Method and apparatus for resource allocation for integrated sensing and communication

Abstract

A communication and sensing resource allocation method of a base station in a wireless communication system provided by an embodiment of the present invention comprises the steps of: separately allocating a communication resource and a sensing resource on a time-frequency resource; transmitting resource allocation information including at least one of the communication resource or the sensing resource to terminals; and transmitting a signal using the sensing resource, wherein the symbol length of a sensing signal may be set differently from the length of a communication symbol.

Description

Resource allocation method and device for the integration of communication and sensing

[0001] The present invention relates to wireless communication, and more specifically, to a resource allocation method and apparatus for simultaneously supporting communication and sensing in a wireless communication system.

[0002] Recently, communication systems are required to go beyond simple data transmission and integrate sensing functions that detect and analyze the surrounding environment. In particular, in mobile communication systems beyond 5G, Integrated Sensing and Communication (ISAC) is emerging as a key technology.

[0003] Conventional radar-based sensing systems had problems such as high implementation costs and low frequency resource efficiency due to the need for dedicated frequency bands and separate hardware. Additionally, since the communication and sensing systems operated separately, interference occurred between the systems, making it difficult to provide integrated services.

[0004] ISAC technology is expected to enable new services and use cases across various industries. For instance, a wide range of applications are possible, including collision avoidance for autonomous vehicles, safety management for robot workers in smart factories, intruder detection in smart homes, and flight path tracking for drones. However, these diverse applications each require different levels of sensing performance, necessitating resource management technologies to support them efficiently.

[0005] Particularly in high-frequency bands such as millimeter wave (mmWave), there is a challenge in that communication and sensing must satisfy their respective performance requirements while sharing the same frequency resources. Furthermore, when multiple base stations have adjacent cell coverage and perform ISAC independently, or when multiple base stations cooperate to perform ISAC over a wide area, the coordination of resource allocation and interference management between base stations emerge as critical issues.

[0006] According to the above-described aspect, embodiments of the present invention provide communication and sensing functions efficiently integrated in a single system.

[0007] Specifically, the present invention aims to provide a method for efficiently distinguishing and allocating transmission intervals for communication symbols (or signals) and transmission intervals for sensing symbols (or signals) on time-frequency resources.

[0008] In addition, the present invention aims to systematically classify the requirements of various sensing applications and provide a differentiated resource allocation method accordingly.

[0009] In addition, the present invention aims to provide a method for supporting inter-cell cooperative sensing and avoiding interference through the sharing of resource allocation information between adjacent cells.

[0010] A method for allocating communication and sensing resources of a base station in a wireless communication system provided by an embodiment of the present invention comprises: a step of allocating communication resources and sensing resources by distinguishing them on time-frequency resources; a step of transmitting resource allocation information including at least one of the communication resources and the sensing resources to terminals; and a step of transmitting a sensing signal using the sensing resources, wherein the sensing signal has a symbol of a first length and the communication signal transmitted using the communication resources has a symbol of a second length.

[0011] Here, the first length may be different from or the same as the second length.

[0012] Here, the sensing signal may be transmitted using a frequency resource with the same or a larger bandwidth as the communication signal.

[0013] Here, the method further includes a step of verifying sensing requirements, wherein the sensing requirements may include at least one of distance measurement accuracy, speed measurement accuracy, sensing processing delay time, and sensing result transmission delay time.

[0014] Here, the length and period of the transmission interval of the sensing signal can be determined based on the sensing requirements.

[0015] Here, the sensing signal may have the form of a pulse train that is periodically transmitted within the transmission interval of the sensing signal.

[0016] Here, the period of the pulse train can be determined based on at least one of distance resolution and speed resolution.

[0017] Here, the method further includes the step of sharing the resource allocation information with adjacent cells, and can perform inter-cell cooperative sensing or avoid interference between sensing signals based on the resource allocation information shared with the adjacent cells.

[0018]

[0019] Here, the step of sharing the resource allocation information with the adjacent cells may include: the step of setting a resource pool for the sensing resource; the step of exchanging resource allocation information with the adjacent cells within the set resource pool; and the step of dynamically allocating resources within the resource pool based on the exchanged resource allocation information.

[0020] Here, the resource allocation information may be transmitted to the terminals via system information or wireless resource control (RRC) signaling.

