Method and apparatus for sensing-based beamforming in integrated sensing and communication system

Abstract

A method by which a terminal performs sensing in a wireless communication system, provided by an embodiment of the present invention, comprises the steps of: receiving sensing configuration information from a base station; and performing terminal self-sensing or base station indication-based sensing on the basis of the sensing configuration information, wherein the sensing configuration information includes at least one of sensing resource allocation information, sensing signal configuration information, or sensing requirements.

Description

Sensing-based beamforming method and device in a system integrating communication and sensing

[0001] The present invention relates to wireless communication, and more specifically, to an efficient beamforming method and apparatus utilizing sensing results in a wireless communication system in which communication and sensing are integrated.

[0002] Recently, as the utilization of the millimeter wave band has increased in communication systems, the importance of beamforming technology is growing. In particular, as the integration of communication and sensing (ISAC) emerges as a key technology in post-5G mobile communication systems, there is a demand for more efficient and accurate beamforming technology.

[0003] However, since existing beamforming technologies primarily rely on channel state or feedback information to form beams, they have limitations in finding the optimal beam in rapidly changing wireless environments. Furthermore, it is currently difficult to effectively control beamforming for moving objects that change in real time.

[0004] Based on the above-described aspects, embodiments of the present invention provide a method for performing efficient beamforming by utilizing sensing results including spatial information of an object (e.g., location, object presence area, etc.) and / or mobility information of an object (e.g., direction of movement, speed, acceleration, etc.) obtained through sensing in an ISAC system.

[0005] Specifically, the present invention provides a method for determining the precise location of an object through two-stage sensing and determining optimal beamforming parameters based thereon.

[0006] In addition, the present invention provides a method for performing efficient beamforming tailored to the requirements of each application by applying a differentiated beamforming strategy according to the sensing QoS level.

[0007] A communication method provided by an embodiment of the present invention comprises: a step of performing a first sensing to obtain a sensing result for an object; a step of determining beamforming parameters based on the sensing result; and a step of performing beamforming using the determined beamforming parameters, wherein the sensing result includes at least one of spatial information of the object and mobility information of the object.

[0008] Here, the spatial information includes at least one of location information where the object is located and area information where the object exists, and the mobility information may include at least one of the object's direction of movement, speed, and acceleration.

[0009] Here, the step of obtaining the sensing result may further include the step of performing a second sensing based on the obtained sensing result to re-obtain a sensing result for the object.

[0010] Here, the first sensing is performed at a first sensing QoS level, and the second sensing is performed at a second sensing QoS level higher than the first sensing QoS level, and the first and second sensing QoS levels may include a required level for at least one of accuracy, resolution, latency, reliability, and update rate.

[0011] Here, the step of performing the beamforming may include the step of performing beamforming or beam sweeping using a beam generated using the determined beamforming parameters.

[0012] Here, the beam sweeping is performed at a preset beam width interval, and the beam width interval can be determined based on the sensing QoS level.

[0013] Here, the first sensing is performed for a plurality of objects, and the beamforming parameters are determined for each object based on at least one of spatial and mobility information for the plurality of objects, and may include precoding for MU-MIMO.

[0014] Here, the beamforming parameters can be determined based on the pattern between the sensing results and the beamforming parameters, which are pre-learned using AI / ML.

[0015] Here, the first sensing may be bistatic sensing or multistatic sensing by a plurality of RUs.

[0016] Here, the object is a preset object including an aerial vehicle or a maritime vehicle, and the beamforming parameter can be determined according to the communication coverage of the object.

[0017] A communication device provided by one embodiment of the present invention comprises a memory and a processor, wherein the processor performs a first sensing to obtain a sensing result for an object, determines beamforming parameters based on the sensing result, and performs beamforming using the determined beamforming parameters, and the sensing result includes at least one of spatial information of the object and mobility information of the object.

[0018] Here, the spatial information includes at least one of location information where the object is located and area information where the object exists, and the mobility information may include at least one of the object's direction of movement, speed, and acceleration.

[0019] Here, the processor can perform a second sensing based on the acquired sensing result to re-acquire the sensing result for the object.

[0020] Here, the processor is configured to perform the first sensing at a first sensing QoS level and the second sensing at a second sensing QoS level higher than the first sensing QoS level, and the first and second sensing QoS levels may include a required level for at least one of accuracy, resolution, latency, reliability, and update rate.

[0021] Here, the processor can perform beamforming or beam sweeping using a beam generated using the determined beamforming parameters.

[0022] Here, the beam sweeping is performed at a preset beam width interval, and the beam width interval can be determined based on the sensing QoS level.

[0023] Here, the first sensing is performed for a plurality of objects, and the beamforming parameters are determined for each object based on at least one of spatial and mobility information for the plurality of objects, and may include precoding for MU-MIMO.

[0024] Here, the beamforming parameters can be determined based on the pattern between the sensing results and the beamforming parameters learned using AI / ML.

[0025] Here, the first sensing may be bistatic sensing or multistatic sensing by a plurality of RUs.

[0026] Here, the object is a preset object including an aerial vehicle or a maritime vehicle, and the beamforming parameter can be determined according to the communication coverage of the object.

[0027] According to the present invention, the accuracy and efficiency of beamforming can be improved by utilizing precise spatial information of an object and / or mobility information of the object obtained through sensing. In addition, spatial information and / or mobility information of the object required for beamforming can be efficiently obtained through a two-stage sensing method. Furthermore, through a differentiated beamforming strategy based on the sensing QoS level, system resources can be efficiently utilized while satisfying the performance requirements of each application.

[0028] The following drawings are made to illustrate a specific example of the present specification. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of the present specification are not limited to the specific names used in the following drawings.

[0029] Figure 1 is a configuration diagram of a communication system to which the present invention is applied.

[0030] FIG. 2 is a block diagram showing the configuration of a communication node according to the present invention.

[0031] FIG. 3 is a diagram showing an AI / ML-based RAN intelligence framework according to the present invention.

[0032] FIG. 4 illustrates an AI / ML framework that can be applied to embodiments of the present invention.

[0033] Figure 5 is a diagram showing the basic configuration of the ISAC system.

[0034] Figures 6a, 6b, and 6c are diagrams illustrating various structures for transmitting and receiving sensing signals.

[0035] FIG. 7 is a diagram illustrating the basic concept of beamforming using sensing according to an embodiment of the present invention.

[0036] FIG. 8 is a diagram illustrating the beamforming operation of a base station using sensing according to an embodiment of the present invention.

