A wireless probe communication joint optimization method and system

Abstract

The application discloses a wireless detection communication joint optimization method and system, and the method comprises the following steps: a controller determines a to-be-optimized area and deployment information of an associated intelligent reflecting surface; when the intelligent reflecting surface is deployed, the controller sends optimization demand parameters of the to-be-optimized area to a base station; the base station determines area-level parameters according to the optimization demand parameters; after the base station controls the intelligent reflecting surface to reflect cell-level control signals to scheduled users in the to-be-optimized area according to the area-level parameters, the base station receives corresponding first echo signals and determines user-level parameters in combination with the optimization demand parameters; and after the base station controls the intelligent reflecting surface to reflect user data signals to the scheduled users according to the user-level parameters, the base station receives corresponding second echo signals and re-determines user-level parameters in combination with the optimization demand parameters. The application can reliably optimize the detection and coverage performance of the to-be-optimized area.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, specifically to a method and system for joint optimization of wireless detection and communication. Background Technology

[0002] Coverage and capacity are two key performance indicators in mobile communication networks. Insufficient capacity to meet user needs can be considered a special case of coverage problems. To address network coverage or capacity optimization issues, manual or self-organizing network techniques are typically used to adjust relevant base station parameters (such as base station antenna azimuth, base station antenna downtilt angle, and base station transmit power parameters).

[0003] Because mobile communication networks are prone to weak coverage areas or coverage gaps due to obstructions from buildings, especially in urban centers with many tall buildings, adjusting the parameters of relevant base stations to optimize weak coverage areas or coverage gaps is ineffective and may even affect the configuration of neighboring base stations, leading to instability in the mobile communication network. In addition, the wireless positioning methods used to solve optimization problems usually rely on line-of-sight wireless signals, but weak coverage areas or coverage gaps may not have line-of-sight channels, making it impossible to efficiently and reliably optimize the detection and coverage performance in weak coverage areas or coverage gaps. Summary of the Invention

[0004] This invention provides a method and system for joint optimization of wireless detection and communication to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0005] Firstly, a joint optimization method for wireless detection and communication is provided, including:

[0006] The controller determines the area to be optimized and the deployment information of its associated smart reflective surfaces;

[0007] When the intelligent reflective surface is deployed, the controller sends the optimization requirement parameters of the area to be optimized to the base station;

[0008] The base station determines the regional parameters based on the received optimization requirement parameters;

[0009] According to the regional parameters, the base station controls the intelligent reflector to reflect the cell-level control signal to the scheduled users in the area to be optimized, and then receives the corresponding first echo signal and determines the user-level parameters in combination with the optimization requirement parameters.

[0010] According to the user-level parameters, the base station controls the intelligent reflector to reflect user data signals toward the scheduled user, then receives the corresponding second echo signal and, in conjunction with the optimization requirement parameters, redetermines the user-level parameters.

[0011] Furthermore, the controller determines the deployment information of the region to be optimized and its associated smart reflective surface, including:

[0012] The controller determines the region to be optimized and performs rasterization on it to obtain several raster regions;

[0013] The controller determines the optimal configuration parameters and optimal deployment location of the smart reflector based on the given coverage optimization requirements of the area to be optimized, with the optimization objective of maximizing the received signal strength of the single grid area farthest from the base station.

[0014] Furthermore, the controller sending the optimization requirement parameters of the area to be optimized to the base station includes:

[0015] The controller autonomously sends the optimization requirement parameters of the area to be optimized to the base station;

[0016] Alternatively, the base station generates optimization request information and sends it to the controller, the optimization request information carrying the identification information of the area to be optimized; after receiving the optimization request information, the controller generates corresponding optimization response information, the optimization response information carrying the optimization requirement parameters of the area to be optimized, and then sends the optimization response information to the base station.

[0017] Furthermore, the region to be optimized is composed of several grid regions, and the optimization requirement parameters of the region to be optimized include the given coverage optimization requirement, the given detection requirement, the optimization strategy and the location information of each grid region associated with the region to be optimized, as well as the deployment location and basic operating parameters of the intelligent reflective surface.

[0018] Furthermore, the base station determines the region-level parameters based on the received optimization requirement parameters, including:

[0019] When the optimization strategy is an integrated detection and communication optimization strategy, the base station determines the regional parameters based on the given coverage optimization requirements, the given detection requirements, and the location information of each grid area, with the goal of maximizing the average received signal strength of the area to be optimized as the coverage optimization objective and the goal of maximizing the average detection accuracy of the area to be optimized as the detection optimization objective.

[0020] When the optimization strategy is a communication optimization strategy, the base station determines the regional parameters based on the given coverage optimization requirements and the location information of each grid area, with the goal of maximizing the average received signal strength of the area to be optimized.

