Information processing method, communication device, and computer-readable storage medium

Abstract

Provided in the present application is an information processing method. The method comprises: a first access point acquiring channel state information of a plurality of user equipments; sending the channel state information to a network device; and cyclically executing the following operations, and ending the cycle until a first preset condition is met: acquiring first information, wherein the first information comprises a reception vector, a weight vector, precoded crossing information between a plurality of access points, and phase information of a plurality of reconfigurable intelligence surfaces; optimizing first precoding vectors of the plurality of user equipments according to the first information, so as to obtain second precoding vectors; and sending the second precoding vectors to the network device. Further provided in the present application are a communication device and a computer-readable storage medium.

Description

Information processing method, communication device and computer-readable storage medium

[0001] Cross-references

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on September 5, 2023, with application number 202311142233.0 and title “Information Processing Method, Communication Equipment and Computer-Readable Storage Medium”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of communication technology, and in particular to an information processing method, communication equipment, and computer-readable storage medium. Background Art

[0004] To meet the demands of future communication networks for large-scale connectivity, high spectral efficiency, and highly reliable, energy-efficient communications, cell-free, massively multi-input, multi-output (MIMO) systems and reconfigurable intelligence surfaces (RIS) are key candidate technologies. In a typical application scenario, intelligent metasurfaces can be introduced into cell-free networks. Multiple access points (APs) in the cell-free network can collaborate with multiple intelligent metasurfaces and network devices (such as core network equipment) to provide communication services to multiple user devices.

[0005] In the case of a cell-free network assisted by a smart metasurface, it is often necessary to design the precoding vectors of the access point and the phase information of the smart metasurface. In related technologies, network equipment can process the relevant information and implement the design of precoding vectors and phase information. However, this approach often results in high processing costs and computational complexity for network equipment, which in turn affects system performance.

[0006] Summary of the Invention

[0007] The present application provides an information processing method, a communication device, and a computer-readable storage medium.

[0008] In a first aspect, an information processing method is provided, which is applied to a first access point, including: obtaining channel state information of multiple user devices; sending the channel state information to a network device; looping and performing the following operations until the loop ends when a first preset condition is met: obtaining first information, the first information including a receiving vector, a weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces; optimizing the first precoding vectors of the multiple user devices according to the first information to obtain a second precoding vector; sending the second precoding vector to the network device; wherein, in the first loop, the first information and the first precoding vector are initialized by the first access point; in the Nth loop, the first information is the first information sent to the first access point after being optimized and updated by the network device in the N-1th loop, and the first precoding vector is the second precoding vector in the N-1th loop.

[0009] In a second aspect, an information processing method is provided, which is applied to a network device, comprising: receiving channel state information of multiple user equipments sent by multiple access points; and looping and performing the following operations until a first preset condition is satisfied:

[0010] Receiving second precoding vectors for the multiple user devices 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 devices 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; and sending the second information to the multiple access points; wherein, in a first cycle, the first information and the first precoding vector are initialized by the multiple access points; and in an 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.

[0011] In a third aspect, a communication device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the method described in the first aspect or the second aspect.

[0012] In a fourth aspect, a computer-readable storage medium is provided. 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 described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0014] FIG1 is a schematic diagram of a scenario of a smart metasurface-assisted cell-free network according to an embodiment of the present application;

[0015] FIG2 is a flow chart of an information processing method according to an embodiment of the present application;

[0016] FIG3 is a flow chart of an information processing method according to an embodiment of the present application;

[0017] FIG4 is a flow chart of an information processing method according to an embodiment of the present application;

[0018] FIG5 is a schematic diagram of interaction between network elements in a cell-free network assisted by an intelligent metasurface according to an embodiment of the present application;

[0019] FIG6 is a schematic diagram of convergence performance of an information processing method according to an embodiment of the present application;

[0020] FIG7 is a schematic diagram showing the performance effect of an information processing method according to an embodiment of the present application;

[0021] FIG8 is a schematic diagram showing the effects of an information processing method according to an embodiment of the present application under different smart metasurface deployment modes;

[0022] FIG9 is a schematic structural diagram of a communication device according to an embodiment of the present application;

[0023] FIG10 is a schematic structural diagram of an information processing device according to an embodiment of the present application;

[0024] FIG11 is a schematic structural diagram of an information processing device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] To meet the demands of future green 6G networks for large-scale connectivity, high spectral efficiency, and highly reliable, energy-efficient communications, cell-free massive Multiple-Input Multiple-Output (MIMO) systems and reconfigurable intelligence surfaces (RIS) are key candidate technologies. To address the performance limitations of traditional cellular systems due to inter-cell interference, cell-free networks combine the advantages of massive MIMO and distributed systems to eliminate interference at cell boundaries. Furthermore, they employ a user-centric transmission design, where all access points (APs) in the network collaborate to provide cooperative services for all users. This addresses the marginalization issue inherent in traditional cellular scenarios, achieving high-capacity coverage and improving system fairness.