[0037] FIG. 9 is a diagram illustrating an example in which the beam width of beamforming or the beam sweeping beam width interval is set differently according to the sensing QoS level according to an embodiment of the present invention.

[0038] FIG. 10 is a flowchart illustrating the process of determining beamforming parameters from sensing results using AI / ML according to an embodiment of the present invention.

[0039] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0040] Terms such as first, second, A, B, etc., may be used to describe various components, but said components shall not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0041] In embodiments of the present invention, "at least one of A and B" may mean "at least one of A or B" or "at least one of one or more combinations of A and B". Additionally, in embodiments of the present invention, "at least one of A and B" may mean "at least one of A or B" or "at least one of one or more combinations of A and B".

[0042] In the embodiments of the present application, (re)transmission may mean "transmission," "retransmission," or "transmission and retransmission"; (re)setting may mean "setting," "resetting," or "setting and resetting"; (re)connection may mean "connection," "reconnection," or "connection and reconnection"; and (re)connection may mean "connection," "reconnection," or "connection and reconnection".

[0043] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0044] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0045] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0046] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the attached drawings. In order to facilitate an overall understanding of the present invention, the same reference numerals are used for identical components in the drawings, and redundant descriptions of identical components are omitted.

[0047] The communication network to which the embodiments according to the present invention are applied is not limited to the details described below, and the embodiments according to the present invention may be applied to various communication networks. Here, the term "communication network" may be used interchangeably with "communication system."

[0048] Throughout the specification, the network may include, for example, 5G mobile communication networks such as 5G and 5G-Advance, 4G mobile communication networks such as LTE (Long Term Evolution) / LTE-Advanced, next-generation wireless LANs such as WiFi 6 / 6E, 6G mobile communication networks, satellite communication networks, etc.

[0049] Throughout the specification, the terminal may be referred to as a terminal, access terminal, mobile terminal, station, subscriber station, mobile station, portable subscriber station, node, device, etc.

[0050] Here, desktop computers, laptop computers, tablet PCs, wireless phones, mobile phones, smartphones, smart watches, smart glasses, e-book readers, PMPs (portable multimedia players), portable game consoles, navigation devices, digital cameras, DMB (digital multimedia broadcasting) players, digital audio recorders, digital audio players, digital picture recorders, digital picture players, digital video recorders, digital video players, automobiles, robots, drones, and unmanned aerial vehicles (UAVs) can be used.

[0051] Throughout the specification, base stations may be referred to as Node B, evolved Node B, gNodeB, BTS (base transceiver station), radio base station, radio transceiver, access point, access node, roadside unit (RSU), DU (digital unit), CDU (cloud digital unit), RRH (radio remote head), RU (radio unit), TP (transmission point), TRP (transmission and reception point), relay node, etc.

[0052] In the following, embodiments according to the present invention are described with reference to a 3GPP 5G NR (New Radio) mobile communication system, and prior art documents defining the operation of a 3GPP 5G NR mobile communication system may be referenced. The names of specific devices or specific signals / messages / fields described in the drawings are presented as examples, and therefore the technical features of this specification are not limited to the specific names used in the drawings below.

[0053] FIG. 1 illustrates an example of a wireless communication system to which embodiments of the present invention can be applied.

[0054] Referring to FIG. 1, a communication system (100) may include a plurality of communication nodes (110-1, 110-2, 110-3, 120-1, 120-2, 130-1, 130-2, 130-3, 130-4, 130-5, 130-6). The plurality of communication nodes may support 4G communication (e.g., LTE (long term evolution), LTE-A (advanced)), 5G communication (e.g., 5G, 5G-Advanced), etc., as defined in 3GPP (3rd generation partnership project) standards. 4G communication may be performed in a frequency band of 6 GHz or lower, and 5G communication may be performed not only in a frequency band of 6 GHz or lower but also in a frequency band of 6 GHz or higher.

[0055] For example, for 4G communication and 5G communication, multiple communication nodes can support communication protocols based on CDMA (code division multiple access), WCDMA (wideband CDMA), TDMA (time division multiple access), FDMA (frequency division multiple access), OFDMA (orthogonal frequency division multiple access), Filtered OFDM, CP (cyclic prefix)-OFDM, DFT-s-OFDM (discrete Fourier transform-spread-OFDM), OFDM (orthogonal frequency division multiplexing), SC (single carrier)-FDMA, NOMA (Non-orthogonal Multiple Access), GFDM (generalized frequency division multiplexing), FBMC (filter bank multi-carrier) based communication protocol, UFMC (universal filtered multi-carrier) based communication protocol, SDMA (Space Division Multiple Access) based communication protocol, etc.

[0056] Additionally, the communication system (100) may further include a core network (not shown). If the communication system (100) supports 4G communication, the core network may include an S-GW (serving-gateway), a P-GW (PDN (packet data network)-gateway), an MME (mobility management entity), etc. If the communication system (100) supports 5G communication, the core network may include a UPF (user plane function), an SMF (session management function), an AMF (access and mobility management function), etc.

[0057] Meanwhile, each of the plurality of communication nodes (110-1, 110-2, 110-3, 120-1, 120-2, 130-1, 130-2, 130-3, 130-4, 130-5, 130-6) constituting the communication system (100) may have a structure described later in FIG. 2. In addition, as an example, the communication system (100) described above may be applied not only to 5G communication but also to subsequent next-generation communication systems (e.g., 6G), and is not limited to a specific form.

[0058] Multiple base stations (110-1, 110-2, 110-3) can each form a macro cell, and base stations (120-1, 120-2) can each form a small cell. For example, the cell coverage of the first base station (110-1) may include the fourth base station (120-1), the third terminal (130-3), and the fourth terminal (130-4). The cell coverage of the second base station (110-2) may include the second terminal (130-2), the fourth terminal (130-4), and the fifth terminal (130-5). The cell coverage of the third base station (110-3) may include the fifth base station (120-2), the fourth terminal (130-4), the fifth terminal (130-5), and the sixth terminal (130-6).

[0059] In particular, each base station may operate as part of a Radio Access Network (RAN) domain that includes AI / ML (Artificial Intelligence / Machine Learning) functions. According to an embodiment of the present invention, each base station may include at least one of ML pre-training, ML training, and AI / ML inference functions, and these functions may be flexibly implemented within the base station. For example, base stations forming a macro cell may include all three functions, and base stations forming a small cell may include only the AI / ML inference function.