[0021] Furthermore, the step of receiving the corresponding first echo signal and combining it with the optimized requirement parameters to determine the user-level parameters includes:

[0022] The base station receives the first echo signal corresponding to the cell-level control signal, and then, in conjunction with the deployment location of the smart reflector, determines the current location information of the scheduled user.

[0023] The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information.

[0024] The base station redetermines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

[0025] Furthermore, the step of receiving the corresponding second echo signal and, in conjunction with the optimized requirement parameters, re-determining the user-level parameters includes:

[0026] The base station receives the second echo signal corresponding to the user data signal, and then, in conjunction with the deployment location of the intelligent reflector, determines the current location information of the scheduled user.

[0027] The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information.

[0028] The base station redetermines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

[0029] Secondly, a joint optimization method for wireless detection and communication is provided, applied to a base station, including:

[0030] Receive optimization requirement parameters for the region to be optimized from the controller, and then determine the region-level parameters;

[0031] Based on the region-level parameters, after the deployed intelligent reflector reflects the cell-level control signal to the scheduled users in the region to be optimized, it receives the corresponding first echo signal and determines the user-level parameters by combining it with the optimization requirement parameters; wherein, the deployment information of the intelligent reflector is obtained by the controller after determining the region to be optimized;

[0032] Based on the user-level parameters, after controlling the intelligent reflector to reflect user data signals toward the scheduled user, the corresponding second echo signal is received and combined with the optimization requirement parameters to redetermine the user-level parameters.

[0033] Furthermore, the optimization requirement parameters for the region to be optimized sent by the receiving controller include:

[0034] An optimization request message is generated and sent to the controller, the optimization request message carrying the identification information of the region to be optimized;

[0035] The system receives optimization response information sent by the controller, which is generated by the controller based on the optimization request information and carries optimization requirement parameters for the region to be optimized.

[0036] Thirdly, a joint optimization system for wireless detection and communication is provided, comprising:

[0037] The controller is used to determine the deployment information of the area to be optimized and its associated smart reflective surface; when the smart reflective surface is deployed, it sends the optimization requirement parameters of the area to be optimized to the base station.

[0038] The base station is configured to determine region-level parameters based on the received optimization requirement parameters; based on the region-level parameters, control the intelligent reflector to reflect cell-level control signals to the scheduled users within the region to be optimized, then receive the corresponding first echo signal and, in conjunction with the optimization requirement parameters, determine user-level parameters; based on the user-level parameters, control the intelligent reflector to reflect user data signals to the scheduled users, then receive the corresponding second echo signal and, in conjunction with the optimization requirement parameters, re-determine the user-level parameters.

[0039] The present invention has at least the following beneficial effects: by adaptively setting the optimal deployment location and optimal configuration parameters of the intelligent reflector for the area to be optimized by the controller, the coverage and detection performance of the area to be optimized can be maximized; by controlling the intelligent reflector to reflect corresponding control signals to the scheduled users in the area to be optimized by the base station, and then predicting the next location information of the scheduled users based on the echo signals corresponding to the control signals, the user-level parameters associated with the scheduled users in the intelligent reflector can be further optimized, which can improve the detection and communication performance of the scheduled users, thereby increasing the throughput of the area to be optimized and reducing detection overhead, and has good practicality. Attached Figure Description

[0040] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0041] Figure 1 This is a flowchart illustrating a joint optimization method for wireless detection and communication in an embodiment of the present invention.

[0042] Figure 2 This is another flowchart illustrating the joint optimization method for wireless detection and communication in this embodiment of the invention.

[0043] Figure 3 This is a schematic diagram of the composition of a wireless detection and communication joint optimization system according to an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of another component of the wireless detection and communication joint optimization system in this embodiment of the invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart. The terms "first," "second," etc., in the specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0048] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0049] The flowchart shown in the attached figures is merely illustrative and does not necessarily include all content and operations / steps, nor does it require them to be performed in the described order. For example, some operations / steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0050] First, the following is an explanation of some of the terms used in this application:

[0051] An Intelligent Reflection Surface (IRS) consists of a three-layer panel and an intelligent controller. The outermost layer comprises metal sheets (elemental structures) printed on a dielectric substrate that directly interact with the incident signal. The middle layer uses copper or other metals to prevent signal energy leakage. The innermost layer is a control circuit board that adjusts the reflection amplitude / phase shift of each element, controlled by the IRS's intelligent controller. In practical applications, the wireless channel is actively modified by adjusting the reflection characteristics of numerous passive reflective elements on the reflective surface (i.e., adjusting the phase, frequency, amplitude, and polarization direction of the incident signal), providing new degrees of freedom to further improve the performance of the wireless link. The signal transmitted by the base station, after being reflected by the IRS, constructively superimposes or cancels out signals propagating along other paths, thereby improving the signal-to-noise ratio at the desired receiver or effectively suppressing co-channel interference.

