optimization vector; the iteration unit sequentially repeats the optimization unit, the judgment unit, and the update unit at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; the output unit, when the target difference is less than the preset value, outputs a first optimal optimization vector and outputs a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputs a second optimal optimization vector and outputs a second maximum sum rate, where the first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process. This device optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constrained quadratic programming optimization problem with the constraint conditions including the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition and the objective function being the preliminary sum rate. When the target difference is less than the preset value, it outputs the first maximum sum rate. When the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, it outputs the second maximum sum rate. That is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constrained quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This device solves the problem of poor communication quality of long-distance users in the existing collaborative rate-splitting multiple access system.

[0058] In an embodiment of the present application, the apparatus for determining the maximum sum rate of the collaborative rate-splitting multiple access system further includes a first decoding module, a second decoding module, a third decoding module, a fourth decoding module, a fifth decoding module, a first calculation module, and an execution module. The first decoding module is configured to decode the common signal received by the first user to obtain a first common interference value, a first common signal-to-interference-plus-noise ratio, and a first common rate. The second decoding module is configured to decode the common signal received by the second user to obtain a second common interference value and a second common signal-to-interference-plus-noise ratio. The third decoding module is configured to decode the first private signal received by the first user to obtain a first private interference value, a first private signal-to-interference-plus-noise ratio, and a first private rate. The fourth decoding module is configured to decode the second private signal received by the second user to obtain a second private interference value, a second private signal-to-interference-plus-noise ratio, and a second private rate. The fifth decoding module is configured to jointly decode the common signal and the relay signal received by the second user to obtain the second common rate. The first common interference value is the sum of the noise signal and the interference signal in the common signal received by the first user. The second common interference value is the sum of the noise signal and the interference signal in the common signal received by the second user. The first private interference value is the sum of the noise signal and the interference signal in the first private signal received by the first user. The second private interference value is the sum of the noise signal and the interference signal in the second private signal received by the second user. The first calculation module is configured to calculate the sum of the sum rate of the first user and the sum rate of the second user according to the first common rate, the second common rate, the first private rate, and the second private rate to obtain the preliminary sum rate. The sum rate of the first user is the sum of the first common rate and the first private rate. The sum rate of the second user is the sum of the second common rate and the second private rate. The execution module is configured to use the precoding matrix, the time allocation parameter, the first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private rate, and the second private rate as elements to form the optimization vector. The time allocation parameter is the ratio of the first transmission time to the second transmission time. The first transmission time is the sum of the time required to transmit the common signal and the private signal transmission time. The private signal transmission time is the sum of the time required to transmit the first private signal and the time required to transmit the second private signal. The second transmission time is the sum of the first transmission time and the time required to transmit the relay signal. In this embodiment, first, in the direct transmission stage, according to the signal received by the first user. Decode the common signal received by the first user to obtain the first common interference value The first common signal-to-interference-plus-noise ratio and the first common rate After the first user successfully decodes the common signal, use SIC to remove the common signal from and decode the first private signal received by the first user to obtain the first private interference value The first private signal-to-interference-plus-noise ratio and the first private rate R 1,p , according to the signal received by the second user Decode the common signal received by the second user to obtain the second common interference value and the second common signal-to-interference-plus-noise ratio After the second user successfully decodes the common signal, use SIC to remove the common signal from and decode the second private signal received by the second user to obtain the first private interference value The first private signal-to-interference-plus-noise ratio and the first private rate R 2,p , in the cooperative transmission stage, at the end of the cooperative transmission stage, according to the signal received by the second user and the relay signal Use maximum ratio combining to jointly decode the common signal and the relay signal received by the second user to obtain the second common rate Wherein, Then, calculate R 1,c 、R 1,p 、R 2,p 、R 2,c The sum of, to obtain the preliminary sum rate r, that is, to obtain the objective function of the convex quadratic constrained quadratic programming optimization problem, using P, θ, α, α c , ρ, ρ c , β, β c As elements to form an optimization vector, wherein, β includes and β c includes and ρ includes and ρ c includes and α includes R 1,p and R 2,p , α c includes R 1,c and R 2,c .

