Electronic device for integrated sensing and communication system, method, and storage medium

Abstract

The present invention relates to an electronic device for an integrated sensing and communication system, a method, and a storage medium. A first electronic device for an integrated sensing and communication system, comprising: at least one processor; and at least one memory comprising a computer program code, wherein the at least one memory and the computer program code are configured to enable, by means of the at least one processor, the first electronic device to: receive sensing data, wherein the sensing data is obtained by performing preliminary sensing by one or more low-frequency base stations; determine policy formulation data for formulating a communication policy and / or a sensing policy; and send the policy formulation data to a second electronic device, wherein the policy formulation data at least comprises information indicating one or more high-frequency base stations which are about to participate in secondary sensing.

Description

Electronic device, method and storage medium for communication and perception integrated system

[0001] Cross-reference to related applications

[0002] This application is based on and claims priority from Chinese patent application No. 202410024486.6, filed on January 8, 2024, entitled “Electronic device, method and storage medium for communication perception integrated system,” the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to wireless communication systems, and more particularly to technologies related to Integrated Sensing and Communication (ISAC). Background Art

[0004] As wireless communication needs evolve, a growing number of wireless communication application scenarios have emerged, some of which require wireless communication systems to integrate both communication and perception capabilities. Traditionally, communication and perception have existed independently. This separate design not only wastes wireless spectrum and hardware resources, but also introduces latency and other issues due to the functional independence. With the advancement of wireless communication technology, the communication spectrum may overlap with the traditional perception spectrum. Therefore, discussions have begun on technologies related to the integration of communication and perception (or simply, synaesthesia integration).

[0005] A wireless communication system may contain multiple base stations. In modern wireless communication systems, due to the evolution of communication technologies, multiple types of base stations may exist. With the upcoming implementation of 5G-A and 6G standards, in addition to the current architecture primarily based on the sub-6GHz frequency band, new base stations will support operation in millimeter wave and terahertz frequency bands. Therefore, future base stations will be categorized as high-frequency and low-frequency.

[0006] In view of this, there is a need for a solution for inter-sensory integration that allows base stations (especially high-frequency base stations and low-frequency base stations) to collaborate for communication and / or perception. Summary of the Invention

[0007] The present disclosure proposes a solution related to data transmission / notification in a wireless communication system. Specifically, the present disclosure provides an electronic device, method, and storage medium for a communication-awareness integrated system.

[0008] One aspect of the present disclosure relates to a first electronic device for a wireless communication system, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, enable the first electronic device to: receive perception data, wherein the perception data is obtained by preliminary perception by one or more low-frequency base stations; determine policy-making data for formulating communication strategies and / or perception strategies; and send the policy-making data to a second electronic device, wherein the policy-making data at least includes information indicating one or more high-frequency base stations to participate in secondary perception.

[0009] Another aspect of the present disclosure relates to a second electronic device for a communication-awareness integrated system, comprising at least one processor; and at least one memory, comprising computer program code, wherein the at least one memory and the computer program code are configured to enable the second electronic device, through the at least one processor, to: receive the policy-making data from the first electronic device as described above; and formulate a communication policy and / or a perception policy based on the policy-making data.

[0010] Another aspect of the present disclosure relates to a method for a first electronic device of a communication perception integrated system, comprising: receiving perception data, wherein the perception data is obtained by preliminary perception by one or more low-frequency base stations; determining policy-making data for formulating communication strategies and / or perception strategies; and sending the policy-making data to a second electronic device, wherein the policy-making data at least includes information indicating one or more high-frequency base stations to participate in secondary perception.

[0011] Another aspect of the present disclosure relates to a method for a second electronic device of a communication-awareness integrated system, comprising: receiving the policy-making data from the first electronic device as described above; and formulating a communication policy and / or a perception policy based on the policy-making data.

[0012] Another aspect of the present disclosure relates to a non-transitory computer-readable storage medium storing executable instructions, which, when executed, implement the method as described in the above aspect.

