Reconfigurable intelligent surface control equipment and method

Abstract

This disclosure provides equipment for wireless communication. [Solution] The equipment includes a processor configured to transmit a request to a first base station regarding the connection status of the user equipment; transmit a sensing command to the first base station to scan an area in the coverage of the first base station and generate environmental feature data; perform neural network inference based on the environmental feature data to generate a predicted location of the user equipment; and control a first reconfigurable intelligent surface (RIS) equipment based on the predicted location.

Description

Technical Field

[0001] The present disclosure relates to reconfigurable intelligent surface control equipment and methods. In particular, the present disclosure relates to reconfigurable intelligent surface control equipment and methods that integrate sensing and communication.

Background Art

[0002] A reconfigurable intelligent surface (RIS), also referred to as an intelligent reflecting surface (IRS), is a surface structure composed of a plurality of reflecting units. The RIS is used to manipulate electromagnetic signals. Specifically, by adjusting the arrangement of the reflecting units, the reflection angle of the RIS can be controlled. By strategically changing the angles of multiple RISs between a transmitter and a receiver, the quality and coverage of electromagnetic signals can be significantly enhanced.

Summary of the Invention

[0003] In some embodiments, equipment for wireless communication is provided. The equipment is arranged to include a processor that transmits a request regarding the connection state of a user equipment to a first base station, transmits a sensing command to the first base station to scan an area within the coverage of the first base station to generate environmental feature data, executes neural network inference based on the environmental feature data to generate a predicted position of the user equipment, and controls a first reconfigurable intelligent surface (RIS) equipment based on the predicted position.

[0004] In some embodiments, methods for wireless communication have been provided. The methods include transmitting a request to a first base station regarding the connection status of user equipment; transmitting a sensing command to the first base station to scan an area in the coverage of the first base station to generate environmental feature data; performing neural network inference based on the environmental feature data to generate a predicted location of the user equipment; and controlling a first RIS equipment based on the predicted location. [Brief explanation of the drawing]

[0005] The present disclosure can be more fully understood by referring to the attached drawings and reading the detailed description of the following embodiments. [Figure 1] This is a schematic diagram illustrating a system for wireless communication according to each embodiment of the present disclosure. [Figure 2] Figure 1 is a schematic diagram illustrating an example of the system according to each embodiment of this disclosure. [Figure 3] This disclosure includes a flow chart illustrating the sensing module, prediction module, routing module, decision module, RIS control module, handover module, controller, and methods for operating the system as shown in Figures 1 and 2, according to each embodiment of this disclosure. [Figure 4] This is a schematic diagram illustrating an example of a machine learning model for a prediction module according to each embodiment of the present disclosure. [Figure 5] This is a schematic diagram illustrating an example of determining the controller optimization path according to each embodiment of the present disclosure. [Figure 6] This is a schematic diagram illustrating an example of determining the controller handover type according to each embodiment of the present disclosure. [Modes for carrying out the invention]

[0006] The embodiments of this disclosure are described herein by reference in detail, and the embodiments shown in the accompanying drawings are exemplary. In the drawings and description, the same reference numerals are used for identical or similar parts whenever possible. Detailed descriptions or explanations of well-known methods of implementation and operations are omitted to avoid obscuring the essential aspects of the various embodiments of this disclosure.

[0007] It should be noted that terms such as “first” and “second” used in this specification to describe various elements or processes are intended to distinguish one element or process from another. However, elements, processes, and their order are not limited by these terms. For example, as long as it does not deviate from the scope of this disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0008] In the following discussion and claims, "comprising," "including," "having," "containing," and "related to" are all open terms, meaning "including but not limited to." Furthermore, "and / or" as used in the text are not mutually exclusive and include any one or more of the related enumerated items and all combinations thereof.

[0009] This disclosure relates to a reconfigurable intelligent surface (RIS) control system and method for determining an optimized transmission path from a base station and RIS to user equipment, and for controlling the RIS based on the determined optimized transmission path. Specifically, when a base station communicates with user equipment, the movement of the user equipment, as well as obstacles between the RIS, base station, and user equipment, affect the quality of communication. To ensure quality of communication, it is necessary to strategically adjust the RIS in response to the movement of the user equipment and obstacles to establish an optimized transmission path.

