Signal enhancement method and device based on intelligent metasurface and storage medium

Abstract

The invention discloses a signal enhancement method and device based on an intelligent metasurface and a storage medium, and the method comprises the steps: determining target user equipment according to the measurement report data of to-be-selected user equipment; and determining a prediction signal enhancement period number according to the motion perception data of the target user equipment. And predicting predicted position information of the target user equipment at the plurality of predicted time points. And based on the predicted position information, allocating an intelligent metasurface unit to each target user equipment, and determining a beam output parameter. And signal enhancement is performed on the target user equipment through the intelligent metasurface unit configured with the beam output parameters. According to the technical scheme, the user equipment needing signal enhancement can be accurately judged, and flexible enhancement is achieved according to different conditions of each user. The signal quality and the communication experience are effectively improved, the motion trail of the user is predicted in real time, the accuracy and the practicability of signal enhancement are improved, signal enhancement coverage can be kept in the face of continuous motion, and a powerful guarantee is provided for safe and stable signal connection.

Description

Technical Field

[0001] The present application belongs to the field of wireless communication technology, and in particular, relates to a signal enhancement method, device and storage medium based on smart metasurface. Background Art

[0002] Reconfigurable Intelligent Surface (RIS) is an intelligent reflective array composed of a large number of programmable units. It can enhance signal coverage by dynamically controlling the propagation path of electromagnetic waves. It has important application value in base station blind spots (such as basements, tunnels, etc.).

[0003] By reasonably adjusting the corresponding beam emission parameters of the intelligent metasurface, the base station beam can be effectively reflected or refracted, thereby achieving directional signal enhancement. At present, most traditional technologies can only uniformly adjust the beam emission parameters of the intelligent metasurface for the cell range corresponding to the base station. When facing the different signal enhancement requirements of low-altitude user equipment (such as drones, etc.) and ground user equipment (such as cars, etc.), it is impossible to have more targeted processing and adjustment, which seriously affects the user's communication experience. In addition, traditional technologies can often only set the parameters of the intelligent metasurface based on historical data and human experience. In actual application scenarios, it lacks timeliness and adaptability, and cannot flexibly face a variety of actual situations, resulting in unsatisfactory signal enhancement effects. At the same time, it is also impossible to handle the signal enhancement scenario of continuous movement of user equipment, which seriously affects the signal quality and user communication experience.

[0004] Therefore, how to achieve more accurate and timely signal enhancement for user devices through smart metasurfaces is an important issue that needs to be solved urgently. Summary of the invention

[0005] The embodiments of the present application provide a signal enhancement method, device and storage medium based on an intelligent metasurface, which can accurately and timely enhance the signal of a user device, thereby improving signal quality and user communication experience.

[0006] In a first aspect, an embodiment of the present application provides a signal enhancement method based on a smart metasurface, comprising:

[0007] Obtaining motion sensing data and measurement report data corresponding to multiple candidate user equipments;

[0008] Determine, according to the measurement report data corresponding to the plurality of candidate user equipments, at least one target user equipment for which signal enhancement is required from the plurality of candidate user equipments;

[0009] For each target user equipment, determining the number of predicted signal enhancement cycles corresponding to the target user equipment according to the motion perception data corresponding to the target user equipment;

[0010] Predict the predicted location information of the target user equipment at multiple predicted time points corresponding to the predicted signal enhancement cycle number;

[0011] Based on the multiple predicted location information corresponding to the target user equipment, allocate at least one intelligent metasurface unit to the target user equipment, and determine the beam output parameters corresponding to each intelligent metasurface unit;

[0012] Enhance the signal of at least one target user equipment through at least one intelligent metasurface unit configured with corresponding beam output parameters.

[0013] In a second aspect, an embodiment of the present application provides a signal enhancement device based on an intelligent metasurface, including:

[0014] A data acquisition module for acquiring motion perception data and measurement report data corresponding to multiple candidate user equipment;

[0015] A target determination module for determining at least one target user equipment that needs signal enhancement from multiple candidate user equipment according to the measurement report data corresponding to the multiple candidate user equipment;

[0016] A cycle determination module for, for each target user equipment, determining the predicted signal enhancement cycle number corresponding to the target user equipment according to the motion perception data corresponding to the target user equipment;

[0017] A location prediction module for predicting the predicted location information of the target user equipment at multiple predicted time points corresponding to the predicted signal enhancement cycle number;

[0018] A unit allocation module for allocating at least one intelligent metasurface unit to the target user equipment based on the multiple predicted location information corresponding to the target user equipment, and determining the beam output parameters corresponding to each intelligent metasurface unit;

[0019] A signal enhancement module for enhancing the signal of at least one target user equipment through at least one intelligent metasurface unit configured with corresponding beam output parameters.

[0020] In a third aspect, an embodiment of the present application provides a signal enhancement system based on an intelligent metasurface, including: a target base station, an active antenna unit, an intelligent metasurface panel, an edge user port function unit, and an intelligent metasurface unit control server. The active antenna unit is arranged in the target base station, and the intelligent metasurface panel is composed of multiple intelligent metasurface units;

[0021] The active antenna unit is used to acquire the measurement report data and motion perception data corresponding to each candidate user equipment;

[0022] The target base station is used to send the measurement report data and motion perception data corresponding to each candidate user equipment to the edge user port function unit;

[0023] The edge user port function unit is used to send the measurement report data and motion perception data corresponding to each candidate user equipment received to the intelligent metasurface unit control server;

[0024] The intelligent metasurface unit control server is used to determine the target user equipment according to the measurement report data and motion perception data corresponding to each candidate user equipment, and determine the beam output parameters corresponding to each intelligent metasurface unit on the intelligent metasurface panel, and send the beam output parameters corresponding to each intelligent metasurface unit to the intelligent metasurface panel through the edge user port function unit;

[0025] The intelligent metasurface panel is used to adjust the parameters of each intelligent metasurface unit according to the beam output parameters corresponding to each intelligent metasurface unit, and enhance the target signal through the intelligent metasurface unit after parameter adjustment.

[0026] In a fourth aspect, an embodiment of the present application provides a terminal device, which includes: a processor and a memory storing computer program instructions;

[0027] When the processor executes the computer program instructions, it implements the signal enhancement method based on the intelligent metasurface as in the first aspect.

[0028] In a fifth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the signal enhancement method based on the intelligent metasurface as in the first aspect is implemented.

[0029] In a sixth aspect, an embodiment of the present application provides a computer program product, and when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the signal enhancement method based on the intelligent metasurface as in the first aspect.

[0030] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:

[0031] A signal enhancement method based on an intelligent metasurface provided by an embodiment of the present application includes:

[0032] Based on the measurement report data corresponding to multiple candidate user devices, at least one target user device that needs signal enhancement is determined. Then, according to the motion perception data corresponding to each target user device, the predicted signal enhancement cycle number corresponding to each target user device is determined. Immediately afterwards, according to the predicted signal enhancement cycle number, the predicted position information of each target user device at multiple predicted time points corresponding to the predicted signal enhancement cycle number is predicted. Finally, based on the multiple predicted position information corresponding to each target user device, at least one intelligent metasurface unit is assigned to each target user device, and the beam output parameters corresponding to each intelligent metasurface unit are determined. Through at least one intelligent metasurface unit configured with the corresponding beam output parameters, signal enhancement is performed on the corresponding target user device.

[0033] The technical solution provided by the embodiments of the present application can accurately determine the user devices that need signal enhancement according to user data, and perform adaptive signal enhancement according to specific data. Through the technical solution provided by the embodiments of the present application, the accuracy of signal enhancement through intelligent metasurfaces can be effectively improved, and flexible enhancement can be performed according to the different situations of each user, effectively improving the signal quality and the user communication experience. In addition, the present application can predict the future movement trajectory of users in real time, significantly improving the accuracy and practicality of the signal enhancement process, and can always maintain signal enhancement coverage in the face of the continuous movement of users, providing a strong guarantee for users to use a safe and stable signal connection.

