Information processing method, communication equipment and computer readable storage medium

Abstract

The invention provides an information processing method. The method comprises the following steps: a first access point acquires channel state information of a plurality of pieces of user equipment; sending the channel state information to a network device; the following operations are circularly executed until a first preset condition is met: first information is acquired, and the first information comprises a receiving vector, a weight vector, pre-coding cross information among a plurality of access points and phase information of a plurality of intelligent metasurfaces; optimizing the first pre-coding vectors of the plurality of user equipment according to the first information to obtain second pre-coding vectors; and sending the second precoding vector to the network equipment. The invention further provides communication equipment and a computer readable storage medium. The method and the device at least solve the technical problem of how to reduce the calculation complexity of the network equipment.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an information processing method, a communication device, and a computer-readable storage medium. Background Art

[0002] Facing the demand for large-scale connectivity, high spectrum efficiency, and highly reliable energy-saving communications in future communication networks, cell-free large-scale multiple-input multiple-output (MIMO) system networks and reconfigurable intelligent metasurfaces (RIS) are key candidate technologies. In a typical application scenario, intelligent metasurfaces can be introduced into cell-free networks, and multiple access points (APs) in the cell-free network can work together with multiple intelligent metasurfaces to interact with network devices (such as core network devices), thereby providing communication services for multiple user devices.

[0003] In the scenario of intelligent metasurface-assisted cell-free network, it is usually necessary to design the precoding vector of the access point and the phase information of the intelligent metasurface. In the related technology, the network equipment can process the relevant information and implement the design of the precoding vector and phase information. However, this method usually leads to high processing cost and computational complexity of the network equipment, thus affecting the system performance. Summary of the invention

[0004] The present application provides an information processing method, a communication device and a computer-readable storage medium, which are used to solve the problem of how to reduce the computational complexity of network equipment when designing precoding vectors and phase information of smart metasurfaces in a scenario where smart metasurfaces assist in cell-free network communications.

[0005] To solve the above technical problems, this application is implemented as follows:

[0006] In a first aspect, an information processing method is provided, which is applied to a first access point, and includes:

[0007] Acquire channel state information of multiple user equipments;

[0008] Sending the channel state information to a network device;

[0009] The loop performs the following operations until the first preset condition is met:

[0010] Acquire first information, where the first information includes a reception vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces;

[0011] Optimize the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector;

[0012] Sending the second precoding vector to the network device;

[0013] In the first cycle, the first information and the first precoding vector are initialized by the first access point; in the Nth cycle, the first information is sent to the first access point after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0014] In a second aspect, an information processing method is provided, which is applied to a network device, comprising:

[0015] receiving channel state information of multiple user equipments sent by multiple access points;

[0016] The loop performs the following operations until the first preset condition is met:

[0017] receiving second precoding vectors of the multiple user equipments sent by the multiple access points, where the second precoding vectors are obtained by the multiple access points optimizing the first precoding vectors of the multiple user equipments according to first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces;

[0018] Optimizing and updating the first information according to the channel state information and the second precoding vector to obtain second information;

[0019] sending the second information to the multiple access points;

[0020] In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0021] In a third aspect, a communication device is provided, comprising:

[0022] processor;

[0023] a memory for storing instructions executable by the processor;

[0024] The processor is configured to execute the instructions to implement the method as described in the first aspect or the second aspect.

[0025] In a fourth aspect, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of a communication device, the communication device is enabled to execute the method described in the first aspect or the second aspect.

[0026] Based on the technical solution provided by the embodiment of the present application, in the scenario of intelligent metasurface assisting cell-free network communication, when designing the precoding vector and the phase information of the intelligent metasurface, it can be realized by multiple access points in the cell-free network and network devices performing multiple cyclic interactions, wherein multiple access points can optimize the precoding vector through multiple interactions, and the network devices can optimize the phase information of the intelligent metasurface through multiple interactions. Thus, part of the design work of the network device can be handed over to multiple access points for implementation, and the design of the precoding vector and the phase information of the intelligent metasurface can be realized through the interactive collaboration between multiple access points and network devices, which can effectively reduce the computational complexity of the network device, shorten the processing time, and improve the efficiency, and effectively guarantee the system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0028] Figure 1 This is a schematic diagram of a scenario of an intelligent metasurface assisted cell-free network according to an embodiment of the present application;

[0029] Figure 2 It is a flowchart of an information processing method according to an embodiment of the present application;

[0030] Figure 3 It is a flowchart of an information processing method according to an embodiment of the present application;

[0031] Figure 4 It is a flowchart of an information processing method according to an embodiment of the present application;

[0032] Figure 5 This is a schematic diagram of the interaction between network elements in a cell-free network assisted by an intelligent metasurface according to an embodiment of the present application;

[0033] Figure 6 is a schematic diagram of convergence performance of an information processing method according to an embodiment of the present application;

[0034] Figure 7 It is a performance effect diagram of an information processing method according to an embodiment of the present application;

[0035] Figure 8 This is a schematic diagram of the effect of an information processing method according to an embodiment of the present application under different intelligent metasurface deployment modes;

[0036] Fig. 9 is a schematic diagram of the structure of a communication device according to an embodiment of the present application;

[0037] Fig.10 is a schematic diagram of the structure of an information processing device according to an embodiment of the present application;

[0038] Fig.11 It is a structural diagram of an information processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] Facing the demand for large-scale connection, high spectrum efficiency, and highly reliable energy-saving communication in the future green 6G network, cell-free massive multiple-input multiple-output (MIMO) system network and reconfigurable intelligence surface (RIS) are key candidate technologies. In order to solve the problem of severe performance limitation caused by inter-cell interference in traditional cellular systems, cell-free networks combine the advantages of massive MIMO systems and distributed systems to eliminate interference at the cell boundaries, and adopt a user-centric transmission design. All access points (APs) in the network jointly provide cooperative services for all users and cooperate with each other, thus solving the marginalization problem existing in traditional cellular scenarios, achieving high-capacity coverage and improving system fairness.

[0040] The demand for networks will increase significantly in the future. To further increase network capacity, the traditional methods of densifying sites or antennas have the problem of high hardware cost and high energy consumption that cannot be ignored. As a passive reflective device with programmable electromagnetic characteristics, configurable smart surfaces have the advantages of low energy consumption and low cost, and can reflect and regulate signals, further enhancing network coverage and improving system capacity. Introducing smart metasurfaces into cell-free networks to assist in communication can replace some access points without affecting user service quality, thereby improving the system's spectrum efficiency in a more energy-efficient and low-cost manner. Therefore, the system model analysis and system-level beamforming algorithm design under the assistance of smart metasurfaces in cell-free networks are urgently needed to promote the application of configurable smart surface technology in future networks.

