Method and system for optimizing total rate of rate division multiple access communication network

Abstract

The present application discloses a method and system for optimizing the total rate of a rate-splitting multiple access communication network, belonging to the field of communication technology. The method includes: constructing a communication system model of downlink rate-splitting multiple access based on an intelligent reflector surface; the communication system model includes a base station, an intelligent reflector surface, and a user terminal; grouping users in the user terminal to construct a layered rate-splitting multiple access model based on user grouping; the layered rate-splitting multiple access model includes a correspondence between user groups and information flows generated by base stations; and assisting downlink communication between base stations and user terminals based on the layered rate-splitting multiple access model and the intelligent reflector surface. The present application constructs a layered rate-splitting multiple access model by grouping users, and combines the passive beamforming of the intelligent reflector surface and the active beamforming of the rate-splitting multiple access to improve system performance. It can cope with the problems of tight spectrum resources and signal interference, and greatly improves spectrum efficiency.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and in particular relates to a method and system for optimizing the total rate of a rate-division multiple access communication network. Background Art

[0002] With the explosive growth of IoT devices, spectrum resources are becoming increasingly scarce, and signal interference between devices is becoming increasingly severe, resulting in low network spectrum efficiency. To address this issue, multiple access technology can be used. Multiple access technology is a key technology in wireless communications. It allows multiple users to share the same communication medium, enabling more efficient use of spectrum resources and increasing network capacity.

[0003] However, under the condition of limited time and frequency resources, traditional multiple access technologies such as frequency division multiple access and time division multiple access may face problems such as limited spectrum resources, uneven time slice allocation, increased access collisions and conflicts, resulting in a serious decline in the spectrum efficiency of multi-user access. Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method and system for optimizing the total rate of a rate-divided multiple access communication network to efficiently utilize spectrum resources and improve the spectrum efficiency of multi-user access.

[0005] In a first aspect, the present application provides a method for optimizing the total rate of a rate-splitting multiple access communication network, comprising:

[0006] Constructing a downlink rate division multiple access communication system model based on an intelligent reflective surface; the communication system model includes a base station, an intelligent reflective surface and a user terminal;

[0007] Grouping users in the user terminal and constructing a layered rate division multiple access model based on the user groups; the layered rate division multiple access model includes a correspondence between user groups and information flows generated by a base station;

[0008] Downlink communication between the base station and the user terminal is assisted according to the layered rate division multiple access model and the smart reflecting surface.

[0009] According to the total rate optimization method of the rate division multiple access communication network of the present application, a layered rate division multiple access model is constructed by grouping users in a downlink rate division multiple access communication system model based on an intelligent reflecting surface. The model significantly improves the communication efficiency between the base station and the user end by designing the correspondence between user groups and information flows, and combines the passive beamforming of the intelligent reflecting surface and the active beamforming of the rate division multiple access to jointly improve the system performance, so that the solution can more efficiently deal with the problems of spectrum resource shortage and signal interference when processing a large number of IoT devices, thereby greatly improving the spectrum efficiency.

[0010] According to one embodiment of the present application, the communication system model includes a link between the base station and the smart reflecting surface, a link between the smart reflecting surface and a user terminal, and a link between the base station and the user terminal.

[0011] In this embodiment, the smart reflecting surface can optimize the signal propagation path by adjusting the phase and amplitude of the reflected signal, thereby enhancing the signal coverage and quality. The direct link between the base station and the user end and the auxiliary link between the smart reflecting surfaces work together to effectively reduce interference, help achieve higher spectrum efficiency, and support more user connections and higher data transmission rates under the same spectrum resources.

[0012] According to one embodiment of the present application, in the layered rate division multiple access model, the base station sends information flow, the information flow including private information streams and public information flows; among them, is the number of users.

[0013] In this embodiment, all users in the user set in the generalized rate division multiple access model can take out part of the information to form a common information flow, so that the generalized rate division multiple access model will form Information flows, and in this embodiment, there is a correspondence between the user groups and the information flows generated by the base station, and the number of information flows is This not only greatly reduces the coding complexity of the base station, but also greatly reduces the decoding complexity of the user.

[0014] According to one embodiment of the present application, in the layered rate division multiple access model, by Indicates the correspondence between information flow and user group; Each element in Is a binary indicator that indicates the flow of information Whether the corresponding user group contains users ,like , then the user Decode the information stream ;in, Indicates the number of information flows.

[0015] In this embodiment, by introducing binary indicators to map the correspondence between information flows and user groups, fine management and dynamic allocation of information flows are achieved. This design allows the system to flexibly adjust resource allocation based on the user's channel conditions and business needs, thereby maximizing spectrum utilization.

[0016] According to one embodiment of the present application, the method further includes:

[0017] When the number of user groups corresponding to the information flow is greater than 1, determining that the information flow is a public information flow;

[0018] When the number of user groups corresponding to the information flow is equal to 1, the information flow is determined to be a private information flow.

[0019] In this embodiment, public information flows and private information flows are distinguished according to the number of user groups corresponding to the information flows, thereby achieving more efficient information transmission and resource management.

[0020] According to one embodiment of the present application, in the layered rate division multiple access model,

[0021] The users in the user terminal determine the decoding order according to the order of each information flow in the information flow set; wherein the order represents the number of users in the user group corresponding to the information flow;

[0022] When decoding each information stream in the information stream set according to the decoding order, the user in the user terminal first decodes the public information stream and then decodes the private information stream by using the string interference elimination technology.

[0023] In this embodiment, the decoding order is determined according to the order of the information stream in a layered rate division multiple access model. During the decoding process, the user decodes the public information stream containing information of multiple users using string interference cancellation technology, and then decodes the private information stream containing only its own information. This strategy not only improves spectrum efficiency, but also enhances the signal-to-interference-and-noise ratio by prioritizing the processing of public information, thereby improving communication reliability.

[0024] According to one embodiment of the present application, grouping users in the user terminal includes:

[0025] Calculate the channel difference matrix of different users based on the channel similarity and channel strength between different users;

[0026] The users of the user terminal are grouped according to the channel difference matrix.

[0027] In this embodiment, by taking into account the differences in channel similarity and channel strength of different users during the user grouping process, the channel characteristics of different users can be optimized so that each user group can be allocated to a resource block suitable for its channel conditions, further optimizing the overall network performance.