[0010] Please refer to Figure 1. Figure 1 is a schematic diagram showing a system 10 for wireless communication according to each embodiment of the present disclosure. In some embodiments, system 10 is a mobile communication system. In some embodiments, system 10 is a radio access network (RAN) system.

[0011] As shown in Figure 1, the system 10 includes a controller 100, one or more base stations 200, one or more base stations 300, and one or more RISs 400. The controller 100 is coupled to the base stations 200, 300, and RISs 400 via electrical connections or wireless communication.

[0012] Base stations 200 and 300 are positioned to communicate with one or more user devices (user equipment), such as smartphones and tablets. For example, base stations 200 and 300 transmit radio signals to and receive radio signals from user equipment.

[0013] In some embodiments, base station 200 is configured to transmit signals within a first frequency range having a first coverage (i.e., the maximum area over which communication is possible), and base station 300 is configured to transmit signals within a second frequency range having a second coverage smaller than the first coverage.

[0014] In some embodiments, the first frequency range is frequency range 1 (FR1), for example, a frequency range of 410 MHz to 7125 MHz. The second frequency range is frequency range 2 (FR2), which is higher than FR1, for example, a frequency range of 24.25 GHz to 52.6 GHz.

[0015] The RIS400 is positioned to transmit signals between the base station 300 and the user equipment. For example, multiple RISs form a signal transmission path to send radio signals from the base station 300 to the user equipment.

[0016] The controller 100 is positioned to control the base stations 200, 300 and RIS 400. For example, the controller 100 selects one base station 200 to transmit a radio signal to the user equipment and determines the reflection angle of the RIS 400 to transmit the radio signal.

[0017] In some embodiments, the controller 100 includes a processor 110 and a memory 120. The processor 110 is electrically connected to the memory 120. In some embodiments, the processor 110 and the memory 120 cooperate to determine the placement of base stations 200, 300 and RIS 400. For example, the processor 110 determines the reflection angle of RIS 400 based on data in the memory 120.

[0018] According to each embodiment, the processor 110 may include a central processing unit (CPU), or other programmable general-purpose or dedicated microcontroller (MCU), microprocessor, digital signal processor (DSP), programmable controller, application-specific integrated circuit (ASIC), graphics processing unit (GPU), arithmetic logic unit (ALU), composite programmable logic device (CPLD), field-programmable gate array (FPGA), or other similar components, or a combination of the above components.

[0019] According to each embodiment, the memory 120 may be a hard disk, random access memory, other storage media, or a combination thereof.

[0020] Please refer to Figure 2. Figure 2 is a schematic diagram showing an example of the system 10 in Figure 1 according to each embodiment of the present disclosure.

[0021] As shown in Figure 2, the controller 100 includes a sensing module 131, a prediction module 132, a routing module 136, a decision module 133, a RIS control module 134, and a handover module 135.

[0022] In some embodiments, the sensing module 131, the prediction module 132, the path module 136, the decision module 133, the RIS control module 134, and the handover module 135 are implemented by the processor 110.

[0023] Those skilled in the art will further understand that any of the various modules described in connection with the present disclosure may be implemented as electronic hardware (e.g., processor - 110), various forms of programs or design codes incorporating instructions, or a combination of both. To clearly illustrate this interchangeability of hardware and software, each of the following exemplary modules is mainly described in terms of its functionality. Whether such functionality is implemented as hardware or as software is determined by specific applications and design constraints on the overall system. Those skilled in the art can implement the above functions in different ways according to each specific application, but such determination of implementation should not be construed as departing from the scope of the present disclosure.

[0024] Please refer to FIGS. 2 and 3 here. FIG. 3 is a flowchart showing a method 20 for operating the sensing module 131, the prediction module 132, the path module 136, the decision module 133, the RIS control module 134, the handover module 135, the controller 100, and the system 10 as shown in FIGS. 1 - 2 according to each embodiment of the present disclosure. It should be understood that additional operations may be provided before, during, and after the operations shown in FIG. 3, and for additional embodiments of the method, some of the operations may be exchanged or deleted. The order of the operations is interchangeable with each other. In each drawing and exemplary embodiment, similar elements are assigned similar reference numerals. The method 20 includes operations o1 - o11, and the above operations will be described below with reference to the system 10 shown in FIGS. 1 - 2. In some embodiments, the method 20 is implemented as program code stored in a non - volatile computer - readable medium (e.g., memory 120).