[0041] At present, most designs in cell-free networks are processed in a centralized manner. Specifically, they can be processed uniformly by a central processing unit (CPU) deployed on the network equipment side (such as the core network or 5G centralized unit (CU), etc.). However, this approach also has problems. For example, when the number of access points and users increases, this solution needs to collect all instantaneous channel state information from all access points, which is obviously impractical. In addition, since the information to be processed is high-dimensional, the optimization cost and complexity of CPU processing are huge problems. The fully distributed design requires frequent interaction of channel state information between access points APs due to the mutual coupling of optimization variables, which will further lead to the problem of increased latency.

[0042] In order to solve the above technical problems, the embodiments of the present application provide an information processing method, a communication device and a computer-readable storage medium. In the scenario of intelligent metasurface-assisted cell-free network communication, when designing precoding vectors and phase information, part of the design work of the network device can be handed over to multiple access points. The design of the precoding vector and the phase information of the intelligent metasurface is realized through the interactive collaboration between multiple access points and network devices. Frequent channel state information interaction between multiple access points can be avoided, the computational complexity of the network device is reduced, the processing time is shorter, the efficiency is higher, and the system performance can be effectively guaranteed.

[0043] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in this application will be described clearly and completely below in conjunction with the drawings in one or more embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0044] The terms "first", "second", etc. in the present application and claims are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the data used in this way can be interchanged where appropriate so that the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the present application and claims means at least one of the connected objects, and the character " / " generally means that the objects associated with each other are in an "or" relationship.

[0045] The following is a detailed description of the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings. It should be noted that in the following embodiments, RIS and smart metasurface refer to smart metasurfaces with reflective functions.

[0046] Figure 1 This is a schematic diagram of a scenario of an intelligent metasurface assisted cell-free network according to an embodiment of the present application.

[0047] Figure 1 The network shown includes multiple user devices, multiple access points, multiple smart metasurfaces, and network devices. Among them, the multiple smart metasurfaces can be reflective smart metasurfaces, each of which includes multiple reflection units, and each smart metasurface can correspond to a controller, and each controller can regulate the phase of the smart metasurface corresponding to it. Multiple user devices and multiple access points can each have multiple transceiver antennas. The network device can be a core network or a 5G centralized unit (CU), etc.

[0048] exist Figure 1 In the network shown, multiple access points and multiple smart super surfaces can provide communication services for multiple user devices by collaboratively interacting with network devices. Taking user device k as an example, when performing downlink communication, multiple access points can send downlink information to the user device through a direct channel between the user device k. At the same time, for each access point, the access point can also send a downlink signal to the user device through a cascade channel between multiple smart super surfaces and user device k. When performing uplink communication, similarly, user device k can send uplink information to multiple access points through a direct channel between multiple access points and a cascade channel between multiple smart super surfaces and multiple access points. The network device can exchange information with multiple access points. In an embodiment of the present application, the network device can exchange channel state information, reception vectors, weight vectors, precoding cross information between multiple access points, and phase information of multiple smart super surfaces with multiple access points to optimize the precoding vector and the phase information of the smart super surface, so as to better provide communication services for user device k.

[0049] Figure 2 It is a flowchart of an information processing method according to an embodiment of the present application. Figure 2 The information processing method shown can be applied to Figure 1 In other words, the information processing method can be performed by installing any access point Figure 1 The software or hardware in any of the access points shown may be executed, and for ease of distinction, may be represented by the first access point. Figure 2 The information processing method shown is as follows.

[0050] S202: Acquire channel state information of multiple user equipments.

[0051] The multiple user devices here can be Figure 1The multiple user devices shown are, that is, multiple user devices in a cell-free network. The channel state information (CSI) corresponding to the multiple user devices may be the downlink channel state information of the multiple user devices when the first access point communicates downlink with the multiple user devices with the assistance of the multiple smart super surfaces. The channel state information of each user device includes the channel state information of the direct channel between the first access point and the user device, and the channel state information of the cascade channel between the first access point, the multiple smart super surfaces and the user device. The multiple smart super surfaces are smart super surfaces that assist the first access point in communicating with the user device through reflected signals.

[0052] When acquiring channel state information of multiple user equipments, the same method may be used for each user equipment to acquire the channel state information. Optionally, as an embodiment, for each user equipment, the channel state information of the user equipment may be acquired by the following steps:

[0053] Sending a pilot signal to a user equipment;

[0054] Receive channel state information fed back by the user equipment according to the pilot signal.

[0055] Specifically, when it is necessary to obtain channel state information, the first access point can send a pilot signal to the user equipment, and the pilot signal can be sent to the user equipment through the first channel and the second channel. The first channel is a direct channel between the first access point and the user equipment, and the second channel is a cascade channel between the first access point, multiple smart super surfaces and the user equipment (including a direct channel between the first access point and multiple smart super surfaces, and a direct channel between multiple smart super surfaces and the user equipment) sent to the user equipment. After receiving the pilot signal, the user equipment can perform channel estimation on the above-mentioned first channel and the second channel, and can obtain the channel state information corresponding to the user equipment according to the channel estimation result, and send the channel state information to the first access point, so that the first access point can obtain the channel state information corresponding to a user equipment.

[0056] After the first access point obtains the channel state information corresponding to one user equipment through the above method, it can obtain the channel state information corresponding to other user equipment through the same method, thereby obtaining the channel state information corresponding to multiple user equipments.

[0057] S204: Send the channel status information to the network device.

[0058] S206: Acquire first information, where the first information includes a receiving vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces.

[0059] The receiving vector and the weight vector are auxiliary vectors introduced to optimize the precoding vector. The receiving vector can be the receiving vector of the user equipment when receiving the downlink signal. For example, the receiving vector can be the linear receiving filter vector on the user equipment side. The weight vector is also a vector for the user equipment. Corresponding to the receiving vector, it can represent the weight information of the user equipment receiving the downlink signal. Multiple access points and multiple smart metasurfaces can be Figure 1 Multiple access points and multiple smart metasurfaces shown.

[0060] Optionally, as an embodiment, when acquiring the first information, the first access point may obtain the first information by initialization, that is, the receiving vector, the weight vector, the precoding cross information between multiple access points, and the phase information of multiple smart metasurfaces may be initialized by the first access point. Alternatively, the first information may also be sent by the network device to the first access point.

[0061] S208: Optimize the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector.

[0062] The first precoding vector is a downlink precoding vector used by the first access point when performing downlink communications with multiple user devices. In an embodiment of the present application, the first precoding vector can be obtained by the first access point through initialization, that is, the first precoding vector is a set of initial precoding vectors designed by the first access point for multiple user devices. The specific implementation method of the first access point initializing the first precoding vector can refer to the specific implementation in the related technology, which will not be described in detail here. Alternatively, the first precoding vector can also be sent to the first access point by the network device. The method for obtaining the first precoding vector is not specifically limited here. Among them, the first precoding vector is not the optimal precoding vector and needs to be optimized. During the optimization, it can be optimized according to the first information obtained in S206 to obtain the optimized second precoding vector.