[0052] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a joint optimization method for wireless detection and communication provided in an embodiment of the present invention. The method is mainly implemented by the cooperation of two devices: a controller and a base station, and specifically includes the following:

[0053] Step S110: The controller determines the deployment information of the area to be optimized and its associated smart reflective surfaces;

[0054] Step S120: When the smart reflective surface is deployed, the controller sends the optimization requirement parameters of the area to be optimized to the base station;

[0055] Step S130: The base station determines the regional parameters based on the received optimization requirement parameters;

[0056] Step S140: After the base station controls the intelligent reflector to reflect the cell-level control signal to the scheduled user in the area to be optimized according to the regional-level parameters, it receives the corresponding first echo signal and determines the user-level parameters in combination with the optimization requirement parameters.

[0057] Step S150: After the base station controls the intelligent reflector to reflect user data signals toward the scheduled user according to the user-level parameters, it receives the corresponding second echo signal and, in conjunction with the optimized requirement parameters, redetermines the user-level parameters.

[0058] In this embodiment of the invention, the specific implementation process of step S110 includes, but is not limited to, the following:

[0059] Step S111: The controller determines the region to be optimized. The region to be optimized consists of location regions without line-of-sight (LOS) channels, specifically as follows:

[0060] An area with weak coverage or coverage holes is identified, and the area is probed using existing drive test (DT) or minimum drive test (MDT) methods to determine the area to be optimized. Alternatively, the base station actively reports its capability information and deployment location to the controller. The base station's capability information includes waveform capability information and antenna capability information. The waveform capability information includes the waveform type and its corresponding waveform (such as radar waveform, communication waveform, and integrated sensing waveform). The antenna capability information includes the number of antennas and beamforming vector. Then, line-of-sight channel analysis is performed on the capability information, the deployment location, and the three-dimensional electronic map associated with the area to determine the area to be optimized.

[0061] Step S112: The controller performs rasterization processing on the area to be optimized to obtain several raster areas, and then obtains the single raster area that is farthest from the base station and records it as the farthest raster area.

[0062] Step S113: The controller obtains the pre-given coverage optimization requirements of the area to be optimized, where the coverage optimization requirements are the minimum received signal strength of the area to be optimized. Further, with maximizing the received signal strength of the farthest grid area as the optimization objective, the controller determines the optimal deployment location and optimal configuration parameters of the intelligent reflector. The mathematical expression used in this implementation process is:

[0063] (d i * ,d r * ,θ i * ,s * ) = argmax[P t G IRS (d i ,d r ,θ i ,s)];

[0064] P t G IRS (d i ,d r ,θ i ,s)≥P r,thr ;

[0065]

[0066] In the formula, (d i * ,dr * ) represents the optimal deployment location of the intelligent reflective surface, and d i * d is the optimal distance between the base station and the smart reflector. i * That is, d i The optimal solution, d r * d is the optimal distance between the smart reflective surface and the individual grid area. r * That is, d r The optimal solution, (θ) i * ,s * θ represents the optimal configuration parameter for the intelligent reflective surface. i * θ is the optimal incident angle of the intelligent reflective surface. i * That is, θ i The optimal solution, s * s represents the optimal size of the intelligent reflective surface. * That is, P is the optimal solution for s. t G represents the maximum transmit power of the base station. IRS (d i ,d r ,θ i ,s) is the cascaded channel gain generated when the base station reaches the farthest grid area via the smart reflector, P r,thr G is the minimum received signal strength. t G represents the antenna gain of the base station. r This refers to the antenna gain of the mobile terminal.

[0067] It should be noted that different sizes of areas to be optimized should be adapted to smart reflectors with different capabilities. The size of the smart reflector determines its ability to reflect wireless signals. At the same time, the deployment location of the smart reflector will also affect the received signal strength of the area to be optimized. Therefore, the above step S113 is proposed to determine the optimal deployment information of the smart reflector.

[0068] After performing step S113 above, the controller determines that the smart reflective surface conforming to the optimal configuration parameters can be deployed at the optimal deployment location, so that staff can carry out on-site deployment; after the smart reflective surface is deployed, the base station can use the reflective capability of the smart reflective surface to complete the detection and communication of the area to be optimized.

[0069] In this embodiment of the invention, the parameter sending methods mentioned in step S120 above mainly include the following two:

[0070] Firstly, the controller actively reports the optimization requirement parameters of the area to be optimized to the base station, specifically as follows:

[0071] The controller generates a first optimization request and sends it to the base station. The first optimization request carries optimization requirement parameters for the area to be optimized. The base station generates corresponding optimization confirmation information based on the received first optimization request, indicating that the base station has received the optimization requirement parameters. The base station then sends the optimization confirmation information to the controller.