[0025] In operation o1, the sensing module 131 is positioned to collect data from the base station 300 and the RIS 400. For example, the sensing module 131 receives the position of the base station 300 from the base station 300. The sensing module 131 also receives the position of each RIS 400 and the adjustable range of the reflection angle from the RIS 400.

[0026] In some embodiments, user equipment 500 is connected to the first base station 200. For simplicity, only the first base station 200 is shown in Figure 2, and the other base stations 200 and 300 are omitted.

[0027] The sensing module 131 is further configured to periodically send status check commands to the first base station 200 to check the connection status between the first base station 200 and the user equipment 500.

[0028] The first base station 200 transmits a connection status request to the user equipment 500. The user equipment 500 transmits connection status feedback to the first base station 200 in response to the connection status request. The first base station 200 then transmits connection status feedback to the sensing module 131.

[0029] If the sensing module 131 receives connection status feedback, it determines that the user equipment 500 is connected to the first base station 200. Conversely, if the sensing module 131 does not receive connection status feedback, it determines that the user equipment 500 is disconnected from the first base station 200.

[0030] When the sensing module 131 determines that the user equipment 500 has been disconnected from the first base station 200, it sends a command to the first base station 200 or multiple base stations 200 so that the first base station 200 or any of the other base stations 200 senses the environment and connects to the user equipment 500. For example, the sensing module 131 sends a command to the first base station 200, and the first base station 200 senses the environment (an area within the coverage of the first base station 200) to search for the user equipment 500. If the user equipment 500 in the coverage is sensed by the first base station 200, the first base station 200 connects to the user equipment 500.

[0031] In some embodiments, the sensing module 131 repeatedly sends commands to the first base station 200 or multiple base stations 200 until the sensing module receives connection status feedback from the first base station 200 or any other base station 200.

[0032] In some embodiments, when user equipment 500 is connected to the first base station 200, operation o2 is performed.

[0033] In operation o2, the sensing module 131 generates sensing commands for base stations 200 and 300. The base stations 200 and 300 respond to the sensing commands, sense the environment, and generate environmental feature data. For example, the base stations 200 and 300 sense the environment in response to the sensing commands using Integrated Sensing and Communication (ISAC) technology.

[0034] Specifically, ISAC technology integrates sensing into communications. For example, base stations 200 and 300 each use the base station's radio signals to sense (scan) the environment. For example, base stations 200 and 300 each use the base station's radio signals to sense features such as the position, orientation, size, and velocity of objects in the base station's coverage to generate environmental feature data. In some embodiments, the environmental feature data includes ISAC data (e.g., point cloud) acquired by scanning the environment using radio signals.

[0035] In operation o3, the sensing module 131 determines the type of object (obstacle) sensed in the environment. For example, the sensing module 131 determines whether the obstacle is stationary or dynamic (e.g., a moving car).

[0036] In some embodiments, the memory 120 stores environmental feature data (e.g., ISAC data) corresponding to different times, and the sensing module 131 determines the type of obstacle by comparing it with the environmental feature data corresponding to different times. For example, if the position of the obstacle differs at different times according to the environmental feature data corresponding to different times, the sensing module 131 determines the type of obstacle to be dynamic.

[0037] In some embodiments, base stations 200 and 300 generate channel status information (CSI) through ISAC. In some embodiments, sensing module 131 is further configured to request CSI from base stations 200 and / or 300. For example, sensing module 131 requests the CSI of the channel between equipment 500 and base station 200 or 300. Through the CSI, sensing module 131 can determine information about the signal between equipment 500 and base station 200 or 300 (e.g., phase, amplitude, and delay).

[0038] In some embodiments, the sensing module 131 receives location data of the equipment 500 from the first base station 200 or 300. In some embodiments, the memory 120 stores the location data of the equipment 500. In some embodiments, the memory 120 stores historical location data of the equipment 500 corresponding to different times.

[0039] In operation o4, the sensing module 131 constructs an environmental model (i.e., a set of data) based on environmental feature data, CSI, and historical location data. In some embodiments, the sensing module 131 generates an environmental model by concatenating the environmental feature data, CSI, and historical location data.