[0059] It should be noted that

[0060] It should also be noted that the total power P of the transmitting end t and the total power P of the first user R are equal, that is, P t = P R .

[0061] It should also be noted that the cooperative rate-splitting multiple access system of this application consists of a transmitting end, a first user, and a second user. The first user closer to the transmitting end acts as a relay and uses the decode-and-forward protocol to help the second user transmit the common signal. At the transmitting end, according to the rate-splitting principle, the signals sent to the first user and the second user are divided into a common part and a private part. The common parts are jointly encoded into the common signal, and the private parts are respectively encoded into a first private signal and a second private signal. The superimposed signal s = [s c , s1, s2] T sent by the transmitting end reaches the first user and the second user through the linear precoding matrix P = [p c , p1, p2]. The signals received by the first user and the second user are x = Ps = p c s c + p1s1 + p2s2, where s c is the common signal, s1 is the first private signal, and s2 is the second private signal.

[0062] It should also be noted that the entire communication process of the cooperative rate-splitting multiple access system includes a direct transmission stage and a cooperative transmission stage. In the direct transmission stage, the transmitting end simultaneously sends a superimposed signal to the first user and the second user. The signal received by U k , k ∈ {1, 2} is In the cooperative transmission stage, the first user re-encodes the decoded common signal and then forwards the re-encoded common signal to the second user with power P R . The relay signal received by the second user is U1 is the first user and U2 is the second user.

[0063] It should also be noted that the frequency- and distance-dependent attenuation A i (d i , f) = exp(-(b0 + b1f m )d i ) in the PLC channel, i ∈ {S1, S2, 12}, where d S1 represents the distance between the transmitting end and the first user, d S2 represents the distance between the transmitting end and the second user, and d S1represents the distance between the first user and the second user, f is the communication frequency, m is the exponent of the attenuation factor, and b0 and b1 are attenuation constants obtained from measurement data.

[0064] It should also be noted that the time allocation parameter θ is the time ratio allocated to the direct transmission phase when the relay operates in the half-duplex mode, and the remaining part (1 - θ) is the time ratio allocated to the cooperative transmission phase.

[0065] In an embodiment of the present application, the above initialization unit is used to initialize the above precoding matrix, the above time allocation parameter, the above first common interference value, the above second common interference value, the above first private interference value, the above second private interference value, the above first common signal-to-interference-plus-noise ratio, the above second common signal-to-interference-plus-noise ratio, the above first common rate, the above second common rate, the above first private signal-to-interference-plus-noise ratio, the above second private signal-to-interference-plus-noise ratio, the above first private rate, and the above second private rate. In this embodiment, as Figure 2 shown, the optimization variables are initialized. First, the precoding matrix P = [p c , p1, p2] is initialized by using maximum ratio transmission and singular value decomposition. Then, the time allocation parameter θ is initialized to 0.8. Finally, the first common interference value (when k = 1) and the second common interference value (when k = 2) are sequentially initialized to The first private interference value (when k = 1) and the second private interference value (when k = 2) are initialized to The first common signal-to-interference-plus-noise ratio (when k = 1) and the second common signal-to-interference-plus-noise ratio (when k = 2) are initialized to The first common rate (when k = 1) and the second common rate (when k = 2) are initialized to The first private signal-to-interference-plus-noise ratio (when k = 1) and the second private signal-to-interference-plus-noise ratio (when k = 2) are initialized to The first private rate (when k = 1) and the second private rate (when k = 2) are initialized to

[0066] It should be noted that the above is the above The above is the above The above is the above R k,c The above is the above R k,p .

[0067] In an embodiment of the present application, the above initialization unit further includes an initialization module, and the above initialization module is used to initialize the above common precoding vector, the above first private precoding vector, and the above second private precoding vector. The above common precoding vector is used to encode the above common signal, the above first private precoding vector is used to encode the above first private signal, and the above second private precoding vector is used to encode the above second private signal. In this embodiment, the first private precoding vector p1 and the second private precoding vector p2 are initialized by maximum ratio transmission as where 0 ≤ λ ≤ 1, and the common precoding vector p c is initialized as where p c =(1 - λ)P t , u c is the eigenvector corresponding to the largest left singular value of the channel matrix H = [h s1 A S1 , h s2 A S2 , and is calculated by u c = U(:,1), where H = USV H .