[0026] Network demand will increase significantly in the future. To further increase network capacity, traditional methods of site or antenna densification have the unavoidable problem of high hardware cost and high energy consumption. As a passive reflective device with programmable electromagnetic properties, configurable smart surfaces have the advantages of low energy consumption and low cost. They can also reflect and control 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, research is urgently needed on system model analysis and system-level beamforming algorithm design in cell-free networks assisted by smart metasurfaces to promote the application of configurable smart surface technology in future networks.

[0027] Currently, most designs in cell-free networks are processed in a centralized manner. In one embodiment, the central processing unit (CPU) deployed on the network equipment side (such as the core network or 5G centralized unit (CU)) can perform unified processing. 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 scheme 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.

[0028] 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 the precoding vector 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. This can avoid frequent channel state information interaction between multiple access points, reduce the computational complexity of the network device, shorten the processing time, and increase the efficiency, which can effectively ensure the system performance.

[0029] In order to help those skilled in the art better understand the technical solutions of this application, the following will clearly and completely describe the technical solutions of this application in conjunction with the drawings of one or more embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0030] The terms "first," "second," and the like in this application and the claims are used to distinguish similar objects and are not used to describe a particular order or precedence. It should be understood that such terms are interchangeable where appropriate so that this application can be implemented in sequences other than those illustrated or described herein. In addition, the term "and / or" in this application and the claims refers to at least one of the connected objects, and the character " / " generally indicates that the connected objects are in an "or" relationship.

[0031] The following describes in detail 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.

[0032] FIG1 is a schematic diagram of a scenario of an intelligent metasurface-assisted cell-free network according to an embodiment of the present application.

[0033] The network shown in Figure 1 includes multiple user devices, multiple access points, multiple smart metasurfaces, and network devices. The multiple smart metasurfaces may be reflective, each including multiple reflective units. Each smart metasurface may correspond to a controller, and each controller may regulate the phase of the corresponding smart metasurface. The multiple user devices and multiple access points may each have multiple transceiver antennas. The network device may be a core network or a 5G centralized unit (CU).

[0034] In the network shown in FIG1 , multiple access points and multiple smart metasurfaces 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 downlink signals to the user device through a cascade channel between the multiple smart metasurfaces and the user device k. When performing uplink communication, similarly, user device k can send uplink information to multiple access points through a direct channel between the multiple access points and a cascade channel between the multiple smart metasurfaces and the 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 metasurfaces with multiple access points to optimize the precoding vector and the phase information of the smart metasurface, thereby better providing communication services for user device k.

[0035] Figure 2 is a flowchart illustrating an information processing method according to an embodiment of the present application. The information processing method illustrated in Figure 2 can be applied to any access point illustrated in Figure 1. In other words, the information processing method can be executed by software or hardware installed in any access point illustrated in Figure 1. For ease of distinction, the first access point may be used to represent the information processing method. The information processing method illustrated in Figure 2 is described as follows.

[0036] S202: Acquire channel state information of multiple user equipments.

[0037] The multiple user devices here can be the multiple user devices shown in Figure 1, that is, multiple user devices in a cell-free network. The channel state information (CSI) corresponding to the multiple user devices can be the downlink channel state information of the multiple user devices when the first access point, with the assistance of multiple smart metasurfaces, communicates downlink with the multiple user devices. 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 metasurfaces and the user device. The multiple smart metasurfaces are smart metasurfaces that assist the first access point in communicating with the user device through reflected signals.

[0038] When acquiring channel state information of multiple user equipments, the same method can be used for each user equipment to acquire the channel state information. As an embodiment, the channel state information of each user equipment can be acquired by the following steps:

[0039] Send a pilot signal to the user equipment; receive channel state information fed back by the user equipment based on the pilot signal.

[0040] In one embodiment, when channel state information needs to be obtained, a first access point may send a pilot signal to a user device. The pilot signal may be sent to the user device via a first channel and a second channel. The first channel is a direct channel between the first access point and the user device, and the second channel is a cascaded channel between the first access point, multiple smart metasurfaces, and the user device (including a direct channel between the first access point and the multiple smart metasurfaces, and a direct channel between the multiple smart metasurfaces and the user device). After receiving the pilot signal, the user device may perform channel estimation on the first and second channels, obtain channel state information corresponding to the user device based on the channel estimation results, and send the channel state information to the first access point. In this way, the first access point can obtain channel state information corresponding to a user device.

[0041] 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 equipment.

[0042] S204: Send the channel state information to the network device.

[0043] 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.

[0044] The receive vector and weight vector are auxiliary vectors introduced to optimize the precoding vector. The receive vector can be the receive vector of the user device when receiving a downlink signal. For example, the receive vector can be a linear receive filter vector on the user device side. The weight vector is also a vector specific to the user device. Corresponding to the receive vector, it can represent the weight information of the user device receiving the downlink signal. The multiple access points and multiple smart metasurfaces can be the multiple access points and multiple smart metasurfaces shown in Figure 1.

