RIS-based Communication Transmission Optimization Method, System, Storage Medium and Device

Abstract

The present invention relates to the field of wireless communication technologies, and discloses a communication transmission optimization method based on RIS. The method is characterized in that the method includes: initializing a wireless edge caching system, where the wireless edge caching system includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and a plurality of user devices; the intelligent controller receives information requests of the plurality of user devices, classifies the information requests, and determines information transmission nodes; before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS; during information transmission, an optimization objective function based on maximizing the achievable sum rate of transmission is constructed; the optimization objective function based on maximizing the achievable sum rate of transmission is solved to obtain optimal parameters; and communication transmission is performed based on the optimal parameters. The present invention can maximize the achievable sum rate of the system on the premise of satisfying the quality of service of user devices.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to a communication transmission optimization method, system, storage medium, and device based on RIS. Background Art

[0002] With the rapid development of the mobile Internet and the Internet of Things, wireless communication networks are facing increasing traffic pressure. Traditional wireless communication technologies, such as orthogonal frequency division multiplexing (OFDM) and single input single output (SISO) technologies, are no longer able to meet the requirements of future wireless communication networks for high capacity and high speed. To address this issue, researchers have proposed various new technologies, such as multiple input multiple output (MIMO) technology, non-orthogonal multiple access (NOMA) technology, and reconfigurable intelligent surface (RIS) technology.

[0003] RIS technology is a new type of wireless communication technology that can dynamically adjust the propagation direction and intensity of wireless signals by using programmable reflection units, thereby improving the coverage and signal quality of wireless communication networks. RIS technology has the advantages of low cost, low power consumption, and easy deployment, and is considered one of the key technologies for future wireless communication networks.

[0004] With the rapid development of the Internet of Things, narrowband Internet of Things has put forward higher requirements for the performance and resource management of edge caching systems. How to reasonably utilize the storage resources of edge nodes, reduce the traffic burden on the backhaul link, and maximize the achievable sum rate of the system while meeting the quality of service (QoS) of user equipment has become an urgent problem to be solved. Summary of the Invention

[0005] Based on this, in view of the above problems, it is necessary to propose a communication transmission optimization method based on RIS.

[0006] A communication transmission optimization method based on RIS, the method comprising the following steps:

[0007] Initialize a wireless edge caching system, the wireless edge caching system including a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and a plurality of user equipments;

[0008] The intelligent controller receives information requests from the plurality of user equipments, classifies the information requests, and determines information transmission nodes;

[0009] Before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS;

[0010] When transmitting information, an optimization objective function based on maximizing the achievable sum rate of transmission is constructed;

[0011] Solve the optimization objective function based on maximizing the achievable sum rate of transmission to obtain the optimal parameters;

[0012] Perform communication transmission based on the optimal parameters.

[0013] In the above solution, the intelligent controller receives the information requests of the several user devices, classifies the information requests, and determines the information transmission node. After that, the method further includes:

[0014] Based on the information requests of the several user devices, the intelligent controller transmits the information requests in different ways; among them, before transmitting the information requests in different ways, it is determined whether the information transmission node is the base station BS or the intelligent controller.

[0015] In the above solution, based on the information requests of the several user devices, the intelligent controller transmits the information requests in different ways, which specifically includes:

[0016] If the file requested by the user device is in the local cache of the intelligent controller, the intelligent controller uses non-orthogonal multiple access to transmit the file requested by the user device;

[0017] If the file requested by the user device is in the server, the intelligent controller uses the RIS to transmit the file requested by the user device through the base station BS beamforming vector.

[0018] In the above solution, before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS, which specifically includes:

[0019] The intelligent controller performs energy harvesting through the reconfigurable intelligent surface RIS, and the harvested energy is used for information transmission and local file caching;

[0020] The popularity of local files is characterized by the Zipf distribution;

[0021] The probability of caching the request for the

[0022]

[0023] Among them, is the skewness parameter, is the probability of caching the request for the th local file, is the number of files, F is the total number of files, This is the file serial number.

[0024] In the above solution, the caching power of the intelligent controller for caching the local file is:

[0025]

[0026] Wherein, is the caching policy, is the file scale, is the energy consumed for caching a file of unit scale, is the caching power, F is the total number of files, is the file serial number;

[0027] Preset the power received by the reconfigurable intelligent surface RIS as , according to the non-linear energy modeling, the harvesting power of the energy collected by the intelligent controller is:

[0028]

[0029] Wherein, is the caching power, is a function based on the power ;

[0030] The transmission power of the intelligent controller for transmitting information files is:

[0031]

[0032] Wherein, is the harvesting power, is the caching power, is a function based on the power ; is the caching policy, is the file scale, is the energy consumed for caching a file of unit scale, F is the total number of files, is the file serial number, is the transmission power of the intelligent controller for transmitting information files.