[0072] Secondly, the controller sends the optimization requirement parameters of the area to be optimized to the base station via a passive reporting method, specifically as follows:

[0073] The base station generates a second optimization request and sends it to the controller. The second optimization request carries the identification information of the area to be optimized. The controller generates corresponding optimization response information based on the received optimization request, and the optimization response carries the optimization requirement parameters of the area to be optimized. The controller then sends the optimization response to the base station.

[0074] In this embodiment of the invention, the controller may, but is not limited to, an OAM (Operation Administration and Maintenance) server or a SON (Self Organizing Networks) server.

[0075] In this embodiment of the invention, after executing the above step S112, it can be determined that the area to be optimized is composed of the plurality of grid areas. The optimization requirement parameters of the area to be optimized specifically include the basic operating parameters and optimal deployment position of the intelligent reflector, as well as the optimization strategy associated with the area to be optimized, the given detection requirement, the given coverage optimization requirement, and the position information of each grid area (mainly the entire area). Among them, the basic operating parameters of the intelligent reflector include the optimal size, the number of reflective units, the operating frequency band, and the scanning range. The optimization strategy includes the integrated detection and communication optimization strategy and the communication optimization strategy. The given detection requirement is the minimum detection accuracy of the area to be optimized, and the given coverage optimization requirement is the minimum received signal strength of the area to be optimized.

[0076] In this embodiment of the invention, the specific implementation process of step S130 includes, but is not limited to, the following:

[0077] Step S131: The base station determines whether the optimization strategy is an integrated exploration and communication optimization strategy; if yes, then proceed to step S132; if no, then it indicates that the optimization strategy is a communication optimization strategy, and proceed to step S133.

[0078] Step S132: The base station takes maximizing the average detection accuracy of the area to be optimized as the detection optimization objective and maximizing the average received signal strength of the area to be optimized as the coverage optimization objective. It then combines the given detection requirements, the given coverage optimization requirements, and the location information of each grid area to determine the region-level parameters. The mathematical expression used in this implementation process is:

[0079]

[0080]

[0081]

[0082]

[0083] Step S133: The base station takes maximizing the average received signal strength of the area to be optimized as the coverage optimization objective, and then determines the area-level parameters by combining the given coverage optimization requirements and the location information of each grid area. The mathematical expression used in this implementation process is:

[0084]

[0085] P r,n ≥P r,thr ;

[0086]

[0087] In the formula, Let θ be the region-level parameter. i * θ is the optimal incident angle of the intelligent reflective surface. i * That is, θ i The optimal solution. The optimal phase of the intelligent reflective surface is... That is The optimal solution, where N is the number of grid regions contained in the region to be optimized, and P r,n Let q be the received signal strength of the nth grid area. n =[q n,x ,q n,y ] T This represents the two-dimensional location information of the nth raster region (primarily based on the region center), where T is the transpose symbol. The mean square position error, P, can be understood as the detection accuracy of the nth grid region. r,thr Q is the minimum received signal strength. q,thr For the minimum detection accuracy, For the reflection coefficient vector and position parameter q n The relevant Fisher Information Matrix (FIM), P t The maximum transmit power of the base station. This is the cascaded channel gain generated when the base station reaches the nth grid area via the smart reflector.

[0088] In this embodiment of the invention, the specific implementation process of step S140 includes, but is not limited to, the following:

[0089] Step S141: The base station sends a cell-level control signal to the smart reflector using an integrated sensing waveform, and controls the smart reflector to reflect the cell-level control signal to the scheduled users in the area to be optimized according to the area-level parameters. The cell-level control signal may be, but is not limited to, a synchronization signal, a paging signal, etc.

[0090] Step S142: The base station receives the first echo signal, which corresponds to the cell-level control signal, reflected back via the smart reflector.

[0091] Step S143: The base station determines the current location information of the scheduled user based on the optimal deployment location of the smart reflector and the first echo signal, thereby determining the grid area where the scheduled user is located at the current moment.

[0092] Step S144: The base station uses the extended Kalman filter algorithm to analyze the current location information of the scheduled user in order to predict the next location information of the scheduled user, thereby determining the grid area where the scheduled user is located at the next moment.