[0013] Another aspect of the present disclosure relates to a computer program product comprising executable instructions, which, when executed, implement the method according to the above aspect.

[0014] The above summary is provided to summarize some exemplary embodiments in order to provide a basic understanding of various aspects of the subject matter described herein. Therefore, the above features are merely examples and should not be construed as narrowing the scope or spirit of the subject matter described herein in any way. Other features, aspects, and advantages of the subject matter described herein will become apparent from the detailed description described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] A better understanding of the present disclosure may be obtained when the following detailed description of the embodiments is considered in conjunction with the accompanying drawings. The same or similar reference numerals are used in the various drawings to represent the same or similar components. The accompanying drawings, together with the following detailed description, are incorporated into and form a part of this specification and are used to illustrate the embodiments of the present disclosure and to explain the principles and advantages of the present disclosure. In particular:

[0016] FIG1 schematically illustrates an exemplary communication and perception integration system that allows a high-frequency base station to collaborate with a low-frequency base station to communicate and / or perceive according to the present disclosure;

[0017] FIG2 shows a conceptual configuration of a first electronic device according to the present disclosure;

[0018] FIG3 schematically illustrates a conceptual operation flow of a first electronic device according to an embodiment of the present disclosure;

[0019] FIG4 schematically illustrates an exemplary solution process of a high frequency base station selection model according to an embodiment of the present disclosure;

[0020] FIG5 schematically illustrates an exemplary interface between network elements according to an embodiment of the present disclosure;

[0021] FIG6 schematically illustrates a conceptual configuration of a second electronic device according to an embodiment of the present disclosure;

[0022] FIG7 schematically illustrates a conceptual operation flow of a second electronic device according to an embodiment of the present disclosure;

[0023] FIG8A schematically illustrates an exemplary interaction according to an embodiment of the present disclosure;

[0024] FIG8B schematically illustrates another exemplary interaction according to an embodiment of the present disclosure;

[0025] FIG9 is a block diagram illustrating an example structure of a computing device that can implement the first electronic device or the second electronic device according to the present disclosure.

[0026] While the embodiments described in this disclosure may be susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and are herein described in detail. However, it should be understood that the drawings and detailed description thereof are not intended to limit the embodiments to the particular forms disclosed, but on the contrary, the intent is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the claims. DETAILED DESCRIPTION

[0027] The following describes representative applications of various aspects of the apparatus and method of the present disclosure. The description of these examples is only to add context and help understand the described embodiments. Therefore, it is clear to those skilled in the art that the embodiments described below can be implemented without some or all of the specific details. In other cases, well-known process steps are not described in detail to avoid unnecessarily obscuring the described embodiments. Other applications are also possible, and the solutions of the present disclosure are not limited to these examples.

[0028] For ease of description, the abbreviations used in this article are as follows:

[0029] ISAC (Integrated Sensing and Communication)

[0030] NFV (Network Function Virtualization)

[0031] SDN (Software Defined Network)

[0032] UE (User Equipment)

[0033] NWDAF (Network Data Analytics Function), network data analysis function

[0034] SF (Sensing Function), perception function

[0035] PCF (Policy Control Function), policy control function

[0036] SINR (Signal to Interference plus Noise Ratio)

[0037] SNR (Signal to Noise Ratio)

[0038] RSRP (Reference Signal Receiving Power), reference signal receiving power

[0039] CRB (Cramér-Rao Bound), Cramér-Rao Bound

[0040] Ndccf (Network-to-Data Center Coherent Foundation), Network-Data Center Coherence

[0041] DCCF (Data Collection Coordination Function), data collection coordination function

[0042] UDM (Unified Data Management)

[0043] OAM (Operation Administration and Maintenance)

[0044] AMF (Access and Mobility Management Function), access and mobility management function

[0045] QoS (Quality of Service)

[0046] QoE (Quality of Experience)

[0047] AnLF (Analytics Logical Function), data analysis logical network element

[0048] MTLF (Model Training Logical Function), model training logical network element

[0049] PCF (Policy Control Function), policy control function

[0050] ADRF (Analytics Data Repository Function), Analytics Data Repository Function