[0063] It should be noted that in the embodiment of the present application, the optimization problem of the first precoding vector is a non-convex problem and is not easy to solve. Therefore, when optimizing the first precoding vector, the optimization problem can be converted into an easy-to-solve convex problem to achieve the optimization of the first precoding vector.

[0064] Optionally, as an embodiment, when optimizing the first precoding vector according to the first information to obtain the second precoding vector, the following steps may be included:

[0065] An objective function is determined according to a receiving vector, a weight vector, precoding cross information, and phase information in the first information; and the first precoding vector is optimized according to the objective function to obtain a second precoding vector.

[0066] The objective function refers to a function used to optimize the first precoding vector, and may also be referred to as an optimization objective expression of the precoding vector (or precoding matrix). The objective function is a convex problem and is easy to solve.

[0067] In one implementation, when determining the objective function according to the reception vector, the weight vector, the precoding cross information, and the phase information, the following S2081 to S2083 may be included.

[0068] S2081: Under the transmission power constraint and the phase constraint, construct a maximum weighted sum rate optimization problem according to the first precoding vector to obtain a first function.

[0069] The transmit power constraint condition refers to the transmit power constraint condition of the first access point. Specifically, when the first access point sends downlink information to multiple user devices, the actual transmit power of the first access point needs to be less than or equal to the maximum transmit power of the first access point. The phase constraint condition refers to the phase constraint condition of multiple smart metasurfaces. When the smart metasurface reflects the received signal, the reflection unit in the smart metasurface needs to meet the unit mode constraint condition, which is the phase constraint condition.

[0070] Under the transmission power constraint and the phase constraint, a maximum weighted sum rate optimization problem can be constructed according to the first precoding vector to obtain a first function. The first function is related to the first precoding vector, the weighting coefficients of multiple user devices, the signal to interference noise ratio of the received signals of the multiple user devices, and the phase information of the multiple smart metasurfaces, and is constrained by the transmission power constraint and the phase constraint.

[0071] It should be noted that the first function is a non-convex problem and is not easy to solve. Therefore, after obtaining the first function, it needs to be transformed through the following S2082 and S2083.

[0072] S2082: Perform equivalent transformation on the first function according to the receiving vector, the weight vector, the weighted rate sum of multiple user equipments, and the corresponding summed mean square error to obtain a second function.

[0073] When transforming the first function, the relationship between the weighted rate sum of multiple user devices (i.e., the rate obtained by weighted summing the rate of each user device and the weighting coefficient) and the summed mean square error (i.e., the error obtained by weighted summing the mean square errors of the rate of each user device) can be explored, and then the first function can be equivalently transformed according to the receiving vector and the weight vector, that is, the problem of maximizing the weighted sum rate optimization is equivalently transformed into the problem of minimizing the mean square error. In this way, the first function can be transformed into the second function.

[0074] S2083: Simplify the second function according to the received vector, the weight vector, the precoding cross information and the phase information to obtain the target function.

[0075] After obtaining the second function, the second function can be further simplified to obtain the objective function, which is a convex problem.

[0076] Thus, through the above S2081 to S2083, the non-convex problem of precoding vector optimization can be transformed into a convex problem, and an easily solvable objective function can be obtained. After obtaining the objective function, the first precoding vector can be optimized according to the objective function to obtain an optimized second precoding vector.

[0077] Optionally, as an embodiment, optimizing the first precoding vector according to the objective function to obtain the second precoding vector may include the following S2084 to S2086.

[0078] S2084: Introduce the transmit power constraint into the objective function using the Lagrange multiplier method, and apply the first-order optimization condition to the objective function to obtain the optimal expression of the precoding vector.

[0079] Here, the Lagrange multiplier method can be used to process the transmit power constraint condition, and the objective function is applied to the first access point and multiple user equipments. The closed form of the active beamformer of the first access point can be derived, that is, the optimal expression of the precoding vector is obtained. The optimal expression of the precoding vector is related to the multiplier introduced by the transmit power constraint condition and the precoding cross information between multiple access points, wherein the multiplier introduced by the transmit power constraint condition can be obtained by the dichotomy method.

[0080] S2085: Determine a third precoding vector according to the optimal expression of the precoding vector.

[0081] After the optimal expression of the precoding vector is obtained, a precoding vector may be calculated based on the optimal expression. Here, for the sake of distinction, a third precoding vector may be used to represent it.

[0082] S2086: Optimize the first precoding vector according to the third precoding vector to obtain a second precoding vector.

[0083] After obtaining the third precoding vector, the first precoding vector may be optimized according to the third precoding vector, thereby obtaining an optimized second precoding vector. Wherein, when optimizing the first precoding vector according to the third precoding vector, the first precoding vector and the third precoding vector may be weighted summed (the sum of the weight coefficient of the first precoding vector and the weight coefficient of the third precoding vector is 1), and the weighted summation result is used as the second precoding vector. Of course, it may also be implemented in other ways, which are not specifically limited here.

[0084] At this point, the first precoding vector can be optimized according to the first information, and the optimized second precoding vector can be obtained.

[0085] S210: Send the second precoding vector to the network device.

[0086] After obtaining the second precoding vector, it can be sent to the network device. For the network device, after receiving the second precoding vector and channel state information sent by the first access point, it can optimize and update the first information in S206 in combination with the second precoding vector and channel state information sent by other access points to obtain the second information, and send the second information to multiple access points. The specific implementation method of the network device optimizing and updating the first information can be found in Figure 3 The illustrated embodiment will not be described in detail here.

[0087] S212: Determine whether a first preset condition is met.

[0088] After the second precoding vector is sent to the network device, it can be determined whether the first preset condition is met. The first preset condition can be a condition on whether the rate value converges, and specifically can be determined according to the second precoding vector whether the rate value of the maximum weighted sum rate optimization problem constructed above converges. Meeting the first preset condition can be obtaining a converged rate value, and not meeting the first preset condition can be not obtaining a converged rate value.

[0089] If the first preset condition is met, it can be said that the second precoding vector obtained in the above S208 is the optimal precoding vector, and S214 can be executed at this time. If the first preset condition is not met, it can be said that the second precoding vector obtained in the above S208 is not the optimal precoding vector, and the second precoding vector needs to be further optimized. When the second precoding vector is further optimized, the above S206 to S212 can be returned to execute. Among them, in the Nth (an integer greater than or equal to 2)th loop, the first information in S206 is the second information obtained after the network device optimizes and updates the first information in the N-1th loop, and the first precoding vector is the second precoding vector in the N-1th loop. Therefore, the precoding vectors of multiple user devices can be continuously optimized by performing a loop iteration with the network device until the first preset condition is met.

[0090] It should be noted that, since the network device can know the channel states of the multiple user devices after the first access point sends the channel state information of the multiple user devices to the network device, in the above-mentioned loop iteration process, the first access point does not need to loop through S202 and S204, that is, it does not need to repeatedly send the channel state information of the multiple user devices to the network device, and only needs to loop through S206 to S212.