[0040] In operation o5, the prediction module 132 makes predictions based on the environmental model and generates predicted locations for the user equipment 500.

[0041] In some embodiments, the prediction module 132 performs inference using a machine learning model (e.g., a neural network) to generate a predicted location of the user's equipment 500.

[0042] In some embodiments, the machine learning model receives an environmental model as input to the machine learning model and generates the predicted location of the user equipment 500 as the output of the machine learning model.

[0043] Please refer to Figures 1 to 4. Figure 4 is a schematic diagram showing an example of a machine learning model of the prediction module 132 according to each embodiment of the present disclosure.

[0044] As shown in Figure 4, the environmental model may include data 1 to data M (e.g., environmental feature data, CSI, and historical location data). The machine learning model may include model 1 to model N, where M and N are natural numbers. For example, the machine learning model may include a convolutional neural network (CNN), a recurrent neural network (RNN), a graph convolutional network (GCN), etc.

[0045] In some embodiments, the machine learning model is an ensemble model combining models 1 to N. For example, model 1 receives data 1 to data M as input and generates a first output, model 2 receives data 1 to data M as input and generates a second output, ..., and model N receives data 1 to data M as input and receives the Nth output. The machine learning model performs an ensemble voting operation based on the first to Nth outputs to generate predicted positions.

[0046] In operation o6, the routing module 136 determines the optimized route for signals transmitted between the first base station 300 and the user equipment 500. In some embodiments, the routing module 136 determines the optimized route based on predicted position, location of base station 300, location of RIS 400, reflection angle range of RIS 400, and environmental feature data.

[0047] For example, the routing module 136 determines the RIS 400 to form a route for transmitting signals between the first base station 300 and the user equipment 500. The routing module 136 can generate different routes in the RIS for transmitting signals between the first base station 300 and the user equipment 500.

[0048] In some embodiments, the route module 136 selects the shortest path in the route as the optimized path. In each embodiment, the route module 136 selects the path formed with the fewest RIS400 as the optimized path.

[0049] Please refer to Figures 1 to 5. Figure 5 is a schematic diagram showing an example of determining the optimization path of the controller 100 according to each embodiment of the present disclosure. As shown in Figure 4, the prediction module 132 generates a predicted position of the user equipment 500 corresponding to time t based on an environmental model at time t-1, which is earlier than time t.

[0050] The routing module 136 then determines the shortest or least used optimized route P of the RIS 400 that connects to the base station 300 and the user equipment 500.

[0051] In some embodiments, after determining the optimization path, operation o7 is performed to determine whether to perform a handover.

[0052] In operation o7, the decision module 133 generates a result indicating whether a handover is necessary based on the optimized path.

[0053] In some embodiments, memory 120 stores the previously optimized routes generated by the routing module 136. In other words, memory 120 stores the base stations 300 and RIS 400 connected to the user equipment 500.

[0054] In some embodiments, the decision module 133 compares the current optimization path with the previous optimization path to determine whether to replace the RIS400 used in the previous optimization path with another RIS400 to form the current optimization path.

[0055] Similarly, the decision module 133 compares the current optimized route with the previous optimized route to determine whether to replace the base station 300 used in the previous optimized route with another base station 300 to form the current optimized route.

[0056] The decision module 133 determines that a handover is necessary if it determines that the RIS400 or base station 300 used in the previous route has been replaced to form the current optimized route.

[0057] Conversely, if the decision module 133 determines that neither the RIS400 nor the base station 300 used in the previous route have been replaced to form the current optimized route, it determines that a handover is not necessary.

[0058] In operation o8, the decision module 133 determines the handover type. If the decision module 133 determines that the base station 300 used in the previous optimized route has been replaced by another base station 300 to form the current optimized route, it determines the handover type as the base station handover type.

[0059] Conversely, if the decision module 133 determines that only the RIS400 was replaced to form the current optimized path, it determines the handover type as the RIS handover type.

[0060] In operation o9, if the handover type is a base station handover type, the handover module 135 generates handover commands to the first base station 300 and the second base station 300, and the second base station 300 replaces the first base station 300 to form the current optimized route. Then, the first base station 300 and the second base station 300 perform the handover operation in response to the handover command.