[0051] RFSP (RAT / Frequency Selection Priority), access / frequency selection priority

[0052] eMBB (enhance Mobile Broadband)

[0053] URSP (UE Route Selection Policy), UE routing selection policy

[0054] CoMP (Coordinated Multiple Points)

[0055] JT (Joint Transmission)

[0056] RSSI (Received Signal Strength Indicator), received signal strength indicator

[0057] CQI (Channel Quality Indicator), channel quality indicator

[0058] BLER (Block Error Rate), block error rate

[0059] VA (virtual anchor), virtual anchor point

[0060] GPS (Global Positioning System)

[0061] AF (Application Function), application function

[0062] NEF (Network Exposure Function), network exposure function

[0063] NRF (Network Repository Function), network repository function

[0064] SMF (Session Management Function), session management function

[0065] NAS (Non-Access Stratum), non-access layer

[0066] NSSAI (Network Slice Selection Assistance Information), network slice selection assistance information

[0067] UPF (User Plane Function), user plane function

[0068] Typically, the system for communication and perception integration according to the present disclosure includes at least a network element device, a base station and a user equipment.

[0069] In this disclosure, a network element device (or simply referred to as a network element) has the full breadth of its usual meaning, and unless explicitly specified, generally refers to a virtualized or non-virtualized device that implements one or more functions of a core network. For example, a single function may be implemented by a single network element device, multiple functions may be implemented by a single network element device, or a single function may be implemented by multiple network element devices. In the case of a virtualized deployment, the network element device may run as a software instance on general-purpose hardware rather than traditional dedicated hardware. For example, NFV or SDN may be used to implement such a virtualized deployment. The network element device may be deployed on one or more servers of the core network. In some cases, some network element devices may also be deployed on edge computing nodes located at locations such as base stations and city data centers, or deployed in a distributed manner between one or more servers of the core network and edge computing nodes.

[0070] In this disclosure, the term "base station" or "control device" has its full breadth of ordinary meaning and includes at least a wireless communication station that facilitates communications as part of a wireless communication system or radio system. For example, a base station may be, for example, an eNB for the 4G communication standard, a gNB for the 5G NR communication standard, a remote radio head, a wireless access point, a drone control tower, or a communication device that performs similar functions. In this disclosure, "base station" and "control device" may be used interchangeably, or a "control device" may be implemented as part of a "base station."

[0071] In the present disclosure, the term "terminal device" or "user equipment (UE)" has the full breadth of its usual meaning and includes at least a terminal device that is part of a wireless communication system or radio system to facilitate communication. As an example, the terminal device may be a terminal device such as a mobile phone, a laptop, a tablet computer, an in-vehicle communication device, a wearable device, a sensor, or the like, or an element thereof. In the present disclosure, "terminal device" and "user equipment" (hereinafter referred to as "UE") may be used interchangeably, or a "terminal device" may be implemented as a part of a "user equipment".

[0072] In the present disclosure, the term "perception" has the full breadth of its usual meaning and can broadly refer to the detection of the state, characteristics, etc. of things in the environment. For example, self-driving car / drone detection, real-time environmental monitoring, weather or air pollution monitoring, etc. The target of perception can be, for example, the perception of obstacles, the perception of the position of objects, the perception of the speed of movement of objects, the perception of air humidity / particulate matter concentration, etc. In addition, perception can also include the detection of the state, characteristics, etc. of wireless communication networks. For example, the perception of spectrum, the perception of traffic, and the perception of network status, etc. For example, the perception results can be obtained by monitoring and / or processing the perception signals, using equipment such as spectrum analyzers, and / or using network traffic monitoring tools.

[0073] In the present disclosure, the term "perception signal" may refer to a signal used to obtain a perception result based thereon. For example, the perception signal may be transmitted as a separate signal, or, in the synaesthesia integration system described in the present disclosure, the perception signal may also be a perception signal component of a synaesthesia integration signal that is multiplexed and transmitted with a communication signal.