[0091] S214: End.

[0092] After the above-mentioned iterative process is finished, for the first access point, the second precoding vector obtained in the last cycle can be used as the optimal precoding vector.

[0093] Optionally, after obtaining the optimal precoding vector, the first access point can perform downlink transmission with multiple user devices based on the optimal precoding vector. Wherein, since the precoding vector used for downlink transmission is the optimal precoding vector, the first access point can better provide communication services for multiple user devices when performing downlink transmission with multiple user devices based on the optimal precoding vector.

[0094] In an embodiment of the present application, in a scenario where a smart metasurface assists in cell-free network communication, when designing the precoding vector and the phase information of the smart metasurface, it can be achieved through multiple access points in the cell-free network and network devices performing multiple cyclic interactions, wherein multiple access points can optimize the precoding vector through multiple interactions, and the network devices can optimize the phase information of the smart metasurface through multiple interactions. Thus, part of the design work of the network device can be handed over to multiple access points for implementation, and the design of the precoding vector and the phase information of the smart metasurface can be achieved through interactive collaboration between multiple access points and network devices, which can effectively reduce the computational complexity of the network device, shorten the processing time, and increase the efficiency, and effectively guarantee the system performance.

[0095] Figure 3 It is a flowchart of an information processing method according to an embodiment of the present application. Figure 3 The information processing method shown can be applied to Figure 1 The network device shown, in other words, the information processing method can be executed by software or hardware installed in the network device. Optionally, the information processing method can be specifically executed by a CPU in the network device. Figure 3 The information processing method shown is as follows.

[0096] S302: Receive channel state information of multiple user equipments sent by multiple access points.

[0097] For each of the plurality of access points, a Figure 2 The method described in the illustrated embodiment determines channel state information corresponding to multiple user devices and sends the channel state information to a network device. After multiple access points send the obtained channel state information to the network device, the network device can receive the channel state information corresponding to multiple user devices sent from the multiple access points. For each access point, the channel state information sent can be the downlink channel state information of each of the multiple user devices when the access point communicates downlink with the multiple user devices with the assistance of multiple smart metasurfaces.

[0098] S304: Receive second precoding vectors of multiple user equipments sent by multiple access points.

[0099] For each of the plurality of access points, a Figure 2 The method recorded in the illustrated embodiment optimizes the first precoding vectors of multiple user devices according to the first information, and sends the optimized second precoding vectors to the network device, wherein the first information may include a receiving vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces.

[0100] After the multiple access points send the optimized second precoding vectors to the network device, the network device may receive the second precoding vectors of the multiple user equipments sent by the multiple access points.

[0101] S306: Optimize and update the first information according to the channel state information and the second precoding vector to obtain second information.

[0102] After receiving the channel state information and the second precoding vector sent by multiple access points, the network device can optimize and update the first information according to the channel state information and the second precoding vector to obtain the second information. Corresponding to the first information, the second information may include an updated receiving vector, an updated weight vector, an updated precoding cross information, and an optimized phase information.

[0103] Optionally, as an embodiment, optimizing and updating the first information according to the channel state information and the second precoding vector to obtain the second information may include the following S3061 to S3065.

[0104] S3061: Obtain the second function.

[0105] Here the second function is Figure 2 The second function in the embodiment shown is obtained by equivalently transforming the first function according to the receiving vector, the weight vector, the weighted rate sum of multiple user equipments and the corresponding summed mean square error. The first function is obtained by constructing a maximum weighted sum rate optimization problem according to the first precoding vector under the conditions of transmit power constraints and phase constraints. The specific implementation method of determining the first function and the specific implementation method of transforming the first function to obtain the second function can be found in Figure 2 The corresponding contents in the illustrated embodiments will not be repeated here.

[0106] Optionally, in one implementation, the second function may be sent by the access point to the network device, or may be determined by the network device, or may be pre-input into the network device, etc. The method for obtaining the second function is not specifically limited here.

[0107] S3062: Apply a first-order optimization condition to the second function according to the channel state information and the second precoding vector, update the receiving vector, and obtain an updated receiving vector.

[0108] After obtaining the second function, a first-order optimization condition may be applied to the second function according to the channel state information received in S302 and the second precoding vector received in S304, so that the receiving vector in the first information may be updated to obtain an updated receiving vector. The expression of the updated receiving vector may be determined according to the channel state information and the second precoding vector.

[0109] S3063: Update the weight vector according to the channel state information, the second precoding vector, and the updated receiving vector to obtain an updated weight vector.

[0110] After obtaining the updated receiving vector, the weight vector in the first information can be updated by further combining the channel state information and the second precoding vector to obtain an updated weight vector. The expression of the updated weight vector can be determined based on the channel state information, the second precoding vector and the updated receiving vector.

[0111] S3064: Update the precoding cross information according to the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector to obtain updated precoding cross information.

[0112] After obtaining the updated receiving vector and weight vector, the precoding cross information in the first information can be further updated in combination with the channel state information and the second precoding vector to obtain updated precoding cross information. The expression of the updated precoding cross information can be determined based on the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector.

[0113] S3065: Optimize the phase information according to the channel state information, the second precoding vector, the updated receiving vector, the updated weight vector and the second function to obtain optimized phase information.

[0114] After obtaining the updated receiving vector and the updated weight vector, the phase information in the first information may be further optimized in combination with the channel state information, the second precoding vector and the second function obtained in S3062 to obtain the optimized phase information.

[0115] Optionally, as an embodiment, when optimizing the phase information, the following steps may be included:

[0116] Introducing the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector into a second function to determine a first expression for optimizing the phase information;

[0117] The first expression is vectorized to obtain a second expression.

[0118] The second expression is solved using a preset algorithm to obtain optimized phase information.

[0119] The first expression is in the form of a matrix. In order to optimize the phase information, after obtaining the first expression, the first expression can be vectorized, so that the first expression can be transformed to obtain a more concise and easy-to-solve second expression.

[0120] After obtaining the second expression, the second expression can be solved using a preset algorithm under the phase constraint, so as to obtain the optimized phase information. The preset algorithm may be a conjugate gradient descent method, MM (Majorization-Minimization) method, alternating direction multiplier method (ADMM) or block-by-block coordinate descent method in the manifold optimization method, which is not specifically limited here. Optionally, since the conjugate gradient descent method in the manifold optimization method has low complexity and is more efficient, the conjugate gradient descent method in the manifold optimization method may be preferred to solve the second expression.

[0121] It should be noted that after solving the second expression using the preset algorithm, the optimized phase information obtained is a continuous phase. Optionally, in one implementation, if a discrete phase is required, after obtaining the continuous phase, the continuous phase can also be processed to obtain the corresponding discrete phase. For example, the continuous phase can be mapped to a discrete set using the nearest principle to obtain a discrete phase.