[0061] In operation o10, if the handover type is the RIS handover type, the handover module 135 generates handover commands to the first RIS400 and the second RIS400 and performs a handover between the first RIS400 and the second RIS400. The second RIS400 replaces the first RIS400 to form the current optimized path. The first RIS400 and the second RIS400 then perform the handover operation in response to the handover command.

[0062] Please refer to Figures 1 to 6. Figure 6 is a schematic diagram showing an example of determining the handover type of the controller 100 according to each embodiment of the present disclosure.

[0063] As shown in Figure 6, at time t-1, the base station 300a (first base station 300) and RIS400a (first RIS400) form an optimized route to the user equipment 500.

[0064] The prediction module 132 then generates a predicted position for the user equipment 500 corresponding to time t after time t-1. If the path module 136 determines that the distance between RIS400a and the predicted position for the user equipment 500 corresponding to time t is greater than the maximum signal range d1 of RIS400a (i.e., the user equipment 500 is outside the coverage of RIS400a), it replaces RIS400a with RIS400b (second RIS400) to form an optimized path corresponding to time t. The predicted position for the user equipment 500 corresponding to time t is within the coverage of RIS400b.

[0065] The decision module 133 determines the handover type corresponding to time t as the RIS handover type. The handover module 135 generates commands to perform RIS handover operations on RIS 400a and 400b.

[0066] The prediction module 132 then generates a predicted position for the user equipment 500 corresponding to time t+1 after time t. As shown in Figure 6, if the path module 136 determines that the user equipment 500 at the predicted position corresponding to time t+1 is isolated from RIS400b by an obstacle, it replaces RIS400b with RIS400c (third RIS400) to form an optimized path corresponding to time t+1.

[0067] If the routing module 136 determines that the distance between RIS400c and base station 300a is greater than the maximum signal range d2 of base station 300a (i.e., RIS400c is outside the coverage of base station 300a), it replaces base station 300a with base station 300b (second base station 300) to form an optimized route corresponding to time t+1. RIS400c is within the coverage of base station 300b.

[0068] The decision module 133 determines the handover type corresponding to time t+1 as the base station handover type. The handover module 135 generates commands for base stations 300a and 300b to perform the base station handover operation.

[0069] In some embodiments, after performing a handover operation, operation o11 is performed to adjust the RIS angle. In some embodiments, if the decision module 133 determines that a handover is not necessary, operation o11 is performed to adjust the RIS angle.

[0070] In operation o11, the decision module 133 determines the placement of the RIS400 in the optimized path based on environmental feature data and the optimized path (location of the base station 300, location of the RIS400 in the optimized path, and predicted location). For example, the decision module 133 determines the reflection angle of the RIS400 in the optimized path and transmits radio signals between the optimized paths. For example, the reflection angle of the RIS400 in the optimized path is adjusted so that the radio signal is reflected from the source RIS400 to the destination RIS400 in the optimized path.

[0071] In some embodiments, the RIS control module 134 is a service management and orchestration (SMO) module that controls the RIS400. The RIS control module 134 controls the RIS400 in an optimized path based on the placement determined by the decision module 133. For example, the RIS control module 134 generates an angle adjustment command to the RIS400 based on the placement determined by the decision module 133, and the RIS400 changes the reflection angle in response to the angle adjustment command.

[0072] In some embodiments, after performing operation o11, operation o2 is performed again to sense the environment, generate the next predicted position of equipment 500, and control base station 300 and RIS 400.

[0073] The arrangements in Figures 1 to 6 are illustrative. Various implementations are within the scope of this disclosure. For example, in some embodiments, base stations 200 and 300 may be integrated into a single base station.

[0074] In light of the above, a system, equipment, and method for wireless communication are provided. The provided system, equipment, and method utilize ISAC technology to sense the environment and predict the location of user equipment based on the sensed environmental data. The provided system, equipment, and method further adjust the RIS placement based on the prediction and environmental data to construct a signal transmission path to the user equipment. The provided system, equipment, and method contribute to improving wireless communication performance.