[0033] In the above solution, during information transmission, an optimization objective function based on maximizing the transmission achievable sum rate is constructed, specifically including:

[0034] Determine the rate of information transmission of the k-th user equipment according to the following expression:

[0035]

[0036]

[0037]

[0038]

[0039] Among them, is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS, is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access, and are the communication bandwidths allocated to the base station BS and the intelligent controller for non-orthogonal multiple access transmission respectively, is the user equipment to the channel gain of the base station BS, is the channel gain from the base station BS to the reconfigurable intelligent surface RIS, is the channel gain from the reconfigurable intelligent surface RIS to the second user equipment ; is the phase shift matrix of the reconfigurable intelligent surface RIS, is the power allocation factor assigned by the intelligent controller to the second user equipment ; is the variance of Gaussian white noise, is the first user equipment, is the second user equipment, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set, is the conjugate transpose of the channel gain matrix from the first user equipment to the base station BS, is the base station BS beamforming vector of the first user equipment , is the base station BS beamforming vector of the user equipment ; is the index of the summation symbol, is the power allocation factor assigned by the intelligent controller to the user equipment with index l, is the transmission power of the intelligent controller information transmission file;

[0040] Determine the optimization objective function according to the rate information of all user equipment for information transmission:

[0041]

[0042] Among them, is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS, is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access, To optimize the objective function, is the maximum value of the first set of user equipment, is the maximum value of the second set of user equipment.

[0043] In the above solution, solving the optimization objective function based on maximizing the transmission achievable sum rate to obtain the optimal parameters specifically includes:

[0044] Using fractional programming FP and quadratic transformation QT to transform the optimization objective function, and converting the optimization objective function into several sub-problems;

[0045] Solving the several sub-problems respectively through an alternating iteration algorithm to obtain the optimal parameters; the optimal parameters include the base station BS beamforming vector, the phase shift matrix of the reconfigurable intelligent surface RIS, and the NOMA power allocation factor.

[0046] This application also proposes a communication transmission optimization system based on RIS, and this system includes:

[0047] A wireless edge cache unit, an information request processing unit, a radio frequency signal energy harvesting unit, an optimization objective function establishment unit, and a communication transmission unit;

[0048] The wireless edge cache unit includes:

[0049] A base station BS for providing wireless communication services;

[0050] A server for storing and providing data content;

[0051] A reconfigurable intelligent surface RIS for dynamically adjusting the wireless propagation environment;

[0052] An intelligent controller for processing system operations and communication processes;

[0053] Several user equipment for initiating information requests and receiving communication transmissions;

[0054] The information request processing unit is used to receive information requests from several user equipment and classify these information requests to determine information transmission nodes;

[0055] The radio frequency signal energy harvesting unit is used to harvest radio frequency signal energy from the base station BS through RIS before information transmission; the optimization objective function establishment unit is used to construct an optimization objective function based on maximizing the transmission achievable sum rate during information transmission; and is used to solve the optimization objective function based on maximizing the transmission achievable sum rate to obtain the optimal parameters; the communication transmission module is used to perform communication transmission based on the obtained optimal parameters.

[0056] The present application also provides a readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0057] Initialize a wireless edge caching system, which includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and a plurality of user devices;

[0058] The intelligent controller receives information requests from the plurality of user devices, classifies the information requests, and determines information transmission nodes;

[0059] Before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS;

[0060] During information transmission, construct an optimization objective function based on maximizing the achievable transmission sum rate;

[0061] Solve the optimization objective function based on maximizing the achievable transmission sum rate to obtain optimal parameters;

[0062] Perform communication transmission based on the optimal parameters.

[0063] The present application also provides a computer device including a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor to perform the following steps:

[0064] Initialize a wireless edge caching system, which includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and a plurality of user devices;

[0065] The intelligent controller receives information requests from the plurality of user devices, classifies the information requests, and determines information transmission nodes;

[0066] Before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS;

[0067] During information transmission, construct an optimization objective function based on maximizing the achievable transmission sum rate;

[0068] Solve the optimization objective function based on maximizing the achievable transmission sum rate to obtain optimal parameters;

[0069] Perform communication transmission based on the optimal parameters.

[0070] Adopting the embodiment of the present invention has the following beneficial effects: First, initialize a wireless edge caching system including a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and several user devices, so that the intelligent controller can receive and classify information requests from user devices to determine transmission nodes. Before information transmission, utilize the reconfigurable intelligent surface RIS to collect the radio frequency signal energy of the base station BS to reserve energy for subsequent transmission. When information is transmitted, construct an optimization objective function based on maximizing the transmission achievable sum rate and solve it to obtain optimal parameters, and finally perform communication transmission according to these optimal parameters, thereby effectively improving the efficiency of communication transmission, enhancing transmission reachability, and increasing the sum rate, achieving the optimization of communication transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0072] Wherein:

[0073] Figure 1 It is a schematic flow chart of a communication transmission optimization method based on RIS in an embodiment;

[0074] Figure 2 It is a schematic diagram of a wireless edge caching system in an embodiment;

[0075] Figure 3 It is a schematic simulation diagram based on the number of iterations and the objective function (optimization objective function) in an embodiment;

[0076] Figure 4 It is a schematic simulation diagram based on the system achievable sum rate in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0078] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention; however, it will be apparent to one skilled in the art that the present invention may be practiced without one or more of these details; in other instances, some well-known technical features are not described in order to avoid obscuring the present invention. It should be understood that the present invention can be implemented in different forms and should not be construed as limited to the embodiments set forth herein.

[0079] To thoroughly understand the present invention, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present invention; alternative embodiments of the present invention are described in detail below. However, in addition to these detailed descriptions, the present invention may have other implementations.