[0045] As an embodiment, when obtaining the first information, the first access point may obtain the first information through initialization. That is, the reception vector, weight vector, precoding cross information between multiple access points, and phase information of multiple smart metasurfaces may be initialized by the first access point. Alternatively, the first information may be sent to the first access point by the network device.

[0046] S208: Optimize the first precoding vectors of the plurality of user equipments according to the first information to obtain a second precoding vector.

[0047] The first precoding vector is a downlink precoding vector used by the first access point when performing downlink communications with multiple user devices. In the 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 implementation method of the first access point initializing the first precoding vector can refer to the implementation in the relevant technology and 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. There is no limitation on the method for obtaining the first precoding vector 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.

[0048] 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.

[0049] 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:

[0050] An objective function is determined according to the reception vector, the weight vector, the precoding cross information, and the phase information in the first information; and the first precoding vector is optimized according to the objective function to obtain a second precoding vector.

[0051] The objective function is a function used to optimize the first precoding vector, and may also be referred to as an optimization target expression of the precoding vector (or precoding matrix). The objective function is a convex problem and is easy to solve.

[0052] 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.

[0053] S2081: Under the transmit 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.

[0054] The transmit power constraint refers to the transmit power constraint of the first access point. In one embodiment, when the first access point sends downlink information to multiple user devices, the actual transmit power of the first access point must be less than or equal to the maximum transmit power of the first access point. The phase constraint refers to the phase constraint of multiple smart metasurfaces. When the smart metasurface reflects a received signal, the reflective elements in the smart metasurface must satisfy the unit mode constraint, which is the phase constraint.

[0055] Under transmit power and phase constraints, a weighted sum rate optimization problem can be constructed based on the first precoding vector to obtain a first function. The first function is related to the first precoding vector, weighting coefficients of multiple user devices, signal-to-interference-and-noise ratios of received signals of the multiple user devices, and phase information of the multiple smart metasurfaces, and is subject to the transmit power and phase constraints.

[0056] 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.

[0057] S2082: Perform equivalent transformation on the 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 to obtain a second function.

[0058] When transforming the first function, the relationship between the weighted sum of the rates 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 rates 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 maximization weighted sum rate optimization problem is equivalently transformed into a mean square error minimization problem. In this way, the first function can be transformed into the second function.

[0059] 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.

[0060] After obtaining the second function, the second function can be further simplified to obtain the objective function, which is a convex problem.

[0061] Thus, through the above steps S2081 to S2083, the non-convex problem of precoding vector optimization can be transformed into a convex problem, thereby obtaining an easily solvable objective function. After obtaining the objective function, the first precoding vector can be optimized according to the objective function to obtain an optimized second precoding vector.

[0062] 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.

[0063] S2084: Use the Lagrange multiplier method to introduce the transmit power constraint condition into the objective function, and apply the first-order optimization condition to the objective function to obtain the optimal expression of the precoding vector.

[0064] Here, the Lagrange multiplier method can be used to address the transmit power constraint. By applying first-order optimization conditions to the objective function for the first access point and multiple user devices, a closed-form form of the active beamformer for the first access point can be derived, yielding the optimal expression for the precoding vector. This optimal expression for the precoding vector is related to the multiplier introduced by the transmit power constraint and the precoding cross-information between multiple access points. The multiplier introduced by the transmit power constraint can be obtained using a bisection method.

[0065] S2085: Determine a third precoding vector according to the optimal expression of the precoding vector.

[0066] After obtaining the optimal expression of the precoding vector, a precoding vector may be calculated based on the optimal expression. Here, for easy distinction, a third precoding vector may be used to represent it.

[0067] S2086: Optimize the first precoding vector according to the third precoding vector to obtain a second precoding vector.

[0068] After obtaining the third precoding vector, the first precoding vector can be optimized based on the third precoding vector, thereby obtaining an optimized second precoding vector. When optimizing the first precoding vector based on the third precoding vector, the first precoding vector and the third precoding vector can be weightedly summed (where the sum of the weight coefficients of the first precoding vector and the third precoding vector is 1), and the weighted sum result is used as the second precoding vector. This can also be achieved using other methods, which are not limited here.

[0069] At this point, the first precoding vector can be optimized according to the first information, and the optimized second precoding vector can be obtained.

[0070] S210: Send the second precoding vector to the network device.

[0071] After obtaining the second precoding vector, it can be sent to the network device. After receiving the second precoding vector and channel state information sent by the first access point, the network device can optimize and update the first information in S206 in combination with the second precoding vectors and channel state information sent by other access points to obtain second information, and send the second information to multiple access points. The implementation of optimizing and updating the first information by the network device can be seen in the embodiment shown in FIG3 and will not be described in detail here.

[0072] S212: Determine whether a first preset condition is met.