[0034] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0036] Figure 1 It is a schematic flowchart of a signal enhancement method based on an intelligent metasurface provided by an embodiment of the present application;

[0037] Figure 2 It is a schematic diagram of an intelligent metasurface panel provided by an embodiment of the present application;

[0038] Figure 3 It is a schematic flowchart of signal enhancement through a signal enhancement system based on an intelligent metasurface provided by an embodiment of the present application;

[0039] Figure 4 It is a schematic structural diagram of a signal enhancement device based on an intelligent metasurface provided by another embodiment of the present application;

[0040] Figure 5 Schematic diagram of the hardware structure of the terminal device provided by another embodiment of the present application. Detailed implementation manners

[0041] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0042] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, the elements defined by the statement "comprising..." do not exclude the presence of additional identical elements in the process, method, article or device comprising the elements.

[0043] A Reconfigurable Intelligent Surface (RIS) is an electromagnetic regulation device composed of an array of programmable units. By dynamically adjusting the electromagnetic characteristics (such as phase, amplitude) of the units, it can actively reconstruct the propagation path of wireless signals and effectively enhance the directional signal in blind areas covered by base stations (such as basements, tunnels, etc.).

[0044] The intelligent surface can intelligently reflect or refract the beam emitted by the base station. By optimizing parameters such as the directivity and beam width of the outgoing beam, the signal strength and communication quality of the target cell can be significantly improved. There are problems in the traditional technology for signal enhancement through the intelligent surface: traditional methods usually take the cells covered by the base station as a unit and perform global beam emission parameter adjustment for the intelligent surface, resulting in difficulty in simultaneously meeting the different requirements of low-altitude users (such as drones) and ground users (such as vehicle-mounted terminals) in practical applications. The signal enhancement process has low accuracy and lacks pertinence, affecting the signal quality and seriously reducing the user communication experience at the same time.

[0045] Moreover, in traditional technologies, the parameter configuration and adjustment of intelligent surfaces often rely on historical data and manual experience preset, lacking real-time perception and flexible adjustment capabilities, and having poor adaptability to different and changing application scenarios and users. In the face of continuous user movement scenarios, there is also a lack of more continuous and stable signal enhancement methods, significantly reducing the signal quality and user communication experience in continuous movement scenarios.

[0046] Based on the above technical problems, the embodiments of the present application provide a signal enhancement method, device, and storage medium based on intelligent surfaces. Specifically, according to the measurement report data corresponding to the user equipment to be selected, the target user equipment that needs signal enhancement can be determined. According to the motion perception data corresponding to the target user equipment, the predicted signal enhancement cycle number can be determined. According to the predicted signal enhancement cycle number, the predicted position information of the target user equipment at multiple predicted time points can be accurately predicted.

[0047] Then, based on the multiple predicted position information, at least one corresponding intelligent surface unit can be allocated to the target user equipment, and the beam output parameters corresponding to each intelligent surface unit can be accurately determined according to the corresponding predicted position information. Finally, through at least one intelligent surface unit configured with the corresponding beam output parameters, signal enhancement can be performed on the corresponding target user equipment.

[0048] Through the technical solutions provided by the embodiments of the present application, the accuracy of signal enhancement through intelligent surfaces can be effectively improved, and flexible enhancement can be performed according to the different situations of each user, effectively improving the signal quality and user communication experience. In addition, the technical solutions provided by the embodiments of the present application can predict the future movement trajectory of the user equipment in real time according to user data, significantly improving the accuracy and timeliness of the signal enhancement process, and being able to maintain signal enhancement coverage at all times in the face of continuous user movement, optimizing the signal quality and improving the user communication experience, providing a more secure and stable signal connection for users.

[0049] Regarding the execution subject adopted by the technical solutions provided by the embodiments of the present application, specifically, it can be a terminal device, such as a desktop computer, a laptop computer, etc., or a remote device, such as a server, etc. In addition, the execution subject adopted by the embodiments of the present application can also be an execution subject in the form of software, such as a client, a software program, etc. installed in the terminal device. Here, the specific type of the execution subject corresponding to the signal enhancement method, device, and storage medium based on intelligent surfaces provided in the embodiments of the present application is not strictly limited, and can be flexibly selected and set according to the application scenario and actual requirements.

[0050] It should be noted that in the embodiments provided in this application, the specific application scenarios corresponding to the above-mentioned signal enhancement method, device, and storage medium based on intelligent metasurfaces are not limited. The technical solutions provided in the embodiments of this application can be flexibly applied to various application scenarios that require signal enhancement according to actual needs.

[0051] For example, in the actual scenario of signal enhancement for each user device in a large outdoor activity venue, such as a stadium for sports events, a concert hall, an amusement park, etc. Through the technical solutions provided in the embodiments of this application, the target user devices that need signal enhancement can be determined according to the measurement report data corresponding to each user device in the activity venue. Then, for the target user devices, the corresponding predicted signal enhancement cycle numbers can be determined according to the operation perception data, and further the predicted position information of the target user devices at multiple predicted time points corresponding to the predicted signal enhancement cycle numbers can be predicted.

[0052] Finally, the corresponding intelligent metasurface units are allocated according to the predicted position information and the corresponding beam output parameters are determined, and the intelligent metasurface units are used to effectively enhance the signal of the future movement path of the target device. The technical solutions provided in the embodiments of this application can significantly enhance the signal connection strength of each user device in a large outdoor activity venue through the above process, optimize the communication experience of users in the venue, and enable devices such as drones and mobile terminals to maintain stable and secure signal connections.

[0053] It should be noted that the application scenarios described in the above embodiments of this application are for more clearly explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. Those of ordinary skill in the art know that with the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems. The signal enhancement method, device, and storage medium based on intelligent metasurfaces provided in the embodiments of this application can be applied to various actual application scenarios that require signal enhancement of user devices.

[0054] Figure 1 It is a schematic flowchart of a signal enhancement method based on intelligent metasurfaces provided in an embodiment of this application.

[0055] As Figure 1 shown, the signal enhancement method based on intelligent metasurfaces provided in the embodiments of this application includes steps S101 to S106.

[0056] S101: Obtain the motion perception data and measurement report data corresponding to multiple candidate user devices.

[0057] In step S101, the technical solution provided by the embodiment of the present application can obtain the operation perception data and measurement report data corresponding to multiple candidate user equipments within the cell range through the target base station.

[0058] Among them, the target base station can be a signal base station that combines communication functions and perception functions. Such a base station can not only provide mobile communication services for user equipments and regularly obtain the measurement report data corresponding to user equipments, but also perform operations such as environmental perception, target detection, and motion tracking on user equipments, so as to collect the operation perception data corresponding to user equipments.

[0059] The operation perception data can be obtained by the target base station through sensing and detecting each candidate user equipment within the cell range according to a preset sensing detection period. For each candidate user equipment, the data content of the operation perception data corresponding to the candidate user equipment can include but is not limited to: the unique identification number ID corresponding to the candidate user equipment i , the timestamp TS for obtaining the operation perception data i (Timestamp), the moving speed Vel corresponding to the candidate user equipment i , the timing advance TA between the target base station and the candidate user equipment i (Timing Advance), the radar cross section corresponding to the candidate user equipment , the horizontal angle of arrival AoA of the antenna between the target base station and the candidate user equipment in this sensing detection period i (Azimuth Angle of Arrival) and ZoA i The vertical angle of arrival of the antenna (Zenith Angle of Arrival).