[0074] In this disclosure, the term "communication signal" may refer to a signal used for communication. For example, the communication signal may be transmitted as a separate signal, or, in the synaesthesia integration system described in this disclosure, the communication signal may be a communication signal component of a synaesthesia integration signal that is multiplexed with a sensory signal and transmitted.

[0075] As introduced in the background art, there is a need for a solution for inter-sensory integration that allows base stations (especially high-frequency base stations and low-frequency base stations) to collaborate to perform communication and / or perception.

[0076] Figure 1 schematically illustrates an exemplary integrated communication and perception system that allows a high-frequency base station to collaborate with a low-frequency base station for communication and / or perception, according to the present disclosure. As shown in Figure 1 , in the present disclosure, the low-frequency base station may be a sub-6 GHz base station, while the high-frequency base station may be a millimeter wave (mmW) base station. It will be appreciated that the high-frequency base station is not limited to a millimeter wave base station and may, for example, also support a higher frequency spectrum, such as a terahertz base station.

[0077] Low-frequency and high-frequency base stations each have different characteristics. For example, low-frequency base stations offer wide coverage, strong diffraction resistance, low dielectric energy loss, strong penetration, and slow attenuation, but they also have limited bandwidth and limited precision positioning and imaging capabilities. High-frequency base stations have larger bandwidths and extensive multipath, resulting in excellent imaging and positioning. However, due to their high operating frequency, their coverage is limited and they are more sensitive to obstacles. Therefore, low-frequency base stations can support longer-range communication and perception, while high-frequency base stations can only support shorter-range communication and perception.

[0078] Taking into account the different characteristics of the above-mentioned low-frequency base stations and high-frequency base stations, in the integrated synaesthesia system disclosed in the present invention, the low-frequency base station can be designed as a continuous and wide-coverage underlying network, so as to achieve wide coverage while completing the user's initial communication and / or perception needs, and then use the high-frequency base station to complete the user's more refined communication and / or perception needs, such as duplex communication and fine perception.

[0079] The synaesthesia integration system according to the present disclosure makes it possible to fully utilize the respective advantages of high-frequency and low-frequency base stations, thereby optimizing network performance while meeting diverse business needs.

[0080] Below, various aspects of the scheme for allowing base stations (especially high-frequency base stations and low-frequency base stations) to collaborate for communication and / or perception for inter-sensory integration according to the present disclosure will be described in detail in conjunction with embodiments, such as the selection of high-frequency base stations and the formulation of communication strategies and perception strategies.

[0081] First, the conceptual structure of the first electronic device 20 according to an embodiment of the present disclosure will be described with reference to Figure 2. The electronic device 20 shown in Figure 2 may include various units to implement corresponding operations according to an embodiment of the present disclosure. In this example, the electronic device 20 includes a communication unit 202 and a computing unit 204. According to the present disclosure, the electronic device 20 may be the network element device described above. In one embodiment, the electronic device 20 is implemented as the network element device itself or a part thereof, or is implemented as a device for controlling the network element device or otherwise being related thereto or a part of the device. In particular, the electronic device may act as an NWDAF. The various operations described below in conjunction with the first electronic device may be implemented by units 202, 204 or other possible units of the electronic device 20.

[0082] As shown in Figure 2, the electronic device 20 may include a communication unit 202. The communication unit 202 may be configured to receive data from other electronic devices and send data to other electronic devices. For example, the data received from other devices may be perception data obtained by preliminary perception by one or more low-frequency base stations, and the data sent to other devices may be policy-making data determined by the electronic device 20 (e.g., the computing unit 204) for formulating communication strategies and / or perception strategies. More generally, the communication unit 202 may be configured to receive or send any appropriate data from other electronic devices to other electronic devices, for example, secondary perception data signals related to secondary perception measurement results obtained by collaborative perception by one or more low-frequency base stations and one or more high-frequency base stations, information related to user communication, information related to the network, or signals received from other electronic devices, and perception results obtained by fusing secondary perception data, etc.