[0080] As Figure 1 shown, in one embodiment, a RIS-based communication transmission optimization method is provided. The RIS-based communication transmission optimization method includes steps S101 to S106, which are described in detail as follows:

[0081] S101. Initialize the wireless edge caching system. The wireless edge caching system includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and several user devices.

[0082] As Figure 2 shown in a schematic diagram of a wireless edge caching system, it includes a base station BS with M antennas, a single-antenna intelligent controller with caching and energy harvesting functions, K single-antenna user devices, and a reconfigurable intelligent surface RIS equipped with N passive reflection elements. The base station BS does not have caching capabilities and can be connected to the server through a backhaul link. Multiple user devices are evenly distributed in the considered network. The reconfigurable intelligent surface RIS is located between the base station BS and the user devices to facilitate communication. The user devices cannot communicate directly with the base station BS and must rely on the reconfigurable intelligent surface RIS for assisted communication. The reconfigurable intelligent surface RIS is equipped with an intelligent controller with energy harvesting and limited caching capabilities.

[0083] Among them, the base station BS serves as the core communication node and is responsible for connecting to a wider network; the server can store a large amount of data information and provide data support for user devices; the reconfigurable intelligent surface RIS can flexibly adjust the electromagnetic environment and enhance the signal transmission effect; the intelligent controller serves as the brain of the system and coordinates and manages the entire system; multiple user devices are the demand sides of communication. The initialization of this system makes it possible for all parts to work together and lays a foundation for subsequent communication optimization.

[0084] S102. The intelligent controller receives information requests from several user devices, classifies the information requests, and determines information transmission nodes;

[0085] The reception and classification of user device requests by the intelligent controller can achieve targeted processing of different types of requests. By determining the information transmission nodes, unnecessary transmission paths can be avoided, transmission delays and interference can be reduced, and the accuracy and efficiency of transmission can be improved. For example, for urgent information requests with high real-time requirements, high-efficiency transmission nodes can be preferentially allocated for transmission.

[0086] In some embodiments, the intelligent controller receives information requests from several user devices, classifies the information requests, and determines information transmission nodes. After that, the method further includes:

[0087] Based on the information requests of several user devices, the intelligent controller transmits the information requests in different ways; among them, before transmitting the information requests in different ways, it is judged whether the information transmission node is the base station BS or the intelligent controller.

[0088] In some embodiments, based on the information requests of several user devices, the intelligent controller transmits the information requests in different ways, specifically including:

[0089] If the file requested by the user device is in the local cache of the intelligent controller, the intelligent controller uses non-orthogonal multiple access to transmit the file requested by the user device;

[0090] If the file requested by the user device is in the server, the intelligent controller uses the base station BS beamforming vector through the RIS to transmit the file requested by the user device.

[0091] To avoid inter-layer interference, the spectra occupied by the base station BS and the intelligent controller are orthogonal to each other.

[0092] S103. Before information transmission, the intelligent controller collects the radio frequency signal energy from the base station BS through the reconfigurable intelligent surface (RIS);

[0093] The reconfigurable intelligent surface (RIS) collects the radio frequency signal energy of the base station BS. On the one hand, it can provide additional energy support for subsequent information transmission, reduce the energy consumption of the device, and extend the service life of the device; on the other hand, by collecting and utilizing this energy, the signal propagation environment can be improved, the signal strength can be enhanced, and the signal coverage and transmission quality can be improved.

[0094] In some embodiments, before information transmission, the intelligent controller collects the radio frequency signal energy from the base station BS through the reconfigurable intelligent surface (RIS), specifically including:

[0095] The intelligent controller performs energy harvesting through a reconfigurable intelligent surface (RIS), and the harvested energy is used for information transmission and local file caching;

[0096] The popularity of local files is characterized by the Zipf distribution;

[0097] The probability of caching the request for the

[0098] n-th local file is:

[0099] where is the skewness parameter, is the probability of caching the request for the n-th local file, is the number of files, F is the total number of files, is the file serial number.

[0100] The larger the value of

[0101] is, the more concentrated the video requests of the user equipment are on popular video files.

[0102] In some embodiments, the caching power of the intelligent controller for caching local files is:

[0103] where is the caching policy, is the file scale, is the energy consumed for caching a unit-scale file, is the caching power, F is the total number of files, is the file serial number;

[0104] The preset power received by the reconfigurable intelligent surface (RIS) is , and according to the non-linear energy modeling, the harvesting power of the energy harvested by the intelligent controller is:

[0105]

[0106] where is the caching power, is a function based on the power ;

[0107] The transmission power of the intelligent controller for transmitting information files is:

[0108] (3)

[0109] where is the harvesting power, is the caching power, Based on power The function of For the cache strategy, is the file size, is the energy consumed by caching unit-scale files, F is the total number of files, is the file number, Transmission power for intelligent controller information transmission files.

[0110] S104, during information transmission, constructing an optimization objective function based on maximizing transmission reachability and rate;

[0111] With the goal of maximizing transmission reachability and rate, it can comprehensively consider the transmission requirements of multiple user devices in the system, and optimize the transmission efficiency of the entire system while ensuring that each user device can obtain a certain transmission rate. This helps to make full use of system resources, improve system throughput and performance, and meet the user equipment's needs for high-speed and stable communication.