[0122] At this point, the first information can be optimized and updated according to the channel state information and the second precoding vector to obtain the second information. The second information includes an updated receiving vector, an updated weight vector, an updated precoding cross information, and an optimized phase information. Among them, the optimized phase information can be continuous phase information and / or discrete phase information, which can be determined according to actual needs and is not specifically limited here.

[0123] S308: Send the second information to multiple access points.

[0124] After optimizing and updating the first information, the network device may send the first information to multiple access points. For each access point, after receiving the second information, the second information may be used as the first information, and the first precoding vector may be optimized according to the first information. The specific implementation method of optimizing the first precoding vector may refer to Figure 2 The embodiments shown will not be described again here.

[0125] S310: Determine whether a first preset condition is met.

[0126] After the second information is sent to multiple access points, it can be determined whether the first preset condition is met. The first preset condition here can be related to Figure 2 The first preset condition in the embodiment shown is the same, and can be specifically referred to Figure 2 The relevant explanations of the illustrated embodiment will not be repeated here.

[0127] If the first preset condition is met, it can be said that the optimized phase information obtained in the above S306 is the optimal phase information, and S312 can be executed at this time. If the first preset condition is not met, the optimized phase information obtained in the above S306 is not the optimal phase information, and the phase information needs to be further optimized, that is, the second information is further optimized. When the second information is further optimized, the above S304 to S310 can be returned to execute. Among them, in the Nth (an integer greater than or equal to 2)th cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle. Therefore, the network device can continuously optimize and update the second information by performing a cyclic iteration with multiple access points, that is, continuously optimize the phase information until the first preset condition is met.

[0128] It should be noted that, since the network device can know the channel status of multiple user devices after multiple access points send the channel status information of multiple user devices to the network device, during the loop iteration process, the network device does not need to loop S302, that is, there is no need to repeatedly receive the channel status information of multiple user devices from multiple access points, and only needs to loop S304 to S310.

[0129] S312: End.

[0130] After the above-mentioned iterative process is finished, for the network device, the phase information obtained in the last cycle can be used as the optimal phase information.

[0131] Optionally, after obtaining the optimal phase information, the network device can send the phase information as target information to the controllers of multiple smart metasurfaces. After receiving the target information, the controller can perform phase control on multiple smart metasurfaces according to the target information. Among them, since the phase information after the control of multiple smart metasurfaces is the optimal phase information, multiple smart metasurfaces can better provide communication services for multiple user devices when combined with multiple access points and multiple user devices for downlink transmission.

[0132] In an embodiment of the present application, in a scenario where a smart metasurface assists in cell-free network communication, when designing the precoding vector and the phase information of the smart metasurface, it can be achieved through multiple access points in the cell-free network and network devices performing multiple cyclic interactions, wherein multiple access points can optimize the precoding vector through multiple interactions, and the network devices can optimize the phase information of the smart metasurface through multiple interactions. Thus, part of the design work of the network device can be handed over to multiple access points for implementation, and the design of the precoding vector and the phase information of the smart metasurface can be achieved through interactive collaboration between multiple access points and network devices, which can effectively reduce the computational complexity of the network device, shorten the processing time, and increase the efficiency, and effectively guarantee the system performance.

[0133] In order to facilitate understanding of the technical solution provided in the embodiment of the present application, a more specific implementation method will be used as an example for explanation below. Figure 4 and Figure 5 . Figure 4 It is a flowchart of an information processing method according to an embodiment of the present application. Figure 5 This is a schematic diagram of the interaction between network elements in an intelligent metasurface-assisted cell-free network according to an embodiment of the present application.

[0134] exist Figure 4 and Figure 5 In the embodiment shown, AP represents access point and RIS represents intelligent hypersurface. Assume that the cell-free network includes B APs and L RISs, which serve K user devices in a collaborative manner. The number of transmitting antennas at the AP end and the number of receiving antennas at the user device end are M and L, respectively. t and M r , the number of reflection units included in each RIS is N, and the definition is Respectively represent a set of AP, RIS, user equipment and RIS reflection unit, then the information processing method provided in the embodiment of the present application may include the following steps.

[0135] S501: Multiple APs obtain channel state information of multiple user equipments.

[0136] Specifically, due to the regulation function of RIS on the signal, the channel from the access point AP to the user equipment actually consists of three parts, namely the direct channel from AP to the user equipment, the channel from AP to RIS, and the reflected channel from RIS to the user equipment. The reflection phase matrix representing the lth RIS regulation signal is constructed as in, Then, the channel estimation method proposed in the related research is used to obtain the channel state information of the related channel. represents the equivalent channel from the bth AP to the kth user equipment, represents the equivalent channel from the lth RIS to the kth user's device, represents the equivalent channel from the bth AP to the lth RIS, then the channel state information of the bth AP corresponding to the kth user can be expressed as:

[0137]

[0138] S502: Multiple APs locally initialize or receive relevant receiving vectors, weight vectors, AP precoding cross information items, and RIS phase information transmitted by network devices.

[0139] In the first cycle, the AP may initialize the receiving vector, weight vector, AP precoding cross information item and RIS phase information locally, and in the Nth (greater than or equal to 2)th cycle, the AP may receive the receiving vector, weight vector, AP precoding cross information item and RIS phase information sent by the network device. The receiving vector, weight vector, AP precoding cross information item and RIS phase information sent by the network device are the updated receiving vector, weight vector, AP precoding cross information item and RIS phase information.

[0140] S503: The multiple APs optimize the first precoding vector according to the receiving vector, the weight vector, the AP precoding cross information item, and the RIS phase information.

[0141] When optimizing the first precoding vector, the following steps 1 to 3 may be included.

[0142] Step 1: Construct the maximum weighted sum rate optimization problem and set w b,k The precoding vector designed by the b-th AP for user equipment k (i.e., the first precoding vector to be optimized) is then the problem of maximizing the weighted sum rate objective under the constraints of transmit power and phase can be expressed as the following function (i.e. Figure 2 The first function in the embodiment shown):

[0143]

[0144] Among them, ω k and P max They represent the weighting coefficient of user equipment k and the maximum transmit power of AP, and the signal-to-interference-noise ratio γ k It is expressed as:

[0145]

[0146] Among them, σ 2 is the variance of Gaussian noise on the user equipment side, and the unit mode constraint of the RIS reflection unit is expressed as Precoding combination vector

[0147] Step 2: Introduce a user-side linear receive filter u k vector (i.e., receiving vector), and further introduces the weight vector by exploring the relationship between the weighted rate and the summed mean square error Ignoring irrelevant constant terms, the problem of maximizing the weighted sum rate objective is transformed into (Right now Figure 2 The second function in the illustrated embodiment), wherein the mean square error term can be expressed as:

[0148]