[0021] Here, the sensing signal is one of a pulse-shaped signal for radar sensing or a reference signal for communication, and the reference signal for communication may be a demodulation reference signal (DM-RS) or a phase tracking reference signal (PT-RS).

[0022] Here, configuration information for allocating the time-frequency resources is received from a central SON (Self Organizing Network) or OAM (Operation and Maintenance), and the resource allocation information can be determined based on the configuration information.

[0023] In a wireless communication system provided by an embodiment of the present invention, a base station comprises: a transceiver; and a processor, wherein the processor distinguishes and allocates a communication resource and a sensing resource on a time-frequency resource, controls the transceiver to transmit resource allocation information including at least one of the communication resource and the sensing resource to terminals, controls the transceiver to transmit a sensing signal using the sensing resource, wherein the sensing signal has a symbol of a first length, and the communication signal transmitted using the communication resource has a symbol of a second length.

[0024] Here, the first length may be different from or the same as the second length.

[0025] Here, the sensing signal may be transmitted using a frequency resource with the same or a larger bandwidth as the communication signal.

[0026] Here, the processor checks the sensing requirements, and the sensing requirements may include at least one of distance measurement accuracy, speed measurement accuracy, sensing processing delay time, and sensing result transmission delay time.

[0027] Here, the length and period of the transmission interval of the sensing signal can be determined based on the sensing requirements.

[0028] Here, the sensing signal has the form of a pulse train that is periodically transmitted within the transmission interval of the sensing signal, and the period of the pulse train can be determined based on at least one of distance resolution and speed resolution.

[0029] Here, the processor shares the resource allocation information with adjacent cells, performs inter-cell cooperative sensing or avoids interference between sensing signals based on the resource allocation information shared with the adjacent cells, sets up a resource pool for the sensing resources, exchanges resource allocation information with the adjacent cells within the set resource pool, and can dynamically allocate resources within the resource pool based on the exchanged resource allocation information.

[0030] Here, the processor controls the transceiver to transmit the resource allocation information to the terminals through system information or wireless resource control (RRC) signaling, and the sensing signal is one of a pulse-shaped signal for radar sensing or a reference signal for communication, and the reference signal for communication is a demodulation reference signal (DM-RS) or a phase tracking reference signal (PT-RS), and receives configuration information for the allocation of the time-frequency resource from a central SON (Self Organizing Network) or OAM (Operation and Maintenance), and can determine the resource allocation information based on the configuration information.

[0031] According to embodiments of the present invention, the utilization of frequency resources can be increased by efficiently integrating and managing time-frequency resources used for communication and sensing. Furthermore, by allocating resources differentially according to sensing QoS requirements, the performance requirements of various sensing applications can be effectively satisfied. In addition, cooperative sensing between cells is possible through the sharing of resource allocation information with adjacent cells, and interference between sensing signals can be effectively avoided. In particular, high distance resolution and velocity resolution can be achieved simultaneously through the periodic transmission of sensing pulses having a length shorter than communication symbols. Moreover, the seamless coexistence of communication and sensing functions is possible by sharing resource allocation information with terminals through system information or RRC signaling. Finally, the communication and sensing performance of the entire network can be optimized through resource management via a central network management system.

[0032] The effects obtainable through the specific examples of this specification are not limited to those listed above. For example, there may be various technical effects that a person with ordinary skill in the related art can understand or derive from this specification. Accordingly, the specific effects of this specification are not limited to those explicitly described herein, but may include various effects that can be understood or derived from the technical features of this specification.

[0033] The following drawings are prepared 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.

[0034] FIG. 1 illustrates an example of a wireless communication system to which embodiments of the present invention can be applied.

[0035] FIG. 2 is a block diagram illustrating an example of the configuration of each communication node constituting the communication system of FIG. 1.

[0036] FIG. 3 illustrates a functional framework for RAN intelligence utilizing artificial intelligence (AI) / machine learning (ML) according to embodiments of the present invention.

[0037] FIG. 4 illustrates an AI / ML framework according to an embodiment of the present invention.

[0038] Figure 5 illustrates the basic configuration of an ISAC system.

[0039] Figures 6a, 6b, and 6c are diagrams illustrating the transmission and reception structures of sensing signals, respectively showing monostatic, bistatic, and multistatic sensing structures.