[0112] S105, solving the optimization objective function based on maximizing transmission reachability and rate to obtain optimal parameters;

[0113] Solving the optimization objective function to obtain the optimal parameters is the key to achieving communication transmission optimization. These parameters can guide the configuration of the reconfigurable intelligent surface RIS, the allocation of transmission power, the selection of transmission paths, etc., so that the system can perform communication transmission in the best way in actual operation, thereby maximizing the transmission reachability and rate and improving the communication quality.

[0114] In some embodiments, during information transmission, an optimization objective function based on maximizing transmission reachability and rate is constructed, specifically including:

[0115] The information transmission rate of the kth user equipment is determined according to the following expression:

[0116] (4)

[0117] (5)

[0118]

[0119]

[0120] in, is the rate at which the kth user equipment transmits information through the reconfigurable smart surface RIS using the base station BS beamforming vector, is the rate at which the kth user equipment uses non-orthogonal multiple access to transmit information, and are the communication bandwidths allocated to the base station BS and the intelligent controller for non-orthogonal multiple access transmission, respectively. is the first user equipment channel gain from the first user equipment to the base station BS is the channel gain from the base station BS to the reconfigurable intelligent surface RIS is the channel gain from the reconfigurable intelligent surface RIS to the second user equipment is the phase shift matrix of the reconfigurable intelligent surface RIS is the power allocation factor allocated by the intelligent controller to the second user equipment is the variance of Gaussian white noise is the first user equipment is the second user equipment is the maximum value of the first user equipment set is the maximum value of the second user equipment set is the first user equipment conjugate transpose of the channel gain matrix from the first user equipment to the base station BS is the first user equipment beamforming vector of the base station BS for the first user equipment is the user equipment beamforming vector of the base station BS for the user equipment is the index of the summation symbol is the power allocation factor allocated by the intelligent controller to the user equipment with index l is the transmission power of the intelligent controller information transmission file;

[0121] Determine the optimization objective function according to the rate information of information transmission of all user equipment:

[0122] (6)

[0123] where is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access is the optimization objective function is the first user equipment is the second user equipment is the maximum value of the first user equipment set is the maximum value of the second user equipment set.

[0124] S106. Perform communication transmission based on the optimal parameters.

[0125] ​​Performing communication transmission according to the obtained optimal parameters can ensure that the communication system reaches an optimal state in terms of resource allocation, signal propagation, etc. This can reduce signal interference and loss, improve the reliability and stability of transmission, reduce communication costs, and provide high-quality communication services for user equipment.

[0126] In some embodiments, solving the optimization objective function based on maximizing the achievable sum rate of transmission to obtain the optimal parameters specifically includes:

[0127] Using fractional programming FP and quadratic transformation QT to transform the optimization objective function, and converting the optimization objective function into several sub-problems;

[0128] Solving the several sub-problems respectively through an alternating iteration algorithm to obtain the optimal parameters; the optimal parameters include the base station BS beamforming vector, the phase shift matrix of the reconfigurable intelligent surface RIS, and the NOMA power allocation factor.

[0129] In some embodiments, using fractional programming FP and quadratic transformation QT to transform the optimization objective function, and converting the optimization objective function into several sub-problems, including:

[0130] Calculating the rate of each user equipment transmission according to the Shannon formula and adding these rates. On the premise of satisfying the quality of service QoS constraints of the user equipment, maximizing the achievable rate of the user equipment, that is, the optimization objective function is as follows:

[0131] (7)

[0132] Wherein, is the phase shift matrix constraint of the reconfigurable intelligent surface RIS; gives the constraint of the non-orthogonal multiple access transmission power allocation coefficient; is the maximum power constraint of the base station BS transmitting the base station BS beamforming vector; and are the user equipment QoS constraints of the first user equipment set and the second user equipment set respectively, and are the user equipment constraints of the first user equipment set and the second user equipment set respectively, is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS, is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set, is the first user equipment, is the second user equipment.

[0133] Due to the difficulty in solving the non - convexity of the optimization objective function, the present invention first uses the FP and QT methods to process the optimization objective function:

[0134] The objective function after being transformed by fractional programming FP is as follows:

[0135] (8)

[0136] (9)

[0137] Wherein, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set, is the first user equipment, is the second user equipment, and are the first auxiliary variable and the second auxiliary variable respectively, is the channel gain matrix of user equipment k, is the base station BS beamforming vector of user equipment , is the base station BS beamforming vector of user equipment , is the index of the summation symbol, is the product of the power allocation factor of user equipment k and the transmission power of the intelligent controller, is the product of the power allocation factor of user equipment and the transmission power of the intelligent controller, is the channel parameter of user equipment ;

[0138] The above - introduced first auxiliary variable and second auxiliary variable are used to reconstruct the log(·) term in the Shannon formula based on Lagrange.