[0073] After the second precoding vector is sent to the network device, a determination may be made as to whether a first preset condition is satisfied. The first preset condition may be a condition regarding whether a rate value has converged. In one embodiment, the determination may be made as to whether a rate value for the aforementioned maximum weighted sum rate optimization problem has converged based on the second precoding vector. Satisfying the first preset condition may mean obtaining a converged rate value, while failing to satisfy the first preset condition may mean not obtaining a converged rate value.

[0074] 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 further optimizing the second precoding vector, the above S206 to S212 can be returned to be executed. 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. In this way, the precoding vectors of multiple user devices can be continuously optimized by performing a cyclic iteration with the network device until the first preset condition is met.

[0075] 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, during the above-mentioned iterative process, the first access point does not need to repeatedly execute 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 repeatedly execute S206 to S212.

[0076] S214: End.

[0077] After the above iterative process is completed, for the first access point, the second precoding vector obtained in the last cycle can be used as the optimal precoding vector.

[0078] In one embodiment, after obtaining the optimal precoding vector, the first access point may perform downlink transmission with multiple user devices based on the optimal precoding vector. Since the precoding vector used for downlink transmission is the optimal precoding vector, the first access point can better provide communication services for the multiple user devices when performing downlink transmission with the multiple user devices based on the optimal precoding vector.

[0079] In an embodiment of the present application, in a scenario where a smart metasurface assists in cell-free network communication, the design of the precoding vector and the phase information of the smart metasurface can be achieved through multiple cyclic interactions between multiple access points in the cell-free network and network devices. The 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 the multiple access points. The design of the precoding vector and the phase information of the smart metasurface can be achieved through interactive collaboration between the multiple access points and the network devices. This can effectively reduce the computational complexity of the network device, shorten processing time, improve efficiency, and effectively ensure system performance.

[0080] Figure 3 is a flow chart of an information processing method according to an embodiment of the present application. The information processing method shown in Figure 3 can be applied to the network device shown in Figure 1. In other words, the information processing method can be executed by software or hardware installed in the network device. In one embodiment, the information processing method can be executed by a CPU in the network device. The information processing method shown in Figure 3 is described as follows.

[0081] S302: Receive channel state information of multiple user equipments sent by multiple access points.

[0082] For each of the multiple access points, channel state information corresponding to multiple user devices can be determined based on the method described in the embodiment shown in FIG. 2 , and the channel state information can be sent to the network device. After the multiple access points send the obtained channel state information to the network device, the network device can receive the channel state information corresponding to the multiple user devices sent from the multiple access points. The channel state information sent by each access point can be the downlink channel state information of each of the multiple user devices when the access point, with the assistance of the multiple smart metasurfaces, conducts downlink communications with the multiple user devices.

[0083] S304: Receive second precoding vectors of multiple user equipments sent by multiple access points.

[0084] For each of the multiple access points, the first precoding vectors of the multiple user devices can be optimized according to the first information based on the method described in the embodiment shown in Figure 2, and the optimized second precoding vectors are sent to the network device, wherein the first information may include a receiving vector, a weight vector, precoding cross information between the multiple access points, and phase information of the multiple smart metasurfaces.

[0085] 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.

[0086] S306: Optimize and update the first information according to the channel state information and the second precoding vector to obtain second information.

[0087] After receiving channel state information and second precoding vectors from multiple access points, the network device can optimize and update the first information based on the channel state information and the second precoding vector to obtain second information. The second information, corresponding to the first information, may include an updated receive vector, an updated weight vector, updated precoding cross information, and optimized phase information.

[0088] 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.

[0089] S3061: Obtain the second function.

[0090] The second function here and the second function in the embodiment shown in FIG2 are a single function. In one embodiment, the first function is equivalently transformed based on the receive vector, the weight vector, the weighted sum of the rates of multiple user equipments, and the corresponding summed mean square error. The first function is obtained by constructing a maximization weighted sum rate optimization problem based on the first precoding vector under transmit power and phase constraints. The implementation methods for determining the first function and converting the first function to the second function can be found in the corresponding contents of the embodiment shown in FIG2 and will not be repeated here.

[0091] 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 limited here.

[0092] 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.

[0093] After obtaining the second function, a first-order optimization condition may be applied to the second function based on the channel state information received in S302 and the second precoding vector received in S304, thereby updating the receive vector in the first information to obtain an updated receive vector. The expression of the updated receive vector may be determined based on the channel state information and the second precoding vector.

[0094] 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.

[0095] After obtaining the updated receive vector, the weight vector in the first information can be further updated in combination with 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 receive vector.

[0096] 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.

[0097] After obtaining the updated receive 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 for the updated precoding cross information can be determined based on the channel state information, the second precoding vector, the updated receive vector, and the updated weight vector.

[0098] 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.

[0099] 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 optimized phase information.