[0093] Step S145: The base station optimizes by maximizing the data rate of the scheduled user, and then determines the user-level parameters based on the next location information of the scheduled user. The mathematical expression used in this implementation process is as follows:

[0094]

[0095] R k ≥R k，req ;

[0096]

[0097]

[0098] In the formula, The user-level parameters are defined as follows: the intelligent reflective surface is composed of several reflective units; the region to be optimized is composed of several grid regions; there is a one-to-one correspondence between the several reflective units and the several grid regions; S is the total number of scheduled users and S≥1; θ i_S * θ is the optimal incident angle of each reflecting unit associated with the scheduled user on the intelligent reflecting surface. i_S * That is, θ i_S The optimal solution. The optimal phase is defined as the phase of each reflective unit associated with the scheduled user on the intelligent reflective surface. That is The optimal solution, R k R is the data rate of the k-th scheduled user. k,req B is the minimum data rate for the k-th scheduled user. k For the radio resources allocated to the k-th scheduled user, P k Let σ be the received signal strength of the grid area where the k-th scheduled user is located. 2 P is the noise power. t The maximum transmit power of the base station. The cascaded channel gain is generated when the base station reaches the grid area where the k-th scheduled user is located via the smart reflector. The grid area where the k-th scheduled user is located is determined by the next location information of the k-th scheduled user.

[0099] More specifically, assuming the number of scheduled users is 1, the implementation process of step S143 above includes, but is not limited to, the following:

[0100] Step S143.1: The base station records the time when it first sends the cell-level control signal and records it as the sending time, and the time when it receives the first echo signal and records it as the receiving time. Then, based on the sending time and the receiving time, the base station determines the measurement distance from the base station to the scheduled user via the smart reflector.

[0101] Step S143.2: The base station determines the current location information of the scheduled user based on the measured distance and the optimal deployment location of the smart reflector. The mathematical expression used in this implementation process is as follows:

[0102]

[0103]

[0104] In the formula, a three-dimensional spatial coordinate system is constructed with the center of the region to be optimized as the origin, [x(t),y(t),0] represents the current location information of the scheduled user, and (0,y) represents the current location information of the scheduled user. IRS ,z IRS ) represents the position information of the intelligent reflective surface relative to the region to be optimized, which is obtained by geometric transformation based on the optimal deployment position of the intelligent reflective surface, d r (X t The distance from the base station to the scheduled user via the smart reflective surface at the current moment is [missing information]. For the measured distance at the current moment, v t The measured noise value of the base station at the current moment.

[0105] It should be noted that when the number of scheduled users is S, it is only necessary to execute the above steps S143.1 and S143.2 S times to obtain the current location information of each scheduled user.

[0106] More specifically, assuming the number of scheduled users is 1, when the scheduled user moves within the area to be optimized, the implementation process of step S144 above includes, but is not limited to, the following:

[0107] Step S144.1: The base station defines the motion state of the scheduled user at the current moment as follows, based on the current location information of the scheduled user:

[0108] X t =[x(t),v x (t),Y(t),V y (T)] T ;

[0109] The motion state of the scheduled user at the next moment is further defined as follows:

[0110] X t+1 =x t X t +G t w t ;

[0111]

[0112] Step S144.2: The base station uses the extended Kalman filter algorithm to track and predict the scheduled user in order to obtain the next location information of the scheduled user. The corresponding implementation process is as follows:

[0113] Given the motion state estimate of the scheduled user at the current moment And the corresponding estimation error covariance matrix p t And select the measurement distance of the base station at the next moment. As observed values;

[0114] The motion state estimate of the scheduled user at the next time step is then obtained by iteratively solving the following equations. And the corresponding estimation error covariance matrix p t+1 :

[0115] System state prediction:

[0116] Error covariance prediction:

[0117] System status update:

[0118] Error covariance update:

[0119] in:

[0120]

[0121]

[0122]

[0123] In the formula, X t Let [X(T),Y(T),0] represent the motion state of the scheduled user at the current moment, and let [V] represent the current location information of the scheduled user. x (T),v y [(t), 0] represents the current movement speed of the scheduled user, T is the transpose sign, and X is the current movement speed of the user. t+1 F represents the motion state of the scheduled user at the next moment. t Let G be the state transition matrix at the current time. t Let w be the input transformation matrix at the current time step, ΔT be the sampling step size at adjacent time steps, and w be the input transformation matrix at the current time step. t =[w tx ,w ty [ T A vector is defined by a Gaussian distribution, where the mean is 0 and the standard deviation is the noise covariance matrix Q. t , (w tx ,w ty (0) represents the current motion acceleration of the scheduled user, and w tx and w ty Generally unrelated For the prediction of the motion state of the scheduled user at the next moment, Kt+1 Let ε be the Kalman gain matrix at the next time step. k+1 Let P be the error matrix at the next time step. t+1|t Let S be the prediction error covariance matrix of the scheduled user at the next time step. t+1 Let be the residual covariance matrix at the next time step. H is the predicted actual distance at the next time step. t+1 Let N be the control matrix at the next time step. k+1 The measured noise value v of the base station at the next moment. t+1 The covariance matrix, d r (X t+1 ) represents the actual distance at the next moment.