[0149] The AP initializes locally or receives relevant information from the network device, fixes the receiving vector u, weight vector f, cross information term ξ and RIS phase information Φ, and after simplifying and processing the above second function, the target problem of precoding optimization can be further expressed as the following function (i.e. Figure 2 The objective function in the embodiment shown, that is, the optimization objective expression of the precoding vector):

[0150]

[0151] Among them, in the optimization target expression of the precoding vector, the coefficient matrix And define the matrix Each element

[0152] Step 3: Using the Lagrange multiplier method to handle the transmit power constraint, the objective function is applied to each AP b and user equipment k with the first-order optimization condition, and the closed form of the active beamformer of all APs (that is, the optimal expression of the precoding vector) can be derived as follows:

[0153]

[0154] in, is the multiplier introduced by the transmit power constraint, which can be obtained by bisection, ξ b,k is the cross information item for the precoding design between APs. For user equipment k, in each iteration t, based on the fixed ξ b,k , each AP b and other APs simultaneously calculate w locally according to the above formula b,k . In order to ensure the convergence of each update and the convergence speed and accuracy, the step size α∈(0,1] can be fixed, and the precoding vector can be updated by the following expression:

[0155]

[0156] in, represents the precoding vector updated at the tth iteration (i.e., the second precoding vector), represents the precoding vector updated at the t-1th iteration, that is, the precoding vector to be optimized at the tth iteration (that is, the first precoding vector), w b,k Indicates that at the tth iteration, The calculated precoding vector (ie, the third precoding vector) is obtained.

[0157] S504: The multiple APs send the channel state information and the optimized second precoding vector to the network device.

[0158] S505: The network device optimizes and updates the receiving vector, the weight vector, the AP precoding cross information, and the RIS phase information according to the channel state information and the second precoding vector sent by the multiple APs.

[0159] When optimizing and updating the receiving vector, the weight vector, the AP precoding cross information item, and the RIS phase information, the following steps 1 to 4 may be included.

[0160] Step 1: Based on the channel state information obtained from the AP and the second precoding vector of each AP, for the above formula By optimizing the first-order optimization conditions of the variables respectively, the access vector is updated, and the updated receiving vector can be expressed as:

[0161]

[0162] Based on the updated receiving vector, combined with the channel state information and the second precoding vector, the weight vector is updated. The updated weight vector can be expressed as:

[0163]

[0164] Afterwards, the AP precoding cross information may be updated based on the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector. The updated AP precoding cross information may be expressed as:

[0165]

[0166] Step 2: Channel state information Substitute into Ignoring the constant term that is irrelevant to the phase, the expression for optimizing the phase information can be derived (i.e. Figure 3 The first expression in the illustrated embodiment) is:

[0167]

[0168] Among them, in the first term of the above formula, the coefficient matrix is Coefficient Matrix In the second and third terms of the above formula, the phase matrix Φ l The coefficient matrix of the first-order terms is expressed as

[0169] Next, define the following combination of coefficient matrix and vector The expression used to optimize the phase information is vectorized, φ l =[φ l,1 ,...,φ l,n ,...,φ l,N ] T , Then the phase optimization problem can be transformed into the following expression (i.e. Figure 3 The second expression in the embodiment shown):

[0170]

[0171] Step 3: Observe that the unit module constraints of the above problem actually form a complex circular manifold The phase optimization problem caused by the above multi-RIS can be solved by the conjugate gradient descent method in the manifold optimization method.

[0172] Specifically, in each iteration r, it is mainly divided into the following steps:

[0173] First, calculate the Euclidean gradient

[0174] Second, calculate the Riemann gradient

[0175] Again, update the tangent space search vector to Among them, β r is the Polak-Ribiere parameter, Represents a mapping between two tangent vectors in different tangent spaces.

[0176] Finally, pull the optimization variables back into the constraint space The pullback operation is defined as α r The step size for the Armijo backtracking line search.

[0177] The optimal phase value, that is, the optimized phase information, can be obtained by iterating the above steps.

[0178] Step 4: Based on the above method, the optimal continuous phase can be obtained After that, the continuous phase can be simply mapped to the discrete set by the nearest principle is the number of discrete bits, so that the optimal discrete phase

[0179] S506: The network device sends the updated receiving vector, weight vector, AP precoding cross information and RIS phase information to multiple APs.

[0180] The coefficient vector {u (t+1) ,f (t+1) ,ξ (t+1) ,φ (t+1) The phase information fed back may be discrete phase information or continuous phase information, which may be determined according to actual needs.

[0181] For each AP, after receiving the updated receiving vector, weight vector, AP precoding cross information and RIS phase information, it can return to execute the above S502. Thus, multiple loop iterations can be performed between multiple APs and network devices. During the loop iteration process, each AP optimizes the first precoding vector according to the updated receiving vector, weight vector, AP precoding cross information and RIS phase information of the network device to obtain the second precoding vector. The network device updates the RIS phase information according to the channel state information and the second precoding sent by each AP, and updates the receiving vector, weight vector, and AP precoding cross information at the same time. Among them, in the Nth (an integer greater than or equal to 2)th loop, for the above S504, multiple APs can send the optimized second precoding vector to the network device without repeatedly sending the channel state information.

[0182] S507: When the convergence rate value is obtained, the loop ends.

[0183] S508: The multiple APs perform downlink transmission with the multiple user equipments based on the second precoding vector obtained in the last loop iteration.

[0184] S509: The network device sends the RIS phase information obtained in the last iteration to the RIS controller.

[0185] S510: The RIS controller performs phase control on the RIS according to the RIS phase information.

[0186] In an embodiment of the present application, when designing the phase information of the precoding vector and the intelligent metasurface, part of the design work of the network device can be handed over to multiple access points, and the design of the phase information of the precoding vector and the intelligent metasurface can be achieved through the interactive collaboration between multiple access points and network devices. Therefore, the computational complexity of the network device can be effectively reduced, the processing time is shorter, the efficiency is higher, and the system performance can be effectively guaranteed.

[0187] The technical solution provided based on the embodiment of the present application is a decentralized collaborative transmission design method. By comparing the simulation with the existing centralized processing method, the performance of RIS under continuous and discrete and different deployment modes can be analyzed to guide the actual deployment.

[0188] In the simulation comparison, the medium and large scale fading is set based on the UMa path loss model in the 3GPP 38.901 protocol, with the center frequency at 5.8 GHz. The small scale fading assumes that all channels use the Rice fading channel model, κ BU =0,κ BR =3,κ RU →∞ represent the channel Rician factors from AP to user, AP to RIS, and RIS to user. The three APs are located at (0, 100m, 3m), Users are randomly distributed in a circle with a radius of 5 and a height of 1.5m, with the origin as the center. The number of users is K = 3. At the same time, three RIS are distributed near the users, located at (0, 20m, 3m), (-15m, -15m, 3m), and (15m, -15m, 3m). Usually, the scale of APs in cell-free networks is small, and the number of antennas for APs and users is set to M respectively. t =2,M r =2, the user weight coefficient is set to The precoding vector update step size is set to α = 0.5, and the AP transmit power constraint P max =20dBm, noise power σ 2 =-80dBm. For specific simulation results, please refer to Figures 6 to 8 .