[0100] As an embodiment, when optimizing the phase information, the following steps may be included:

[0101] Channel state information, a second precoding vector, an updated receiving vector, and an updated weight vector are introduced into a second function to determine a first expression for optimizing phase information; and the first expression is vectorized to obtain a second expression.

[0102] The second expression is solved using a preset algorithm to obtain optimized phase information.

[0103] 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 easier to solve second expression.

[0104] After obtaining the second expression, the second expression can be solved using a preset algorithm under the phase constraint condition, so as to obtain the optimized phase information. The preset algorithm can be a conjugate gradient descent method, an MM (Majorization-Minimization) method, an alternating direction multiplier method (ADMM), or a block-by-block coordinate descent method in a manifold optimization method, and is not limited here. In one embodiment, since the conjugate gradient descent method in a manifold optimization method has low complexity and is more efficient, the conjugate gradient descent method in a manifold optimization method can be preferably used to solve the second expression.

[0105] It should be noted that after solving the second expression using the preset algorithm, the optimized phase information obtained is a continuous phase. In one implementation, if a discrete phase is desired, the continuous phase can 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 the discrete phase.

[0106] At this point, the first information can be optimized and updated based on the channel state information and the second precoding vector to obtain second information. The second information includes the updated receive vector, the updated weight vector, the updated precoding cross information, and the optimized phase information. The optimized phase information can be continuous phase information and / or discrete phase information, which can be determined based on actual needs and is not limited here.

[0107] S308: Send the second information to multiple access points.

[0108] After optimizing and updating the first information, the network device may send the first information to multiple access points. Upon receiving the second information, each access point may use the second information as the first information and optimize the first precoding vector based on the first information. The implementation of optimizing the first precoding vector is described in the embodiment shown in FIG2 and is not repeated here.

[0109] S310: Determine whether a first preset condition is met.

[0110] After sending the second information to multiple access points, it can be determined whether the first preset condition is met. The first preset condition here can be the same as the first preset condition in the embodiment shown in Figure 2. For details, please refer to the relevant explanation of the embodiment shown in Figure 2, and will not be repeated here.

[0111] 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 needs to be further optimized. When the second information is further optimized, the above S304 to S310 can be returned to be executed. Among them, in the Nth (an integer greater than or equal to 2)th loop, the first information is the second information obtained by the network device in the N-1th loop, and the first precoding vector is the second precoding vector in the N-1th loop. Therefore, the network device can continuously optimize and update the second information by performing cyclic iterations with multiple access points, that is, continuously optimize the phase information, until the first preset condition is met.

[0112] It should be noted that, since the network device can know the channel states of the multiple user devices after the multiple access points send the channel state information of the multiple user devices to the network device, during the loop iteration process, the network device does not need to repeatedly execute S302, that is, it does not need to repeatedly receive the channel state information of the multiple user devices from the multiple access points, and only needs to repeatedly execute the above S304 to S310.

[0113] S312: End.

[0114] After the above-mentioned iterative process is completed, for the network device, the phase information obtained in the last cycle can be used as the optimal phase information.

[0115] In one embodiment, after obtaining the optimal phase information, the network device may send the phase information as target information to the controllers of multiple smart metasurfaces. After receiving the target information, the controllers may perform phase control on the multiple smart metasurfaces based on the target information. Since the phase information after control by the multiple smart metasurfaces is optimal, the multiple smart metasurfaces, when combined with multiple access points and multiple user devices for downlink transmission, can better provide communication services for multiple user devices.

[0116] In an embodiment of the present application, in a scenario where a smart metasurface assists in cell-free network communication, the design of the precoding vector and the phase information of the smart metasurface can be achieved through multiple cyclic interactions between multiple access points in the cell-free network and network devices. The 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 the multiple access points. The design of the precoding vector and the phase information of the smart metasurface can be achieved through interactive collaboration between the multiple access points and the network devices. This can effectively reduce the computational complexity of the network device, shorten processing time, improve efficiency, and effectively ensure system performance.

[0117] To facilitate understanding of the technical solutions provided by the embodiments of this application, a more specific implementation method will be used as an example for illustration below. Please refer to Figures 4 and 5. Figure 4 is a flow chart of an information processing method according to an embodiment of this application. Figure 5 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 this application.

[0118] In the embodiments shown in Figures 4 and 5, AP represents an access point and RIS represents an intelligent metasurface. Assume that a cell-free network includes B APs and L RISs, which serve K user devices in a coordinated manner. The number of transmitting antennas on the AP side and receiving antennas on the user device side are M and L, respectively. t and M r , the number of reflection units included in each RIS is N, and the definition is Representing a set of AP, RIS, user equipment and RIS reflection unit respectively, then the information processing method provided in the embodiment of the present application may include the following steps.

[0119] S501: Multiple APs obtain channel state information of multiple user equipments.