[0083] The electronic device 20 may further include a computing unit 204. The computing unit 204 may be configured to determine policy formulation data for formulating communication policies and / or perception policies. For example, the determined policy formulation data includes at least information indicating one or more high-frequency base stations to participate in secondary perception, and may further include at least one of data related to communication user profile reasoning, data related to network performance and status, and data assisting secondary perception. For example, the computing unit 204 may determine policy formulation data based at least in part on perception data obtained by preliminary perception performed by one or more low-frequency base stations received by the communication unit 202. More generally, the computing unit 204 may determine policy formulation data based at least on one or more of perception data, information related to user communication, and information related to the network. The computing unit 204 may also be configured to perform any required computing operations, for example, fusing the secondary perception data received from the communication unit 202 to obtain a perception result.

[0084] It should be noted that the above-mentioned units are only logical modules divided according to the specific functions implemented by them, rather than being used to limit specific implementation methods, for example, they can be implemented in software, hardware or a combination of software and hardware. The functions of the units disclosed herein can be implemented using circuits or processing circuits. Processing circuits can refer to various implementations of digital circuit systems, analog circuit systems or mixed signal (a combination of analog and digital) circuit systems that perform functions in a computing system. Processing circuits can include, for example, circuits such as integrated circuits (ICs), application specific integrated circuits (ASICs), parts or circuits of separate processor cores, entire processor cores, separate processors, programmable hardware devices such as field programmable gate arrays (FPGAs), and / or systems including multiple processors. Processors are considered to be processing circuits or circuits because they include transistors and other circuits therein.

[0085] In the present disclosure, a circuit, unit, device or apparatus is hardware that performs or is programmed to perform the function. The hardware can be any hardware disclosed herein or otherwise known to be programmed or configured to perform the function. When the hardware is a processor that can be considered as a type of circuit, the circuit, device or unit is a combination of hardware and software, and the software is used to configure the hardware and / or processor. In the implementation of hardware, the hardware can be programmed or configured to perform the function. In the implementation of software or a combination of software and hardware, the software can be used to configure the hardware and / or processor. In actual implementation, the above-mentioned various units can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.).

[0086] Hereinafter, various operations implemented by the first electronic device 20 will be described in detail with reference to the conceptual operation flow 30 of the transmitting end shown in FIG. 3 .

[0087] The operation of the first electronic device starts at S302.

[0088] At S304, the first electronic device receives the perception data. The perception data may be obtained by performing preliminary perception by one or more low-frequency base stations. For example, the first electronic device may receive the perception data from a network element acting as an SF, and the SF may receive reported perception data from one or more low-frequency base stations. The low-frequency base station may use wide beam scanning to quickly and roughly determine the approximate area where the perception target is located, thereby obtaining the perception data for preliminary perception. For example, the obtained perception data may be used by the first electronic device to determine preliminary portrait information of the perception target, for example, the preliminary portrait information may include the location, state, shape and / or motion status of the perception target. In the present disclosure, the perception target may be one or more objects with communication capabilities and communication requirements, such as one or more UEs. Alternatively, the perception target may also be one or more objects without communication requirements, such as buildings, vehicles, pedestrians, environmental characteristics (such as air humidity / particulate matter concentration), and network characteristics (such as spectrum, traffic, network status), etc.

[0089] According to the present disclosure, the one or more low-frequency base stations participating in the preliminary perception may be one or more low-frequency base stations that are closest to the perception target and whose signal strength is greater than a predetermined threshold. Different perception methods may be used. For example, a low-frequency base station may send a perception signal (for example, a synaesthesia signal) to the perception target, and obtain perception data based on the perception signal received by another low-frequency base station. For another example, a low-frequency base station may send a perception signal to the perception target, and obtain perception data based on the echo signal of the perception signal received by the low-frequency base station. For another example, a UE may send a perception signal to a low-frequency base station, and obtain perception data based on the received perception signal. According to the present disclosure, for example, the low-frequency base stations participating in the preliminary perception and the perception method to be used may be decided by the SF.