[0040] Figure 7 illustrates various types of signals used in radar sensing and their respective characteristics.

[0041] FIG. 8 illustrates the detailed structure and key parameters of the coherent pulse train used in the present invention.

[0042] Figure 9 illustrates the allocation structure of communication signals and sensing signals in time-frequency resources.

[0043] Figure 10 illustrates in more detail the integrated allocation structure of communication and sensing signals in time-frequency resources.

[0044] FIG. 11 is a diagram illustrating the operation of performing sensing by allocating sensing resources of a base station according to an embodiment of the present invention.

[0045] FIG. 12a is a diagram illustrating the operation in which a terminal receives information of a sensing resource from a base station according to an embodiment of the present invention and performs sensing without a separate request to the base station.

[0046] FIG. 12b is a diagram illustrating an operation of requesting sensing resources from a base station of a terminal and allocating sensing resources to perform sensing according to an embodiment of the present invention.

[0047] FIG. 13 is a diagram illustrating a resource allocation method in the structure of inter-cell cooperative sensing using a network management system according to an embodiment of the present invention.

[0048] 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.

[0049] 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.

[0050] 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".

[0051] 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".

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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."

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] FIG. 1 illustrates an example of a wireless communication system to which embodiments of the present invention can be applied.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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).

[0068] 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.

[0069] 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.

[0070] FIG. 2 is a block diagram illustrating an example of the configuration of each communication node constituting the communication system of FIG. 1.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] Meanwhile, embodiments of the present invention may be carried out by AI (Artificial Intelligence) machine learning or deep learning technology.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] FIG. 4 illustrates an AI / ML framework that can be applied to embodiments of the present invention.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] In the following, Integrated Sensing and Communication (ISAC) related to embodiments of the present invention is described.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.)."

[0100] 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.

[0101] Figure 5 is a diagram showing the basic configuration of the ISAC system.

[0102] 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.

[0103] 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.

[0104] Figures 6a, 6b, and 6c are diagrams illustrating the specific concept of transmitting and receiving sensing signals.

[0105] 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.

[0106] 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.

[0107] 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, and direction of an object can be determined more accurately using 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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%.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Sensing QoS can be broadly defined in terms of parameters such as accuracy, resolution, latency, reliability, and update rate, as shown in .

[0117]

[0118] 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.

[0119]

[0120] A 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. Furthermore, a processing latency of less than 5ms enables response within a travel distance of less than 14cm, even when driving at 100km / h.

[0121] 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 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.

[0122] For example, in the case of Automated Guided Vehicles (AGVs) within 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the following, a resource allocation method for communication signals and sensing signals is described according to an embodiment of the present invention.

[0134] Figure 7 shows various types of signals used in radar sensing and their respective characteristics.

[0135] Figure 7(a) shows a continuous wave (CW) signal, which is a continuously transmitted sinusoidal signal that can precisely measure the velocity of a target through the Doppler effect. The CW signal provides excellent Doppler resolution of 1 / Tcw due to its long observation time (Tcw). However, it has the characteristic of not providing distance resolution because the round-trip time of the signal cannot be measured due to the absence of a start and end of the pulse.

[0136] Figure 7(b) shows a single short pulse, which is a signal transmitted for only a very short period of time. Since the beginning and end of the short pulse are clearly distinguished, the round-trip time of the signal can be measured precisely, providing excellent distance resolution of c·Tp / 2 (where c is the speed of light and Tp is the pulse width). However, a single pulse alone has the disadvantage of lower accuracy in speed measurement because it is difficult to measure the Doppler frequency.

[0137] Figure 7(c) shows a pulse train signal proposed in the present invention, which is a form in which short pulses are transmitted repeatedly at a constant period. Since each pulse of the pulse train is short, it provides excellent distance resolution (ΔR = c·Tp / 2), and at the same time, it can precisely measure the Doppler frequency through the phase change of multiple pulses, thereby providing excellent speed resolution (Δf = 1 / Np·Ti, where Np is the number of pulses and Ti is the pulse repetition period).

[0138] Figure 8 shows the detailed structure and key parameters of the coherent pulse train used in the present invention.

[0139] The pulse train (810) consists of several pulses that are coherent in phase. Each pulse has a pulse width (Tp, 820), which is an important parameter for determining distance resolution. The pulses are repeated every pulse repetition interval (PRI: Pulse Repetition Interval, Ti, 830) and, together with the total number of pulses (Np, 840), determine the Doppler resolution.