[0075] This disclosure is described using examples and based on preferred embodiments, but should be understood as not being limited to these embodiments. Those skilled in the art can make various changes, substitutions, and modifications without departing from the spirit and scope of this disclosure. Thus, this disclosure is intended to cover modifications and changes to this disclosure as long as they fall within the scope of the appended claims. [Explanation of symbols]

[0076] 10: System 20: Method 100: Controller 110: Processor 120: Memory 131: Sensing Module 132: Prediction Module 133: Decision Module 134: RIS control module 135: Handover Module 136: Route Module 200, 300, 300a, 300b: Base station 400, 400a, 400b, 400c: RIS 500: User equipment d1: Maximum signal range d2: Maximum signal range o1, o2, o3, o4, o5, o6, o7, o8, o9, o10, o11: Operation obstacke, obstackes: obstacles P: Route t: time t+1, t-1: Time

Claims

1. Equipment for wireless communication, To transmit a request regarding the connection status of the user equipment to the first base station, Sending a sensing command to the first base station to scan the area in the coverage of the first base station and generate environmental feature data, Based on the aforementioned environmental feature data, neural network inference is performed to generate a predicted position of the user equipment, Controlling a first reconfigurable intelligent surface (RIS) equipment based on the predicted position, Equipment including a processor arranged to perform the following tasks.

2. The processor is further configured to determine a plurality of signal paths between the second base station and the user equipment, and to determine the shortest signal path among the plurality of signal paths as the optimized path, The processor is further configured to adjust the arrangement of multiple RIS equipment in the optimized path to transmit signals between the second base station and the user equipment. The equipment according to claim 1, wherein the first base station is arranged to generate radio signals within a first frequency range, and the second base station is arranged to generate radio signals within a second frequency range higher than the first frequency range.

3. It is equipment, Further arranged to request location data from a first plurality of RIS equipment, including the first RIS equipment, The aforementioned processor, Based on the position data, the environmental feature data, and the predicted position, the second plurality of RIS equipment are determined in the first plurality of RIS equipment to form a signal path. Based on the aforementioned location data, environmental feature data, and predicted location, multiple signal paths between the second base station and the user equipment are determined. The first path formed with the fewest RIS equipment among the aforementioned multiple signal paths is determined as the optimized path, Based on the positions of the third plurality of RIS equipment, the reflection angles of the third plurality of RIS equipment in the optimized path are determined, and a signal is transmitted between the second base station and the user equipment. The apparatus according to claim 1, further arranged to perform the following:

4. The apparatus according to claim 1, wherein the processor is further configured to determine the first RIS equipment and the second RIS equipment that performs the handover operation based on the environmental feature data and the predicted position.

5. The aforementioned user equipment is connected to the second base station in the first hour. The equipment according to claim 1, wherein the processor is further configured to determine, based on the environmental feature data and the predicted location, a third base station to perform a handover operation with the second base station if, based on the predicted location corresponding to a second time after the first time, it is determined that the first RIS equipment for forming a signal path for the user equipment is outside the coverage of the second base station.

6. The apparatus according to claim 1, wherein the processor is further configured to transmit the sensing command to the first base station, and the first base station generates a point cloud as environmental feature data by scanning the area using radio signals.

7. The apparatus according to claim 1, further comprising a memory device, the memory device being configured to store historical environmental feature data, and the processor being configured to compare the historical environmental feature data with the environmental feature data to determine whether an object in the region is dynamic or not.

8. A method for wireless communication, To transmit a request regarding the connection status of the user equipment to the first base station, Sending a sensing command to the first base station to scan the area in the coverage of the first base station and generate environmental feature data, Based on the aforementioned environmental feature data, neural network inference is performed to generate a predicted position of the user equipment, Controlling the first RIS equipment based on the predicted position, A method that includes this.

9. The method according to claim 8, further comprising repeatedly transmitting the sensing command to the first base station until the base station transmits the connection status indicating the connection between the user equipment.

10. The second base station to be connected to the user equipment is determined based on the locations of multiple second base stations, the locations of multiple RIS equipment, the environmental characteristic data, and the predicted location. The signal path between the second base station and the user equipment is determined based on the locations of the plurality of RIS equipment, the environmental characteristic data, and the predicted location. To determine the optimal signal path having the fewest RIS devices in the aforementioned signal path, The reflection angle of the RIS equipment in the optimized signal path is adjusted to connect the second base station and the user equipment. The method according to claim 8, further comprising:

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