[0090] Next, in S306, the first electronic device may determine policy-making data for formulating a communication policy and / or a perception policy. The policy-making data may include at least information indicating one or more high-frequency base stations to participate in secondary perception. For example, at least a portion of the policy-making data may be determined based on the received perception data. The detailed operation of determining the policy-making data will be described below.

[0091] Subsequently, in S308, the first electronic device may send the determined policy formulation data to the second electronic device. In particular, the second electronic device may be a network element acting as a PCF. The second electronic device may use the received policy formulation data to specify a specific communication policy and / or perception policy.

[0092] Finally, the operation of the first electronic device ends at S310.

[0093] It should be noted that the operating steps of the first electronic device shown in Figure 3 are merely schematic. In practice, the operation of the transmitting end may also include some additional or alternative steps. For example, after S308, the first electronic device may also receive secondary perception data related to the secondary perception measurement results obtained by collaborative perception with one or more low-frequency base stations and one or more high-frequency base stations, and perform fusion processing on the secondary perception data to obtain the perception result. For another example, before S306, the first electronic device may also receive a request for policy formulation data from the second electronic device. For another example, in parallel with S304 or immediately following S304, the first electronic device may also receive information related to user communications and / or network-related information from one or more network elements in order to determine policy formulation data. The detailed operations of these additional steps will be described below.

[0094] The following describes in detail the process of determining one or more high-frequency base stations to participate in the secondary sensing.

[0095] According to the present disclosure, one or more high-frequency base stations to participate in the secondary perception can be determined based on multiple factors calculated at least in part based on the perception data of the preliminary perception. Specifically, the one or more high-frequency base stations to participate in the secondary perception can be determined based on the perception effectiveness and system efficiency. For example, the perception effectiveness can be determined based on the combined signal-to-noise ratio of the perception signals sent by one or more high-frequency base stations. For example, the system efficiency can be determined based on the spectrum efficiency, energy efficiency and base station switching efficiency of one or more high-frequency base stations. For scenarios where there are still communication needs, for example, for scenarios where the perception target needs to communicate with the help of one or more high-frequency base stations participating in the secondary perception, the one or more high-frequency base stations can be further determined based on the communication effectiveness. For example, the communication effectiveness can be determined based on the combined signal-to-interference-and-noise ratio and the total received signal receiving power of the communication signals sent by one or more high-frequency base stations.

[0096] According to the present disclosure, when determining one or more high-frequency base stations to participate in secondary sensing, in addition to considering sensing effectiveness, system efficiency, and additional communication effectiveness, one or more of the following constraints may also be considered:

[0097] - Maximum rated transmit power of the base station. That is, when calculating perceived performance, system efficiency, and communication performance, the transmit power of the high-frequency base station considered cannot be higher than the maximum rated transmit power of the base station;

[0098] - the minimum signal-to-interference-and-noise ratio required for communication, i.e., when calculating the communication performance, the combined signal-to-interference-and-noise ratio of the communication signals calculated based on the considered high-frequency base stations cannot be lower than the minimum signal-to-interference-and-noise ratio required for communication by the synaesthesia integrated system;

[0099] - The minimum signal-to-noise ratio required for sensing, i.e., when calculating the sensing performance, the combined signal-to-noise ratio of the sensing signals calculated based on the considered high-frequency base stations cannot be lower than the minimum signal-to-interference-and-noise ratio required for sensing by the synaesthesia integration system;

[0100] - The minimum received signal strength required for communication, that is, when calculating the communication performance, the total received signal power calculated based on the high-frequency base station considered cannot be lower than the minimum received signal power required by the integrated system for communication;

[0101] - the maximum Cramer-Rao bound required for sensing, i.e., when estimating the parameters of sensing based on the considered high-frequency base station, the Cramer-Rao bound cannot be larger than the maximum Cramer-Rao bound required for sensing by the synaesthesia integration system;

[0102] - the minimum distance accuracy required for sensing, i.e. the distance accuracy achieved by sensing based on the considered high-frequency base station cannot be lower than the minimum distance accuracy required for sensing by the synaesthesia integration system;