[0140] Figure 8 also defines the duty cycle (d, 850) and pulse repetition frequency (PRF: fr, 860). The duty cycle represents the ratio of the time the pulse is actually transmitted to the total period (d = Tp / Ti), which determines the ratio of the system's average power to its maximum power. The pulse repetition frequency is defined as the reciprocal of the pulse repetition period (fr = 1 / Ti) and is an important parameter for determining the maximum unambiguous velocity.

[0141] Figure 9 shows an example of the allocation structure of communication signals and sensing signals in time-frequency resources.

[0142] FIG. 9(a) shows resource allocation in the time-frequency plane, where the resources (910) shown in gray represent resources used for sensing, and the other RE (Resource Elements) represent resources for communication. Each communication symbol occupies a specific time-frequency range and can be transmitted via a multi-carrier method such as OFDM. For example, data communication by a smartphone user is performed through these communication resources.

[0143] According to one embodiment of the present invention, resources used for sensing and communication can be flexibly configured depending on the situation. For example, more resources may be allocated to sensing during periods of low communication traffic, such as at night, while the proportion of resources for communication may be increased during the day when communication traffic is high. Additionally, resource allocation may be adjusted according to regional characteristics, such as setting a high proportion of resources for sensing for safety in factory areas and a high proportion of resources for communication in residential areas.

[0144] Figure 9(b) shows the time domain structure of the sensing signal in detail.

[0145] The sensing signal length (920) indicates the duration of each pulse, which can be set to be equal to the length of a standard communication symbol or much shorter than a communication symbol to provide high distance resolution. The sensing signal period (930) indicates the repetition interval between pulses, which is an important factor in determining the maximum detection distance and velocity measurement range of the target.

[0146] According to another embodiment of the present invention, a sensing function may be performed using a reference signal for communication (e.g., DM-RS, PT-RS, etc.). This has the advantage of enabling basic sensing without transmitting a separate sensing signal. For example, this method can be utilized in situations where high precision is not required, such as when detecting only the presence or absence of a person in an indoor environment.

[0147] Figure 10 shows the integrated allocation structure of communication and sensing signals in time-frequency resources in more detail.

[0148] FIG. 10(a) shows resource allocation in the time-frequency plane, specifically showing how a sensing signal train (1010) is allocated separately from communication symbols. Information regarding the resources allocated for sensing is known in advance to general communication terminals through system information or RRC configuration information. For example, a terminal for an autonomous vehicle can perform communication by avoiding the resources through which the sensing signal is transmitted using this information.

[0149] In addition, according to another embodiment of the present invention, inter-cell cooperative sensing or interference avoidance is possible by sharing resource allocation information with adjacent cells. For example, multiple base stations can cooperate to perform omnidirectional sensing without blind spots at an urban intersection, or continuous vehicle tracking can be performed on a highway.

[0150] The allocation of such sensing resources can be managed in two main ways.

[0151] First, it is a method in which resources for multiple cells are centrally pre-configured and managed within the network operating system. This approach is useful in environments requiring close cooperation among many cells, such as complex intersections in urban areas.

[0152] Second, there is a method in which only the entire resource pool is set in advance, and adjacent cells dynamically manage resources by exchanging resource allocation information as needed. This method is suitable for environments where cells are arranged in a line and situational changes are relatively simple, such as highways. The two methods are explained in more detail in Fig. 13.

[0153] FIG. 10(b) shows the time domain characteristics of the sensing signal in detail. The sensing signal is transmitted with a fixed length (1020) and period (1030), and this periodic structure enables velocity measurement through the Doppler effect. The length and period of the sensing signal can be dynamically adjusted according to the sensing QoS requirements.

[0154] As another embodiment of the present invention, sensing for different purposes can be performed simultaneously by diversifying the period and pattern of the sensing signal. For example, it can be configured to detect fast objects at close range with short-period pulses and slow objects at long range with long-period pulses. This can be usefully applied, for example, in situations where pedestrians at close range and vehicles at long range need to be detected simultaneously in a road environment.