[0028] According to one embodiment of the present application, grouping users of the user terminal according to the channel difference matrix includes:

[0029] Initialize the user group; the initialized user group includes a single-user user group, the single-user user group receiving a corresponding private information stream;

[0030] Calculating two users with the largest channel differences among the users of the user terminal according to the channel difference matrix as the multi-user user group corresponding to the first public information flow;

[0031] The following steps are performed cyclically: a new user is added to the multi-user user group corresponding to the previous public information flow so that the channel difference of the multi-user user group corresponding to the next public information flow obtained after the new user is added is maximized, until the number of users in the multi-user user group corresponding to the last public information flow is .

[0032] In this embodiment, by initializing user groups and assigning a private information stream to each user, the two users with the largest channel differences are then selected as the multi-user user group corresponding to the first public information stream, ensuring efficient distribution of information streams among users. The process of adding new users is repeated until the multi-user user group corresponding to the last public information stream includes all users. This grouping method helps the system allocate resources more rationally, allowing each user group to receive information streams under optimal channel conditions, thereby improving spectrum utilization efficiency. Furthermore, users can determine the decoding order based on the order of each information stream in the information stream set, helping users prioritize decoding public information streams containing more user information before decoding private information streams, thereby improving decoding efficiency and accuracy.

[0033] According to one embodiment of the present application, the method further includes:

[0034] Taking maximizing spectrum efficiency in the communication system model as the goal, a first objective function for optimizing the total rate of the communication network is constructed using user grouping, base station active precoding design, rate division multiple access rate, and passive beamforming of the smart reflector as optimization variables;

[0035] Solve the optimization variables in the first objective function to optimize the total communication network rate of the communication system model.

[0036] In this embodiment, a spectrum efficiency maximization optimization problem is constructed by optimizing user grouping in the communication system model, base station active precoding design, rate-splitting multiple access rate allocation, and passive beamforming of smart reflective surfaces. By mapping information flows to user groups, the system intelligently manages interference between users, improves signal quality, and fully utilizes the beamforming capabilities of smart reflective surfaces to optimize signal propagation paths. Furthermore, the base station's active precoding design enables dynamic signal adjustments based on user channel conditions, further improving signal transmission efficiency. This comprehensive optimization approach not only increases the overall network rate but also enhances the system's adaptability to varying network loads and channel conditions, thereby achieving more efficient and reliable data transmission in modern communication networks.

[0037] According to one embodiment of the present application, the first objective function is as follows:

[0038]

[0039] in, represents the first objective function, represents the information flow index, represents the phase shift response matrix of the smart reflector, Indicates the The phase shift of the reflective element, Represents a collection of users, is the number of users, Indicates the correspondence between information flow and user groups. Elements in Is a binary indicator that indicates the flow of information Whether the corresponding user group contains users , represents the number of information flows, Indicates the number of reflective elements of the smart reflective surface, Represents the precoding matrix of the base station for all information streams, and the superscript H represents the conjugate transpose of the matrix. represents the common rate matrix of all users, For users Information In the information flow The corresponding public rate is Representing information flow The corresponding user group, Representing information flow The achievable rate, Represents a user kThe achievable rate, Indicates the maximum transmit power of the base station, Indicates the minimum rate that the user can achieve. represents the trace of the matrix in brackets.

[0040] According to one embodiment of the present application, solving the optimization variables in the objective function includes:

[0041] Converting the objective of maximizing spectrum efficiency in the first objective function into the objective of weighted minimum mean square error to obtain a second objective function;

[0042] The optimization variables in the second objective function are solved by performing alternating optimization iterations on the optimization variables.

[0043] In this embodiment, a new optimization objective function, the second objective function, is constructed by transforming the original spectrum efficiency maximization problem into a weighted minimum mean square error (WMSE) problem. This transformation exploits the relationship between WMSE and signal-to-noise ratio (SNR), transforming the original non-convex optimization problem into a more manageable form. An alternating optimization iteration method is then used to solve the optimization variables in the second objective function. In each iteration, a portion of the optimization variables is fixed while another portion is optimized, and this process continues until convergence. This method is effective in solving complex optimization problems because it decomposes a large, difficult-to-solve problem into a series of smaller, more manageable subproblems. Through this iterative optimization process, the optimal solution to the original spectrum efficiency maximization problem can be gradually approached, thereby achieving efficient utilization of network spectrum resources while ensuring communication quality.

[0044] According to one embodiment of the present application, the second objective function is as follows:

[0045]

[0046] in, represents the second objective function, the equalizer matrix , Represents a user Receiving information flow The equalizer, weight matrix , Representation and Information Flow In the user The weights associated with the mean squared error at , , is a user Information Flow The weighted mean square error of , Represents a user The weighted mean square error of Represents a user The private information flow rate.

[0047] In a second aspect, the present application provides a system for optimizing the total rate of a rate-splitting multiple access communication network, comprising:

[0048] A first building module is used to build a downlink rate division multiple access communication system model based on a smart reflecting surface; the communication system model includes a base station, a smart reflecting surface and a user terminal;

[0049] A second building module is configured to group users in the user terminal and build a layered rate division multiple access model based on the user groups; the layered rate division multiple access model includes a correspondence between user groups and information flows generated by a base station;

[0050] A communication module is used to assist downlink communication between the base station and the user terminal according to the layered rate division multiple access model and the smart reflecting surface.

[0051] According to the rate division multiple access communication network total rate optimization system of the present application, a layered rate division multiple access model is constructed by grouping users in a downlink rate division multiple access communication system model based on an intelligent reflecting surface. This model significantly improves the communication efficiency between the base station and the user end by designing the correspondence between user groups and information flows. It combines the passive beamforming of the intelligent reflecting surface and the active beamforming of the rate division multiple access to jointly improve the system performance, so that the solution can more efficiently deal with the problems of spectrum resource shortage and signal interference when processing a large number of IoT devices, thereby greatly improving the spectrum efficiency.

[0052] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored on the memory and runnable on the processor. When the processor executes the computer program, it implements the method for optimizing the total rate of a rate-divided multiple access communication network as described in the first aspect above.

[0053] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing the total rate of a rate-division multiple access communication network as described in the first aspect above.

[0054] In the fifth aspect, the present application provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the total rate optimization method of the rate-division multiple access communication network as described in the first aspect above.