[0120] In one embodiment, due to the signal regulation function of the RIS, the channel from the access point AP to the user device actually consists of three parts, namely the direct channel from the AP to the user device, the channel from the AP to the RIS, and the reflected channel from the RIS to the user device. 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 relevant channel, and let 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:

[0121] S502: Multiple APs locally initialize or receive relevant reception vectors, weight vectors, AP precoding cross information items, and RIS phase information transmitted by network devices.

[0122] In the first cycle, the AP may locally initialize the receive vector, weight vector, AP precoding cross information item, and RIS phase information. In the Nth (greater than or equal to 2)th cycle, the AP may receive the receive vector, weight vector, AP precoding cross information item, and RIS phase information sent by the network device. The receive vector, weight vector, AP precoding cross information item, and RIS phase information sent by the network device are updated receive vector, weight vector, AP precoding cross information item, and RIS phase information.

[0123] 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.

[0124] When optimizing the first precoding vector, the following steps 1 to 3 may be included.

[0125] Step 1: Construct the maximum weighted sum rate optimization problem and set w b,k For the precoding vector designed by the b-th AP for user equipment k (i.e., the first precoding vector to be optimized), the problem of maximizing the weighted sum rate objective under the transmit power and phase constraints can be expressed as the following function (i.e., the first function in the embodiment shown in FIG2 ):

[0126] Among them, ω k and P max They represent the weighting coefficient of user equipment k and the maximum transmit power of AP, signal-to-interference-and-noise ratio γ k Expressed as:

[0127] Among them, σ 2 is the variance of the Gaussian noise on the user equipment side, and the unit mode constraint of the RIS reflection unit is expressed as Precoding combination vector

[0128] Step 2: Introduce a user-side linear receive filter u k Vector (i.e., receiving vector), by exploring the relationship between weighted rate and summed mean square error, we further introduce the weight vector Ignoring irrelevant constant terms, the problem of maximizing the weighted sum rate objective is transformed into (i.e., the second function in the embodiment shown in FIG2 ), wherein the mean square error term can be expressed as:

[0129] The AP initializes locally or receives relevant information from a network device, fixes the reception vector u, weight vector f, cross information item, and RIS phase information Φ, and then, by simplifying and processing the second function, the objective problem of precoding optimization can be further expressed as the following function (i.e., the objective function in the embodiment shown in FIG2 , i.e., the optimization objective expression of the precoding vector):

[0130] Among them, in the optimization target expression of the precoding vector, the coefficient matrix And define the matrix Each element

[0131] Step 3: Using the Lagrange multiplier method to handle the transmit power constraint, apply the first-order optimization condition to the objective function for each AP b and user device k. The closed-form form of the active beamformer for all APs (i.e., the optimal expression of the precoding vector) can be derived as follows:

[0132] in, is the multiplier introduced by the transmit power constraint and can be obtained by bisection. b,k For user equipment k, in each iteration t, based on the fixed ξ b,k Each AP b and other APs calculate w locally according to the above formula at the same time 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:

[0133] 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 the tth iteration is based on The calculated precoding vector (ie, the third precoding vector) is obtained.

[0134] S504: The multiple APs send the channel state information and the optimized second precoding vector to the network device.

[0135] 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.

[0136] 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.

[0137] 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, the access vector is updated. The updated receiving vector can be expressed as:

[0138] 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:

[0139] Afterwards, the AP's 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:

[0140] Step 2: Channel state information Substitute into Ignoring the constant term that is irrelevant to the phase, the expression for optimizing the phase information (i.e., the first expression in the embodiment shown in FIG3 ) can be derived as follows:

[0141] 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 linear coefficient matrix of

[0142] 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 ,...,φl,N ] T , The phase optimization problem can be transformed into the following expression (i.e., the second expression in the embodiment shown in FIG3 ):

[0143] Step 3: Observe that the unit module constraint of the above problem actually forms a complex circular manifold The phase optimization problem caused by the above-mentioned multi-RIS can be solved by the conjugate gradient descent method in the manifold optimization method.

[0144] In one embodiment, in each iteration r, the following steps are mainly performed:

[0145] First, calculate the Euclidean gradient

[0146] Second, calculate the Riemann gradient

[0147] 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.

[0148] Finally, pull the optimization variables back into the constraint space The pullback operation is defined as α r The search step size for the Armijo backtracking line.

[0149] The optimal phase value, that is, the optimized phase information, can be obtained by iterating the above steps.

[0150] Step 4: Based on the above method, the optimal continuous phase can be obtained Afterwards, the continuous phase can be simply mapped to the discrete set via the nearest principle is the number of discrete bits, so that the optimal discrete phase can be obtained

[0151] S506: The network device sends the updated receiving vector, weight vector, AP precoding cross information, and RIS phase information to multiple APs.

[0152] The coefficient vector {u (t+1) ,f (t+1) ,ξ (t+1) ,,φ (t+1) The phase information fed back can be discrete or continuous, depending on actual needs.