[0139] Specifically, the present invention defines , and , wherein, is the channel gain matrix of user equipment k, is the channel gain from user equipment to base station BS, is the channel gain from base station BS to the reconfigurable intelligent surface RIS, is the phase - shift matrix of the reconfigurable intelligent surface RIS, is the product of the power allocation factor of user equipment and the transmission power of the intelligent controller, The power allocation factor assigned by the intelligent controller to the user equipment with index l The transmission power of the intelligent controller information transmission file The product of the power allocation factor of user equipment k and the transmission power of the intelligent controller

[0140] When the first auxiliary variable and the second auxiliary variable have the following optimal values, the optimization objective function given in formula (7) is equivalent to the sum of formulas (8) and (9):

[0141] (10)

[0142] (11)

[0143] where is the channel gain matrix of user equipment k is the base station BS beamforming vector of user equipment is the base station BS beamforming vector of user equipment is the index of the summation symbol is the variance of Gaussian white noise is the maximum value of the first user equipment set is the maximum value of the second user equipment set, and k is the serial number of the user equipment is the optimized first auxiliary variable is the optimized second auxiliary variable is the product of the power allocation factor of user equipment k and the transmission power of the intelligent controller is the power allocation factor of user equipment and the product of the transmission power of the intelligent controller is the power allocation factor of user equipment channel parameter

[0144] Continue to use the quadratic transformation QT to transform it, and the optimized objective function after transformation is as follows:

[0145] (12)

[0146] (13)

[0147] where and are the first auxiliary variable and the second auxiliary variable respectively and are the third auxiliary variable and the fourth auxiliary variable respectively is the channel gain matrix of user equipment k​​ is the base station BS beamforming vector of the user equipment ; is the base station BS beamforming vector of the user equipment ; is the index of the summation symbol is the variance of the Gaussian white noise is the maximum value of the first user equipment set is the maximum value of the second user equipment set, and k is the serial number of the user equipment is the optimized first auxiliary variable is the optimized second auxiliary variable is the product of the power allocation factor of user equipment k and the transmission power of the intelligent controller is the user equipment ; the product of the power allocation factor and the transmission power of the intelligent controller is the user equipment ; the channel parameter

[0148] Introduce the third auxiliary variable and the fourth auxiliary variable . When the two auxiliary variables have the following optimal values, the objective function given in formula (7) is equivalent to the sum of formulas (12) and (13):

[0149] (14)

[0150] (15)

[0151] where is the optimized third auxiliary variable is the optimized fourth auxiliary variable and are the first auxiliary variable and the second auxiliary variable respectively is the channel gain matrix of user equipment k is the base station BS beamforming vector of the user equipment ; is the base station BS beamforming vector of the user equipment ; is the index of the summation symbol is the variance of the Gaussian white noise is the maximum value of the first user equipment set is the maximum value of the second user equipment set, and k is the serial number of the user equipment is the product of the power allocation factor of user equipment k and the transmission power of the intelligent controller is the user equipment ; the product of the power allocation factor and the transmission power of the intelligent controller For the user equipment channel parameters.

[0152] After transformation by fractional programming FP and quadratic transformation QT, the transformed optimized objective function is shown in Equation (16).

[0153] Since there are variables in the newly formulated optimized objective function of Equation (16), the phase shift matrix of the reconfigurable intelligent surface RIS and the product of the power allocation factor of user equipment k and the transmission power of the intelligent controller

[0154] are coupled, and the present invention proposes to use an algorithm based on alternating iteration to solve this problem.

[0155] (16)

[0156] where B1 is the bandwidth of the base station BS, B2 is the bandwidth of the intelligent controller, is the constraint of the phase shift matrix of the reconfigurable intelligent surface RIS; gives the constraint of the non-orthogonal multiple access transmission power allocation coefficient; is the maximum power constraint for the base station BS to transmit the base station BS beamforming vector; and are the user equipment QoS constraints of the first user equipment set and the second user equipment set respectively, and are the user equipment constraints of the first user equipment set and the second user equipment set respectively, and are the i th first auxiliary variable and the i th second auxiliary variable respectively, and are the th third auxiliary variable and the th fourth auxiliary variable respectively, is the base station BS beamforming vector of the user equipment , is the index of the summation symbol, is the channel gain matrix of user equipment i, is the base station BS beamforming vector of user equipment i, is the product of the power allocation factor of the user equipment and the transmission power of the intelligent controller, is the user equipment channel parameter, is the user equipment The product of the power allocation factor and the transmission power of the intelligent controller.

[0157] Separate the three coupled variables using the alternating iteration algorithm for formula (16), and the specific steps are as follows.

[0158]

[0159]

[0160]

[0161] Among them, is the phase shift matrix of the reconfigurable intelligent surface RIS (the first coupled variable), and are the user equipment constraints of the first user equipment set and the second user equipment set respectively, is the parameter in the phase shift matrix of the reconfigurable intelligent surface RIS, is the second coupled variable and is the third coupled variable, is the channel gain from the base station BS to the reconfigurable intelligent surface RIS, is the first user equipment the conjugate transpose of the channel gain matrix to the base station BS, is the base station BS beamforming vector of the first user equipment , is the user equipment the base station BS beamforming vector of, is the variance of Gaussian white noise, is the power allocation factor assigned by the intelligent controller to the second user equipment , is the reconfigurable intelligent surface RIS to the second user equipment the channel gain of, is the transmission power of the intelligent controller information transmission file.

[0162] Step (1) gives the phase shift matrix of the reconfigurable intelligent surface RIS and the NOMA power allocation factor , and optimally solve the base station BS beamforming vector of the base station BS.

[0163] This sub-problem is expressed as:

[0164] (17)

[0165] Among them, B1 is the bandwidth of the base station BS, and are respectively thei a first auxiliary variable and the i th second auxiliary variable, is the channel gain matrix of user equipment i, is the base station BS beamforming vector of user equipment i, is for user equipment 's base station BS beamforming vector, is the index of the summation symbol, is the base station BS beamforming vector of base station BS, is for user equipment 's base station beamforming vector, is the channel matrix parameter between the user equipment and the base station through the RIS, is the variance of the Gaussian white noise, is the QoS constraint of the user equipment in the first user equipment set, is the first user equipment, is the second user equipment, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set.