[0060] The measurement report data (Measurement Report, MRO) can be obtained by the target base station from each candidate user equipment within the cell range according to a preset data measurement period. For each candidate user equipment, the data content of the measurement report data corresponding to the candidate user equipment can include but is not limited to: the identification number AMF_UE_NGAP_ID of the next generation application protocol (Next Generation Application Protocol, NGAP) corresponding to the candidate user equipment j , the area number AMF_Region_ID of the access and mobility management function (Access and Mobility Management Function, AMF) area to which the candidate user equipment belongs j, the set number AMF_Set_ID of the access and mobility management function set to which the candidate user equipment belongs j , the pointer number AMF_Pointer of the access and mobility management function pointer to which the candidate user equipment belongs j , the time stamp TS for obtaining measurement report data j , the reference signal received power (RSRP) of the cell corresponding to the candidate user equipment s,j (Reference Signal Received Power), the reference signal received power (RSRP) of the neighboring cell of the cell corresponding to the candidate user equipment n,j , the time advance TA between the target base station and the candidate user equipment j , the azimuth of arrival (AoA) of the horizontal antenna between the target base station and the candidate user equipment in this data measurement period j and ZoA j The vertical angle of arrival of the antenna.

[0061] It should be noted that the duration of the data measurement period corresponding to the above measurement report data and the perception detection period corresponding to the operation perception data may be different. Generally, for the same candidate user equipment, the data measurement period can be set with a relatively long time interval, for example, obtaining the data measurement period of the candidate user equipment once every about 5 seconds. The perception detection period can be set shorter to more accurately locate the position and movement state of the candidate user equipment. For example, the acquisition interval can be set within 1 second.

[0062] Based on the measurement report data, the signal strength of the candidate user equipment can be accurately judged, and based on the operation perception data, the future movement trajectory of the user can be accurately located and predicted. Different acquisition periods may result in the acquisition time stamps of the measurement report data and the operation perception data corresponding to the same candidate user equipment not being exactly the same. Therefore, the user signal can be determined first according to the measurement report data, and the corresponding operation perception data can be matched based on the measurement report data, so as to perform subsequent position prediction and effective allocation of intelligent metasurface units.

[0063] S102: Determine at least one target user equipment that needs signal enhancement from multiple candidate user equipment according to the measurement report data corresponding to the multiple candidate user equipment.

[0064] In step S102, the technical solution provided in the embodiment of the present application can accurately determine, from multiple candidate user equipment, at least one candidate user equipment with a weak signal strength that needs to be enhanced by an intelligent metasurface unit in the future according to the measurement report data corresponding to the multiple candidate user equipment obtained in step S102, as the target user equipment.

[0065] Specifically, in an embodiment provided by the present application, for each candidate user equipment, the signal strength corresponding to the cell where the candidate user equipment is located in the measurement report data (such as the above-mentioned RSRP s,j ), and the signal strength corresponding to the neighboring cells of the cell where it is located (such as the above-mentioned RSRP n,j ) can be used to determine whether the candidate user equipment is the target user equipment.

[0066] Among them, when it is determined that the signal strength corresponding to the candidate user equipment in the cell where it is located and the signal strength corresponding to the neighboring cells are both less than the preset signal strength threshold, it means that the signal connection quality of the candidate user equipment in the current cell is poor, and since the signal strength corresponding to the neighboring cells also does not meet the standard, the signal quality cannot be improved by switching the connection. In this case, the candidate user equipment can be determined as one of the target user equipment, and subsequent location prediction and intelligent metasurface unit allocation processes can be performed for the target user equipment.

[0067] Through the above judgment process, the target user equipment with weak signal strength among the candidate user equipment can be accurately determined. From the traditional technology that can only adjust the beam output parameters of the intelligent metasurface unit for the cell or a specific area, it is accurate to the situation analysis of each user equipment individual. While providing a practical basis for the subsequent location prediction and unit allocation processes, it effectively improves the processing accuracy and pertinence of the overall signal enhancement method, significantly enhancing the signal connection strength of each target user equipment and optimizing the user communication experience.

[0068] It should be noted that in the embodiment of the present application, considering that when there are many candidate user equipments whose signal strength does not meet the signal strength threshold, there may be a problem that there are too many target user equipments that need to be allocated intelligent metasurface units subsequently. In this case, the processing efficiency and effect of the overall signal enhancement process may be affected due to the limitation of the number of intelligent metasurface units or the large amount of subsequent parameter calculations.

[0069] Based on this, in another embodiment provided by the present application, the candidate user equipment can be pre-classified into special user equipment and non-special user equipment according to the actual equipment type and application scenario. The specific classification method can be based on the identification number of the next-generation application protocol AMF_UE_NGAP_ID corresponding to each candidate user equipment in the measurement report data j , the area number AMF_Region_ID of the access and mobility management function area to which the candidate user equipment belongs j , the set number AMF_Set_ID of the access and mobility management function set to which the candidate user equipment belongs j and the pointer number AMF_Pointer of the access and mobility management function pointer to which the candidate user equipment belongs j, determined by matching with preset special user device identification data.

[0070] For each special user device, the above judgment process can continue to determine whether the special user device is the target user device according to the signal strength of the cell where it is located and the signal strength of adjacent cells, and then perform subsequent location prediction and unit allocation processes.

[0071] For each non-special user device, when it is determined in a certain data measurement period that the signal strength of the cell where the special user device is located and the signal strength of adjacent cells do not meet the preset signal strength threshold, the measurement report data of the special user device in subsequent data measurement periods can be continuously monitored. When it is determined that in a preset number of consecutive data measurement periods, the signal strength of the cell where the special user device is located and the signal strength of adjacent cells do not meet the preset signal strength threshold, it can be determined that the special user device is the target user device that needs signal enhancement.

[0072] Among them, in the embodiments of the present application, the distinction criteria and specific types of the above special user devices and non-special user devices are not limited. The special user device can be, for example, a safety and emergency response device in a public place, a monitoring and sensor device in an important facility, a medical health monitoring device, etc. The non-special user device can be, for example, a smart home appliance device, an agricultural automation device, a smart lighting device, etc. In addition to the above examples, other devices can also be used as special user devices or non-special user devices, which can be flexibly set and divided according to the actual scenario and application requirements.

[0073] S103: For each target user device, determine the predicted signal enhancement period number corresponding to the target user device according to the motion perception data corresponding to the target user device.

[0074] In step S103, the technical solution provided in the embodiments of the present application can determine the predicted signal enhancement period number corresponding to each target user device according to the motion perception data corresponding to each target user device.

[0075] It should be noted in advance that for each target user device, when the perception detection period corresponding to the target user device is less than the data measurement period, the technical solution provided in the embodiments of the present application can perform data matching on the measurement report data and perception detection data corresponding to the target user device, so as to determine the motion perception data that matches the measurement report data.

[0076] Specifically, in an embodiment provided by the present application, for each target user device, at least one motion perception data with the shortest measurement time difference from the measurement report data can be determined according to the acquisition timestamp of the measurement report data corresponding to the target user device and the acquisition timestamps of the motion perception data corresponding to multiple perception detection periods.

[0077] Subsequently, data matching can be performed between the measurement report data and the motion perception data with the shortest measurement time difference from at least one measurement report data. The specific matching process can refer to the following inequality formula (1):

[0078]

[0079] Among them, for each target user device, [AoA j , ZoA j , TA j are respectively the antenna horizontal arrival angle AoA between the target user device and the target base station in the measurement report data j , the antenna vertical arrival angle ZoA j and the time advance TA j . [AoA i , ZoA i , TA i are respectively the antenna horizontal arrival angle AoA between the target user device and the target base station in the motion perception data with the shortest measurement time difference from the measurement report data i , the antenna vertical arrival angle ZoA i and the time advance TA i . a, b, c, and d are respectively preset parameter thresholds.

[0080] Through the above formula (1), when it is determined that the differences in the antenna horizontal arrival angle, antenna vertical arrival angle, and time advance between the target user device and the target base station when the measurement report data and the motion perception data are respectively acquired, and the sum value of the three data differences are all less than the corresponding preset parameter thresholds, it can be determined that the motion perception data of the target user device corresponding to the perception detection period is the motion perception data matching the measurement report data.

[0081] Then, according to the motion perception data matching the measurement report data, the predicted signal enhancement period number corresponding to the target user device can be determined.