[0055] In a sixth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method for optimizing the total rate of a rate division multiple access communication network as described in the first aspect above.

[0056] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0057] According to the total rate optimization method of the rate division multiple access communication network of the present application, a layered rate division multiple access model is constructed by grouping users in a downlink rate division multiple access communication system model based on an intelligent reflecting surface. The model significantly improves the communication efficiency between the base station and the user end by designing the correspondence between user groups and information flows, and combines the passive beamforming of the intelligent reflecting surface and the active beamforming of the rate division multiple access to jointly improve the system performance, so that the solution can more efficiently deal with the problems of spectrum resource shortage and signal interference when processing a large number of IoT devices, thereby greatly improving the spectrum efficiency.

[0058] Furthermore, in some embodiments, the smart reflecting surface can optimize the signal propagation path by adjusting the phase and amplitude of the reflected signal, thereby enhancing the signal coverage and quality. The direct link between the base station and the user end and the auxiliary link between the smart reflecting surfaces work together to effectively reduce interference, help achieve higher spectrum efficiency, and support more user connections and higher data transmission rates under the same spectrum resources.

[0059] Furthermore, in some embodiments, all users in the user set in the generalized rate division multiple access model can take out part of the information to form a common information flow, so that the generalized rate division multiple access model will form Information flows, and in this embodiment, there is a correspondence between the user groups and the information flows generated by the base station, and the number of information flows is This not only greatly reduces the coding complexity of the base station, but also greatly reduces the decoding complexity of the user.

[0060] Furthermore, in some embodiments, by introducing binary indicators to map the correspondence between information flows and user groups, fine management and dynamic allocation of information flows are achieved. This design allows the system to flexibly adjust resource allocation according to the user's channel conditions and business needs, thereby maximizing spectrum utilization.

[0061] Furthermore, in some embodiments, public information flows and private information flows are differentiated according to the number of user groups corresponding to the information flows, thereby achieving more efficient information transmission and resource management.

[0062] Furthermore, in some embodiments, the decoding order is determined according to the order of the information stream in a layered rate division multiple access model. During the decoding process, the user decodes the public information stream containing information of multiple users through the string interference cancellation technology, and then decodes the private information stream containing only its own information. This strategy not only improves the spectrum efficiency, but also enhances the signal-to-interference-and-noise ratio of the signal by prioritizing the processing of public information, thereby improving the reliability of communication.

[0063] Furthermore, in some embodiments, by taking into account the differences in channel similarity and channel strength of different users during the user grouping process, the channel characteristics of different users can be optimized so that each user group can be allocated to resource blocks suitable for its channel conditions, further optimizing the overall network performance.

[0064] Furthermore, in some embodiments, by initializing user groups and assigning a private information stream to each user, the two users with the largest channel differences are then selected as the multi-user user group corresponding to the first public information stream, ensuring efficient distribution of information streams among users. By repeatedly adding new users until the multi-user user group corresponding to the last public information stream includes all users, this grouping method helps the system allocate resources more rationally, allowing each user group to receive information streams under optimal channel conditions, thereby improving spectrum utilization efficiency. Furthermore, users can determine the decoding order based on the order of each information stream in the information stream set, helping users prioritize decoding public information streams containing more user information before decoding private information streams, thereby improving decoding efficiency and accuracy.

[0065] Furthermore, in some embodiments, a spectrum efficiency maximization optimization problem is constructed by optimizing user grouping in the communication system model, base station active precoding design rate-splitting multiple access rate allocation, and the passive beamforming of the smart reflector. Through the correspondence between information flows and user groups, the system can intelligently manage interference between users, improve signal quality, and fully utilize the beamforming capabilities of the smart reflector to optimize the signal propagation path. In addition, the base station's active precoding design enables the signal to be dynamically adjusted according to the user's channel conditions, further improving signal transmission efficiency. This comprehensive optimization method not only improves the overall network rate, but also enhances the system's adaptability to different network loads and channel conditions, thereby achieving more efficient and reliable data transmission in modern communication networks.

[0066] Furthermore, in some embodiments, a new optimization objective function, or secondary objective function, is constructed by transforming the original spectrum efficiency maximization problem into a weighted minimum mean square error (WMSE) problem. This transformation exploits the relationship between WMSE and signal-to-noise ratio (SNR), transforming the original non-convex optimization problem into a more manageable form. An alternating optimization iteration method is then used to solve the optimization variables in the secondary objective function. In each iteration, a portion of the optimization variables is fixed while another portion is optimized, alternating until convergence. This method is effective in solving complex optimization problems because it decomposes a large, difficult-to-solve problem into a series of smaller, more manageable subproblems. Through this iterative optimization process, the optimal solution to the original spectrum efficiency maximization problem can be gradually approached, thereby achieving efficient utilization of network spectrum resources while ensuring communication quality.

[0067] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become obvious from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0069] Figure 1 1 is a flow chart of a method for optimizing the total rate of a rate-division multiple access communication network provided in an embodiment of the present application;

[0070] Figure 2 This is a schematic diagram of the architecture of the communication system model provided in the embodiment of the present application;

[0071] Figure 3 Schematic diagram of the architecture of the layered rate division multiple access model provided in an embodiment of the present application;

[0072] Figure 4 This is a schematic diagram of the structure of a rate division multiple access communication network total rate optimization system provided by an embodiment of the present application;

[0073] Figure 5 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0075] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0076] Under the condition of limited time and frequency resources, traditional multiple access technologies such as frequency division multiple access and time division multiple access may face problems such as limited spectrum resources, uneven time slice allocation, increased access collisions and conflicts, resulting in a serious decline in the spectrum efficiency of multi-user access.

[0077] To address at least one of the above technical issues, the present application proposes a method and system for optimizing the total rate of a rate-splitting multiple access communication network. The method and system for optimizing the total rate of a rate-splitting multiple access communication network provided by the present application are described in detail below with reference to the accompanying drawings, using specific embodiments and their application scenarios.

[0078] The method for optimizing the total rate of a rate division multiple access communication network may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0079] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer having a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0080] In the following embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse, and a joystick.

[0081] The embodiment of the present application provides a method for optimizing the total rate of a rate-splitting multiple access communication network. The execution subject of the method can be an electronic device or a functional module or functional entity in the electronic device that can implement the method for optimizing the total rate of a rate-splitting multiple access communication network. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras, and wearable devices. The method for optimizing the total rate of a rate-splitting multiple access communication network provided in the embodiment of the present application is explained below using an electronic device as an example of the execution subject.