[0068] In an embodiment of the present application, the above determination unit includes a first determination module, a second determination module, and a third determination module. The above first determination module is used to determine that the above target optimization vector satisfies the above sum rate constraint condition when the above target optimization vector simultaneously satisfies the first sum rate constraint condition and the second sum rate constraint condition; the above second determination module is used to determine that the above target optimization vector satisfies the above sum rate constraint condition; the above third determination module is used to determine that the above target optimization vector satisfies the above common rate constraint condition when the above target optimization vector simultaneously satisfies the first common rate constraint condition and the second common rate constraint condition; and to determine that the above target optimization vector satisfies the above total power constraint condition when the above target optimization vector satisfies the constraint condition of the total power at the transmitter. In this embodiment, the first sum rate constraint condition (when k = 1) and the second sum rate constraint condition (when k = 2) are Ω [n] (p k , h Sk , β k ) ≥ ρ k , k ∈ {1, 2}, where The first common rate constraint condition (when k = 1) and the second common rate constraint condition (when k = 2) are c1 + c2 ≤ Z [n] (θ, α 1,c ), Ω [n] (pc , h Sk , β k,c ) ≥ ρ k,c , k ∈ {1, 2}, where The total power constraint condition of the transmitter is tr(pp H ) ≤ P t .

[0069] In an embodiment of the present application, the above optimization unit includes a second calculation module, a third calculation module, and an output module. The second calculation module is used to calculate the product of the first preliminary vector of the previous optimization process and a preset coefficient to obtain a second preliminary vector. The first preliminary vector includes the preliminary optimization vector and the preliminary sum rate. The third calculation module is used to calculate the sum of the first preliminary vector and the second preliminary vector of the previous optimization process to obtain a target vector. The output module is used to output the target optimization vector and the target sum rate according to the target vector. In this embodiment, the Newton steepest descent gradient method is used to optimize the preliminary optimization vector and the preliminary sum rate, that is, to solve the convex quadratic constrained quadratic programming optimization problem with the preliminary sum rate r as the objective function. Its iteration process is η(m) = (r(m), P(m), θ, α(m), α c (m), ρ(m), ρ c (m), β(m), β c (m)), where β = 0.39, is the gradient of η(m). After the m-th iteration ends, the obtained target sum rate r * and the target optimization vector are If the condition |r [m] - r [m-1] | < ε does not hold, update the preliminary sum rate in the (m + 1)-th iteration to r * , and update the preliminary optimization vector in the (m + 1)-th iteration to When the condition |r [m] - r [m-1] | < ε holds, the iteration ends, and the output target sum rate is the optimal objective function and the optimal optimization vector That is, the maximum sum rate of the cooperative rate splitting multiple access system is obtained.

[0070] The device for determining the maximum sum rate of the above cooperative rate splitting multiple access system includes a processor and a memory. The above initialization unit, optimization unit, judgment unit, update unit, iteration unit, and output unit are all stored in the memory as program units, and the processor executes the above program units stored in the memory to implement corresponding functions.

[0071] The processor contains a kernel, which retrieves the corresponding program units from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problem of poor communication quality of long-distance users in the cooperative rate-splitting multiple access system in the prior art can be solved.

[0072] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flashRAM), and the memory includes at least one memory chip.

[0073] An embodiment of the present invention provides a storage medium, on which a program is stored, and when the program is executed by a processor, the method for determining the maximum sum rate of the above-mentioned cooperative rate-splitting multiple access system is implemented.

[0074] An embodiment of the present invention provides a processor, and the above-mentioned processor is used to run a program, wherein when the above-mentioned program runs, the method for determining the maximum sum rate of the above-mentioned cooperative rate-splitting multiple access system is executed.