[0124] It should be noted that when the number of scheduled users is S, it is only necessary to execute the above steps S144.1 and S144.2 S times to obtain the next location information of each scheduled user.

[0125] In this embodiment of the invention, the specific implementation process of step S150 includes, but is not limited to, the following:

[0126] Step S151: The base station sends user data signals to the smart reflector using an integrated sensing waveform, and controls the smart reflector to reflect the user data signals to the scheduled user according to the user-level parameters;

[0127] Step S152: The base station receives the second echo signal corresponding to the user data signal reflected back via the smart reflector.

[0128] Step S153: The base station determines the current location information of the scheduled user (hereinafter referred to as the first location information) based on the optimal deployment position of the smart reflector and the second echo signal, thereby determining the grid area where the scheduled user is located at the current moment;

[0129] Step S154: The base station uses the extended Kalman filter algorithm to parse the first location information of the scheduled user in order to predict the next location information of the scheduled user (hereinafter referred to as the second location information), thereby determining the grid area where the scheduled user is located at the next moment.

[0130] Step S155: The base station optimizes by maximizing the data rate of the scheduled user, and then redetermines the user-level parameters based on the second location information of the scheduled user.

[0131] It should be noted that the specific implementation process of steps S152 to S155 is similar to that of steps S142 to S145, and will not be described again in this invention.

[0132] In response to steps S145 and S155 above, the present invention also proposes other alternative preferred embodiments, such as: the base station, while ensuring the data rate of the scheduled user, takes minimizing the base station's transmission power as the optimization objective, and then determines the user-level parameters based on the next location information of the scheduled user.

[0133] It should be noted that, for a new batch of scheduled users, the base station determines the user-level parameters associated with the new batch of scheduled users based on the echo signal corresponding to the cell-level control signal, that is, by executing the above step S140 to determine the final required user-level parameters; while for the original batch of scheduled users, the base station re-determines the user-level parameters associated with the original batch of scheduled users based on the echo signal corresponding to the user data signal, that is, by executing the above step S150 to determine the final required user-level parameters.

[0134] In this embodiment of the invention, by adaptively setting the optimal deployment location and configuration parameters of the intelligent reflector for the area to be optimized by the controller, the coverage and detection performance of the area to be optimized can be maximized. By controlling the intelligent reflector to reflect corresponding control signals to the scheduled users in the area to be optimized by the base station, and then predicting the next location information of the scheduled users based on the echo signals corresponding to the control signals, the user-level parameters associated with the scheduled users in the intelligent reflector can be further optimized, thereby improving the detection and communication performance of the scheduled users, thereby increasing the throughput of the area to be optimized and reducing detection overhead, which has good practicality.

[0135] Please refer to Figure 2 , Figure 2 This is another flowchart illustrating the joint optimization method for wireless detection and communication provided in this embodiment of the invention. The method is mainly applied to base stations and specifically includes the following:

[0136] Step S210: Receive the optimization requirement parameters of the region to be optimized sent by the controller, and then determine the region-level parameters;

[0137] Step S220: Based on the regional parameters, after controlling the deployed intelligent reflector to reflect the cell-level control signal to the scheduled user in the area to be optimized, receive the corresponding first echo signal and determine the user-level parameters in combination with the optimization requirement parameters.

[0138] Step S230: Based on the user-level parameters, after controlling the intelligent reflector to reflect user data signals toward the scheduled user, receive the corresponding second echo signal and, in conjunction with the optimization requirement parameters, redetermine the user-level parameters.

[0139] In this embodiment of the invention, the parameter receiving situations mentioned in step S210 above mainly include the following two types:

[0140] Firstly, when the controller sends the first optimization request information it generates to the base station through an active reporting method, the first optimization request information carries the optimization requirement parameters of the area to be optimized. After receiving the first optimization request information, the base station extracts the optimization requirement parameters of the area to be optimized from it, generates optimization confirmation information corresponding to the first optimization request information, and then sends it to the controller. The optimization confirmation information indicates that the base station has received the optimization requirement parameters.

[0141] Secondly, the base station actively generates optimization request information and sends it to the controller. The optimization request information carries the identification information of the area to be optimized. When the controller generates corresponding optimization response information after receiving the optimization request information and sends it to the base station, the optimization response information carries the optimization requirement parameters of the area to be optimized. After receiving the optimization response information, the base station extracts the optimization requirement parameters of the area to be optimized from it.

[0142] In this embodiment of the invention, the specific deployment information of the intelligent reflective surface mentioned in step S220 above is obtained by the controller after determining the area to be optimized.

[0143] The content of the first method embodiment described above is applicable to this method embodiment. The functions implemented in this method embodiment are the same as those in the first method embodiment described above, and the beneficial effects achieved are the same as those in the first method embodiment described above. Therefore, they will not be repeated here.