[0082] like Figure 1 As shown, the method for optimizing the total rate of a rate-division multiple access communication network includes: step 110, step 120 and step 130.

[0083] Step 110: Construct a communication system model of downlink rate division multiple access based on a smart reflecting surface; the communication system model includes a base station, a smart reflecting surface, and a user terminal.

[0084] Intelligent Reflecting Surface (IRS) is an emerging technology that uses electromagnetic materials and intelligent control mechanisms on a reflective surface to adjust the propagation characteristics of wireless signals. By modifying parameters such as the phase and amplitude of the reflected signal, IRS can enhance signal strength, improve signal quality, and mitigate interference in complex wireless environments.

[0085] In one example, if Figure 2 As shown in Figure 2, the communication system model may include a base station, an IRS, and a user terminal. Base station service downlink of root antenna single-antenna users, IRS consists of The IRS consists of a set of reflectors deployed between the base station and users to assist in downlink communications. Due to severe path loss, signals reflected twice or more by the IRS are ignored. Both the base station's antenna array and the IRS's reflector array follow the uniform linear array (ULA) model.

[0086] In some embodiments, the communication system model includes a link between a base station and a smart reflecting surface, a link between a smart reflecting surface and a user terminal, and a link between a base station and a user terminal.

[0087] In this embodiment, the phase and amplitude of the reflected signal are adjusted by the intelligent reflecting surface, which can optimize the signal propagation path, thereby enhancing the signal coverage and quality. The direct link between the base station and the user end and the auxiliary link between the intelligent reflecting surfaces work together to effectively reduce interference, help achieve higher spectrum efficiency, and support more user connections and higher data transmission rates under the same spectrum resources.

[0088] like Figure 2 As shown, the equivalent channels of the base station and IRS link can be represented by the symbols Indicates that the IRS and users The equivalent channel of the link can be represented by the symbol Indicates that base stations and users k The equivalent channel of the link can be represented by the symbol The superscript H represents the conjugate transpose of the matrix. The IRS is deployed in a location where it can establish a line-of-sight link with the base station and the user. Therefore, all channels obey Rician fading. The equivalent channel between the base station and the IRS can be expressed as:

[0089]

[0090] in, is the Rice factor, For large-scale fading, are the elements of the non-line-of-sight component, obeying the complex Gaussian distribution is the sight distance component, and its expression is:

[0091]

[0092] in, is the departure angle of the signal at the base station, is the arrival angle of the signal at the IRS, and All are ULA responses, and the array response is calculated as:

[0093]

[0094] in, is the distance between adjacent elements of the ULA array, is the signal carrier wavelength, is the angle between the signal and the ULA array. Similarly, the IRS and the user Equivalent channels of links, base stations, and users k The equivalent channels of the link can be expressed as:

[0095]

[0096] Among them, the sight distance component , non-line-of-sight component and Each element of follows a complex Gaussian distribution , is the Rice factor, It is a large-scale fading; For IRS to users The departure angle, From base station to user The phase shift response matrix of IRS is defined as ,in Indicates the The phase shift of each reflective element.

[0097] Step 120: Group users in the user terminal and construct a layered rate division multiple access model based on the user groups; the layered rate division multiple access model includes a correspondence between user groups and information flows generated by the base station.

[0098] Rate-Splitting Multiple Access (RSMA) is a multi-user access technology that allows multiple users to communicate simultaneously at the same time and frequency by dividing the user's transmission rate into different parts. The core concept of RSMA is to divide the channel capacity so that users can obtain different rates to meet different service requirements.

[0099] In the existing generalized RSMA model, it is assumed that is the set of all users, the set of all users Users in any subset of can take out part of the information to form a public information flow, and all users in the subset need to decode the public information flow to obtain the corresponding information. Finally, in the generalized RSMA model, This not only greatly increases the coding complexity of the base station, but also greatly increases the decoding complexity of the user.

[0100] In the embodiment of the present application, a new hierarchical RSMA model based on user grouping is designed, such as Figure 3 As shown, the hierarchical RSMA model includes a correspondence between user groups and information flows generated by base stations. According to the correspondence, the user at the user end can judge the information flow and decide whether to decode the information flow.

[0101] In some embodiments, the hierarchical RSMA model base station sends Information flows include private information streams and public information flows; among them, is the number of users.

[0102] In this embodiment, all users in the user set in the generalized rate division multiple access model can take out part of the information to form a common information flow, so that the generalized rate division multiple access model will form Information flows, and in this embodiment, there is a correspondence between the user groups and the information flows generated by the base station, and the number of information flows is This not only greatly reduces the coding complexity of the base station, but also greatly reduces the decoding complexity of the user.

[0103] In some embodiments, in a layered rate division multiple access model, Indicates the correspondence between information flow and user group; Each element in Is a binary indicator that indicates the flow of information Whether the corresponding user group contains users ,like , then the user Decode the information stream ;in, Indicates the number of information flows.

[0104] Specifically, after determining the user group, the base station transmits with the assistance of IRS Information flow service downlink User. Definition Represents a collection of information flows. Define a To indicate the user group corresponding to the information flow. Each element in Is a binary indicator that indicates the flow of information Whether the corresponding user group contains users ,like , then the user Decode the information stream ;in, Indicates the number of information flows.

[0105] In this embodiment, by introducing binary indicators to map the correspondence between information flows and user groups, fine management and dynamic allocation of information flows are achieved. This design allows the system to flexibly adjust resource allocation based on the user's channel conditions and business needs, thereby maximizing spectrum utilization.

[0106] In some embodiments, the method further comprises:

[0107] When the number of user groups corresponding to the information flow is greater than 1, the information flow is determined to be a public information flow;

[0108] When the number of user groups corresponding to the information flow is equal to 1, the information flow is determined to be a private information flow.

[0109] In this embodiment, the information flow The corresponding user group in can be expressed as , represents a set of users, if ,but For public message streams, user groups All users in the .if ,but It is a private message stream and will only be decoded by one user.

[0110] In this embodiment, public information flows and private information flows are distinguished according to the number of user groups corresponding to the information flows, thereby achieving more efficient information transmission and resource management.