[0155] As an additional embodiment, the waveform of the sensing signal can be optimized according to the application environment. For example, a waveform considering multipath reflection can be used in an indoor environment, and a waveform considering weather effects can be used in an outdoor environment. In addition, in environments where GPS signals do not reach, such as underground parking lots, the positioning function utilizing the sensing signal can be enhanced.

[0156] FIG. 11 is a diagram illustrating the operation of a base station performing sensing by allocating sensing resources according to an embodiment of the present invention.

[0157] The base station determines the parameters of the sensing signal (S1105). This is a step of setting the basic characteristics of the signal for sensing the surrounding environment, and the parameters of the sensing signal may be determined according to the sensing QoS requirements. The sensing QoS requirements include at least one of distance measurement accuracy, speed measurement accuracy, sensing processing delay time, and sensing result transmission delay time.

[0158] The above parameters may include pulse length (Tp), pulse repetition period (PRI), number of pulses (Np), bandwidth (BW), etc. These parameters can be set differently depending on the sensing QoS requirements. For example, if high-precision hazard detection for worker safety is required in a factory, short symbols may be used for distance measurement accuracy of ±0.1m, whereas if only the presence of a vehicle is checked in a parking lot, relatively long symbols may be used as distance measurement accuracy of ±1.0m is sufficient. Additionally, if fast sensing processing within 5ms is required, the pulse train period may be set to a short duration.

[0159] In addition, the base station separates and allocates communication resources and sensing resources on time-frequency resources (S1110). For example, since a high sensing success rate of 99.9% or higher is required to prevent collisions in autonomous vehicles, a wider frequency resource (i.e., a wider bandwidth) can be allocated to the sensing signal to increase precision. On the other hand, in cases where a sensing success rate of about 95% is sufficient, such as for indoor environment monitoring, a bandwidth identical to or similar to that of the communication signal may be allocated.

[0160] The base station transmits resource allocation information containing information about allocated resources to the terminals (S1125). Through this, the terminals can perform stable communication by avoiding the section where the sensing signal is transmitted.

[0161] Finally, the base station performs sensing by transmitting a sensing signal through the allocated sensing resources (S1130). Here, the transmission period of the sensing signal can be determined according to the sensing QoS requirements. For example, to accurately measure the speed of a fast-moving object, the signal is transmitted with a short update period of 10ms, and for monitoring a slowly changing environment, a long transmission period of 100ms can be used.

[0162] In FIG. 12 below, an embodiment is described in which a terminal receives resources for transmitting a sensing signal from a base station and transmits a sensing signal. Sensing by the terminal can be broadly classified into cases where the terminal does not request sensing resources from the base station and cases where the terminal requests sensing resources from the base station. The former is described in FIG. 12a and the latter is described in FIG. 12b.

[0163] FIG. 12a is a diagram illustrating the operation in which a terminal receives information of a sensing resource from a base station according to an embodiment of the present invention and performs sensing without a separate request to the base station.

[0164] Referring to FIG. 12a, a terminal receives sensing resource allocation information (or sensing resource setting information) from a base station via system information or RRC signaling (S1210). The base station allocates communication resources and sensing resources separately from the total time-frequency resources, and pre-configures the sensing resources in the form of a resource pool to notify all terminals. At this time, the base station may configure multiple resource areas with different characteristics within the resource pool by considering various sensing requirements. For example, it may configure a high-resolution sensing resource area with short symbol lengths and periods and a general sensing resource area with relatively long symbol lengths and periods. This sensing resource pool information is also transmitted to general communication terminals so that they can communicate while avoiding the corresponding resources.

[0165] Subsequently, the terminal performs direct communication and / or sensing operations without a separate request procedure based on the received resource allocation information (S1220). For example, a terminal for an autonomous vehicle can perform sensing by selecting a resource area suitable for its sensing purpose from a pool of sensing resources provided in advance by the base station. This method can improve the efficiency of sensing by eliminating the procedure of requesting resources from the base station every time when urgent sensing or periodic sensing is required.

[0166] FIG. 12b is a diagram illustrating an operation in which a terminal requests a sensing resource from a base station and receives a sensing resource to perform sensing according to an embodiment of the present invention.

[0167] First, the terminal receives sensing resource configuration information, such as sensing resource allocation information, from the base station via system information or RRC signaling (1250). The base station sets up communication resource intervals and sensing resource intervals on time-frequency resources and shares this information with the terminals.