[0103] - the minimum angular accuracy required for sensing, i.e., the angular accuracy achieved by sensing based on the considered high-frequency base station cannot be lower than the minimum angular accuracy required for sensing by the synaesthesia integration system;

[0104] - the minimum speed accuracy required for sensing, i.e., the speed accuracy achieved by sensing based on the considered high-frequency base station cannot be lower than the minimum speed accuracy required for sensing by the synaesthesia integration system;

[0105] - The minimum value of the maximum access capability of the base station, that is, when sensing and / or communicating based on the high-frequency base station under consideration, the access status of the base station should not reach the minimum value of its maximum access capability.

[0106] Based on the above factors and constraints, the determination of one or more high-frequency base stations participating in the secondary sensing can be designed to solve the following objective function P under the above constraints.

[0107] The constraints can be expressed as

[0108] Wherein, C1 is the maximum rated transmit power of the base station, C2 is the required minimum signal-to-interference-and-noise ratio for communication, C3 is the required minimum signal-to-noise ratio for sensing, C4 is the required minimum received signal strength for communication, C5 is the required maximum Cramer-Rao bound for sensing, C6 is the required minimum distance accuracy for sensing, C7 is the required minimum angle accuracy for sensing, C8 is the required minimum velocity accuracy for sensing, and C9 is the minimum value of the maximum access capability of the base station. Specifically, σ d , σ θ and σ v They can be calculated as follows:

[0109] Where c represents the speed of light, B represents the bandwidth of the perceived signal, represents the angular resolution, D represents the antenna array aperture, and N sym Indicates the number of pulses or symbols of the echo signal of the sensing signal received by the base station during the period when the base station's beam resides on the sensing target, T r represents the repetition period of the sensing pulse or the sensing symbol, and SNR represents the signal-to-noise ratio of the sensing signal (for example, the signal-to-noise ratio can be calculated in any appropriate manner, such as the calculation method described below when describing the sensing performance).

[0110] In the above objective function, ψ com , ψ sen and ψ eff They are the weight parameters of communication efficiency, perception efficiency and system efficiency, ψ com +ψ sen +ψ eff = 1. The values ​​of each weight can be dynamically adjusted according to business needs in actual scenarios. For example, in the drone intrusion detection scenario, the perception business has a high positioning requirement of sub-meter accuracy, so the communication efficiency and system efficiency weights can be reduced to achieve the best perception efficiency. For another example, under the condition of urban power shortage, ψ eff To save energy. In particular, for scenarios without communication requirements, ψ com Set to 0, that is, determine the high-frequency base station without considering the communication performance. j Indicates whether the jth high-frequency base station is selected to enter the high-frequency base station cooperation set for communication and / or sensing services, where j is a positive integer:

[0111] α represents the communication performance under the currently selected high-frequency base station cooperation set, β represents the perception performance under the currently selected high-frequency base station cooperation set, and κ represents the system efficiency under the currently selected high-frequency base station cooperation set. Therefore, the above objective function can be understood as, for one or more candidate high-frequency base stations, calculating ψ based on the selected one or more high-frequency base stations com α+ψ sen β+ψ eff κ, so that the sum value Ψ is maximized.

[0112] According to the present disclosure, any appropriate method can be used to calculate communication effectiveness, perceived effectiveness, and system efficiency. The following details a preferred calculation method according to the present disclosure. It should be understood that the present disclosure is not limited to this preferred method.

[0113] The communication performance α can be expressed by the normalized combined signal-to-interference-and-noise ratio of the communication signal, for example,

[0114] Among them, SINR com RSRP is the combined signal-to-interference-and-noise ratio of the communication signals sent by each base station in the high-frequency base station cooperation set. r SINR is the total received signal power of the communication signals sent by each base station in the high-frequency base station cooperation set. max and SINR min They represent the maximum signal to interference plus noise ratio (SINR) and minimum SINR that can be achieved by all feasible high-frequency base station cooperation sets, and the maximum SINR and minimum SINR can be estimated by analyzing the received preliminary perception data (for example, the channel state information can be estimated based on the perception data, and the maximum and minimum SINRs can be further estimated), without the need to traverse all base station cooperation sets for accurate calculation. RSRP max and RSRP min They respectively represent the maximum reference signal received power and the minimum reference signal received power that can be achieved by all feasible high-frequency base station cooperation sets, and similarly, the maximum reference signal received power and the minimum reference signal received power can also be estimated by analyzing the received preliminary perceived data.