[0111] user The message set after being segmented is represented as Each piece of information is encoded into a different information stream, and the user The set of information streams that need to be decoded can be expressed as . User segmentation information with the same superscript Encoded into the information stream Representing information flow The corresponding user group. Definition The base station's response to all information flows The precoding matrix, the base station's transmitted signal is expressed as:

[0112]

[0113] in, For information flow The corresponding precoding vector, are independent and identically distributed random variables with zero mean and unit variance and satisfy , Indicates the expected value, Represents the identity matrix. After precoding, the signal passes through the base station and the user Direct link and base station-IRS-user The reflection link reaches the user ,user Receive signal The expression is:

[0114]

[0115]

[0116] Step 130: Assist the downlink communication between the base station and the user terminal according to the layered rate segmentation multiple access model and the smart reflector.

[0117] In the embodiments of the present application, the hierarchical RSMA model and IRS can be combined to facilitate communication between users at the base station and the user end. Specifically, during the communication process, the hierarchical RSMA model can be used to segment user information and encode it into the information stream. Furthermore, the IRS can be used to adjust the phase of its reflective unit to achieve beamforming, which helps to concentrate signal energy in the area where the user is located, improve signal quality, and reduce interference to other user groups.

[0118] According to the total rate optimization method of the rate division multiple access communication network of the present application, a layered rate division multiple access model is constructed by grouping users in a downlink rate division multiple access communication system model based on an intelligent reflecting surface. The model significantly improves the communication efficiency between the base station and the user end by designing the correspondence between user groups and information flows, and combines the passive beamforming of the intelligent reflecting surface and the active beamforming of the rate division multiple access to jointly improve the system performance, so that the solution can more efficiently deal with the problems of spectrum resource shortage and signal interference when processing a large number of IoT devices, thereby greatly improving the spectrum efficiency.

[0119] In some embodiments, in a layered rate division multiple access model,

[0120] The users in the user terminal determine the decoding order according to the order of each information flow in the information flow set; wherein the order represents the number of users in the user group corresponding to the information flow;

[0121] When a user at the user end decodes each information stream in the information stream set according to the decoding order, the user first decodes the public information stream and then decodes the private information stream through the string interference elimination technology.

[0122] In this embodiment, in the hierarchical RSMA model, users Need to set Decode all information flows in the system. Before decoding, the decoding order must be determined. The order of the information flow is defined as the number of users in the user group corresponding to the information flow. The order of private information flows is 1, and the order of public information flows is 2 to 1. Not waiting.

[0123] In the hierarchical RSMA model, The order of each public stream is different, so the decoding order can be determined according to the order of the information stream. Define binary indicator Represents a user Information Flow and information flow Successive Interference Cancellation (SIC) operation, if , it means the user Decoding the information flow Previously, SIC operation was required to decode the information flow first Specifically, if the user Information flow needs to be and Decode, and The order is higher than ,Right now ,but and In the user The decoding order at and Similarly, if , the decoding order is .

[0124] It should be noted that SIC operation only exists in user Between the information streams that need to be decoded. If the information stream There are no users in , the user The stream will not be decoded , and the information flow is regarded as an interference signal. Therefore, we can further obtain ,but , given the user group matrix and the corresponding SIC decoding order matrix ,user Receiving rate It can be expressed as:

[0125]

[0126] Among them, when Because the information stream to be decoded cannot exist in the interference term, For information flow To ensure that the public information flow can be successfully decoded by all users in the corresponding user group, the information flow The achievable rate should not exceed the set Decoding stream for users in The minimum receiving rate, therefore, the information flow Achievable rate Expressed as:

[0127]

[0128] definition For users Information In the flow The corresponding public rate in the , the public rate matrix of all users is expressed as , information flow The achievable rate can also be expressed as .user Achievable rate It is given by:

[0129]

[0130] in, Contains users A collection of public information flows of information, For users The private information rate.

[0131] In this embodiment, the decoding order is determined according to the order of the information stream in a layered rate division multiple access model. During the decoding process, the user decodes the public information stream containing information of multiple users using string interference cancellation technology, and then decodes the private information stream containing only its own information. This strategy not only improves spectrum efficiency, but also enhances the signal-to-interference-and-noise ratio by prioritizing the processing of public information, thereby improving communication reliability.

[0132] In some embodiments, grouping users in a client includes:

[0133] Calculate the channel difference matrix of different users based on the channel similarity and channel strength between different users;

[0134] The users at the user end are grouped according to the channel difference matrix.

[0135] Since the public stream signal is a broadcast signal, the signal reception strength of the broadcast signal is determined by the user with the worst channel quality. Furthermore, if large-scale fading cannot be corrected, the more similar the channels of the broadcast users are, the better the precoding design can achieve. Therefore, this embodiment designs a user grouping method based on channel differences.

[0136] Specifically, in this embodiment, the similarity measurement matrix is first defined To express the similarity between different UE (User Equipment) channels, the channel similarity calculation formula is:

[0137]

[0138] in, is a symmetric matrix with all diagonal elements set to 1. From base station to user The channel coefficient between them, and in the energy collection stage , in the signal reflection stage The channel difference matrix of any two users is defined as , which has the following form:

[0139]

[0140] in, The control parameters are used to control the influence of channel similarity and channel strength on the channel difference matrix in the grouping rule. From base station to user The channel strength between. When it increases, the matrix The value of is gradually dominated by the channel similarity. When the channel strength decreases, The impact of the calculation results gradually increases.

[0141] Users at the user end can be grouped according to the channel difference matrix of different users.

[0142] In this embodiment, by taking into account the differences in channel similarity and channel strength of different users during the user grouping process, the channel characteristics of different users can be optimized so that each user group can be allocated to a resource block suitable for its channel conditions, further optimizing the overall network performance.

[0143] In some embodiments, grouping users of a user terminal according to a channel difference matrix includes:

[0144] Initialize user groups; the initialized user groups include A single-user user group receives the corresponding private information flow;

[0145] According to the channel difference matrix, the two users with the largest channel difference among the users at the user end are calculated as the multi-user user group corresponding to the first public information flow;

[0146] The following steps are performed cyclically: a new user is added to the multi-user user group corresponding to the previous public information flow so that the channel difference of the multi-user user group corresponding to the next public information flow obtained after the new user is added is maximized, until the number of users in the multi-user user group corresponding to the last public information flow is .