[0082] Based on the above matching process, the motion perception data corresponding to each target user device can be accurately determined, making the subsequent determination process of the prediction signal enhancement cycle more accurate and practical. Furthermore, it can effectively improve the prediction accuracy of the subsequent location prediction for the target user device, making the subsequent configuration process of the intelligent metasurface more accurate, effective, and reasonable. It significantly improves the processing accuracy and effect of the overall signal enhancement process, effectively improving the signal connection quality of the user device and the communication experience of the user.

[0083] Furthermore, the number of prediction signal enhancement cycles corresponding to each target user device is used to represent the duration for which the signal of the target user device can be enhanced. The duration corresponding to each prediction signal enhancement cycle can be the same as the perception detection cycle corresponding to the motion perception data.

[0084] The number of prediction signal enhancement cycles corresponding to different types of target user devices may not be the same. The reason is that the device types corresponding to the target user devices can be divided into many types according to the actual application scenarios, such as drone devices, moving intelligent vehicles, mobile terminals such as mobile phones and computers used by pedestrians. The motion speeds and operation trajectories of each type of target user device may not be exactly the same in the next period of time. Determining the corresponding number of prediction signal enhancement cycles for each type of target user device can effectively improve the actual application effect of the subsequent signal enhancement process and save resources.

[0085] Based on this, in an embodiment provided in the present application, for each target user device, according to the motion perception data corresponding to the target user device, the radar parameter data, motion speed data, and motion height data corresponding to the target user device can be determined.

[0086] Among them, the radar parameter data corresponding to the target user device can specifically refer to the radar cross-section area corresponding to the target user device in the motion perception data (such as in the above example). The motion speed data can specifically refer to the moving speed corresponding to the target user device in the motion perception data (such as Vel in the above example i , and the motion height data can be determined according to the horizontal arrival angle of the antenna, vertical arrival angle of the antenna, and time advance amount between the target user device and the target base station in the motion perception data.

[0087] Then, according to the radar parameter data and motion height data corresponding to the target user device, type matching can be performed based on a preset device type correspondence table to determine the target device type corresponding to the target user device.

[0088] According to the target device type, the reference speed data corresponding to the target user device can be determined. Further, based on the motion speed data and the reference speed data corresponding to the target user device, the number of predicted signal enhancement periods corresponding to the target user device can be determined. The specific determination process of the number of predicted signal enhancement periods can refer to the following formula (2):

[0089]

[0090] Among them, for each target user device, T i is used to represent the number of predicted signal enhancement periods corresponding to the target user device, Vel t is used to represent the reference speed data corresponding to the target user device, Vel i is used to represent the motion speed data corresponding to the target user device, T max is used to represent the maximum number of signal enhancement periods that can be supported.

[0091] The calculation process of formula (2) is to select the ratio of the reference speed data Vel t corresponding to the target user device and the motion speed data Vel i rounded up, and the minimum value between the maximum number of signal enhancement periods T max that can be supported. Through the calculation process of the above formula (2), the number of predicted signal enhancement periods T i corresponding to each target user device can be accurately calculated, providing a practical basis for the subsequent position prediction and intelligent metasurface unit allocation processes of each target user device.

[0092] It should be noted that in the embodiments of the present application, the specific content of the device type correspondence table for determining the target device type corresponding to the target user device and the specific correspondence between the target user device and the target device type are not strictly limited. For example, in one embodiment, the device type correspondence table can be as shown in Table 1 below.

[0093]

[0094]

[0095] Table 1 is an example table of a device type correspondence table provided in an embodiment of the present application, which represents the specific correspondence between the target user device and the target device type.

[0096] As shown in Table 1, according to the radar parameter data and motion altitude data corresponding to different types of target user devices, the corresponding target device type can be accurately matched from the device type correspondence table. Then, based on the target device type, the reference speed data corresponding to the target user device can be accurately determined, and then the predicted signal enhancement cycle number corresponding to the target user device can be determined through the above formula (2). Table 1 is only for illustration and understanding, and does not represent a limitation on the device type correspondence table in the embodiments of the present application. The specific content and corresponding relationship in the device type correspondence table can be determined according to actual needs and application scenarios.

[0097] Through the above processing process, the predicted signal enhancement cycle number corresponding to each target user device can be accurately determined, providing a practical basis for the subsequent position prediction process and intelligent metasurface unit allocation process, and significantly improving the signal enhancement effect. Based on the determination process of the target device type, the authenticity and practicality of the predicted signal enhancement cycle number are effectively improved, making the subsequent position prediction and unit allocation more in line with the actual motion conditions of the target user device, thereby improving the enhancement effect of the overall signal enhancement process, and further optimizing the signal quality and enhancing the user communication experience.

[0098] S104: Predict the predicted position information of the target user device at multiple predicted time points corresponding to the predicted signal enhancement cycle number.

[0099] In step S104, the technical solution provided in the embodiments of the present application can predict and determine the predicted position information corresponding to each target user device at multiple predicted time points based on the predicted signal enhancement cycle number corresponding to each target user device.

[0100] Among them, the specific number of predicted time points is the same as the predicted signal enhancement cycle number corresponding to the target user device, and each predicted signal enhancement cycle is the same as the sensing detection cycle corresponding to the motion sensing data. Therefore, the time interval between each predicted time point and the adjacent predicted time point is the sensing detection cycle.

[0101] The predicted position information corresponding to each predicted time point is used to represent the position information that the target user device can correspond to at this predicted time point. The specific manifestation form of the predicted position information can be the longitude, latitude and altitude corresponding to the target user device.

[0102] In an embodiment provided by the present application, for each target user device, according to the predicted signal enhancement cycle number corresponding to the target user device determined through the above step S103, a plurality of predicted time points corresponding to the target user device are determined.

[0103] Then, the multiple predicted time points corresponding to the target user device can be used as input data and input into a location prediction model pre-trained for the target user device, so that the location prediction model can predict and determine the predicted location information corresponding to the target user device at each predicted time point in the future according to the received multiple predicted time points.

[0104] Through the above prediction process, the predicted location information corresponding to each target user device at multiple predicted time points can be accurately determined, providing an accurate and practical basis for the subsequent allocation process and parameter determination process of the intelligent metasurface units, significantly improving the enhancement effect of the overall signal enhancement process, and thus effectively improving the signal quality within the target base station cell range and optimizing the user communication experience.

[0105] Regarding the model training process corresponding to the location prediction model pre-trained for the target user device in the above prediction process, in an embodiment provided in the present application, a location prediction model that can predict the future movement trajectory of the target user device can be trained according to the historical location information of the target user device within a certain time range before each predicted time point.

[0106] Specifically, for each target user device, the technical solution provided in the embodiment of the present application can determine the historical location information of the target user device at multiple historical time points.

[0107] Among them, the specific determination process and implementation method of the historical location information are not strictly limited in the embodiment of the present application. In some embodiments, it can be determined according to the time advance, antenna horizontal arrival angle, and antenna vertical arrival angle between the target user device and the target base station in the motion perception data corresponding to each historical time point. In other embodiments, other feasible historical location information determination methods can also be used, which can be flexibly selected according to the application scenario and actual requirements.

[0108] Then, the multiple historical time points can be used as input information and input into the location prediction model to be trained, so that the location prediction model to be trained can determine the predicted historical location information corresponding to each historical time point according to the historical time points.

[0109] Immediately afterwards, according to the predicted historical location information corresponding to each historical time point and the historical location information determined before, the training error corresponding to the location prediction model to be trained can be accurately calculated. According to the training error, the location prediction model to be trained can be trained to obtain a location prediction model that can determine the predicted location information corresponding to the predicted time point.