[0166] In this transformed sub-problem, since the second constraint condition cannot be directly solved by a convex optimization solver. Introduce the first slack variable and the second slack variable to reconstruct the second constraint, and the specific formula reconstruction is as follows:

[0167] (18)

[0168] (19)

[0169] (20)

[0170] Among them, is the first slack variable, is the second slack variable, is the QoS constraint of the user equipment in the first user equipment set, is for user equipment 's base station beamforming vector, is the channel matrix parameter between the user equipment and the base station through the RIS, is for user equipment 's base station BS beamforming vector, is the index of the summation symbol, is the variance of the Gaussian white noise, is the first user equipment, is the second user equipment, is the maximum value of the first set of user equipment, is the maximum value of the second set of user equipment.

[0171] Since formula (19) is still non-convex, it is transformed into , and after Taylor expansion, formula (19) is transformed into:

[0172] (21)

[0173] where, and are the optimal values of the first relaxation variable and the second relaxation variable obtained from the previous iteration, is the base station beamforming vector of user equipment , is the channel matrix parameter of the user equipment and the base station through the RIS. Thus, the sub-problem of step (1) can be solved by a convex optimization solver.

[0174] The base station BS beamforming vector and the optimal NOMA power allocation factor given in step (2) are used to optimize and solve the phase shift matrix of the reconfigurable intelligent surface RIS.

[0175] This sub-problem is expressed as:

[0176] (22)

[0177] where, B1 is the bandwidth of the base station BS, and are the i th first auxiliary variable and the i th second auxiliary variable respectively, is the first user equipment, is the second user equipment, is the maximum value of the first set of user equipment, is the maximum value of the second set of user equipment, is the phase shift matrix of the reconfigurable intelligent surface RIS, is the channel gain from the base station BS to the reconfigurable intelligent surface RIS, is the channel gain from user equipment i to the base station BS, is the optimal base station BS beamforming vector, is the optimal base station BS beamforming vector of the first user equipment , is the phase shift matrix constraint of the reconfigurable intelligent surface RIS, The QoS constraints of the user equipment for the first set of user equipment

[0178] The problem is transformed through the equation . And the second constraint condition is added to the objective function in the form of a penalty function to ensure that the sub-problem only has the first constraint condition. In this way, the sub-problem can be solved using the Riemannian manifold algorithm. The transformed form of the sub-problem is as follows:

[0179] (23)

[0180] Among them, the relevant parameters are defined as follows:

[0181] (24)

[0182] (25)

[0183] (26)

[0184] (27)

[0185] (28)

[0186] Thus, the sub-problem in step (2) can be solved using the method of Riemannian manifold.

[0187] The phase shift matrix of the reconfigurable intelligent surface RIS given in step (3) and the base station BS beamforming vector of the base station BS are used to optimize and solve the NOMA power allocation factor

[0188] This sub-problem is expressed as:

[0189] (29)

[0190] Among them, B2 is the bandwidth of the intelligent controller, and are respectively the -th third auxiliary variable and the -th fourth auxiliary variable, is the channel parameter of the user equipment , is the product of the power allocation factor of the user equipment and the transmission power of the intelligent controller, is the transmission power of the transmission file of the intelligent controller, is the channel parameter of the user equipment , is the user equipment The product of the power allocation factor and the transmission power of the intelligent controller, is the user equipment The product of the power allocation factor and the transmission power of the intelligent controller, is the variance of the Gaussian white noise, is the QoS constraint of the user equipment in the second user equipment set, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set, is the first user equipment, is the second user equipment.

[0191] Make the following changes to the second constraint condition of the sub-problem:

[0192] (30)

[0193] is the user equipment The product of the power allocation factor and the transmission power of the intelligent controller, is the user equipment The product of the power allocation factor and the transmission power of the intelligent controller, is the variance of the Gaussian white noise, is the QoS constraint of the user equipment in the second user equipment set, is the maximum value of the first user equipment set, is the maximum value of the second user equipment set, is the first user equipment, is the second user equipment, is the user equipment Channel parameter.

[0194] Thus, the sub-problem in step (3) can be solved by a convex optimization solver.

[0195] Solve the base station BS beamforming vector of the base station BS through step (1), and substitute it into step (2) to solve the phase shift matrix of the reconfigurable intelligent surface RIS. Substitute the base station BS beamforming vector of the base station BS and the phase shift matrix of the reconfigurable intelligent surface RIS solved through steps (1) and (2) into step (3) to solve the NOMA power allocation factor In this way, a set of sub-optimal solution parameters is obtained. Substituting these sub-optimal solutions into steps (1), (2), and (3) in sequence gives a second set of sub-optimal solutions. Repeatedly substituting sub-optimal solutions according to the above steps will result in the nth set of sub-optimal solutions. If the difference in the objective function values between the nth set of sub-optimal solutions and the (n - 1)th set of sub-optimal solutions is less than a certain threshold, the parameters of the nth set of sub-optimal solutions are the optimal solutions for optimizing the objective function.