[0060] Each of the multiple base stations may operate in different frequency bands or in the same frequency band. Each of the multiple base stations may be connected to one another via an ideal backhaul link or a non-ideal backhaul link, and may exchange information with one another via an ideal backhaul link or a non-ideal backhaul link.

[0061] FIG. 2 is a block diagram illustrating an example of the configuration of each communication node constituting the communication system of FIG. 1.

[0062] Referring to FIG. 2, the communication node (200) may include at least one processor (210), a memory (220), and a transceiver (230) that is connected to a network to perform communication. Additionally, the communication node (200) may further include an input interface device (240), an output interface device (250), a storage device (260), etc. Each component included in the communication node (200) may be connected by a bus (270) to communicate with one another.

[0063] However, each component included in the communication node (200) may be connected via individual interfaces or individual buses centered around the processor (210), rather than via a common bus (270). For example, the processor (210) may be connected via a dedicated interface to at least one of a memory (220), a transmission / reception device (230), an input interface device (240), an output interface device (250), and a storage device (260).

[0064] The processor (210) can execute a program command stored in at least one of the memory (220) and the storage device (260). The processor (210) may be a central processing unit (CPU), a neural processing unit (NPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed.

[0065] The processor (210) may be configured to execute AI / ML functions according to the present invention. For example, program instructions for ML pre-training, ML training, or AI / ML inference functions may be stored in memory (220) and executed by the processor (210). The processor (210) may include a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor neural network processing unit (NPU) for AI / ML computation.

[0066] Each of the memory (220) and the storage device (260) may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (220) may be composed of at least one of read-only memory (ROM) and random access memory (RAM). The storage device (260) may store AI / ML model parameters, training data, inference results, etc.

[0067] The transmitting and receiving device (230) may provide an interface for wired or wireless communication. For example, the transmitting and receiving device (230) may communicate with other network entities through a fronthole interface.

[0068] Meanwhile, embodiments of the present invention may be performed by AI (Artificial Intelligence) machine learning or deep learning technology.

[0069] FIG. 3 is a diagram showing a functional framework for RAN intelligence utilizing artificial intelligence (AI) / machine learning (ML) that can be applied to embodiments of the present invention.

[0070] Referring to Fig. 3, RAN intelligence with AI / ML enabled can be considered. For example, specific AI / ML algorithms can be configured in various forms and are not limited to a specific form.

[0071] Referring to FIG. 3, the data collection unit (310) may be an entity that provides input data to the model training unit (320) and the model inference unit (330). For example, the input data may be at least one of a measurement value by another network entity, a feedback value by terminals, and a feedback value for the output of an AI / ML model, but is not limited thereto. Here, the training data provided by the data collection unit (310) to the model training unit (320) may be data provided for the AI / ML model training function. Additionally, the inference data provided by the data collection unit (310) to the model inference unit (330) may be data provided for the AI / ML model inference function. Here, the model training unit (320) may be an entity that performs training, validation, and testing of the AI / ML model, thereby providing performance metrics for the AI / ML model. The model training unit (320) can provide and update an AI / ML model to the model inference unit (330), and the model inference unit (330) can provide model performance feedback to the model training unit (320). That is, the model training unit (320) can perform training on the AI / ML model through the feedback from the model inference unit (330) and provide the updated AI / ML model back to the model inference unit (330). In addition, the model inference unit (330) can receive inference data from the data collection unit (310), generate an output through the received AI / ML model, and provide it to an actor (340). Here, the actor (340) may be a subject that performs an action according to the output, and the action performed by the actor (340) may be fed back to the data collection unit (310) and provided to the model training unit (320) as training data.

[0072] In other words, data for learning (or training) an AI / ML model is provided so that the AI / ML model is learned and built, and inference data is provided to the built AI / ML model to produce output, thereby enabling AI / ML model-based operations to be performed.

[0073] FIG. 4 illustrates an AI / ML framework that can be applied to embodiments of the present invention.

[0074] Referring to FIG. 4, the AI / ML framework (400) may be composed of a data collection block (410), a model training block (420), a model management block (430), a model inference block (440), and a model storage block (450). FIG. 4 is merely an example of an AI / ML framework, and various entities / functions / blocks not disclosed in FIG. 4 may be added to the AI / ML framework, and at least some of the blocks disclosed in FIG. 4 may be omitted.

[0075] The data collection block (410) can be performed in the LCM for various purposes such as model training, model inference, model monitoring, model selection, and model updating. The data collection block (410) of FIG. 4 may be a block that conceptually represents data sources and entities holding data for training, inference, and monitoring. Although the data collection block (410) of FIG. 4 is represented as a single block, data collection for training, inference, and monitoring may have various characteristics and requirements. Additionally, the timescale of training and monitoring (e.g., real-time or offline) may require individual consideration.

[0076] Regarding training, training data may be initially generated in the network and UEs. The initial data may be collected (or transmitted) by one or more data collection entities. Data collection entities may be owned by various entities, such as internal or external UEs / chipset / network vendors, network operators, and positioning service providers.

[0077] With respect to inference, inference data for the UE-side model and / or the UE portion of both-sided models may be transmitted or provided directly from the UE. Inference data for the network-side model and / or the network portion of both-sided models may be transmitted or provided directly from the network, or may be transmitted from the UE.

[0078] Regarding monitoring, monitoring data for UE-side monitoring may be transmitted or provided directly from the UE. Monitoring data for network-side monitoring may be transmitted or provided directly from the network, or it may be transmitted from the UE.

[0079] Data collection for real-time operations such as real-time model monitoring, switching, and selection can incur significant signaling overhead. Conversely, infrequent data collection to reduce signaling overhead can result in latency for real-time model monitoring, switching, and selection.

[0080] The model training block (420) may include both initial training and model updates. Generally, model training can be divided into model training conducted alongside model development and subsequent training for the developed model. The model training block (420) in FIG. 4 is represented as a single block for simplification.

[0081] Depending on the location of the dataset and / or the region where the model (or untrained model) is located, training may be performed internally within the network or by external entities such as UEs, chipset / network vendors, network operators, and positioning service providers. Since AI / ML model development is generally an iterative process of data collection, model design, training, and performance validation, careful implementation considerations regarding power consumption, hardware scope, latency, and concurrency with other layer functions are required for AI / ML model development.