[0110] In the embodiments of the present application, the specific model structure and type of the above-mentioned position prediction model are not strictly limited and can be flexibly set according to the application scenario and actual requirements. For example, in one embodiment, the training process of the position prediction model can be a polynomial fitting process, which can specifically refer to the following formula (3):

[0111]

[0112] Among them, for each target user device, t represents each time point, which is the prediction time point in actual application and the historical time point in the training process. x, y, and z are used to represent the position information corresponding to the time point, which is the prediction time point in actual application and the historical time point in the training process. x is the longitude, y is the latitude, and z is the altitude. a, b, and c respectively represent the polynomial parameters corresponding to the three position information, a0, b0, and c0 are the constant terms corresponding to the three position information respectively, and n is the polynomial degree.

[0113] In the training process, the polynomial parameters corresponding to each position information in the above formula (3) can be fitted and trained according to the training error corresponding to the position prediction model to be trained, and finally a polynomial fitting formula that can accurately represent the relationship between the time point and the position information is obtained through training.

[0114] The trained position prediction model can accurately predict the predicted position information corresponding to the target user device at each prediction time point through the polynomial fitting formula. Through the above model training process based on the real-time historical data corresponding to the target user device, the position prediction accuracy and efficiency of the trained position prediction model are greatly guaranteed, thereby significantly improving the accuracy and practicability of the position prediction information, and providing a true and effective basis for the subsequent allocation process and parameter configuration of the intelligent metasurface.

[0115] S105: Based on the multiple predicted position information corresponding to the target user device, allocate at least one intelligent metasurface unit for the target user device, and determine the beam output parameters corresponding to each intelligent metasurface unit.

[0116] In step S105, the technical solution provided in the embodiments of the present application can allocate at least one intelligent metasurface unit for each target user device based on the multiple predicted position information corresponding to each target user device determined in the above step S104. At the same time, the beam output parameters corresponding to each intelligent metasurface unit can be determined according to the predicted position information corresponding to each intelligent metasurface unit.

[0117] Among them, the Reconfigurable Intelligent Surface (RIS) units are on the intelligent surface panel formed by splicing multiple RIS units. Each RIS unit can independently adjust the electromagnetic wave phase, amplitude and other beam characteristics of the incident beam, so as to achieve the beneficial effect of enhancing the signal strength in a specific area. The beam output parameters of each RIS unit can specifically include the corresponding horizontal emission angle and vertical emission angle of the intelligent surface at the current moment when the electromagnetic wave of the base station beam is incident on the intelligent surface.

[0118] Specifically, in an embodiment provided by the present application, for each target user equipment, according to the coverage range corresponding to each pre-configured RIS unit, the RIS unit that covers the most predicted position information corresponding to the target user equipment can be used as one of the RIS units corresponding to the target user equipment.

[0119] If there is remaining predicted position information corresponding to the target user equipment that is not covered by the intelligent surface at this time, the above determination process is continued for the remaining predicted position information, that is, the RIS unit that covers the most remaining predicted position information is determined as one of the RIS units corresponding to the target user equipment.

[0120] If there is still remaining predicted position information after processing, the RIS unit that covers the most predicted position information is determined again, and the determination is repeated until each predicted position information corresponding to the target user equipment is completely covered by the corresponding RIS unit.

[0121] When determining the RIS unit corresponding to each predicted position information, the beam output parameters corresponding to the predicted time point of the corresponding RIS unit at the predicted position information can be determined according to each predicted position information. The specific expression form of the beam output parameters can refer to the following formula (4):

[0122] {ris ux,vy |(vert 1x ,hori 1y ,T1),...,(vert cx ,hori cy ,T c )} Formula (4)

[0123] Among them, for each RIS unit, ris ux,vy is used to represent the beam output parameters corresponding to the RIS unit, T c is used to represent the predicted time point of the c-th predicted position information corresponding to the RIS unit, vert cx and hori cyrespectively represent the vertical beam emission angle and the horizontal beam emission angle of the intelligent metasurface unit at the prediction time point T c corresponding to the target user equipment.

[0124] Through the above processing process, the vertical beam emission angle and the horizontal beam emission angle of the intelligent metasurface unit for the target user equipment at each prediction time point can be determined in sequence according to the time sequence corresponding to the prediction time point, and finally the beam output parameters as shown in formula (4) can be obtained. According to the beam output parameters, the parameters of the intelligent metasurface unit can be adjusted to enhance the signal of the target user equipment at the predicted position, improve the signal quality in the target base station cell area and the communication experience of users.

[0125] It should be noted that in the embodiments of the present application, the specific implementation methods and processes for determining the beam output parameters corresponding to the prediction time points of the intelligent metasurface unit at each predicted position information are not strictly limited. In some embodiments, it can be calculated and determined according to a preset linear function representing the mapping relationship between the predicted position information and the beam output parameters of the intelligent metasurface unit. In other embodiments, other feasible determination methods can also be used, which can be flexibly adjusted according to actual requirements and application scenarios.

[0126] In addition, in the present application, it is considered that the severity of the problem of low signal strength faced by different target user equipment may not be exactly the same. The target user equipment with more severe low signal strength should perform signal enhancement earlier.

[0127] Therefore, in another embodiment provided by the present application, the signal enhancement priorities of each target user equipment can be sorted according to the signal strengths of each target user equipment in the cell where it is located and adjacent cells determined in step S102. The higher the difference between the signal strength and the signal strength threshold of the target user equipment, the higher the priority of signal enhancement.

[0128] At this time, if there are special user equipment among the target user equipment, the above intelligent metasurface unit allocation and parameter configuration can be performed first for the special user equipment with the highest priority, and so on. After all the special user equipment is configured, the intelligent metasurface unit allocation and parameter configuration can be performed for the non-special user equipment.

[0129] In addition to the above content, the signal enhancement range corresponding to each intelligent metasurface unit is mainly determined by the intelligent metasurface panel where it is located. When there are overlapping signal enhancement ranges for multiple intelligent metasurface panels, the intelligent metasurface units on multiple intelligent metasurface panels can be allocated for the prediction position information of a single target user equipment at the same time, so as to achieve multi-stream coverage of signal enhancement.

[0130] Based on this, in another embodiment provided by the present application, for each intelligent metasurface unit corresponding to the target user equipment determined through the above embodiment, it can be determined whether the associated intelligent metasurface unit corresponding to this intelligent metasurface unit covers at least one predicted location information corresponding to the target user equipment.

[0131] Among them, the associated intelligent metasurface unit is an intelligent metasurface unit that has at least partially the same coverage range as this intelligent metasurface unit.

[0132] When it is determined that the associated intelligent metasurface unit covers at least one predicted location information corresponding to the target user equipment, in this case, the associated intelligent metasurface unit corresponding to this intelligent metasurface unit can also be used as one of the intelligent metasurface units corresponding to the target user equipment.

[0133] Intelligent metasurface units with the same coverage area can effectively achieve enhanced multi-stream signal coverage for the target user equipment on its future movement trajectory, thereby significantly improving the signal enhancement effect and maintaining signal enhancement stability. When a certain intelligent metasurface unit has a fault or abnormal problem and cannot enhance the signal normally, under multi-stream coverage, it can still ensure that the target user equipment can obtain the signal enhancement effect, improve and ensure the signal connection quality, and optimize the user communication experience.

[0134] Regarding the layout and determination of the associated intelligent metasurface unit corresponding to each intelligent metasurface unit, in an embodiment provided by the present application, multiple intelligent metasurface units can be combined and arranged on an intelligent metasurface panel, and the deployment parameters and performance parameters of each intelligent metasurface panel will largely determine the coverage range of multiple intelligent metasurface units thereon.

[0135] Among them, the deployment parameters corresponding to each intelligent metasurface panel can include but are not limited to: azimuth angle α, elevation angle β, hanging height H, beam width W, longitude lon, and latitude lat. The performance parameters can include but are not limited to: effective enhanced coverage distance r, beam horizontal schedulable number γ, and beam vertical schedulable number δ.

[0136] Based on the above parameters, the coverage range corresponding to each intelligent metasurface panel is a conical region V formed by adjusting the emission length of the region to the effective enhanced coverage distance r, adjusting the emission width of the region to the beam width W, the beam horizontal schedulable number γ, and the beam vertical schedulable number δ k , and the vertex of the region is the geographical location corresponding to the panel, that is, (lon, lat, H).