[0075] An embodiment of the present invention provides a system for determining the maximum sum rate of a cooperative rate-splitting multiple access system, including: one or more processors, a memory, and one or more programs, wherein the above-mentioned one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned one or more processors, and the above-mentioned one or more programs include methods for executing any of the above, and when the processor executes the program, at least the following steps are implemented:

[0076] Step S101, an initialization step, initializes the optimization vector to obtain a preliminary optimization vector, and the above-mentioned optimization vector includes multiple communication parameters;

[0077] Step S102, an optimization step, uses the Newton steepest descent gradient method to optimize the above-mentioned preliminary optimization vector to obtain a target optimization vector, and uses the Newton steepest descent gradient method to optimize the preliminary sum rate to obtain a target sum rate, and the above-mentioned preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user;

[0078] Step S103, a judgment step, judges whether the target difference is less than a preset value, and judges whether the above-mentioned target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, and the above-mentioned target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process;

[0079] Step S104, Update Step: When the above-mentioned target difference is equal to or greater than the above-mentioned preset value and the above-mentioned target optimization vector simultaneously satisfies the above-mentioned sum rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition, use the above-mentioned target sum rate to replace the preliminary sum rate, and use the above-mentioned target optimization vector to replace the preliminary optimization vector;

[0080] Step S105, Repeat the above-mentioned optimization step, the above-mentioned judgment step, and the above-mentioned update step at least once in sequence until the above-mentioned target difference is less than the above-mentioned preset value or the above-mentioned target optimization vector satisfies at most any one of the above-mentioned rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition;

[0081] Step S106, When the above-mentioned target difference is less than the above-mentioned preset value, output the first optimal optimization vector and output the first maximum sum rate. When the above-mentioned target optimization vector satisfies at most any one of the above-mentioned sum rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition, output the second optimal optimization vector and output the second maximum sum rate. The above-mentioned first optimal optimization vector is the above-mentioned target optimization vector of the current optimization process, the above-mentioned first maximum sum rate is the above-mentioned target sum rate of the current above-mentioned optimization process, the above-mentioned second optimal optimization vector is the above-mentioned target optimization vector of the previous above-mentioned optimization process, and the above-mentioned second maximum sum rate is the above-mentioned target sum rate of the previous above-mentioned optimization process.

[0082] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0083] This application also provides a computer program product, which is suitable for executing a program initialized with at least the following method steps when executed on a data processing device:

[0084] Step S101, Initialization Step: Initialize the optimization vector to obtain a preliminary optimization vector, and the above-mentioned optimization vector includes multiple communication parameters;

[0085] Step S102, Optimization Step: Use the Newton fastest descent gradient method to optimize the above-mentioned preliminary optimization vector to obtain a target optimization vector, and use the Newton fastest descent gradient method to optimize the preliminary sum rate to obtain a target sum rate. The above-mentioned preliminary sum rate is the sum of the sum rate of the above-mentioned first user and the sum rate of the above-mentioned second user;

[0086] Step S103, Judgment Step: Judge whether the target difference is less than the preset value, and judge whether the above-mentioned target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition. The above-mentioned target difference is the difference between the above-mentioned target sum rate of the current optimization process and the above-mentioned target sum rate of the previous above-mentioned optimization process;

[0087] Step S104, update step. When the above-mentioned target difference is equal to or greater than the above-mentioned preset value and the above-mentioned target optimization vector simultaneously satisfies the above-mentioned sum rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition, use the above-mentioned target sum rate to replace the above-mentioned preliminary sum rate, and use the above-mentioned target optimization vector to replace the above-mentioned preliminary optimization vector;

[0088] Step S105, repeat the above-mentioned optimization step, the above-mentioned judgment step, and the above-mentioned update step at least once in sequence until the above-mentioned target difference is less than the above-mentioned preset value or the above-mentioned target optimization vector satisfies at most any one of the above-mentioned rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition;

[0089] Step S106, when the above-mentioned target difference is less than the above-mentioned preset value, output the first optimal optimization vector and output the first maximum sum rate. When the above-mentioned target optimization vector satisfies at most any one of the above-mentioned sum rate constraint condition, the above-mentioned common rate constraint condition, and the above-mentioned total power constraint condition, output the second optimal optimization vector and output the second maximum sum rate. The above-mentioned first optimal optimization vector is the above-mentioned target optimization vector of the current optimization process, the above-mentioned first maximum sum rate is the above-mentioned target sum rate of the current above-mentioned optimization process, the above-mentioned second optimal optimization vector is the above-mentioned target optimization vector of the previous above-mentioned optimization process, and the above-mentioned second maximum sum rate is the above-mentioned target sum rate of the previous above-mentioned optimization process.