[0196] As Figures 3 to 4 shown is the schematic diagram of the simulation implementation test of the present invention. The network model in the test system is the Figure 2 application scenario shown. The results of the simulation test are as Figures 3 to 4 shown, and the simulation is carried out from the aspect of the achievable sum rate of the system. To intuitively reflect the superiority of the method of the present invention, the simulation results of the method of the present invention are also compared with the random RIS scheme. The simulation results show that the present invention can effectively improve the achievable sum rate of the system on the premise of ensuring the minimum QoS requirements of user equipment. The present invention has the advantages of simple method, fast data transmission rate, etc., and can be used in the field of wireless communication technology.

[0197] This application also proposes a communication transmission optimization system based on RIS, which includes:

[0198] a wireless edge cache unit, an information request processing unit, a radio frequency signal energy harvesting unit, an optimization objective function establishment unit, and a communication transmission unit;

[0199] The wireless edge cache unit includes:

[0200] a base station BS for providing wireless communication services;

[0201] a server for storing and providing data content;

[0202] a reconfigurable intelligent surface RIS for dynamically adjusting the wireless propagation environment;

[0203] an intelligent controller for processing system operations and communication processes;

[0204] several user equipments for initiating information requests and receiving communication transmissions;

[0205] The information request processing unit is used to receive information requests from several user equipments and classify these information requests to determine information transmission nodes;

[0206] The radio frequency signal energy harvesting unit is used to harvest the radio frequency signal energy from the base station BS through the RIS before information transmission; the optimization objective function establishment unit is used to construct an optimization objective function based on maximizing the achievable transmission sum rate during information transmission; it is used to solve the optimization objective function based on maximizing the achievable transmission sum rate to obtain the optimal parameters; the communication transmission module is used to perform communication transmission based on the obtained optimal parameters.

[0207] This application also proposes a readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0208] Initialize the wireless edge caching system, which includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and several user devices;

[0209] The intelligent controller receives the information requests of several user devices, classifies the information requests, and determines the information transmission node;

[0210] Before information transmission, the intelligent controller harvests the radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS;

[0211] During information transmission, construct an optimization objective function based on maximizing the achievable transmission sum rate;

[0212] Solve the optimization objective function based on maximizing the achievable transmission sum rate to obtain the optimal parameters;

[0213] Perform communication transmission based on the optimal parameters.

[0214] This application also proposes a computer device, including a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor as follows:

[0215] Initialize the wireless edge caching system, which includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and several user devices;

[0216] The intelligent controller receives the information requests of several user devices, classifies the information requests, and determines the information transmission node;

[0217] Before information transmission, the intelligent controller harvests the radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS;

[0218] During information transmission, construct an optimization objective function based on maximizing the achievable transmission sum rate;

[0219] Solve the optimization objective function based on maximizing the achievable transmission sum rate to obtain the optimal parameters;

[0220] Perform communication transmission based on optimal parameters.

[0221] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories.

[0222] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0223] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. The above-disclosed is only the preferred embodiment of the present invention, and of course, it cannot be used to limit the scope of the rights of the present invention. Therefore, the equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A communication transmission optimization method based on RIS, characterized in that, The method includes: Initializing a wireless edge caching system, which includes a base station BS, a server, a reconfigurable intelligent surface RIS, an intelligent controller, and several user equipments; The intelligent controller receives information requests from the several user equipments, classifies the information requests, and determines information transmission nodes; Before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS; During information transmission, an optimization objective function based on maximizing the achievable transmission sum rate is constructed; The optimization objective function based on maximizing the achievable transmission sum rate is solved to obtain optimal parameters; Communication transmission is performed based on the optimal parameters; During information transmission, constructing the optimization objective function based on maximizing the achievable transmission sum rate specifically includes: Determining the rate of information transmission of the k-th user equipment according to the following expression: Among them, is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS. is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access. B1 and B2 are the communication bandwidths allocated to the base station BS and the intelligent controller for non-orthogonal multiple access transmission, respectively. is the channel gain from user equipment K1 to the base station BS, and g0 is the channel gain from the base station BS to the reconfigurable intelligent surface RIS. is the channel gain from the reconfigurable intelligent surface RIS to the second user equipment K2, and Θ is the phase shift matrix of the reconfigurable intelligent surface RIS. is the power allocation factor assigned by the intelligent controller to the second user equipment K2, and σ 2 is the variance of Gaussian white noise. k1 is the first user equipment, k2 is the second user equipment, K1 is the maximum value of the first user equipment set, and K2 is the maximum value of the second user equipment set. is the conjugate transpose of the channel gain matrix from the first user equipment K1 to the base station BS. is the base station BS beamforming vector of the first user equipment K1, and w l is the base station BS beamforming vector of user equipment l, where l is the index of the summation symbol, and α l is the power allocation factor assigned by the intelligent controller to user equipment with index l, and P IT is the transmission power of the intelligent controller information transmission file. Determining the optimization objective function according to the rate information of all user equipments for information transmission; Among them, R Total is the optimization objective function; Solving the optimization objective function based on maximizing the achievable transmission sum rate to obtain optimal parameters specifically includes: Using fractional programming FP and quadratic transformation QT to transform the optimization objective function, and transforming the optimization objective function into several sub-problems; Solving the several sub-problems respectively through an alternating iteration algorithm to obtain optimal parameters; the optimal parameters include the base station BS beamforming vector, the phase shift matrix of the reconfigurable intelligent surface RIS, and the NOMA power allocation factor.