[0082] When large-scale field data is collected from a data collection entity, the vendor responsible for model development must have access to said data. Typically, model development is an offline engineering process performed by an engineering team that requires access to large datasets collected in the field. In other words, decisions regarding model structure, device-specific optimizations, and the number of models to develop (e.g., generalizable versus specific models) may depend on the large-scale field data. If the vendor owning the data collection entity is different from the vendor responsible for model development, the vendor responsible for model development must have access to the dataset. This can be achieved through explicit dataset sharing or by providing access to the collected dataset. Dataset sharing / access may be relevant to two-sided models where both the gNB vendor and the UE / chipset vendor must participate in the model development and training processes.

[0083] After the model is developed and trained, the model can be stored in a model repository or a model storage block (450) and delivered to a target device. The model can be compiled into an executable file for inference. Here, various methods may exist depending on the location where the model is trained, the model storage / delivery format, the location where the model is hosted before delivery, etc.

[0084] The model inference block (440) is a function that provides AI / ML model inference output, such as prediction or decision. The model inference block (440) may also provide model performance feedback to the model training block (420). The model inference block (440) may be responsible for data preparation, such as data preprocessing, cleaning, formatting, and transformation, based on the inference data delivered by the data collection block (410).

[0085] Model management may include functionality / model monitoring, selection, activation, deactivation, switching, fallback, etc. FIG. 4 illustrates a single model management block (430), but not all aspects of model management may be implemented in a single location. Some aspects of model monitoring, activation / deactivation, selection, switching, and fallback may be performed on the network side, and other aspects may be performed on the UE side. With regard to model selection, activation, deactivation, switching, and fallback for UE-side models and both-side models, mechanisms related to decisions by the network initiated by the network, mechanisms related to decisions by the network initiated by the UE and requested by the network, mechanisms related to decisions by the UE that are event-triggered by the network and where the UE's decision is reported to the network, mechanisms related to decisions by the UE that are UE-autonomous and where the UE's decision is reported to the network, and mechanisms related to decisions by the UE that are UE-autonomous and where the UE's decision is not reported to the network may be considered.

[0086] In the following, Integrated Sensing and Communication (ISAC) related to embodiments of the present invention is described.

[0087] Radar is the most representative example of wireless sensing technology. RADAR stands for Radio Detection And Ranging and refers to an information system that detects objects and determines their direction, distance, and speed by measuring the reflected waves that return after radiated electromagnetic waves strike an object. Radar can detect how far away an object is and in what direction and at what speed it is moving from a considerable distance away. Furthermore, because it uses radio waves to detect objects, it has the advantage of operating effectively even in atmospheric conditions such as rain, fog, snow, and smoke, as well as maintaining the same functionality at night in complete darkness. However, such radar operation requires the allocation of a dedicated frequency with a significant bandwidth, as well as the installation and operation of dedicated transmitters and receivers. This acts as a limitation in terms of the efficient use of frequency resources and system construction costs.

[0088] Recently, active technical discussions have been underway regarding methods that integrate communication and sensing into a single system, offering significant advantages over existing mobile communication systems and sensor networks in terms of investment efficiency and frequency resource utilization. At 3GPP, a technology is being discussed under the name ISAC (Integrated Sensing and Communication) that integrates communication and sensing functions to perform both functions simultaneously within a single system.

[0089] ISAC technology is expected to be fully realized with the future development of 6G networks. ISAC primarily uses millimeter wave (mmWave) and terahertz (THz) bands and is known to require advanced beamforming and new waveform design.

[0090] 3GPP defines 5G wireless sensing as "a 5G system function that uses NR RF signals to obtain information about the characteristics of the environment and / or objects within the environment (e.g., shape, size, orientation, speed, location, distance between objects, or relative movement, etc.)."

[0091] ISAC technology is expected to enable new services and use cases across various industries. For instance, it can be utilized for object detection and tracking, environmental monitoring, and human motion monitoring, and can be applied to diverse fields such as unmanned aerial vehicles (UAVs), smart homes, V2X (Vehicle-to-Everything), and factories. Specifically, in road environments, it can improve traffic safety by detecting the movements of pedestrians or vehicles, while in smart factories, it can enhance operational efficiency and safety by tracking the real-time locations of robots and workers. Furthermore, in smart homes, it can be used as a security system to provide personalized services by analyzing residents' behavioral patterns or to detect intruders.

[0092] Figure 5 is a diagram showing the basic configuration of the ISAC system.

[0093] Referring to FIG. 5, the base station (520) can communicate with the UE (510) via a communication link and simultaneously detect an object (540) through a sensing path (550-1, 550-2). Additionally, the terminal (510) can also detect an object (540) through a sensing path (560-1, 560-2). For example, a base station installed on a roadside can detect pedestrians or obstacles on the road while communicating with a terminal for an autonomous vehicle. Or, a base station inside a factory can determine the location of a worker while communicating with a work robot. In this way, the ISAC can perform communication and sensing functions simultaneously as a single system, and both the base station and the terminal can perform the sensing function.

[0094] The information obtainable through sensing includes not only the basic location, velocity, and acceleration of an object, but also its size, shape, and material properties. This diverse information can be utilized according to the specific application field. For example, on smart roads, vehicle speed and direction information can be used to predict collision risks, while in smart factories, worker posture information can be analyzed to monitor work safety.

[0095] Figures 6a, 6b, and 6c are diagrams illustrating the specific concept of transmitting and receiving sensing signals.

[0096] Referring to FIG. 6a, in a monostatic method, a single sensing transceiver (605) transmits a sensing signal (650), and the same sensing transceiver (605) receives a reflected signal (655) reflected from an object (620). This is a principle similar to how a bat emits ultrasound and perceives its surroundings through the reflected waves. For example, this method can be used to detect visitors with a single sensor installed at the entrance of a smart home, or to confirm the entry of a vehicle with a sensor at the entrance of a parking lot. This method has the advantage of a simple structure and easy installation, but there may be limitations in accuracy as information can only be obtained from a specific angle of the object.

[0097] Referring to FIG. 6b, in the bistatic method, a sensing signal (660) transmitted by a sensing transmitter (610) is reflected from an object (620) and received as a reflected signal (665) at a sensing receiver 1 (630) at a different location. This is similar to the principle of shining a light from one side of a soccer field and observing a player's shadow from the other. For example, it can be effectively utilized to more accurately track the movement of a robot arm in a factory or to detect pedestrians in blind spots on a road. This method allows for more accurate location estimation because it can detect objects from different angles.

[0098] Referring to FIG. 6c, in a multistatic method, a sensing signal (670) transmitted by a sensing transmitter (610) is reflected from an object (620) and received as a reflected signal (675, 680) at sensing receivers (630, 640) at various locations. This is similar to multiple CCTVs capturing a scene simultaneously from various angles. Through this multistatic structure, the location, speed, direction, etc., of an object can be determined more accurately based on measurement information. For example, it can be used to simultaneously track the movement of multiple vehicles and pedestrians at a complex intersection, or to precisely monitor the locations of multiple workers and equipment in an extensive factory workplace.