[0090] In the above-mentioned embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0091] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the above-mentioned unit division can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0092] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0094] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0095] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0096] 1) In the method for determining the maximum sum rate of the cooperative rate-splitting multiple access system of the present application, first, the optimization vector is initialized through an initialization step to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; then, through an optimization step, the Newton steepest descent gradient method is used to optimize the preliminary optimization vector to obtain a target optimization vector, and the Newton steepest descent gradient method is used to optimize the preliminary sum rate to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; then, through a judgment step, it is judged whether the target difference is less than a preset value, and it is judged whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; then, through an update step, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, the target sum rate is used to replace the preliminary sum rate, and the target optimization vector is used to replace the preliminary optimization vector; then, the above optimization step, judgment step, and update step are sequentially repeated at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; finally, when the target difference is less than the preset value, the first optimal optimization vector is output, and the first maximum sum rate is output, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, the second optimal optimization vector is output, and the second maximum sum rate is output, where the first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process. This method optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constraint quadratic programming optimization problem with the constraint conditions including the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition and the objective function being the preliminary sum rate. When the target difference is less than the preset value, the first maximum sum rate is output, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, the second maximum sum rate is output, that is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constraint quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This method solves the problem of poor communication quality of long-distance users in the existing cooperative rate-splitting multiple access system.

[0097] 2) In the device for determining the maximum sum rate of the cooperative rate-splitting multiple access system of the present application, the initialization unit initializes the optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; the optimization unit optimizes the preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes the preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; the judgment unit judges whether the target difference is less than a preset value, and judges whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; the update unit, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replaces the preliminary sum rate with the target sum rate and replaces the preliminary optimization vector with the target optimization vector; the iteration unit sequentially repeats the optimization unit, the judgment unit, and the update unit at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; the output unit, when the target difference is less than the preset value, outputs a first optimal optimization vector and outputs a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputs a second optimal optimization vector and outputs a second maximum sum rate, where the first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process. This device optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constrained quadratic programming optimization problem with the constraint conditions including the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition and the objective function being the preliminary sum rate. When the target difference is less than the preset value, it outputs the first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, it outputs the second maximum sum rate, that is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constrained quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This device solves the problem of poor communication quality of long-distance users in the existing cooperative rate-splitting multiple access system.

[0098] 3) The determination system for the maximum sum rate of the cooperative rate-splitting multiple access system of the present application includes: one or more processors, a memory, and one or more programs. Among them, the above one or more programs are stored in the above memory and are configured to be executed by the above one or more processors. The above one or more programs include those for executing any of the above methods. This system optimizes the preliminary optimization vector and the preliminary sum rate through the Newton steepest descent gradient method, that is, solves the convex quadratic constrained quadratic programming optimization problem with the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition as the constraint conditions and the preliminary sum rate as the objective function through the Newton steepest descent gradient method. When the objective difference is less than the preset value, the first maximum sum rate is output. When the objective optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, the second maximum sum rate is output. That is, when the convergence accuracy of the Newton steepest descent gradient method is satisfied or the constraint conditions of the convex quadratic constrained quadratic programming optimization problem are not satisfied, the maximum sum rate is obtained. This system solves the problem of poor communication quality of long-distance users in the existing cooperative rate-splitting multiple access system.