[0153] For each AP, after receiving the updated receive vector, weight vector, AP precoding cross information, and RIS phase information, it can return to execute the above S502. As a result, multiple loop iterations can be performed between multiple APs and the network device. During the loop iteration process, each AP optimizes the first precoding vector based on the updated receive vector, weight vector, AP precoding cross information, and RIS phase information of the network device to obtain a second precoding vector. The network device updates the RIS phase information based on the channel state information and second precoding sent by each AP, and simultaneously updates the receive vector, weight vector, and AP precoding cross information. 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.

[0154] S507: When the convergence rate value is obtained, the loop ends.

[0155] S508: The multiple APs perform downlink transmission with the multiple user equipments based on the second precoding vector obtained in the last loop iteration.

[0156] S509: The network device sends the RIS phase information obtained in the last iteration to the RIS controller.

[0157] S510: The RIS controller performs phase control on the RIS according to the RIS phase information.

[0158] 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 realized 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.

[0159] The technical solution provided in the embodiments of the present application is a decentralized collaborative transmission design method. By comparing simulations with existing centralized processing methods, the performance of RIS under continuous and discrete conditions and different deployment modes can be analyzed to guide actual deployment.

[0160] In the simulation comparison, the 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→∞ represents 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 AP scale of a cell-free network is small, and the number of antennas of the AP and the user 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. The simulation results can be seen in Figures 6 to 8.

[0161] Figure 6 illustrates the convergence performance of the decentralized collaborative transmission method proposed in this application. As can be seen from Figure 6, when N = 100 and the convergence error does not exceed 0.1%, the proposed method converges within 15 iterations. Despite the decentralized design of active precoding, the convergence performance is almost identical to that of the centralized method and very close to the sum rate performance, with no performance loss. Similarly, the discrete bit case, random phase, and no RIS cases also converge within 15 iterations, effectively demonstrating the convergence of the sum rate optimization algorithm in the proposed method.

[0162] Figure 7 shows the performance comparison between the proposed method and the centralized method in terms of rate performance and CPU running time indicators when the number of reflection units N is 100, 200 and 300. As can be seen from Figure 7, the two methods are almost the same in terms of rate performance under different conditions. However, the collaborative transmission method proposed in this application avoids the calculation of high-dimensional matrices due to the decentralized design. At the same time, the manifold optimization design is faster than the solution 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 orders of magnitude, while 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.

[0163] FIG8 shows 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). Among them, the RIS near the AP side, i.e., 3 RIS, are deployed near the 3 set APs to jointly serve users near the origin. As can be seen from FIG8, in this set scenario, whether it is continuous bits or discrete bits, the deployment method near the user side is better than the AP side deployment. 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 infrastructure scenario where the number of APs B′=B+L, the RIS-assisted architecture still has a significant gain at low cost and low power consumption, fully demonstrating the potential of RIS to improve system capacity.

[0164] The foregoing description describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0165] FIG9 is a schematic diagram of the structure of a communication device according to an embodiment of the present application. Referring to FIG9 , at the hardware level, the communication device includes a processor, and in one embodiment, also includes 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, such as at least one disk storage device. Of course, the communication device may also include hardware required for other services.

[0166] The processor, network interface, and memory can be interconnected via an internal bus, such as an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. These buses can be classified as address buses, data buses, and control buses. For ease of illustration, FIG9 shows only one bidirectional arrow, but this does not imply that there is only one bus or only one type of bus.

[0167] The memory is used to store the program. In one embodiment, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0168] The processor reads the corresponding computer program from the non-volatile memory into the internal 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 used to perform the following operations:

[0169] Obtain channel state information of multiple user devices; send the channel state information to a network device; loop the following operations until the loop ends when a first preset condition is met: obtain first information, the first information including a receiving 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 devices according to the first information to obtain a second precoding vector; send the second precoding vector to the network device; wherein, in the first loop, the first information and the first precoding vector are initialized by the first access point; in the Nth loop, the first information 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.

[0170] Or to do the following:

[0171] Receive channel state information of multiple user devices sent by multiple access points; loop and perform the following operations until the loop ends when a first preset condition is met: receive second precoding vectors of the multiple user devices 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 devices 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; optimize and update the first information according to the channel state information and the second precoding vector to obtain second information; send the second information to the multiple access points; wherein, in the first loop, the first information and the first precoding vector are initialized by the multiple access points; in the Nth loop, the first information is the second information obtained by the network device in the N-1th loop, and the first precoding vector is the second precoding vector in the N-1th loop.

[0172] The method performed by the information processing device disclosed in the embodiment shown in FIG9 of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or software instructions. 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 gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in this application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly implemented as a hardware decoding processor or performed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0173] The communication device can also execute the methods of Figures 2 and 3 and realize the functions of the information processing device in the embodiments shown in Figures 2 and 3, which will not be described in detail in this application.

[0174] Of course, in addition to software implementation, the communication device of this 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.