[0137] For any two intelligent metasurface panels, there are corresponding coverage ranges V i and V j . When determining the coverage areas V i and Vj If there is an overlapping range between them, and the overlapping range is greater than a preset overlapping range threshold, it can be determined that there is an association relationship between these two intelligent metasurface panels, and each intelligent metasurface unit on the two intelligent metasurface panels corresponds to an associated intelligent metasurface unit of the other intelligent metasurface panel. The set representation of the intelligent metasurface panels with an association relationship can be referred to the following formula (5):

[0138] {ris d |ris d1 ,...,ris dk}

[0139] Where ris d is the set expression of the intelligent metasurface panels with an association relationship, and ris dk is the k-th intelligent metasurface panel having an association relationship with the other k - 1 intelligent metasurface panels. Multiple intelligent metasurface units are distributed on each intelligent metasurface panel. Through multiple intelligent metasurface units with an association relationship, multi-stream signal enhanced coverage for target users can be successfully achieved, thereby significantly improving the signal enhancement effect and stability. The schematic diagram of the intelligent metasurface panel can be referred to Figure 2 as shown in

[0140] Figure 2 is the schematic diagram of an intelligent metasurface panel provided by an embodiment of the present application. Among them, 201 is a single intelligent metasurface unit, 202 is the power connection interface of the intelligent metasurface panel, and 203 is the remote controller of the intelligent metasurface panel.

[0141] As Figure 2 shown, multiple intelligent metasurface units 201 are evenly and neatly arranged on the intelligent metasurface panel. Through the remote controller 203, the beam output parameters corresponding to each intelligent metasurface unit determined through the above steps can be received, and then each intelligent metasurface unit can be adjusted in a timely manner to achieve signal enhancement for each target user device.

[0142] In addition, in the embodiments of the present application, the specific manner of how to actually deploy the intelligent metasurface panel is not strictly limited. In some embodiments, it may be to obtain the three-dimensional periodic measurement data (Measurement Report, MRO) corresponding to each user in the target base station cell range in advance through the target base station. According to the signal strength of the user equipment used by the user in the periodic measurement data and the preset signal strength threshold, multiple signal strength angles of the user equipment that may require signal enhancement by the intelligent metasurface can be determined.

[0143] Then, based on the horizontal angle of arrival of the antenna, the vertical angle of arrival of the antenna, and the time advance of the antenna from the user in the periodic measurement data corresponding to the user equipment that needs signal enhancement, the position information corresponding to the user equipment that needs signal enhancement can be determined, such as longitude, latitude, and altitude.

[0144] Finally, the density-based spatial clustering of applications with noise (DBSCAN) algorithm can be used to perform clustering analysis on multiple user equipments that need signal enhancement, and multiple clusters that need signal enhancement can be determined. For each cluster, a corresponding intelligent metasurface panel can be set. Then, in the subsequent actual application process, the above method can be used to effectively and practically enhance the signals of each user equipment.

[0145] S106: Enhance the signals of at least one target user equipment through at least one intelligent metasurface unit configured with corresponding beam output parameters.

[0146] In step S106, the technical solution provided in the embodiment of the present application can configure corresponding parameters for each intelligent metasurface unit according to the beam output parameters corresponding to each intelligent metasurface unit determined in step S105. Based on the intelligent metasurface units after parameter configuration, signal enhancement of the target user equipment can be achieved.

[0147] Specifically, in an embodiment provided by the present application, for each intelligent metasurface unit, the beam output parameters corresponding to the intelligent metasurface unit can be input into the intelligent metasurface panel where the intelligent metasurface unit is located, so that the intelligent metasurface panel where it is located can adjust the parameters of the intelligent metasurface unit according to the beam output parameters.

[0148] Then, the intelligent metasurface panel can enhance the signals on the future movement trajectory of the target user equipment through the adjusted intelligent metasurface unit. The intelligent metasurface panel can be deployed near the target base station in advance according to the deployment method described in the above embodiment. The specific composition can be formed by aggregating multiple intelligent metasurface units according to a preset panel shape, such as the one shown above. Figure 2 In addition, the intelligent metasurface panel can also be other shapes composed of intelligent metasurface units, such as circular, hexagonal, etc. The specific shape of the intelligent metasurface panel is not strictly limited in this application and can be strictly determined according to the application scenario and actual situation.

[0149] In addition to the above, the signal enhancement method based on intelligent metasurface provided in the embodiments of the present application can be implemented through a specific signal enhancement system based on intelligent metasurface. The signal enhancement system based on intelligent metasurface may specifically include: a communication and sensing integrated target base station, an active antenna unit (AUU) with communication and sensing integration on the target base station, an intelligent metasurface panel formed by aggregating multiple intelligent metasurface units, an edge user plane function (UPF), and an intelligent metasurface unit control server.

[0150] In an embodiment provided by the present application, the processing process of signal enhancement for a target user equipment by the signal enhancement system based on intelligent metasurface can refer to Figure 3 as shown in

[0151] Figure 3 FIG. is a schematic flowchart of signal enhancement by the signal enhancement system based on intelligent metasurface provided in the embodiments of the present application, including steps S301 to S305.

[0152] S301: Obtain the measurement report data and motion perception data corresponding to each candidate user equipment through the active antenna unit of the target base station.

[0153] S302: The target base station sends the measurement report data and motion perception data corresponding to each candidate user equipment to the edge user plane function interface, and sends the data to the intelligent metasurface unit control server through the edge user plane function.

[0154] S303: The intelligent metasurface unit control server determines the target user equipment according to the measurement report data and motion perception data corresponding to each candidate user equipment, and determines the beam output parameters corresponding to each intelligent metasurface unit on each intelligent metasurface panel.

[0155] S304: The intelligent metasurface unit control server sends the determined beam output parameters corresponding to each intelligent metasurface unit to the corresponding intelligent metasurface panel through the edge user plane function, so that the intelligent metasurface panel adjusts the parameters of the intelligent metasurface units on its own panel.

[0156] S305: The intelligent metasurface units after parameter adjustment can perform signal enhancement on each target user equipment.

[0157] The specific implementation methods and operation processes corresponding to the above steps are all described in the above method introduction, and will not be elaborated here.

[0158] The above is the specific implementation manner of the signal enhancement method based on intelligent metasurface provided by the embodiments of the present application. Through the technical solutions provided by the embodiments of the present application, the accuracy of signal enhancement through intelligent metasurface can be effectively improved, and flexible enhancement can be performed according to the different situations of each user, effectively improving the signal quality and the user communication experience.

[0159] In addition, the technical solutions provided by the embodiments of the present application can also predict the future movement trajectory of the user equipment in real time according to the user data, significantly improving the accuracy and timeliness of the signal enhancement process. In the face of the continuous movement of the user, signal enhancement coverage can also be maintained at all times, optimizing the signal quality and improving the user communication experience, providing a more secure and stable signal connection for the user. When there is an associated relationship of overlapping coverage ranges between intelligent metasurface units, multi-stream signal enhancement coverage for a single target user equipment can also be achieved through multiple intelligent metasurface units, significantly improving the signal enhancement effect and stability.

[0160] Based on the beam weight configuration method provided in the above embodiments, the present application also provides an embodiment of a signal enhancement device based on intelligent metasurface.

[0161] Figure 4 It is a schematic structural diagram of a signal enhancement device based on intelligent metasurface provided in another embodiment of the present application.