[0099] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for determining the maximum sum rate of a cooperative rate-splitting multiple access system, characterized in that The collaborative rate-splitting multiple access system includes a transmitter, a first user, and a second user. The distance between the first user and the transmitter is less than the distance between the second user and the transmitter. The transmitter sends a superimposed signal to the first user and also sends the superimposed signal to the second user. The superimposed signal includes a common signal, a first private signal, and a second private signal. The first user sends a relay signal to the second user. The method includes: Decoding the common signal received by the first user to obtain a first common interference value, a first common signal-to-interference-plus-noise ratio, and a first common rate; decoding the common signal received by the second user to obtain a second common interference value and a second common signal-to-interference-plus-noise ratio; decoding the first private signal received by the first user to obtain a first private interference value, a first private signal-to-interference-plus-noise ratio, and a first private rate; decoding the second private signal received by the second user to obtain a second private interference value, a second private signal-to-interference-plus-noise ratio, and a second private rate; jointly decoding the common signal and the relay signal received by the second user to obtain a second common rate. The first common interference value is the sum of the noise signal and the interference signal in the common signal received by the first user. The second common interference value is the sum of the noise signal and the interference signal in the common signal received by the second user. The first private interference value is the sum of the noise signal and the interference signal in the first private signal received by the first user. The second private interference value is the sum of the noise signal and the interference signal in the second private signal received by the second user; Calculating the sum of the sum rate of the first user and the sum rate of the second user according to the first common rate, the second common rate, the first private rate, and the second private rate to obtain a preliminary sum rate. The sum rate of the first user is the sum of the first common rate and the first private rate. The sum rate of the second user is the sum of the second common rate and the second private rate; Using a precoding matrix, a time allocation parameter, the first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private rate, and the second private rate as elements to form an optimization vector. The time allocation parameter is the ratio of the first transmission time to the second transmission time. The first transmission time is the sum of the time required to transmit the common signal and the private signal transmission time. The private signal transmission time is the sum of the time required to transmit the first private signal and the time required to transmit the second private signal. The second transmission time is the sum of the first transmission time and the time required to transmit the relay signal; Initialization step: Initialize the optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; Optimization step: Use the Newton steepest descent gradient method to optimize the preliminary optimization vector to obtain a target optimization vector, and use the Newton steepest descent gradient method to optimize the preliminary sum rate to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; Judgment step: Judge whether the target difference is less than a preset value, and judge whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, where the target difference is the difference between the target sum rate of the current optimization process and the target sum rate of the previous optimization process; Update step: When the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, use the target sum rate to replace the preliminary sum rate, and use the target optimization vector to replace the preliminary optimization vector; Repeat the optimization step, the judgment step, and the update step in sequence at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; When the target difference is less than the preset value, output the first optimal optimization vector and the first maximum sum rate. When the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, output the second optimal optimization vector and the second maximum sum rate. The first optimal optimization vector is the target optimization vector of the current optimization process, the first maximum sum rate is the target sum rate of the current optimization process, the second optimal optimization vector is the target optimization vector of the previous optimization process, and the second maximum sum rate is the target sum rate of the previous optimization process.

2. The method according to claim 1, characterized in that, The precoding matrix includes a common precoding vector, a first private precoding vector, and a second private precoding vector. The initialization step further includes: Initialize the common precoding vector, the first private precoding vector, and the second private precoding vector. The common precoding vector is used to encode the common signal, the first private precoding vector is used to encode the first private signal, and the second private precoding vector is used to encode the second private signal.

3. The method according to claim 2, wherein Judging whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition includes: When the target optimization vector simultaneously satisfies the first sum rate constraint condition and the second sum rate constraint condition, determine that the target optimization vector satisfies the sum rate constraint condition; When the target optimization vector simultaneously satisfies the first common rate constraint condition and the second common rate constraint condition, determine that the target optimization vector satisfies the common rate constraint condition; Determine that the target optimization vector satisfies the total power constraint condition when the target optimization vector satisfies the constraint condition of the total power at the transmitter side.

4. The method according to claim 3, characterized in that, The optimization steps include: Calculate the product of the first preliminary vector in the previous optimization process and a preset coefficient to obtain a second preliminary vector, where the first preliminary vector includes the preliminary optimization vector and the preliminary sum rate; Calculate the sum of the first preliminary vector and the second preliminary vector in the previous optimization process to obtain a target vector; Output the target optimization vector and the target sum rate according to the target vector.