[0175] The present application also provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions. When executed by a portable communication device including multiple application programs, the instructions enable the portable communication device to execute the method of the embodiments shown in FIG. 2 and FIG. 3 , and to perform the following operations:

[0176] Obtain channel state information of multiple user devices; send the channel state information to a network device; loop the following operations until the loop ends when a first preset condition is met: obtain first information, the first information including a receiving 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 devices according to the first information to obtain a second precoding vector; send the second precoding vector to the network device; wherein, in the first loop, the first information and the first precoding vector are initialized by the first access point; in the Nth loop, the first information 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.

[0177] Or to do the following:

[0178] Receive channel state information of multiple user devices sent by multiple access points; loop and perform the following operations until the loop ends when a first preset condition is met: receive second precoding vectors of the multiple user devices 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 devices 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; optimize and update the first information according to the channel state information and the second precoding vector to obtain second information; send the second information to the multiple access points; wherein, in the first loop, the first information and the first precoding vector are initialized by the multiple access points; in the Nth loop, the first information is the second information obtained by the network device in the N-1th loop, and the first precoding vector is the second precoding vector in the N-1th loop.

[0179] FIG10 is a schematic structural diagram of an information processing device 100 according to an embodiment of the present application. Referring to FIG10 , in a software implementation, the information processing apparatus 100 may include: an acquisition module 101, a sending module 102, and an optimization module 103, wherein: the acquisition module 101 acquires channel state information of multiple user devices; the sending module 102 sends the channel state information to a network device; and the following operations are performed in a loop until a first preset condition is satisfied and the loop ends: 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; the optimization module 103 optimizes the first precoding vectors of the multiple user devices based on the first information to obtain a second precoding vector; and the sending module 102 sends the second precoding vector to the network device; wherein, in a first loop, the first information and the first precoding vector are initialized by the first access point; and in an Nth loop, the first information 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.

[0180] The information processing device 100 provided in the embodiment of the present application can also execute the method of Figure 2 and realize the functions of the information processing device 100 in the embodiment shown in Figure 2, which will not be repeated in this application.

[0181] FIG11 is a schematic diagram of the structure of an information processing device 110 according to an embodiment of the present application. Referring to FIG11 , 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: the receiving module 111 receives channel state information of multiple user equipments sent by multiple access points; the following operations are performed in a loop until the loop ends when a first preset condition is met: the receiving module 111 receives the second precoding vectors of the multiple user equipments sent by the multiple access points, the second precoding vectors being obtained by the multiple access points after optimizing the first precoding vectors of the multiple user equipments according to the first information, the first information including the receiving vector, weight vector, precoding cross information between the multiple access points, and phase information of multiple smart metasurfaces; 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; a sending module 113 sends the second information to the multiple access points; wherein, 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.

[0182] The information processing device 110 provided in this application can also execute the method of Figure 3 and realize the functions of the information processing device 110 in the embodiment shown in Figure 3, which will not be repeated in this application.

[0183] The embodiment of the present application also provides an information processing system, the information processing system includes multiple access points, multiple smart metasurfaces and network devices, the multiple access points and multiple smart metasurfaces provide communication services for multiple user devices by collaboratively interacting with the network devices, wherein: the multiple access points obtain channel state information of the multiple user devices; the channel state information is sent to the network device; the following operations are performed in a loop until the loop ends when a first preset condition is met: the multiple access points obtain first information, the first information including a receiving vector, a weight vector, precoding cross information between the multiple access points and phase information of the multiple smart metasurfaces; according to the first information, The first precoding vectors of the multiple user devices are optimized to obtain a second precoding vector; the second precoding vector is sent to the network device; 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 the second information is sent to the multiple access points; wherein, in a 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.

[0184] In the information processing system provided in the embodiments of the present application, each access point can implement the functions of the access point (or first access point) in the embodiments shown in Figures 1 to 5, and the network device can implement the functions of the network device in the embodiments shown in Figures 1 to 5, which will not be described in detail here.

[0185] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0186] The systems, devices, modules, or units described in the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. In one embodiment, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, 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.

[0187] Computer-readable media includes 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 RAM (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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (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 transitory computer-readable media such as modulated data signals and carrier waves.

[0188] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0189] The various embodiments in this application are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment.

Claims

1. An information processing method, applied to a first access point, comprising: 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 of claim 1, wherein: 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 of claim 1, wherein: 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 of claim 3, wherein: 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 of claim 3, wherein: The optimizing the first precoding vector according to the objective function to obtain a second precoding vector includes: Introducing a 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 of claim 1, wherein: 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 of claim 1, wherein: 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, applied to a network device, comprising: 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 of claim 8, wherein: 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 of claim 9, wherein: 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 of claim 10, wherein: The optimized phase information includes discrete phase information and / or continuous phase information.

12. The method of claim 8, wherein: 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 of claim 8, wherein: 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 comprising: 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, when 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.

Images