[0189] Figure 6 The convergence performance of the decentralized collaborative transmission method proposed in this application is demonstrated. Figure 6 It can be seen that when N = 100 and the convergence error does not exceed 0.1%, the proposed method converges within 15 iterations. Although the decentralized design of active precoding is adopted in the proposed design, the convergence performance is almost the same as that of the centralized method and the sum rate performance is very close, without causing performance loss. Similarly, the discrete bit case and the random phase and no RIS case also converge within 15 iterations, which effectively proves the convergence of the sum rate optimization algorithm in the proposed method.

[0190] Figure 7 The performance comparison between the proposed method and the centralized method in terms of rate performance and CPU running time is shown when the number of reflection units N is 100, 200 and 300 respectively. Figure 7 It can be seen that the two methods have almost the same performance and rate in different cases. However, the collaborative transmission method proposed in this application adopts a decentralized design, thus avoiding the calculation of high-dimensional matrices. At the same time, the manifold optimization design is faster than the solution speed of the CVX convex tool. Therefore, the overall optimization speed is faster than that of the centralized method. As N increases, the running time (seconds) of the proposed method remains at 10 0 The running time of the centralized method ranges from 10 2 The order of magnitude increases to 10 3 This shows the low complexity advantage of the proposed algorithm.

[0191] Figure 8 The performance curves of the proposed method under two RIS deployment modes (i.e., deploying RIS near the AP side or deploying RIS near the user side) are shown. Among them, the three RIS near the AP side are deployed near the three set APs to jointly serve users near the origin. Figure 8 It can be seen that in this scenario, whether it is continuous bits or discrete bits, the deployment method close to the user side is better than the deployment on the AP side. At the same time, the network architecture proposed in this application is compared with the 3AP no cell scenario without RIS service and the 6AP no cell scenario without RIS service. The remaining 3 APs are located at (0, -100m, 3m), It can be found that even compared with the non-cell-based scenario where the number of APs B′=B+L, the RIS-assisted architecture still has a large gain at low cost and low power consumption, which fully demonstrates the potential of RIS to improve system capacity.

[0192] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0193] Fig. 9 This is a schematic diagram of the structure of a communication device according to an embodiment of the present application. Fig. 9At the hardware level, the communication device includes a processor, and optionally an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the communication device may also include hardware required for other services.

[0194] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0195] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0196] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an information processing device at the logical level. The processor executes the program stored in the memory and is specifically used to perform the following operations:

[0197] Acquire channel state information of multiple user equipments;

[0198] Sending the channel state information to a network device;

[0199] The loop performs the following operations until the first preset condition is met:

[0200] Acquire first information, where the first information includes a reception vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces;

[0201] Optimize the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector;

[0202] Sending the second precoding vector to the network device;

[0203] In which, in the first cycle, the first information and the first precoding vector are initialized by the first access point; in the Nth cycle, the first information is the second information obtained after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0204] Or to do the following:

[0205] receiving channel state information of multiple user equipments sent by multiple access points;

[0206] The loop performs the following operations until the first preset condition is met:

[0207] receiving second precoding vectors of the multiple user equipments sent by the multiple access points, where the second precoding vectors are obtained by the multiple access points optimizing the first precoding vectors of the multiple user equipments according to first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces;

[0208] Optimizing and updating the first information according to the channel state information and the second precoding vector to obtain second information;

[0209] sending the second information to the multiple access points;

[0210] In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0211] The above application Fig. 9The method performed by the information processing device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in this application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in this application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0212] The communication device can also perform Figure 2 and Figure 3 The method is implemented by the information processing device. Figure 2 and Figure 3 The functions in the illustrated embodiments will not be described in detail in this application.

[0213] Of course, in addition to software implementation, the communication device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0214] The present application also proposes a computer-readable storage medium, which stores one or more programs, wherein the one or more programs include instructions, which, when executed by a portable communication device including a plurality of application programs, enable the portable communication device to execute Figure 2 and Figure 3 The method of the embodiment shown is specifically used to perform the following operations:

[0215] Acquire channel state information of multiple user equipments;

[0216] Sending the channel state information to a network device;

[0217] The loop performs the following operations until the first preset condition is met:

[0218] Acquire first information, where the first information includes a reception vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces;

[0219] Optimize the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector;

[0220] Sending the second precoding vector to the network device;

[0221] In which, in the first cycle, the first information and the first precoding vector are initialized by the first access point; in the Nth cycle, the first information is the second information obtained after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0222] Or to do the following:

[0223] receiving channel state information of multiple user equipments sent by multiple access points;

[0224] The loop performs the following operations until the first preset condition is met:

[0225] receiving second precoding vectors of the multiple user equipments sent by the multiple access points, where the second precoding vectors are obtained by the multiple access points optimizing the first precoding vectors of the multiple user equipments according to first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces;

[0226] Optimizing and updating the first information according to the channel state information and the second precoding vector to obtain second information;

[0227] sending the second information to the multiple access points;

[0228] In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0229] Fig.10 This is a schematic diagram of the structure of an information processing device 100 according to an embodiment of the present application. Fig.10 In a software implementation, the information processing device 100 may include: an acquisition module 101, a sending module 102 and an optimization module 103, wherein:

[0230] An acquisition module 101 is used to acquire channel state information of multiple user equipments;

[0231] A sending module 102, sending the channel state information to a network device;

[0232] The loop performs the following operations until the first preset condition is met:

[0233] The acquisition module 101 acquires first information, where the first information includes a receiving vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces;

[0234] An optimization module 103 optimizes the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector;

[0235] The sending module 102 sends the second precoding vector to the network device;

[0236] In which, in the first cycle, the first information and the first precoding vector are initialized by the first access point; in the Nth cycle, the first information is the second information obtained after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0237] The information processing device 100 provided in the embodiment of the present application can also execute Figure 2 The method is implemented by the information processing device 100 Figure 2 The functions of the illustrated embodiments will not be described in detail in this application.

[0238] Fig.11 This is a schematic diagram of the structure of an information processing device 110 according to an embodiment of the present application. Fig.11 In a software implementation, the information processing device 110 may include: a receiving module 111, an optimizing module 112 and a sending module 113, wherein:

[0239] A receiving module 111 receives channel state information of multiple user equipments sent by multiple access points;

[0240] The loop performs the following operations until the first preset condition is met:

[0241] The receiving module 111 receives second precoding vectors of the multiple user equipments sent by the multiple access points, where the second precoding vectors are obtained by the multiple access points after optimizing the first precoding vectors of the multiple user equipments according to first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces;

[0242] An optimization module 112 optimizes and updates the first information according to the channel state information and the second precoding vector to obtain second information;

[0243] A sending module 113 sends the second information to the multiple access points;

[0244] In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0245] The information processing device 110 provided in the present application may also execute Figure 3 The method is implemented by the information processing device 110 Figure 3 The functions of the illustrated embodiments will not be described in detail in this application.