5. A device for determining the maximum sum rate of a cooperative rate-splitting multiple access system, characterized in that, The cooperative rate splitting multiple access system includes a transmitter, a first user, and a second user. The distance between the first user and the transmitter is less than the distance between the second user and the transmitter. The transmitter sends a superimposed signal to the first user and the transmitter sends the superimposed signal to the second user. The superimposed signal includes a common signal, a first private signal, and a second private signal. The first user sends a relay signal to the second user. The device includes: An initialization unit that initializes an optimization vector to obtain a preliminary optimization vector, where the optimization vector includes multiple communication parameters; An optimization unit that optimizes the preliminary optimization vector using the Newton steepest descent gradient method to obtain a target optimization vector, and optimizes the preliminary sum rate using the Newton steepest descent gradient method to obtain a target sum rate, where the preliminary sum rate is the sum of the sum rate of the first user and the sum rate of the second user; A judgment unit that judges whether the target difference is less than a preset value, and judges whether the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition. The target difference is the difference between the target sum rate in the current optimization process and the target sum rate in the previous optimization process; An update unit that, when the target difference is equal to or greater than the preset value and the target optimization vector simultaneously satisfies the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, replaces the preliminary sum rate with the target sum rate and replaces the preliminary optimization vector with the target optimization vector; An iteration unit that sequentially repeats the optimization unit, the judgment unit, and the update unit at least once until the target difference is less than the preset value or the target optimization vector satisfies at most any one of the rate constraint condition, the common rate constraint condition, and the total power constraint condition; An output unit that, when the target difference is less than the preset value, outputs a first optimal optimization vector and outputs a first maximum sum rate, and when the target optimization vector satisfies at most any one of the sum rate constraint condition, the common rate constraint condition, and the total power constraint condition, outputs a second optimal optimization vector and outputs a second maximum sum rate. The first optimal optimization vector is the target optimization vector in the current optimization process, the first maximum sum rate is the target sum rate in the current optimization process, the second optimal optimization vector is the target optimization vector in the previous optimization process, and the second maximum sum rate is the target sum rate in the previous optimization process; The device for determining the maximum sum rate of the collaborative rate-splitting multiple access system further includes a first decoding module, a second decoding module, a third decoding module, a fourth decoding module, a fifth decoding module, a first calculation module, and an execution module. The first decoding module is used to decode the common signal received by the first user to obtain a first common interference value, a first common signal-to-interference-plus-noise ratio, and a first common rate. The second decoding module is used to decode the common signal received by the second user to obtain a second common interference value and a second common signal-to-interference-plus-noise ratio. The third decoding module is used to decode the first private signal received by the first user to obtain a first private interference value, a first private signal-to-interference-plus-noise ratio, and a first private rate. The fourth decoding module is used to decode the second private signal received by the second user to obtain a second private interference value, a second private signal-to-interference-plus-noise ratio, and a second private rate. The fifth decoding module is used to jointly decode the common signal and the relay signal received by the second user to obtain a second common rate. The first common interference value is the sum of the noise signal and the interference signal in the common signal received by the first user. The second common interference value is the sum of the noise signal and the interference signal in the common signal received by the second user. The first private interference value is the sum of the noise signal and the interference signal in the first private signal received by the first user. The second private interference value is the sum of the noise signal and the interference signal in the second private signal received by the second user. The first calculation module is used to calculate the sum of the sum rate of the first user and the sum rate of the second user according to the first common rate, the second common rate, the first private rate, and the second private rate to obtain the preliminary sum rate. The sum rate of the first user is the sum of the first common rate and the first private rate. The sum rate of the second user is the sum of the second common rate and the second private rate. The execution module is used to use the precoding matrix, the time allocation parameter, the first common interference value, the second common interference value, the first private interference value, the second private interference value, the first common signal-to-interference-plus-noise ratio, the second common signal-to-interference-plus-noise ratio, the first private signal-to-interference-plus-noise ratio, the second private signal-to-interference-plus-noise ratio, the first common rate, the second common rate, the first private rate, and the second private rate as elements to form the optimization vector. The time allocation parameter is the ratio of the first transmission time to the second transmission time. The first transmission time is the sum of the time required to transmit the common signal and the time required to transmit the private signal. The private signal transmission time is the sum of the time required to transmit the first private signal and the time required to transmit the second private signal. The second transmission time is the sum of the first transmission time and the time required to transmit the relay signal.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 4.

7. A processor, characterized in that, The processor is configured to run a program, wherein the program, when running, executes the method according to any one of claims 1 to 4.

8. A system for determining the maximum sum rate of a cooperative rate splitting multiple access system, characterized in that, Comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include those for executing the method according to any one of claims 1 to 4.

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