[0168] When a terminal requires sensing resources, it requests the base station to allocate sensing resources (1260). For example, a terminal for an autonomous vehicle may request resources for sensing the surrounding environment. In this case, the terminal may also provide the necessary sensing QoS requirements (e.g., distance measurement accuracy, sensing processing latency, etc.).

[0169] The base station can receive a request from a terminal and select and allocate time-frequency resources capable of satisfying sensing QoS requirements. Additionally, the base station can set the length and period of the sensing signal, taking into account the sensing QoS requirements. Furthermore, the base station may allocate sensing resources according to the set length and period of the sensing signal, taking into account the sensing QoS requirements.

[0170] Accordingly, the terminal receives a sensing resource allocation approval from the base station (1270). This approval information includes sensing resource allocation information, namely, specific time-frequency location information of the allocated resource and configuration information of the sensing resource (length, period, etc.).

[0171] Finally, the terminal performs communication and / or sensing operations according to the received resource allocation approval (1280). For example, it can perform a sensing operation using the allocated sensing resources and perform communication by avoiding the resources allocated for sensing.

[0172] The following describes a method for allocating sensing resources based on a structure for cooperative sensing among multiple cells.

[0173] A mobile communication network is a complex system in which numerous base stations are interconnected. To operate such a network efficiently, a system that manages the entire network from a central location is required. Representative network management systems include Self Organizing Networks (SON) and Operation and Maintenance (OAM) systems.

[0174] SON is a system that enables the network to autonomously configure, optimize, and recover, performing functions such as automatically establishing relationships between adjacent cells and optimizing wireless parameters. OAM is a system responsible for the overall operation and maintenance of the network, performing network performance monitoring, fault management, and configuration management.

[0175] FIG. 13 is a diagram illustrating a resource allocation method in the structure of inter-cell cooperative sensing using a network management system according to an embodiment of the present invention.

[0176] The central network management system (1310) manages and controls the sensing resources of the entire network. For example, in urban areas, many base stations are densely packed in a narrow area, so resources must be allocated to enable efficient sensing while avoiding interference between them. At this time, the central network management system can determine the optimal resource allocation method by considering the location, coverage, and traffic load of each base station.

[0177] Cell 1 (1320) and Cell 2 (1330) have a structure in which they cooperate to sense the same object (1340) at different locations. For example, this applies to cases where multiple base stations cooperate to detect vehicles or pedestrians without blind spots at complex intersections in urban areas, or to cases where vehicles are continuously tracked on highways.

[0178] In the case of a bistatic sensing structure in which cell 1 transmits a transmission signal (1350) and cell 2 receives and processes a reflected signal (1360) reflected from an object, accurate time and frequency synchronization between the two cells is very important. For example, to detect a vehicle moving at 100 km / h, precise time synchronization in microseconds may be required.

[0179] A central network management system can manage resources for this cooperative sensing in two ways. First, it establishes a resource allocation plan for the entire network in advance to assign fixed resources to each cell. This is suitable for situations where traffic is regularly concentrated, such as during rush hour. Second, it defines only the range of resources available to each cell, allowing the actual resource allocation to be adjusted autonomously through mutual communication. This has the advantage of enabling flexible responses to unpredictable situations, such as traffic accidents or demonstrations.

[0180] In addition, the central network management system monitors the resource usage status of each cell and adjusts resource allocation if necessary. For example, if sensing performance degrades in a specific cell, it can allocate additional resources to that cell or adjust resource allocation to strengthen cooperative sensing with adjacent cells.

[0181] Through this structure, by comprehensively analyzing sensing information received from multiple cells, more accurate and reliable sensing than single-cell-based sensing becomes possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The various embodiments of the present invention are not intended to list all possible combinations but are intended 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.

[0189] 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.

Claims

1. A method for allocating communication and sensing resources of a base station in a wireless communication system, A step of distinguishing and allocating communication resources and sensing resources on time-frequency resources; A step of transmitting resource allocation information to terminals, including at least one of the communication resource and the sensing resource; and It includes the step of transmitting a sensing signal using the above-mentioned sensing resource, and A resource allocation method characterized in that the sensing signal has a symbol of a first length, and the communication signal transmitted using the communication resource has a symbol of a second length.

2. In Paragraph 1, A resource allocation method characterized in that the first length is different from or the same as the second length.