[0147] Specifically, you can Get the user group corresponding to the first public information flow Next, to the user group Add a new user Get the second user group ,in . And so on, to the user group Add a new user to get the user group , when the user group containing all users is obtained When , the user grouping process ends. The final grouping result is , among which the former Each group is a single user group that receives the corresponding private information flow. Each user group is a multi-user user group that receives corresponding public information flow.

[0148] The detailed user grouping process is shown in Table 1:

[0149] Table 1

[0150]

[0151] In this embodiment, by initializing user groups and assigning a private information stream to each user, the two users with the largest channel differences are then selected as the multi-user user group corresponding to the first public information stream, ensuring efficient distribution of information streams among users. The process of adding new users is repeated until the multi-user user group corresponding to the last public information stream includes all users. This grouping method helps the system allocate resources more rationally, allowing each user group to receive information streams under optimal channel conditions, thereby improving spectrum utilization efficiency. Furthermore, users can determine the decoding order based on the order of each information stream in the information stream set, helping users prioritize decoding public information streams containing more user information before decoding private information streams, thereby improving decoding efficiency and accuracy.

[0152] In some embodiments, the method further comprises:

[0153] With the goal of maximizing spectrum efficiency in the communication system model, the first objective function for optimizing the total rate of the communication network is constructed using the user grouping method, base station active precoding design, rate division multiple access rate, and passive beamforming of the smart reflector as optimization variables.

[0154] The optimization variables in the first objective function are solved to optimize the total communication network rate of the communication system model.

[0155] In this embodiment, since the user grouping method, base station active precoding design, rate division multiple access rate, and passive beamforming of the smart reflector are all factors that affect the spectrum efficiency, under the condition of meeting the basic communication rate requirements of the users, the user grouping method, base station active precoding design, rate division multiple access rate, and passive beamforming of the smart reflector are used as variables to construct the first objective function for optimizing the total rate of the communication network. The first objective function The expression is as follows:

[0156]

[0157] in, represents the first objective function, represents the information flow index, represents the phase shift response matrix of the smart reflector, Indicates the The phase shift of the reflective element, Represents a collection of users, is the number of users, Indicates the correspondence between information flow and user groups. Elements in Is a binary indicator that indicates the flow of information Whether the corresponding user group contains users , represents the number of information flows, Indicates the number of reflective elements of the smart reflective surface, Represents the precoding matrix of the base station for all information streams, and the superscript H represents the conjugate transpose of the matrix. represents the common rate matrix of all users, For users Information In the information flow The corresponding public rate is Representing information flow The corresponding user group, Representing information flow The achievable rate, Represents a user k The achievable rate, Indicates the maximum transmit power of the base station, Indicates the minimum rate that the user can achieve. represents the trace of the matrix in brackets.

[0158] Among them, (11b) is the public rate constraint, (11c) represents the user The minimum receiving rate constraint, (11d) is the binary variable constraint related to user pairing, and (11e) constrains that each user's information is at least in one information flow and at most in There are information flows, (11f) constrains each information flow to be used by at least one user and at most User decoding, (11g) is the phase shift constraint of the IRS reflector, and (11h) ensures that the maximum transmission power of the base station is not greater than .

[0159] In this embodiment, a spectrum efficiency maximization optimization problem is constructed by optimizing user grouping in the communication system model, base station active precoding design, rate-splitting multiple access rate allocation, and passive beamforming of smart reflective surfaces. By mapping information flows to user groups, the system intelligently manages interference between users, improves signal quality, and fully utilizes the beamforming capabilities of smart reflective surfaces to optimize signal propagation paths. Furthermore, the base station's active precoding design enables dynamic signal adjustments based on user channel conditions, further improving signal transmission efficiency. This comprehensive optimization approach not only increases the overall network rate but also enhances the system's adaptability to varying network loads and channel conditions, thereby achieving more efficient and reliable data transmission in modern communication networks.

[0160] In some embodiments, solving for the optimization variable in the objective function includes:

[0161] The second objective function is obtained by converting the spectral efficiency maximization goal in the first objective function into the weighted minimum mean square error goal;

[0162] The optimization variables in the second objective function are solved by performing alternating optimization iterations on the optimization variables.

[0163] In the first objective function, although constraints (11g) and (11h) are convex, the first objective function is neither convex nor concave with respect to the optimization variable. Therefore, the first objective function is an NP-hard (Non-deterministic Polynomial time Hard) non-convex problem. In this embodiment, an efficient iterative solution algorithm is proposed to obtain a high-quality solution to the spectral efficiency maximization problem.

[0164] In this embodiment, the objective of maximizing spectrum efficiency in the first objective function may be transformed into the objective of weighted minimum mean-square error (WMMSE) to obtain the second objective function.

[0165] The specific calculation process is as follows:

[0166] definition Represents a user Receiving information flow Equalizer, information flow The estimated signal The expression is:

[0167]

[0168] The summation in the first row indicates that Previously, the user The decoded information stream is therefore required from Removed; the summation in the second row indicates that the decoded information flow Post-user Still need to continue to decode or decode the information flow; user Decoding information flow Mean Square Error (MSE) The expression is:

[0169]

[0170] in( i )Depend on get, {} means to find the real part in the brackets, the last item Indicates that the user Information Flow and in the flow The power sum of the decoded signal is expressed as:

[0171]

[0172] By solving , the optimal minimum mean square error (MMSE) equalizer The expression is:

[0173]

[0174] Substituting formula (20) into (18), the minimum mean square error The expression is:

[0175]

[0176] in, Therefore, users Receive information flow Signal-to-Interference-plus-Noise Ratio (SINR) The expression is:

[0177]

[0178] Information Flow In the user The receiving rate at The expression can be rewritten as follows:

[0179]

[0180] This formula indicates the relationship between logarithmic rate and MMSM. However, due to the presence of the logarithmic operator, formula (23) still cannot be directly used to solve the problem of maximizing spectrum efficiency. To further solve this problem, this embodiment introduces an enhanced weighted mean square error (WMSE):

[0181]

[0182] in, is a user Information Flow The weighted mean square error of Is with information flow In the user The weight associated with the MSE at . By observing (24) we can see that and The solution is the same. Therefore, (20) is still the optimal MMSE equalizer. Substitute (20) into (23) and let , the optimal MMSE weight The expression is:

[0183]

[0184] Substituting formula (25) and formula (23) into formula (24), the rate-weighted minimum mean square error (Weighted Minimum Mean Square Error, WMMSE) The relational expression is:

[0185]

[0186] Use the rate-WMMSE relationship and define , the information flow in formula (9) Achievable rate The expression can be rewritten as follows:

[0187]

[0188] In addition, according to formulas (26) and (10), , define the user k The weighted mean square error ,in For users By using WMSE for private information flow, we can obtain:

[0189]

[0190] Similarly, the system achievable rate in the energy harvesting phase can also be expressed as follows:

[0191]

[0192] in, Represents the energy harvesting phase , Represents the energy harvesting phase , Represents the energy harvesting phase .