[0099] These various sensing methods can be appropriately selected based on the characteristics of the application or the required accuracy. For example, monostatic methods may be cost-effective for basic applications such as simple occupancy detection or access monitoring. On the other hand, multistatic methods may be more suitable for situations requiring high accuracy, such as collision avoidance in autonomous vehicles or precision control of industrial robots. Additionally, constraints on the installation environment and cost efficiency can also be important considerations when selecting a sensing method.

[0100] In this invention, 'Sensing QoS (Quality of Sensing)' or 'Sensing Requirements' are newly proposed to quantitatively define the quality of the sensing function in a system where communication and sensing are integrated. The Sensing QoS proposed in the embodiments of this invention is broadly defined in terms of five parameters: accuracy, resolution, latency, reliability, and update rate. Each Sensing QoS parameter is classified into high (Sensing) QoS levels, medium (Sensing) QoS levels, and low (Sensing) QoS levels, and each level is defined by a specific Sensing QoS (or Sensing Requirement) value. In actual ISACs, a number of various levels, ranging from fewer to more, can be defined depending on the situation. This is explained in detail below.

[0101] Various ISAC use cases are being discussed at 3GPP. These use cases include diverse application scenarios such as intruder detection in smart homes, pedestrian / animal detection on highways, rainfall monitoring, sensing at crosswalks, UAV flight trajectory tracking, and AGV collision avoidance in factories. Each of these use cases may require different levels of sensing performance.

[0102] For example, UAV collision avoidance requires a position accuracy of 1-2 m, a speed accuracy of 1-2 m / s, and a latency of 100-1000 ms. AMR collision avoidance in factories requires a position accuracy within 1 m, a speed accuracy of 1 m / s, a latency of 500 ms or less, and an update rate of 20 Hz. On the other hand, intruder detection in smart homes requires a position accuracy within 10 m and a latency of 1000 ms or less, as well as a missed detection rate of less than 5% and a false alarm rate of less than 2%.

[0103] The high QoS level is intended to support use cases requiring high-precision sensing, such as industrial applications, providing, for example, a distance accuracy of ±0.1m and a processing latency of 5ms or less. The medium QoS level is intended to support general use cases, such as UAV / vehicle applications, providing, for example, a distance accuracy of ±0.5m and a processing latency of 20ms or less. The low QoS level is intended to support use cases requiring low precision, such as presence detection applications, providing, for example, a distance accuracy of ±1.0m and a processing latency of 50ms or less.

[0104] In addition, considering use cases where continuous monitoring of a wide area is important rather than sensing accuracy, such as rainfall monitoring or traffic management at tourist destinations, the QoS system of the present invention includes the update rate as a key parameter. For example, at high QoS levels, a short update cycle of 10ms is provided to support applications requiring real-time performance, at medium QoS levels, a longer update cycle of 50ms is used, and at low QoS levels, a long update cycle of 100ms is used to efficiently utilize system resources and energy.

[0105] To support use cases requiring cooperation among multiple sensing entities, such as vehicle maneuvering and navigation or UAV intrusion detection, the system of the present invention includes resource allocation information sharing between adjacent cells and a cooperative sensing mechanism. In particular, at high QoS levels, high-accuracy sensing performance can be provided by utilizing diversity techniques through multiple receivers.

[0106] As such, the sensing QoS system of the present invention provides a framework that comprehensively accommodates the requirements of various use cases while systematically classifying and managing them. Through this, it is possible to ensure an appropriate level of sensing performance suitable for the requirements of each application while efficiently utilizing limited system resources.

[0107] Sensing QoS can be broadly defined in terms of parameters such as accuracy, resolution, latency, reliability, and update rate, as shown in .

[0108]

[0109] Meanwhile, each parameter defined in Sensing QoS can be divided into multiple QoS levels as shown in . In the example in , each QoS parameter can be divided into 'High QoS Level', 'Medium QoS Level', and 'Low QoS Level'. is an example of QoS levels, and depending on the various applications of ISAC, performance indicators required for the application can be configured and required values ​​for each performance indicator can be defined to create a wider variety of QoS levels.

[0110]

[0111] The High QoS level guarantees the highest level of sensing performance, providing distance accuracy of ±0.1m, speed accuracy of ±0.5km / h, and angle accuracy of ±1 degree, while ensuring a processing latency of less than 5ms and a sensing success rate of over 99.9%. This level is suitable for mission-critical applications requiring high precision and reliability, such as autonomous driving or industrial safety. For example, in a collision avoidance system for an autonomous vehicle, it must be possible to measure the distance to surrounding vehicles or pedestrians with an error of within 10cm and determine relative speed with an accuracy of within 0.5km / h to secure a safe braking distance. Additionally, a processing latency of less than 5ms enables response within a travel distance of less than 14cm, even when driving at 100km / h. The Medium QoS level provides intermediate performance suitable for general sensing applications, offering distance accuracy of ±0.5m, speed accuracy of ±2km / h, and angle accuracy of ±3 degrees, while guaranteeing a processing latency of less than 20ms and a sensing success rate of over 99%. This level is suitable for general sensing applications such as indoor positioning. For example, in the case of Automated Guided Vehicles (AGVs) in a factory, a distance measurement error of about 50 cm is acceptable because the cargo transport speed is relatively low (around 5 km / h) and the surrounding environment is well controlled. Additionally, a processing delay of 20 ms corresponds to a distance of 3 cm traveled by an AGV moving at 5 km / h, enabling safe operation.

[0112] The low QoS level guarantees minimum sensing performance, providing distance accuracy of ±1.0m, speed accuracy of ±5km / h, and angle accuracy of ±5 degrees, while ensuring a processing latency of less than 50ms and a sensing success rate of over 95%. This level is suitable for applications requiring relatively low precision, such as environmental monitoring or presence detection.

[0113] For example, in applications such as detecting the presence of vehicles in a parking lot or counting the number of people inside a building, a distance measurement error of about 1 m is acceptable, and a processing delay of 50 ms is not a problem. This is because in such applications, only the presence of an object and approximate location information are required, rather than precise position or speed.