[0162] As Figure 4 shown, the embodiments of the present application also provide a signal enhancement device 400 based on intelligent metasurface, which is applied to an electronic device. The signal enhancement device 400 based on intelligent metasurface includes:

[0163] A data acquisition module 401, configured to acquire motion perception data and measurement report data corresponding to a plurality of candidate user devices;

[0164] A target determination module 402, configured to determine at least one target user device that needs signal enhancement from the plurality of candidate user devices according to the measurement report data corresponding to the plurality of candidate user devices;

[0165] A period determination module 403, configured to determine, for each target user device, the predicted signal enhancement period number corresponding to the target user device according to the motion perception data corresponding to the target user device;

[0166] A position prediction module 404, configured to predict the predicted position information of the target user device at a plurality of predicted time points corresponding to the predicted signal enhancement period number;

[0167] A unit allocation module 405, configured to allocate at least one intelligent metasurface unit to the target user device based on the plurality of predicted position information corresponding to the target user device, and determine the beam output parameters corresponding to each intelligent metasurface unit;

[0168] A signal enhancement module 406, configured to enhance signals for at least one target user equipment through at least one intelligent metasurface unit configured with corresponding beam output parameters.

[0169] Optionally, the above-mentioned target determination module 402 includes:

[0170] For each candidate user equipment, determine the signal strength corresponding to the cell where the candidate user equipment is located and the signal strength of the neighboring cells of the cell where the candidate user equipment is located according to the measurement report data corresponding to the candidate user equipment;

[0171] When it is determined that both the signal strength corresponding to the cell where the candidate user equipment is located and the signal strength of the neighboring cells are less than a preset signal strength threshold, determine that the candidate user equipment is a target user equipment that needs signal enhancement.

[0172] Optionally, the sensing detection period of the motion sensing data is less than the data measurement period of the measurement report data;

[0173] The period determination module 403 includes:

[0174] According to the measurement report data corresponding to the target user equipment, perform data matching on at least one motion sensing data with the shortest measurement time difference between the sensing detection period and the data measurement period corresponding to the target user equipment, and determine the motion sensing data that matches the measurement report data corresponding to the target user equipment;

[0175] Determine the predicted signal enhancement period number corresponding to the target user equipment according to the motion sensing data that matches the measurement report data corresponding to the target user equipment.

[0176] Optionally, the period determination module 403 includes:

[0177] Determine the radar parameter data, motion speed data, and motion height data corresponding to the target user equipment according to the motion sensing data corresponding to the target user equipment;

[0178] Determine the target device type of the target user equipment according to the radar parameter data and motion height data corresponding to the target user equipment;

[0179] Determine the predicted signal enhancement period number corresponding to the target user equipment according to the motion speed data corresponding to the target user equipment and the reference speed data corresponding to the target device type of the target user equipment. The duration corresponding to the predicted signal enhancement period is the same as the sensing detection period of the motion sensing data.

[0180] Optionally, the position prediction module 404 includes:

[0181] Determine multiple prediction time points corresponding to the target user device according to the number of prediction signal enhancement cycles corresponding to the target user device;

[0182] Input the multiple prediction time points into the position prediction model pre-trained for the target user device, and predict the prediction position information corresponding to the target user device at the multiple prediction time points through the position prediction model.

[0183] Optionally, the position prediction module 404 includes:

[0184] For each target user device, determine the historical position information of the target user device at multiple historical time points;

[0185] Input the multiple historical time points into the position prediction model to be trained, and determine the predicted historical position information corresponding to the multiple historical time points through the position prediction model to be trained;

[0186] Determine the training error of the position prediction model to be trained according to the predicted historical position information and the historical position information corresponding to the multiple historical time points;

[0187] Train the position prediction model to be trained according to the training error to obtain the position prediction model corresponding to the target user device.

[0188] Optionally, the cell allocation module 405 includes:

[0189] For each target user unit, according to the coverage range corresponding to each pre-configured intelligent metasurface unit, use the intelligent metasurface unit that covers the most of the multiple prediction position information corresponding to the target user device as one of the intelligent metasurface units corresponding to the target user device;

[0190] If there is remaining predicted position information of the target user device that is not covered, determine the intelligent metasurface unit that covers the most of the remaining predicted position information corresponding to the target user device according to the coverage ranges of other intelligent metasurface units as one of the intelligent metasurface units corresponding to the target user device, and repeatedly determine until all the multiple predicted position information corresponding to the target user device is covered;

[0191] For each intelligent metasurface unit corresponding to the target user device, determine the beam output parameter corresponding to the intelligent metasurface unit according to the predicted position information covered by the intelligent metasurface unit.

[0192] Optionally, the cell allocation module 405 includes:

[0193] For each intelligent metasurface unit corresponding to the target user device, determine whether the coverage range of the associated intelligent metasurface unit corresponding to the intelligent metasurface unit covers at least one predicted location information corresponding to the target user device, where the associated intelligent metasurface unit is an intelligent metasurface unit that has at least a partially overlapping coverage range with the intelligent metasurface unit;

[0194] If so, use the associated intelligent metasurface unit as one of the intelligent metasurface units corresponding to the target user device, and perform multi-stream coverage on the target user device through the intelligent metasurface unit and the associated intelligent metasurface unit.

[0195] It should be noted that the data processing device 400 is a device corresponding to the above signal enhancement method based on intelligent metasurfaces. All implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects.

[0196] Figure 5 This is a schematic diagram of the hardware structure of a terminal device provided in another embodiment of this application.

[0197] The terminal device may include a processor 501 and a memory 502 storing computer program instructions.

[0198] Specifically, the above-mentioned processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0199] The memory 502 may include a mass storage for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 502 may include a removable or non-removable (or fixed) medium. In a suitable case, the memory 502 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0200] In a particular embodiment, the memory 502 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0201] The processor 501 reads and executes the computer program instructions stored in the memory 502 to implement any of the above-described intelligent metasurface-based signal enhancement methods in the embodiments.

[0202] In one example, the terminal device may further include a communication interface 503 and a bus 510. Among them, as Figure 5 shown, the processor 501, the memory 502, and the communication interface 503 are connected through the bus 510 and complete communication with each other.

[0203] The communication interface 503 is mainly used to implement communication between each module, device, unit, and / or device in the embodiments of the present application.

[0204] The bus 510 includes hardware, software, or both, and couples the components of the online data flow charging device to each other. By way of example and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses or a combination of two or more of these. In a suitable case, the bus 510 may include one or more buses. Although the embodiments of the present application describe and illustrate a specific bus, the present application contemplates any suitable bus or interconnect.

[0205] In addition, in combination with the intelligent metasurface-based signal enhancement method in the above embodiments, the embodiments of the present application may provide a computer storage medium to implement. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any of the above-described intelligent metasurface-based signal enhancement methods in the embodiments are implemented.

[0206] The embodiment of the present application also provides a computer program product, including a computer program, which when executed by a processor implements any one of the above-mentioned methods for signal enhancement based on intelligent metasurfaces.

[0207] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, the detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps after understanding the spirit of the present application.

[0208] The functional blocks shown in the above structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. A "machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0209] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0210] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing apparatus enable the implementation of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions.

[0211] The above are only specific embodiments of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should all be covered by the protection scope of the present application.

Claims

1. A signal enhancement method based on intelligent metasurface, characterized in that, Including: Obtaining motion perception data and measurement report data corresponding to multiple candidate user devices; Determining at least one target user device that needs signal enhancement from the multiple candidate user devices according to the measurement report data corresponding to the multiple candidate user devices; For each of the target user devices, determining the predicted signal enhancement cycle number corresponding to the target user device according to the motion perception data corresponding to the target user device, and the duration of each predicted signal enhancement cycle corresponding to the predicted signal enhancement cycle number is the same as the perception detection cycle corresponding to the motion perception data; Predicting the predicted position information of the target user device at multiple predicted time points corresponding to the predicted signal enhancement cycle number; Based on the multiple predicted position information corresponding to the target user device, allocating at least one intelligent metasurface unit to the target user device, and determining the beam output parameters corresponding to each intelligent metasurface unit; Enhancing the signal of the at least one target user device through the at least one intelligent metasurface unit configured with the corresponding beam output parameters.