[0144] Please refer to Figures 3 to 4 , Figures 3 to 4 This is a schematic diagram of a wireless detection and communication joint optimization system provided in an embodiment of the present invention. The system includes a controller and a base station, and the controller and the base station are wirelessly connected, wherein:

[0145] The controller can perform functions including, but not limited to, the following: determining the deployment information of the area to be optimized and its associated smart reflective surface; and sending the optimization requirement parameters of the area to be optimized to the base station when the smart reflective surface is deployed.

[0146] It should be noted that the controller can preferably determine the area to be optimized by the base station capability information and the base station deployment location actively reported by the base station. The base station capability information includes waveform capability information and antenna capability information.

[0147] The functions that the base station can perform include, but are not limited to, the following: determining region-level parameters based on the received optimization requirement parameters; controlling the intelligent reflector to reflect cell-level control signals to the scheduled users in the region to be optimized according to the region-level parameters, receiving the corresponding first echo signal and combining it with the optimization requirement parameters to determine user-level parameters; controlling the intelligent reflector to reflect user data signals to the scheduled users according to the user-level parameters, receiving the corresponding second echo signal and combining it with the optimization requirement parameters to re-determine user-level parameters.

[0148] In one specific embodiment, see Figure 3 As shown, the controller sends the optimization requirement parameters of the area to be optimized to the base station in the following way:

[0149] The controller generates a first optimization request and sends it to the base station. The first optimization request carries optimization requirement parameters for the area to be optimized. The base station generates corresponding optimization confirmation information based on the received first optimization request, indicating that the base station has received the optimization requirement parameters. The base station then sends the optimization confirmation information to the controller.

[0150] In another specific embodiment, see Figure 4 As shown, the controller sends the optimization requirement parameters of the area to be optimized to the base station in the following way:

[0151] The base station generates a second optimization request and sends it to the controller. The second optimization request carries the identification information of the area to be optimized. The controller generates corresponding optimization response information based on the received optimization request, and the optimization response carries the optimization requirement parameters of the area to be optimized. The controller then sends the optimization response to the base station.

[0152] The content of the first method embodiment described above is applicable to this system embodiment. The functions implemented in this system embodiment are the same as those in the first method embodiment described above, and the beneficial effects achieved are the same as those in the first method embodiment described above. Therefore, they will not be repeated here.

[0153] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements a wireless detection and communication joint optimization method as described in the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium by which a device (e.g., a computer, mobile phone, etc.) stores or transmits information in a readable form, and can be a read-only memory, a disk, or an optical disk, etc.

[0154] The terms “comprising” and “having”, and any variations thereof, in the specification and accompanying drawings of this application are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or that are inherent to such process, method, product, or apparatus.

[0155] In this application, it should be understood that "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0156] Although the description of this application has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment. Rather, it should be considered as effectively covering the intended scope of this application by referring to the appended claims and taking into account the prior art, which provides for a broad possible interpretation of these claims. Furthermore, the foregoing description of this application with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this application that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A joint optimization method for wireless detection and communication, characterized in that, include: The controller determines the area to be optimized and the deployment information of its associated smart reflective surfaces; When the smart reflector is deployed, the controller sends the optimization requirement parameters of the area to be optimized to the base station; wherein, the area to be optimized is composed of several grid areas, and the optimization requirement parameters of the area to be optimized include the given coverage optimization requirements, given detection requirements, optimization strategy and location information of each grid area associated with the area to be optimized, as well as the deployment location and basic operating parameters of the smart reflector; The base station determines the regional parameters based on the received optimization requirement parameters; According to the regional parameters, the base station controls the intelligent reflector to reflect the cell-level control signal to the scheduled users in the area to be optimized, and then receives the corresponding first echo signal and determines the user-level parameters in combination with the optimization requirement parameters. According to the user-level parameters, the base station controls the intelligent reflector to reflect user data signals toward the scheduled user, then receives the corresponding second echo signal and, in conjunction with the optimization requirement parameters, redetermines the user-level parameters. The step of receiving the corresponding first echo signal and determining the user-level parameters in conjunction with the optimized requirement parameters includes: The base station receives the first echo signal corresponding to the cell-level control signal, and then, in conjunction with the deployment location of the smart reflector, determines the current location information of the scheduled user. The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information. The base station determines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

2. The joint optimization method for wireless detection and communication according to claim 1, characterized in that, The controller determines the deployment information of the region to be optimized and its associated smart reflective surfaces, including: The controller determines the region to be optimized and performs rasterization on it to obtain several raster regions; The controller determines the optimal configuration parameters and optimal deployment location of the smart reflector based on the given coverage optimization requirements of the area to be optimized, with the optimization objective of maximizing the received signal strength of the single grid area farthest from the base station.