[0114] In the system of the present invention, resource allocation can be differentiated according to the QoS level of each sensing session. At a high QoS level, dedicated frequency-time resources are allocated, maximum transmit power is used, high-frequency sensing is performed with short pulse periods, and diversity through multiple receivers is utilized. At an intermediate QoS level, semi-dedicated frequency-time resources are allocated, intermediate transmit power is used, and regular sensing is performed with appropriate pulse periods. At a low QoS level, shared frequency-time resources are allocated, minimum required transmit power is used, and periodic sensing is performed with long pulse periods.

[0115] In addition, the system of the present invention can dynamically adjust the QoS level of each sensing session according to network load conditions or channel conditions. For example, in situations where network traffic is congested, the QoS level can be temporarily lowered to ensure system stability, and even when channel conditions deteriorate, the QoS level can be lowered to maintain minimum sensing performance. Conversely, when spare resources become available or channel conditions improve, the QoS level can be raised to provide a higher level of sensing performance.

[0116] In the event of an emergency, the necessary sensing performance can be guaranteed by raising the QoS level of the relevant session to a high QoS, even if the QoS level of other sensing sessions is temporarily lowered. For example, if an autonomous vehicle detects an emergency collision risk, resources from sensing sessions with low QoS levels, such as surrounding environment monitoring or parking management, can be temporarily reclaimed and allocated to sensing for collision avoidance.

[0117] Meanwhile, in an embodiment of the present invention, the amount of information when reporting a sensing result may vary depending on the sensing QoS (or sensing QoS level). Specifically, the number of bits representing the sensing result may change depending on the resolution and latency required for sensing. The number of bits of such sensing result reporting information may also affect the latency of the transmission of the sensing result. The sensing result reporting information may have a name such as, for example, 'Sensing Report Indicator (SRI),' and the format of the SRI may be set according to the sensing QoS.

[0118] For example, assume a case where a terminal senses the location of an object within a specific sensing area. If a low QoS level of resolution is required for the sensing, it may be sufficient to divide the entire sensing area into four zones and indicate the zone where the object is located. In this case, the sensing result can be represented using only 2-bit information specifying one of the four zones. This may, for example, satisfy the distance measurement accuracy of ±1.0m and a processing delay of 50ms or less defined at the low QoS level in . Depending on the sensing resolution, the number of bits for the sensing result report can be set to 2 bits, thereby minimizing the number of bits required for transmitting the sensing result.

[0119] On the other hand, if high QoS level resolution is required for the sensing, the entire sensing area can be divided more finely into more zones, for example, 16 zones, to represent the location of the object more precisely. In this case, 4 bits of information are required to specify one of the 16 zones for the sensing result. This may be, for example, to satisfy the distance measurement accuracy of ±0.1m defined at the high QoS level in . As such, although the number of bits to be transmitted increases to satisfy higher sensing resolution, more precise location information can be provided.

[0120] As shown in the example above, in the embodiment of the present invention, by differentiating the number of bits representing the sensing result according to the resolution level of the sensing QoS, the system can provide optimal performance suitable for the characteristics of each application. For example, when detecting the presence of pedestrians on a crosswalk, a 2-bit representation with a low QoS level may be sufficient, but when determining the location of a precision robot in a factory, 4 bits may be set to represent the sensing result according to the sensing QoS level with a high QoS level.

[0121] The variable bit allocation method for reporting these sensing results also affects the sensing processing delay and result transmission delay defined in . That is, while fast transmission is possible using a small number of bits at low QoS levels, relatively longer transmission times are required at high QoS levels because more bits must be transmitted.

[0122] In the following, embodiments of performing beamforming using the sensing proposed in the present invention are described.

[0123] The terms 'object spatial information' and 'object mobility information', which are defined in the following embodiments, are defined.

[0124] The spatial information of an object includes at least one of the current location or size of the object as well as information on the area where the object may exist. In other words, the spatial information of an object refers to information regarding the spatial characteristics where the object exists (or is located) or may exist, which is acquired through various sensing methods.

[0125] Meanwhile, object mobility information includes information on the object's dynamic characteristics, such as its direction of movement, speed, and acceleration. In other words, it refers to information on physical characteristics related to the object's movement obtained through object sensing.

[0126] FIG. 7 is a diagram illustrating the basic concept of beamforming using sensing according to an embodiment of the present invention.

[0127] Referring to FIG. 7, a base station (710) obtains a sensing result, i.e., spatial information and / or mobility information of an object (730), through sensing, and performs beamforming based thereon. In particular, in an embodiment of the present invention, a sensing result, i.e., spatial information and / or mobility information of an object, can be obtained through stepwise sensing, and beamforming can be performed based thereon.

[0128] In the following description, an embodiment is described in which beamforming is performed using the position information of an object as an example of the spatial information and / or mobility information of an object.

[0129] Specifically, the base station (710) first performs a first sensing over a first sensing area (720). The first sensing may cover a relatively wide area but may be performed at a low sensing QoS level (e.g., low sensing accuracy or low sensing resolution). For example, when determining the location of an AGV (Automated Guided Vehicle) inside a factory, the first sensing may be performed over the entire area of ​​the workplace inside the factory (e.g., 50m x 50m) with a relatively low accuracy of about ±2m. Or, when detecting a vehicle on a highway, the first sensing may be performed over a radius of 500m around the base station with an accuracy (or resolution) of about ±5m.

[0130] When the approximate location of an object (730) is determined through the first sensing, the base station (710) performs second sensing on a second sensing area (740) centered on that area. The second sensing targets a narrower area than the first sensing but can be performed with higher accuracy. For example, in the case of an AGV, the second sensing can be performed with high accuracy (or resolution) of ±0.2m on a 10m x 10m area around the location initially determined. In the case of a highway vehicle, the second sensing can be performed with an accuracy of ±0.5m on a 50m radius around the location initially determined.

[0131] When the detailed location of an object (730) is identified through this two-stage sensing, the base station (710) performs optimized beamforming (750) based on this information. Beamforming (750) forms a communication beam focused toward the object (730) by utilizing the precise location information identified by the second sensing. For example, in communication with an AGV, precise beam direction adjustment within ±1 degree is possible, thereby optimizing communication quality. In communication with a highway vehicle, a stable communication link can be maintained even while moving at high speed by forming a beam based on the vehicle's precise location.

[0132] This two-stage sensing-based beamforming method can be effectively utilized in various application scenarios. For example, when communicating with a ship at a port, the first sensing can identify the ship's location within a radius of approximately 2 km with an accuracy of ±50 m, and then the second sensing can detect an area of ​​200 m around the ship with an accuracy of ±5 m. Alternatively, when communicating with a drone, the first sensing can scan the airspace up to 500 m above with an accuracy of ±10 m, and then the second sensing can precisely detect an area of ​​50 m around the drone with an accuracy of ±1 m.