[0246] The embodiment of the present application further provides an information processing system, the information processing system comprising a plurality of access points, a plurality of smart metasurfaces and a network device, wherein the plurality of access points and the plurality of smart metasurfaces provide communication services for a plurality of user devices by collaboratively interacting with the network device, wherein:

[0247] The multiple access points acquire channel state information of the multiple user equipments; and send the channel state information to the network device;

[0248] The loop performs the following operations until the first preset condition is met:

[0249] The multiple access points acquire first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of the multiple smart metasurfaces; optimize the first precoding vectors of the multiple user devices according to the first information to obtain a second precoding vector; and send the second precoding vector to the network device;

[0250] The network device optimizes and updates the first information according to the channel state information and the second precoding vector to obtain second information; and sends the second information to the multiple access points;

[0251] In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

[0252] In the information processing system provided in the embodiment of the present application, each access point can implement Figures 1 to 5 The functions of the access point (or first access point) in the embodiment shown are that the network device can implement Figures 1 to 5 The functions of the network devices in the illustrated embodiment will not be described in detail here.

[0253] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0254] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0255] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0256] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0257] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. An information processing method, characterized in that: Applied to the first access point, including: Acquire channel state information of multiple user equipments; Sending the channel state information to a network device; The loop performs the following operations until the first preset condition is met: Acquire first information, where the first information includes a reception vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces; Optimize the first precoding vectors of the multiple user equipments according to the first information to obtain a second precoding vector; Sending the second precoding vector to the network device; In the first cycle, the first information and the first precoding vector are initialized by the first access point; in the Nth cycle, the first information is sent to the first access point after the network device optimizes and updates the first information in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

2. The method according to claim 1, characterized in that The acquiring channel state information of multiple user equipments includes: For each user equipment, sending a pilot signal to the user equipment; receiving channel state information fed back by the user equipment according to the pilot signal; The channel state information is obtained by performing channel estimation on a first channel and a second channel, wherein the first channel is a direct channel between the user equipment and the first access point, and the second channel is a cascade channel between the first access point, the multiple smart metasurfaces, and the user equipment.

3. The method according to claim 1, characterized in that The optimizing the first precoding vectors of the plurality of user equipments according to the first information to obtain a second precoding vector includes: Determine an objective function according to the receiving vector, the weight vector, the precoding cross information and the phase information in the first information; The first precoding vector is optimized according to the objective function to obtain a second precoding vector.

4. The method according to claim 3, characterized in that The determining of the objective function according to the receiving vector, the weight vector, the precoding cross information and the phase information in the first information includes: Under the transmit power constraint and the phase constraint, constructing a maximum weighted sum rate optimization problem according to the first precoding vector to obtain a first function; According to the receiving vector, the weight vector, the weighted rate sum of the plurality of user equipments and the corresponding summed mean square error, performing equivalent transformation on the first function to obtain a second function; The second function is simplified according to the receiving vector, the weight vector, the precoding cross information and the phase information to obtain the objective function.

5. The method according to claim 3, characterized in that The optimizing the first precoding vector according to the objective function to obtain a second precoding vector includes: Introducing the transmit power constraint condition into the objective function using a Lagrange multiplier method, and applying a first-order optimization condition to the objective function to obtain an optimal expression of a precoding vector; Determine a third precoding vector according to the optimal expression of the precoding vector; The first precoding vector is optimized according to the third precoding vector to obtain the second precoding vector.

6. The method according to claim 1, characterized in that The method further comprises: After the cycle is finished, downlink transmission is performed with the multiple user equipments according to the second precoding vector obtained in the last cycle.

7. The method according to claim 1, characterized in that The first access point is any one of the multiple access points, and the multiple access points and the multiple smart super surfaces provide communication services for the multiple user devices by collaboratively interacting with the network device.

8. An information processing method, characterized in that: Applied to network equipment, including: receiving channel state information of multiple user equipments sent by multiple access points; The loop performs the following operations until the first preset condition is met: receiving second precoding vectors of the multiple user equipments sent by the multiple access points, where the second precoding vectors are obtained by the multiple access points optimizing the first precoding vectors of the multiple user equipments according to first information, where the first information includes a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces; Optimizing and updating the first information according to the channel state information and the second precoding vector to obtain second information; sending the second information to the multiple access points; In which, in the first cycle, the first information and the first precoding vector are initialized by the multiple access points; in the Nth cycle, the first information is the second information obtained by the network device in the N-1th cycle, and the first precoding vector is the second precoding vector in the N-1th cycle.

9. The method according to claim 8, characterized in that The optimizing and updating the first information according to the channel state information and the second precoding vector to obtain the second information includes: Obtain a second function, where the second function is obtained by equivalently transforming the first function according to the receiving vector, the weight vector, the weighted rate sum of the multiple user equipments, and the corresponding summed mean square error, and the first function is obtained by constructing a maximum weighted sum rate optimization problem according to the first precoding vector under a transmit power constraint and a phase constraint; Applying a first-order optimization condition to the second function according to the channel state information and the second precoding vector, updating the receiving vector, and obtaining an updated receiving vector; Updating the weight vector according to the channel state information, the second precoding vector and the updated receiving vector to obtain an updated weight vector; Updating the precoding cross information according to the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector to obtain updated precoding cross information; The phase information is optimized according to the channel state information, the second precoding vector, the updated receiving vector, the updated weight vector and the second function to obtain optimized phase information.

10. The method according to claim 9, characterized in that The optimizing the phase information according to the channel state information, the second precoding vector, the updated receiving vector, the updated weight vector and the second function to obtain the optimized phase information includes: Introducing the channel state information, the second precoding vector, the updated receiving vector, and the updated weight vector into the second function to determine a first expression for optimizing the phase information; Performing vectorization processing on the first expression to obtain a second expression; The second expression is solved using a preset algorithm to obtain optimized phase information.

11. The method according to claim 10, characterized in that The optimized phase information includes discrete phase information and / or continuous phase information.

12. The method according to claim 8, characterized in that The method further comprises: When the cycle ends, the phase information in the second information obtained in the last cycle is sent to the controller of the multiple smart metasurfaces as target information, and the target information is used by the controller of the multiple smart metasurfaces to perform phase control on the multiple smart metasurfaces.

13. The method according to claim 8, characterized in that The network device provides communication services for the multiple user devices by collaboratively interacting with the multiple access points and the multiple smart metasurfaces.

14. A communication device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 13.

15. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of a communication device, the communication device is enabled to execute the method according to any one of claims 1 to 13.

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