3. The joint optimization method for wireless detection and communication according to claim 1, characterized in that, The controller sends the optimization requirement parameters of the area to be optimized to the base station, including: The controller autonomously sends the optimization requirement parameters of the area to be optimized to the base station; Alternatively, the base station generates optimization request information and sends it to the controller, the optimization request information carrying the identification information of the area to be optimized; after receiving the optimization request information, the controller generates corresponding optimization response information, the optimization response information carrying the optimization requirement parameters of the area to be optimized, and then sends the optimization response information to the base station.

4. The joint optimization method for wireless detection and communication according to claim 1, characterized in that, The base station determines the regional-level parameters based on the received optimization requirement parameters, including: When the optimization strategy is an integrated detection and communication optimization strategy, the base station determines the regional parameters based on the given coverage optimization requirements, the given detection requirements, and the location information of each grid area, with the goal of maximizing the average received signal strength of the area to be optimized as the coverage optimization objective and the goal of maximizing the average detection accuracy of the area to be optimized as the detection optimization objective. When the optimization strategy is a communication optimization strategy, the base station determines the regional parameters based on the given coverage optimization requirements and the location information of each grid area, with the goal of maximizing the average received signal strength of the area to be optimized.

5. The joint optimization method for wireless detection and communication according to claim 1, characterized in that, The process of receiving the corresponding second echo signal and, in conjunction with the optimized requirement parameters, re-determining the user-level parameters includes: The base station receives the second echo signal corresponding to the user data signal, and then, in conjunction with the deployment location of the intelligent reflector, determines the current location information of the scheduled user. The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information. The base station redetermines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

6. A joint optimization method for wireless detection and communication, characterized in that, Applied to base stations, including: The system receives optimization requirement parameters for the region to be optimized from the controller, and then determines the region-level parameters. The region to be optimized is composed of several grid regions. The optimization requirement parameters for the region to be optimized include the given coverage optimization requirement, given detection requirement, optimization strategy, and location information of each grid region associated with the region to be optimized, as well as the deployment location and basic operating parameters of the intelligent reflective surface. Based on the region-level parameters, after the deployed intelligent reflector reflects the cell-level control signal to the scheduled users in the region to be optimized, it receives the corresponding first echo signal and determines the user-level parameters by combining it with the optimization requirement parameters; wherein, the deployment information of the intelligent reflector is obtained by the controller after determining the region to be optimized; Based on the user-level parameters, after controlling the intelligent reflection to reflect user data signals toward the scheduled user, the corresponding second echo signal is received and combined with the optimization requirement parameters to redetermine the user-level parameters. The step of receiving the corresponding first echo signal and determining the user-level parameters in conjunction with the optimized requirement parameters includes: The base station receives the first echo signal corresponding to the cell-level control signal, and then, in conjunction with the deployment location of the smart reflector, determines the current location information of the scheduled user. The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information. The base station determines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

7. The joint optimization method for wireless detection and communication according to claim 6, characterized in that, The optimization requirement parameters for the region to be optimized, sent by the receiving controller, include: An optimization request message is generated and sent to the controller, the optimization request message carrying the identification information of the region to be optimized; The system receives optimization response information sent by the controller, which is generated by the controller based on the optimization request information and carries optimization requirement parameters for the region to be optimized.

8. A joint optimization system for wireless detection and communication, characterized in that, include: The controller is used to determine the area to be optimized and the deployment information of its associated smart reflective surfaces; When the smart reflector is deployed, the optimization requirement parameters of the area to be optimized are sent to the base station; wherein, the area to be optimized is composed of several grid areas, and the optimization requirement parameters of the area to be optimized include the given coverage optimization requirements, given detection requirements, optimization strategy and location information of each grid area associated with the area to be optimized, as well as the deployment location and basic operating parameters of the smart reflector. The base station is configured to determine region-level parameters based on the received optimization requirement parameters; based on the region-level parameters, control the intelligent reflector to reflect cell-level control signals to the scheduled users within the region to be optimized, then receive the corresponding first echo signal and combine it with the optimization requirement parameters to determine user-level parameters; based on the user-level parameters, control the intelligent reflector to reflect user data signals to the scheduled users, then receive the corresponding second echo signal and combine it with the optimization requirement parameters to re-determine the user-level parameters. The step of receiving the corresponding first echo signal and determining the user-level parameters in conjunction with the optimized requirement parameters includes: The base station receives the first echo signal corresponding to the cell-level control signal, and then, in conjunction with the deployment location of the smart reflector, determines the current location information of the scheduled user. The base station uses an extended Kalman filter algorithm to predict the next location information of the scheduled user based on the user's current location information. The base station determines user-level parameters based on the next location information of the scheduled user, with the optimization objective of maximizing the data rate of the scheduled user.

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