[0133] As such, the two-stage sensing method of the present invention combines a primary approximate sensing of a wide area with a secondary precise sensing of a narrow area, enabling efficient and accurate identification of the location of an object. This enables optimized beamforming, and consequently, maximizes communication performance.

[0134] Although beamforming by two-stage sensing is described in Fig. 7, beamforming by multi-stage sensing of three or more stages is also possible in the same way.

[0135] In addition, in a manner similar to the method described in FIG. 7, an embodiment of the present invention can perform beamforming by sensing other characteristics included in the spatial information and / or mobility information of an object (e.g., object presence area, direction of movement of the object, speed, acceleration, etc.) in two stages (or multiple stages) and determining beamforming parameters based on the results.

[0136] FIG. 8 is a diagram illustrating the beamforming operation of a base station using sensing according to an embodiment of the present invention.

[0137] Referring to FIG. 8, the base station first checks the sensing QoS level for the object (S810). The sensing QoS level includes parameters such as required sensing accuracy, resolution, latency, reliability, and update rate, and can be set to one of, for example, a high QoS level, a medium QoS level, or a low QoS level depending on service characteristics or application requirements.

[0138] The base station can determine the sensing method based on the confirmed sensing QoS level (S820). For example, if a high QoS level is required, two-stage sensing is performed to ensure high accuracy (or resolution), and if a low QoS level is required, one-stage sensing alone may be sufficient. Additionally, parameters such as the bandwidth and transmission power of the sensing signal may be determined based on the sensing QoS level.

[0139] When the first stage of sensing is determined, the base station performs the first sensing (S830) and obtains the first sensing result (i.e., spatial information and / or mobility information of the object) (S835). That is, the base station performs sensing by applying parameters for the first sensing according to the sensing QoS level, and thereby obtains the spatial information and / or mobility information of the object, so that the area and location where the object exists, and the direction and speed of movement of the object, etc. can be identified.

[0140] If a second stage of sensing is determined at step S820 (e.g., when a high QoS level is required), the base station performs a first sensing (S840) to obtain a first sensing result (spatial information and / or mobility information of an object) (S850) and performs additional sensing. That is, the base station performs a second sensing based on the first sensing result (S860) to obtain a result of the second sensing (i.e., spatial information and / or mobility information of an object) (S870). The second sensing is intended to perform sensing with higher accuracy to achieve a higher QoS level, thereby enabling the acquisition of a more accurate sensing result (detailed spatial information and / or detailed mobility information of an object) than the first sensing. In another embodiment, the base station may perform the second sensing immediately without the first sensing, taking into account the availability of system resources, required response time, or channel conditions, and obtain a sensing result of a high QoS level immediately.

[0141] Next, the base station determines beamforming parameters based on the acquired sensing result (first sensing result or second sensing result) (S880). The beamforming parameters may include the beam direction, width, transmission power level, TCI (Transmission Configuration Indicator) State, QCL (Quasi-CoLocation), etc., and these parameters may be optimized by considering the spatial information and / or mobility information of the object, the sensing QoS level, the channel state, etc. Meanwhile, when the base station performs sensing on multiple objects, the base station may perform the sensing process for each object to acquire spatial information and / or mobility information for each object, and based on this, determine beamforming parameters for each object or determine a precoding matrix for MU-MIMO.

[0142] Subsequently, the base station performs beamforming using the determined beamforming parameters (S890). At this time, different beam width intervals are set according to the QoS level, as shown in FIG. 9, and beamforming or beam sweeping can be performed according to the set beam width. For example, beamforming or beam sweeping can be performed with a precise beam width of 1 degree at a high QoS level, with a beam width of 3 degrees at an intermediate QoS level, and with a beam width of 5 degrees at a low QoS level.

[0143] FIG. 9 is a diagram illustrating an example in which the beam sweeping beam width spacing is set differently according to the sensing QoS level according to an embodiment of the present invention.

[0144] Referring to FIG. 9, (a), (b), and (c) illustrate examples in which different beam width spacings are applied according to QoS levels.

[0145] At the high QoS level of (a), beamforming or beam sweeping is performed with a precise beam width of 1 degree, enabling very accurate object localization in terms of sensing. This is suitable for cases where high positional accuracy is required, such as the control of industrial robots or precision machinery.

[0146] At the intermediate QoS level of (b), beamforming or beam sweeping is performed with a beam width of 3 degrees, which can provide a level of accuracy suitable for tracking general vehicles or moving objects in terms of sensing.

[0147] At the low QoS level of (c), beamforming or beam sweeping is performed with a relatively wide beam width of 5 degrees, so in terms of sensing, it can be used for applications with relatively low accuracy requirements, such as pedestrian detection or approximate location determination.

[0148] The difference in beamwidth used in beamforming or beam sweeping according to each QoS level reflects the trade-off between sensing accuracy and system resource utilization. A narrower beamwidth enables more precise localization, but it requires the transmission of more sensing signals and increases the processing power and the amount of data to be handled. For example, when scanning an area of ​​30 degrees, 31 beams are required at a high QoS level, whereas only 7 beams are sufficient at a low QoS level.

[0149] FIG. 10 is a flowchart illustrating the process of determining beamforming parameters from sensing results using AI / ML according to an embodiment of the present invention.

[0150] Referring to FIG. 10, learning data between the sensing result and the corresponding optimal beamforming parameter is first collected (S1010). Here, the sensing result includes spatial information and / or mobility information of the object, which may include information such as the area and location where the object exists, and the direction and speed of the object's movement.

[0151] Next, training of the AI / ML model is performed using the collected data (S1020). During the training process, the AI / ML model learns the relationship between various sensing results and the corresponding optimal beamforming parameters.

[0152] When sensing of a new object is performed, the acquired sensing result is input into a trained AI / ML model (S1030). The input sensing result includes spatial information and / or mobility information of the object acquired in real time.

[0153] The AI / ML model predicts optimal beamforming parameters based on the input sensing results (S1040). The predicted beamforming parameters include values ​​optimized according to the spatial information and / or mobility information of the object.

[0154] Additionally, the predicted beamforming parameters are applied to actual communication, and their performance can be monitored (S1050). Monitoring may include various performance indicators such as actual communication quality, throughput, and latency.

[0155] Finally, the monitored performance results can be added to the existing training data to continuously update the AI / ML model (S1060). Through this feedback loop, the prediction accuracy of the AI / ML model can be continuously improved.