2. The RIS-based communication transmission optimization method according to claim 1, wherein After the intelligent controller receives information requests from the several user equipments, classifies the information requests, and determines information transmission nodes, the method further includes: Based on the information requests of the several user equipments, the intelligent controller transmits the information requests in different ways; among them, before transmitting the information requests in different ways, it is judged whether the information transmission node is the base station BS or the intelligent controller.

3. The RIS-based communication transmission optimization method according to claim 2, wherein, Based on the information requests of the several user equipments, the intelligent controller transmits the information requests in different ways specifically includes: If the file requested by the user equipment is in the local cache of the intelligent controller, the intelligent controller uses non-orthogonal multiple access to transmit the file requested by the user equipment; If the file requested by the user equipment is in the server, the intelligent controller uses the base station BS beamforming vector through the RIS to transmit the file requested by the user equipment.

4. The RIS-based communication transmission optimization method according to claim 1, wherein Before information transmission, the intelligent controller collects radio frequency signal energy from the base station BS through the reconfigurable intelligent surface RIS specifically includes: The intelligent controller performs energy harvesting through the reconfigurable intelligent surface RIS, and the harvested energy is used for information transmission and local file caching; Determining the popularity of local files through Zipf distribution characterization; The probability of caching the f-th local file request is: where α is the skewness parameter, and P f is the probability of caching the f-th local file request, i is the number of files, F is the total number of files, and f is the file number.

5. The RIS-based communication transmission optimization method according to claim 4, wherein The caching power of the intelligent controller for caching the local file is: P ca = ∑ f∈F c f l f c ca Among them, c f is the caching policy, l f is the file scale, c ca is the energy consumed by the cache unit scale file, P ca is the caching power, F is the total number of files, and f is the file serial number; Preset the power received by the reconfigurable intelligent surface (RIS) as P. According to the non-linear energy model, the power collected by the intelligent controller for the collected energy is: P EH = f(P) Among them, P EH is the collection power, and f(p) is a function based on the power P; The transmission power of the information transmission file of the intelligent controller is: P IT = P EH -P ca = f(P) - ∑ f∈F c f l f c ca 。 6. A RIS-based communication transmission optimization system, characterized in that, The system includes: a wireless edge caching unit, an information request processing unit, a radio frequency (RF) signal energy harvesting unit, an optimization objective function establishment unit, and a communication transmission unit; The wireless edge caching unit includes: A base station (BS) for providing wireless communication services; A server for storing and providing data content; A reconfigurable intelligent surface (RIS) for dynamically adjusting the wireless propagation environment; An intelligent controller for processing system operations and communication processes; A number of user equipments for initiating information requests and receiving communication transmissions; The information request processing unit is configured to receive information requests from a number of user equipments and classify these information requests to determine information transmission nodes; The RF signal energy harvesting unit is configured to harvest RF signal energy from the base station (BS) through the RIS before information transmission; The optimization objective function establishment unit is configured to construct an optimization objective function based on maximizing the transmission achievable sum rate during information transmission; and to solve the optimization objective function based on maximizing the transmission achievable sum rate to obtain optimal parameters; The communication transmission unit is configured to perform communication transmission based on the obtained optimal parameters; The optimization objective function establishment unit is specifically configured to: Determine the rate of information transmission of the k-th user equipment according to the following expression: Among them, is the rate at which the k-th user equipment transmits information using the base station BS beamforming vector through the reconfigurable intelligent surface RIS. is the rate at which the k-th user equipment transmits information using non-orthogonal multiple access. B1 and B2 are the communication bandwidths allocated to the base station BS and the intelligent controller for non-orthogonal multiple access transmission, respectively. is the channel gain from user equipment K1 to the base station BS, and g0 is the channel gain from the base station BS to the reconfigurable intelligent surface RIS. is the channel gain from the reconfigurable intelligent surface RIS to the second user equipment K2, and Θ is the phase shift matrix of the reconfigurable intelligent surface RIS. is the power allocation factor assigned by the intelligent controller to the second user equipment K2, and σ 2 is the variance of Gaussian white noise. k1 is the first user equipment, k2 is the second user equipment, K1 is the maximum value of the first user equipment set, and K2 is the maximum value of the second user equipment set. is the conjugate transpose of the channel gain matrix from the first user equipment K1 to the base station BS. is the base station BS beamforming vector of the first user equipment K1, and w l is the base station BS beamforming vector of user equipment l, where l is the index of the summation symbol, and α l is the power allocation factor assigned by the intelligent controller to user equipment with index l, and P IT is the transmission power of the intelligent controller information transmission file. Determine the optimization objective function according to the rate information of information transmission of all user equipments; Among them, R Total is the optimization objective function; The optimization objective function establishment unit is further specifically configured to: Transform the optimization objective function by using fractional programming (FP) and quadratic transformation (QT), and convert the optimization objective function into a number of sub-problems; Solve the number of sub-problems respectively through an alternating iteration algorithm to obtain optimal parameters; the optimal parameters include the base station (BS) beamforming vector, the phase shift matrix of the reconfigurable intelligent surface (RIS), and the non-orthogonal multiple access (NOMA) power allocation factor.

7. A readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 5.

8. A computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 5.

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