2. The method according to claim 1, characterized in that Determining at least one target user device that needs signal enhancement from the multiple candidate user devices according to the measurement report data corresponding to the multiple candidate user devices includes: For each of the candidate user devices, determining the signal strength of the cell where the candidate user device is located and the signal strength of the neighboring cells of the cell where the candidate user device is located according to the measurement report data corresponding to the candidate user device; When it is determined that both the signal strength of the cell where the candidate user device is located and the signal strength of the neighboring cells are less than a preset signal strength threshold, determining that the candidate user device is the target user device that needs signal enhancement.

3. The method according to claim 1, wherein The perception detection cycle of the motion perception data is less than the data measurement cycle of the measurement report data; For each of the target user devices, determining the predicted signal enhancement cycle number corresponding to the target user device according to the motion perception data corresponding to the target user device includes: According to the measurement report data corresponding to the target user device, performing data matching on at least one motion perception data with the shortest measurement time difference between the perception detection cycle and the data measurement cycle corresponding to the target user device, and determining the motion perception data that matches the measurement report data corresponding to the target user device; Determining the predicted signal enhancement cycle number corresponding to the target user device according to the motion perception data that matches the measurement report data corresponding to the target user device.

4. The method according to claim 1, wherein For each of the target user devices, determining the predicted signal enhancement cycle number corresponding to the target user device according to the motion perception data corresponding to the target user device includes: Determining the radar parameter data, motion speed data, and motion height data corresponding to the target user device according to the motion perception data corresponding to the target user device; Determining the target device type of the target user device according to the radar parameter data and motion height data corresponding to the target user device; Determine the number of predicted signal enhancement cycles corresponding to the target user device according to the motion speed data corresponding to the target user device and the reference speed data corresponding to the target device type of the target user device.

5. The method according to claim 1, characterized in that, Predict the predicted position information of the target user device at multiple predicted time points corresponding to the number of predicted signal enhancement cycles, including: Determine multiple predicted time points corresponding to the target user device according to the number of predicted signal enhancement cycles corresponding to the target user device; Input the multiple predicted time points into the position prediction model pre-trained for the target user device, and predict the predicted position information corresponding to the target user device at the multiple predicted time points through the position prediction model.

6. The method according to claim 5, characterized in that, The method further includes: For each target user device, determine the historical position information of the target user device at multiple historical time points; Input the multiple historical time points into the position prediction model to be trained, and determine the predicted historical position information corresponding to the multiple historical time points through the position prediction model to be trained; Determine the training error of the position prediction model to be trained according to the predicted historical position information and the historical position information corresponding to the multiple historical time points; Train the position prediction model to be trained according to the training error to obtain the position prediction model corresponding to the target user device.

7. The method according to claim 1, characterized in that, Based on the multiple predicted position information corresponding to the target user device, allocate at least one intelligent metasurface unit for the target user device, and determine the beam output parameters corresponding to each intelligent metasurface unit, including: For each target user unit, according to the coverage range corresponding to each pre-configured intelligent metasurface unit, use the intelligent metasurface unit that covers the most of the multiple predicted position information corresponding to the target user device as one of the intelligent metasurface units corresponding to the target user device; If there is remaining predicted position information of the target user device that is not covered, determine the intelligent metasurface unit that covers the most of the remaining predicted position information corresponding to the target user device according to the coverage ranges of other intelligent metasurface units as one of the intelligent metasurface units corresponding to the target user device until all of the multiple predicted position information corresponding to the target user device is covered; For each intelligent metasurface unit corresponding to the target user device, determine the beam output parameter corresponding to the intelligent metasurface unit according to the predicted position information covered by the intelligent metasurface unit.

8. The method according to claim 7, characterized in that, The method further includes: For each intelligent metasurface unit corresponding to the target user device, determine whether the coverage range of the associated intelligent metasurface unit corresponding to the intelligent metasurface unit covers at least one of the predicted position information corresponding to the target user device, where the associated intelligent metasurface unit is an intelligent metasurface unit that has at least a partially same coverage range as the intelligent metasurface unit; If so, use the associated intelligent metasurface unit as one of the intelligent metasurface units corresponding to the target user device, and perform multi-stream coverage on the target user device through the intelligent metasurface unit and the associated intelligent metasurface unit.

9. The method according to claim 1, characterized in that, Enhancing the signal of the at least one target user equipment through the at least one intelligent metasurface unit configured with the corresponding beam output parameters, including: For each of the intelligent metasurface units, input the beam output parameters corresponding to the intelligent metasurface unit into the intelligent metasurface panel where the intelligent metasurface unit is located, and configure the parameters of the intelligent metasurface unit through the intelligent metasurface panel where the intelligent metasurface unit is located. The intelligent metasurface panel is pre-deployed and composed of multiple intelligent metasurface units according to a preset panel shape; Control the at least one intelligent metasurface unit after parameter configuration through at least one of the intelligent metasurface panels to enhance the signal of the at least one target user equipment.

10. The method according to claim 9, wherein Deploying the intelligent metasurface panel includes: Obtain the three-dimensional periodic measurement data corresponding to multiple user equipment to be enhanced in the cell area corresponding to the target base station through the target base station; For each of the user equipment to be enhanced, determine the position information corresponding to the user equipment to be enhanced according to the three-dimensional periodic measurement data corresponding to the user equipment to be enhanced; Based on the position information corresponding to the multiple user equipment to be enhanced, perform clustering analysis to determine the clustering analysis result corresponding to the multiple user equipment to be enhanced; According to the position information corresponding to each cluster in the clustering analysis result, determine the deployment positions of at least one of the intelligent metasurface panels. For each of the intelligent metasurface panels, use other intelligent metasurface panels whose overlapping coverage ranges with the intelligent metasurface panel meet a preset coverage range threshold as the associated intelligent metasurface panels of the intelligent metasurface panel.

11. A signal enhancement system based on intelligent metasurface, characterized in that Including: A target base station, an active antenna unit, an intelligent metasurface panel, an edge user port function unit, and an intelligent metasurface unit control server. The active antenna unit is deployed at the target base station, and the intelligent metasurface panel is composed of multiple intelligent metasurface units; The active antenna unit is used to obtain the measurement report data and motion perception data corresponding to each candidate user equipment; The target base station is used to send the measurement report data and the motion perception data corresponding to each candidate user equipment to the edge user port function unit; The edge user port function unit is used to send the received measurement report data and motion perception data corresponding to each candidate user equipment to the intelligent metasurface unit control server; The intelligent metasurface unit control server is used to determine the target user equipment according to the measurement report data and the motion perception data corresponding to each candidate user equipment, and determine the beam output parameters corresponding to each intelligent metasurface unit on the intelligent metasurface panel, and send the beam output parameters corresponding to each intelligent metasurface unit to the intelligent metasurface panel through the edge user port function unit; The intelligent metasurface panel is used to adjust the parameters of each intelligent metasurface unit according to the beam output parameters corresponding to each intelligent metasurface unit, and enhance the signal of the target user equipment through the intelligent metasurface unit after parameter adjustment.

12. A signal enhancement device based on intelligent metasurface, characterized in that, Including: A data acquisition module, configured to acquire motion perception data and measurement report data corresponding to multiple candidate user devices; A target determination module, configured to determine at least one target user device that needs signal enhancement from the multiple candidate user devices according to the measurement report data corresponding to the multiple candidate user devices; A period determination module, configured to, for each of the target user devices, determine the predicted signal enhancement period number corresponding to the target user device according to the motion perception data corresponding to the target user device; A position prediction module, configured to predict the predicted position information of the target user device at multiple predicted time points corresponding to the predicted signal enhancement period number; A unit allocation module, configured to allocate at least one intelligent metasurface unit for the target user device based on the multiple predicted position information corresponding to the target user device, and determine the beam output parameters corresponding to each intelligent metasurface unit; A signal enhancement module, configured to perform signal enhancement on the at least one target user device through the at least one intelligent metasurface unit configured with the corresponding beam output parameters.

13. A computer-readable storage medium, characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the signal enhancement method based on an intelligent metasurface as described in any one of claims 1-10 is implemented.

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