[0193] Therefore, the first objective function of the spectrum efficiency maximization problem can be reformulated as the following second objective function:

[0194]

[0195] in, represents the second objective function, the equalizer matrix , Represents a user Receiving information flow The equalizer, , Representation and Information Flow In the user The weights associated with the mean squared error at , , is a user Information Flow The weighted mean square error of , Represents a user k The weighted mean square error of Represents a user k The private information flow rate.

[0196] The above WMMSE problem is still a non-convex problem. In this embodiment, the non-convex problem can be decomposed into sub-problems, and then each optimization variable is iteratively solved using the alternating optimization framework. By fixing the remaining variables, Optimize them separately. And, for in The algorithm in Table 1 can be used to solve it and determine the user grouping. and It can be solved with a closed-form expression based on formula (20) and formula (25).

[0197] Specifically, when optimizing variables When given, the second objective function It can be simplified as follows:

[0198]

[0199] Among them, the third objective function It is a quadratic constrained quadratic programming (QCQP) problem, which can be solved using the interior point method integrated in CVX (convex optimization) Among them, CVX is a modeling and solving toolkit for convex optimization problems, which uses the interior point method as the solution algorithm.

[0200] When the variable When fixed, the second objective function It can be simplified as follows:

[0201]

[0202] To deal with this problem, this paper first defines three symbols and Then, the user Decoding information flow The MSE can be reformulated as:

[0203]

[0204] in,

[0205]

[0206] By solving , the optimal MMSE equalizer is given by the following formula:

[0207]

[0208] The perfect square formula will be expanded Substituted into formula (33), MSE It can be re-expressed as follows:

[0209]

[0210] in, The expressions are as follows:

[0211]

[0212]

[0213]

[0214] right and Any value of and are all positive definite matrices. In addition, It's about variables Due to the intractable quadratic constraints and phase shift constraints, the fourth objective function This is still a non-convex and non-homogeneous problem. To deal with this problem, first we add Introducing a slack variable , The new form is as follows:

[0215]

[0216] in,

[0217]

[0218] Going further, we can use the properties of matrix trace and get the following formula:

[0219]

[0220] In addition, the definition And apply the Semidefinite Relaxation (SDR) technique to relax The non-convex rank 1 constraint in . Then, can be rewritten as follows:

[0221]

[0222] Fourth objective function can be re-expressed as follows:

[0223]

[0224] Among them, the equalizer matrix and the weight matrix It can be solved in closed form by using formula (20) and formula (25). Note that the function It is a standard convex semidefinite programming problem, which can be solved by convex optimization solvers such as CVX. The relaxation of the constraints in The optimal solution may not satisfy the constraints , in order to get from the function Obtain a solution that satisfies the rank 1 constraint, and then use the eigenvalue decomposition and Gaussian randomization method to recover the solution that satisfies the rank 1 constraint. .

[0225] Based on the above calculation process, this embodiment designs an alternating optimization algorithm to solve all optimization variables in an iterative manner. Detailed information of the alternating optimization algorithm is shown in Table 2.

[0226] Table 2

[0227]

[0228] As the spectral efficiency increases in each iteration, the alternating optimization algorithm is guaranteed to converge, and for a given power constraint, the algorithm is bounded. The convergence of the alternating optimization algorithm in Table 2 is guaranteed by Proposition 1.

[0229] Proposition 1: The third objective function The non-increasing value in the iterative process of the alternating optimization algorithm can ensure the convergence of the alternating optimization algorithm.

[0230] prove:

[0231] definition is the third objective function The feasible solution of is the target value corresponding to the feasible solution. As shown in steps 7 to 10 of the alternating optimization algorithm, if is the fourth objective function The feasible solution of , then it is also the third objective function Therefore, assuming and The third objective function In the The iteration and The solution is updated in the iteration process. Thus, the following inequality can be obtained:

[0232]

[0233] in, It is established because given , according to step 7 in the alternating optimization algorithm, is the third objective function The optimal solution of . It is established because given , is the fourth objective function The optimal solution of , and the third objective function have the same target value, Proposition 1 is proved.

[0234] In this embodiment, a new optimization objective function, the second objective function, is constructed by transforming the original spectrum efficiency maximization problem into a weighted minimum mean square error (WMSE) problem. This transformation exploits the relationship between WMSE and signal-to-noise ratio (SNR), transforming the original non-convex optimization problem into a more manageable form. An alternating optimization iteration method is then used to solve the optimization variables in the second objective function. In each iteration, a portion of the optimization variables is fixed while another portion is optimized, and this process continues until convergence. This method is effective in solving complex optimization problems because it decomposes a large, difficult-to-solve problem into a series of smaller, more manageable subproblems. Through this iterative optimization process, the optimal solution to the original spectrum efficiency maximization problem can be gradually approached, thereby achieving efficient utilization of network spectrum resources while ensuring communication quality.

[0235] The method for optimizing the total rate of a rate-splitting multiple access communication network provided in the embodiments of the present application can be performed by a system for optimizing the total rate of a rate-splitting multiple access communication network. In the embodiments of the present application, the system for optimizing the total rate of a rate-splitting multiple access communication network provided in the embodiments of the present application is described by taking the system for optimizing the total rate of a rate-splitting multiple access communication network as an example.

[0236] An embodiment of the present application also provides a system for optimizing the total rate of a rate division multiple access communication network.