[0156] As such, according to an embodiment of the present invention, by utilizing AI / ML to determine optimal beamforming parameters from sensing results, efficient beamforming is possible even in a dynamically changing wireless communication environment.

[0157] The various embodiments of the present invention described so far may be implemented by hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, it may be implemented by one or more ASICs (Application Specific Integrated Circuits), DSPs (Digital Signal Processors), DSPDs (Digital Signal Processing Devices), PLDs (Programmable Logic Devices), FPGAs (Field Programmable Gate Arrays), general processors, controllers, microcontrollers, microprocessors, etc.

[0158] The scope of the present invention includes software or machine-executable instructions (e.g., operating systems, applications, firmware, programs, etc.) that enable operations according to the methods of various embodiments to be executed on a device or computer, and a non-transitory computer-readable medium on which such software or instructions, etc. are stored and which are executable on a device or computer. Examples of computer-readable media include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, etc. Examples of program instructions include machine code, such as that produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as at least one software module to perform the operations of the present invention, and vice versa.

[0159] The methods according to the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. A computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the computer-readable medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software. The operation of the method according to an embodiment of the present invention may be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes any type of recording device in which information that can be read by a computer system is stored. Additionally, the computer-readable recording medium may be distributed across networked computer systems, allowing computer-readable programs or code to be stored and executed in a distributed manner.

[0160] Some aspects of the invention have been described in the context of a device, but may also be described according to a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described according to a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, at least one of the most important method steps may be performed by such a device.

[0161] In the embodiments, a programmable logic device (e.g., a field-programmable gate array) may be used to perform some or all of the functions of the methods described herein. In the embodiments, the field-programmable gate array may operate with a microprocessor to perform one of the methods described herein. Generally, it is preferable that the methods be performed by some hardware device.

[0162] The exemplary methods of the present invention are described as a series of operations for clarity of description, but this is not intended to limit the order in which the steps are performed, and if necessary, each step may be performed simultaneously or in a different order. To implement the method according to the present invention, additional steps may be included in addition to the steps exemplified, steps excluding some steps and including the remaining steps, or steps excluding some steps and including additional steps.

[0163] The various embodiments of the present invention are not intended to list all possible combinations but to explain representative aspects of the invention, and the matters described in the various embodiments may be applied independently or in combination of two or more.

[0164] Although the present invention has been described with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

[0165]

Claims

1. Regarding the communication method, A step of performing a first sensing to obtain a sensing result for an object; A step of determining beamforming parameters based on the above sensing results; and The method includes the step of performing beamforming using the determined beamforming parameters above, A communication method wherein the sensing result comprises at least one of spatial information of the object and mobility information of the object.

2. In Paragraph 1, The above spatial information includes at least one of location information where the object is located and area information where the object exists, and A communication method in which the above mobility information includes at least one of the direction of movement, speed, and acceleration of the object.

3. In Paragraph 1, The step of obtaining the above sensing result is, A communication method further comprising the step of performing a second sensing based on the above-mentioned sensing result to re-acquire a sensing result for the object.

4. In Paragraph 3, The above first sensing is performed at a first sensing QoS level, and The above second sensing is performed at a second sensing QoS level higher than the first sensing QoS level, and A communication method wherein the first and second sensing QoS levels include a required level for at least one of accuracy, resolution, latency, reliability, and update rate.

5. In Paragraph 1, The step of performing the above beamforming is, A communication method comprising the step of performing beamforming or beam sweeping using a beam generated using the beamforming parameters determined above.

6. In Paragraph 5, The above beam sweeping is performed at preset beam width intervals, and A communication method in which the above beam width spacing is determined based on the sensing QoS (Quality of Sensing) level.

7. In Paragraph 1, The above first sensing is performed on a plurality of objects, and A communication method comprising: the beamforming parameters are determined for each object based on at least one of spatial and mobility information for the plurality of objects, and precoding for MU-MIMO (Multi-User Multiple Input Multiple Output).

8. In Paragraph 1, A communication method in which the beamforming parameters are determined based on a pattern between a pre-learned sensing result and beamforming parameters using artificial intelligence / machine learning (AI / ML).

9. In Paragraph 1, A communication method in which the first sensing above is bistatic sensing or multistatic sensing by a plurality of RUs (Radio Units).

10. In Paragraph 1, The above object is a preset object including an aerial vehicle or a maritime vehicle, and A communication method in which the beamforming parameters are determined according to the communication coverage of the object.

11. In a communication device, Memory; and Includes a processor, The above processor is, A first sensing is performed to obtain a sensing result for an object, beamforming parameters are determined based on the sensing result, and beamforming is performed using the determined beamforming parameters. A communication device comprising at least one of the above sensing result, the spatial information of the object and the mobility information of the object.

12. In Paragraph 11, The above spatial information includes at least one of location information where the object is located and area information where the object exists, and A communication device comprising at least one of the movement direction, speed, and acceleration of the object, wherein the above mobility information includes 13. In paragraph 11, the processor is, A communication device that performs a second sensing based on the above-mentioned sensing result to re-acquire a sensing result for the object.

14. In Paragraph 13, the above processor, The above first sensing is performed at the first sensing QoS level, and The above second sensing is configured to be performed at a second sensing QoS level higher than the first sensing QoS level, and A communication device wherein the first and second sensing QoS levels include a required level for at least one of accuracy, resolution, latency, reliability, and update rate.

15. In paragraph 11, the above processor, A communication device that performs beamforming or beam sweeping using a beam generated using the beamforming parameters determined above.

16. In Paragraph 15, The above beam sweeping is performed at preset beam width intervals, and A communication device in which the above beam width spacing is determined based on the sensing QoS (Quality of Sensing) level.

17. In Paragraph 11, The above first sensing is performed on a plurality of objects, and A communication device comprising beamforming parameters determined for each object based on at least one of spatial and mobility information for the plurality of objects, and precoding for MU-MIMO (Multi-User Multiple Input Multiple Output).

18. In Paragraph 11, A communication device in which the beamforming parameters are determined based on a pattern between a sensing result and beamforming parameters learned using artificial intelligence / machine learning (AI / ML).

19. In Paragraph 11, A communication device in which the first sensing is bistatic sensing or multistatic sensing by a plurality of RUs (Radio Units).

20. In Paragraph 11, The above object is a preset object including an aerial vehicle or a maritime vehicle, and A communication device in which the beamforming parameters are determined according to the communication coverage of the object.

Images