3. In Paragraph 1, A resource allocation method in which the above sensing signal is transmitted using frequency resources with the same or larger bandwidth as the above communication signal.

4. In Paragraph 1, It further includes a step to verify sensing requirements, and The above sensing requirements include at least one of distance measurement accuracy, speed measurement accuracy, sensing processing delay time, and sensing result transmission delay time, a resource allocation method.

5. In Paragraph 4, A resource allocation method in which the length and period of the transmission interval of the above-mentioned sensing signal are determined based on the above-mentioned sensing requirements.

6. In Paragraph 1, A resource allocation method in which the above sensing signal has the form of a pulse train that is periodically transmitted within the transmission interval of the above sensing signal.

7. In Paragraph 6, A resource allocation method in which the period of the above pulse train is determined based on at least one of distance resolution and speed resolution.

8. In Paragraph 1, It further includes the step of sharing the resource allocation information with adjacent cells, A resource allocation method that performs inter-cell cooperative sensing or avoids interference between sensing signals based on resource allocation information shared with the adjacent cells.

9. In paragraph 8, the step of sharing the resource allocation information with the adjacent cells is, A step of setting up a resource pool for the above-mentioned sensing resources; A step of exchanging resource allocation information with adjacent cells within the resource pool set above; and A resource allocation method comprising the step of dynamically allocating resources within the resource pool based on the exchanged resource allocation information.

10. In Paragraph 1, A resource allocation method in which the above resource allocation information is transmitted to the terminals via system information or wireless resource control (RRC) signaling.

11. In Paragraph 1, The above sensing signal is one of a pulse-shaped signal for radar sensing or a reference signal for communication, and A resource allocation method in which the communication reference signal is a demodulation reference signal (DM-RS) or a phase tracking reference signal (PT-RS).

12. In Paragraph 1, A resource allocation method that receives configuration information for allocating the time-frequency resources from a central SON (Self Organizing Network) or OAM (Operation and Maintenance) and determines the resource allocation information based on the configuration information.

13. In a base station of a wireless communication system, Transmitter / receiver; and Includes a processor, The above processor is, The transceiver controls the communication resource and the sensing resource by distinguishing and allocating them on time-frequency resources, and transmits resource allocation information including at least one of the communication resource and the sensing resource to terminals, and controls the transceiver to transmit a sensing signal using the sensing resource. A base station characterized in that the above-mentioned sensing signal has a symbol of a first length, and the communication signal transmitted using the above-mentioned communication resource has a symbol of a second length.

14. In Paragraph 13, A base station characterized in that the first length is different from or the same as the second length.

15. In Paragraph 13, A base station that transmits the above sensing signal using frequency resources with the same or larger bandwidth as the above communication signal.

16. In Paragraph 13, The above processor checks the sensing requirements, and The above sensing requirements include at least one of distance measurement accuracy, speed measurement accuracy, sensing processing delay time, and sensing result transmission delay time, for a base station.

17. In Paragraph 16, A base station in which the length and period of the transmission interval of the above-mentioned sensing signal are determined based on the above-mentioned sensing requirements.

18. In Paragraph 13, The above sensing signal has the form of a pulse train that is periodically transmitted within the transmission interval of the above sensing signal, and A base station in which the period of the above pulse train is determined based on at least one of distance resolution and speed resolution.

19. In Paragraph 13, the above processor, Sharing the resource allocation information with adjacent cells, performing inter-cell cooperative sensing based on the resource allocation information shared with the adjacent cells, or avoiding interference between sensing signals, A base station that sets a resource pool for the above-mentioned sensing resources, exchanges resource allocation information with adjacent cells within the set resource pool, and dynamically allocates resources within the resource pool based on the exchanged resource allocation information.

20. In Paragraph 13, the above processor, The transceiver is controlled to transmit the above resource allocation information to the terminals via system information or wireless resource control (RRC) signaling, and The above sensing signal is one of a pulse-shaped signal for radar sensing or a reference signal for communication, and the communication reference signal is a demodulation reference signal (DM-RS) or a phase tracking reference signal (PT-RS). A base station that receives configuration information for the allocation of the time-frequency resources from a central SON (Self Organizing Network) or OAM (Operation and Maintenance) and determines the resource allocation information based on the configuration information.

Images