[0237] like Figure 4 As shown, the rate division multiple access communication network total rate optimization system includes:

[0238] The first construction module 410 is used to construct a communication system model of downlink rate division multiple access based on a smart reflecting surface; the communication system model includes a base station, a smart reflecting surface, and a user terminal;

[0239] The second construction module 420 is configured to group users in the user terminal and construct a layered rate division multiple access model based on the user groups; the layered rate division multiple access model includes a correspondence between user groups and information flows generated by the base station;

[0240] The communication module 430 is configured to assist the downlink communication between the base station and the user terminal according to the layered rate segmentation multiple access model and the smart reflector.

[0241] According to the rate division multiple access communication network total rate optimization system of the present application, a layered rate division multiple access model is constructed by grouping users in a downlink rate division multiple access communication system model based on an intelligent reflecting surface. This model significantly improves the communication efficiency between the base station and the user end by designing the correspondence between user groups and information flows. It combines the passive beamforming of the intelligent reflecting surface and the active beamforming of the rate division multiple access to jointly improve the system performance, so that the solution can more efficiently deal with the problems of spectrum resource shortage and signal interference when processing a large number of IoT devices, thereby greatly improving the spectrum efficiency.

[0242] The rate division multiple access communication network total rate optimization system in the embodiments of the present application can be an electronic device or a component of an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal or other device other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an in-vehicle electronic device, a mobile internet device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. It can also be a server, a network attached storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc., and the embodiments of the present application are not specifically limited.

[0243] The rate division multiple access communication network total rate optimization system in the embodiments of the present application can be a device having an operating system. The operating system can be a Microsoft (Windows) operating system, an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0244] In some embodiments, as Figure 5As shown, an embodiment of the present application also provides an electronic device 500, including a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the program is executed by the processor 501, the various processes of the above-mentioned rate-division multiple access communication network total rate optimization method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0245] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0246] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the various processes of the above-mentioned rate-division multiple access communication network total rate optimization method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0247] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0248] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for optimizing the total rate of a rate division multiple access communication network.

[0249] The processor is the processor in the electronic device in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0250] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned rate-division multiple access communication network total rate optimization method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0251] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0252] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0253] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of this application.

[0254] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

[0255] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0256] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for optimizing the total rate of a rate-division multiple access communication network, characterized in that: include: Constructing a downlink rate division multiple access communication system model based on an intelligent reflective surface; the communication system model includes a base station, an intelligent reflective surface and a user terminal; The users in the user terminal are grouped and a layered rate division multiple access model based on user grouping is constructed; the layered rate division multiple access model includes a correspondence between user groups and information flows generated by the base station; in the layered rate division multiple access model, the base station sends 2 K -1 information flow, which includes K private information flows and K -1 public information stream; among them, K is the number of users; in the hierarchical rate division multiple access model, by K × L The matrix α represents the correspondence between information flow and user groups; each element α in the matrix α k,l Is a binary indicator that indicates the flow of information s l Whether the corresponding user group contains users k , if α k,l =1, then the user k Decode the information stream s l ;in, L Indicates the number of information flows; Downlink communication between the base station and the user terminal is assisted according to the layered rate division multiple access model and the smart reflecting surface.

2. The method according to claim 1, characterized in that The communication system model includes a link between the base station and the smart reflecting surface, a link between the smart reflecting surface and a user terminal, and a link between the base station and the user terminal.

3. The method according to claim 1, characterized in that The method further comprises: When the number of user groups corresponding to the information flow is greater than 1, determining that the information flow is a public information flow; When the number of user groups corresponding to the information flow is equal to 1, the information flow is determined to be a private information flow.

4. The method according to claim 1, wherein In the layered rate division multiple access model, The users in the user terminal determine the decoding order according to the order of each information flow in the information flow set; wherein the order represents the number of users in the user group corresponding to the information flow; When decoding each information stream in the information stream set according to the decoding order, the user in the user terminal first decodes the public information stream and then decodes the private information stream by using the string interference elimination technology.

5. The method according to claim 1, wherein The grouping of users in the user terminal includes: Calculate the channel difference matrix of different users based on the channel similarity and channel strength between different users; The users of the user terminal are grouped according to the channel difference matrix.

6. The method according to claim 5, characterized in that The grouping of users of the user terminal according to the channel difference matrix includes: Initialize the user group; the initialized user group includes K a single-user user group, the single-user user group receiving a corresponding private information stream; Calculating two users with the largest channel differences among the users of the user terminal according to the channel difference matrix as the multi-user user group corresponding to the first public information flow; The following steps are performed cyclically: a new user is added to the multi-user user group corresponding to the previous public information flow so that the channel difference of the multi-user user group corresponding to the next public information flow obtained after the new user is added is maximized, until the number of users in the multi-user user group corresponding to the last public information flow is K .

7. The method according to claim 1, characterized in that The method further comprises: Taking maximizing spectrum efficiency in the communication system model as the goal, a first objective function for optimizing the total rate of the communication network is constructed using user grouping, base station active precoding design, rate division multiple access rate, and passive beamforming of the smart reflector as optimization variables; Solve the optimization variables in the first objective function to optimize the total communication network rate of the communication system model.

8. The method according to claim 7, characterized in that The first objective function is as follows:

9. The method according to claim 7 or 8, characterized in that Solving the optimization variables in the objective function includes: Converting the objective of maximizing spectrum efficiency in the first objective function into the objective of weighted minimum mean square error to obtain a second objective function; The optimization variables in the second objective function are solved by performing alternating optimization iterations on the optimization variables.

10. The method according to claim 9, characterized in that The second objective function is as follows:

11. A rate division multiple access communication network total rate optimization system, characterized in that: include: A first building module is used to build a downlink rate division multiple access communication system model based on a smart reflecting surface; the communication system model includes a base station, a smart reflecting surface and a user terminal; The second construction module is used to group the users in the user terminal and construct a layered rate division multiple access model based on the user grouping; the layered rate division multiple access model includes a correspondence between the user group and the information flow generated by the base station; in the layered rate division multiple access model, the base station sends 2 K -1 information flow, which includes K private information flows and K -1 public information stream; among them, K is the number of users; in the hierarchical rate division multiple access model, by K × L The matrix α represents the correspondence between information flow and user groups; each element α in the matrix α k,l Is a binary indicator that indicates the flow of information s l Whether the corresponding user group contains users k , if α k,l =1, then the user k Decode the information stream s l ;in, L Indicates the number of information flows; A communication module is used to assist downlink communication between the base station and the user terminal according to the layered rate division multiple access model and the smart reflective surface.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 10 is implemented.

13. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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