[0115] For example, the signal to interference noise ratio can be calculated as follows

[0116] in is the set of high-frequency base station collaborations that participate in communication and sensing tasks, P i Represents high-frequency base station x i The transmission power, h i It is for high frequency base station x i The fast fading factor that obeys the exponential distribution, ∥x i-y∥ is the user y (for example, a sensing target with communication needs) and the high-frequency base station x i The distance between them, ε is the path loss exponent greater than 2, σ 2 is the ambient noise, where i is a positive integer.

[0117] The advantage of using the above formula to approximate the combined signal to noise ratio of the communication signal is that the signal to noise ratio can be obtained by theoretical estimation without actually obtaining the signal to noise ratio of the received signal. This is because h i It is already known, as long as P is preset in advance i , we can calculate SINR com .

[0118] RSRP r The communication coverage effect of the high-frequency base station for the target user can be measured by defining the average value of the signal power received on all resource elements carrying the reference signal within a symbol. Specifically, the base stations in the high-frequency base station cooperation set can send test signals in sequence, and the target user can record and upload the RSRP of the received test signals. r It can be calculated as the sum of the RSRPs of each base station.

[0119] The perceptual performance β can be expressed as the combined signal-to-noise ratio of the normalized perceptual signal, for example,

[0120] Among them, SNR sen SNR is the combined signal-to-noise ratio of the sensing signals sent by each base station in the high-frequency base station cooperation set. max and SNR min They respectively represent the maximum signal-to-noise ratio and minimum signal-to-noise ratio that can be achieved by all feasible high-frequency base station cooperation sets, and the maximum signal-to-noise ratio or minimum signal-to-noise ratio can be estimated by analyzing the received preliminary perceived data without traversing all base station cooperation sets for accurate calculation.

[0121] For example, the signal-to-noise ratio can be calculated as follows

[0122] in, is the set of high-frequency base station cooperation serving the target user y (e.g., the sensing target), ω j Indicates that for high frequency base station x j The ratio between different weighting coefficients is equivalent to the corresponding path fading coefficient. Represents high-frequency base station x j The wireless signal transmission power, and Represent high-frequency base station x jThe gain of the base station MIMO array transmission and reception after beamforming, λ represents the carrier wavelength of the integrated interawareness system, σ represents the scattering area of ​​the perception signal of the perception target, and τ represents the transmission signal duration of a perception pulse or symbol. Represents high-frequency base station x j The number of pulses or symbols of the echo signal of the sensing signal received by the base station during the period when the beam resides on the sensing target; represents the efficiency of pulse or symbol accumulation of the sensing base station (for example, it is assumed that the efficiency is the same for each base station in the coordinated set), R j Indicates the sensing target to the high-frequency base station x j The distance, k represents the Boltzmann constant, T represents the standard temperature, which is generally 290K. j Represents high-frequency base station x j The noise coefficient of the perception system, L j Represents high-frequency base station x j The loss of the perception system, where j is a positive integer.

[0123] The system efficiency κ can be expressed as the weighted sum of the normalized spectral efficiency, energy efficiency, and base station switching efficiency of the high-frequency base station, for example,

[0124] Among them, η se is the spectrum efficiency of high-frequency base stations, η semax and η semin They represent the maximum and minimum spectrum efficiencies that can be achieved by all feasible high-frequency base station cooperation sets, η ee is the energy efficiency of high-frequency base stations, η eemax and η eemin They represent the maximum energy efficiency and minimum energy efficiency that can be achieved by all feasible high-frequency base station cooperation sets, η sw is the switching efficiency of the high-frequency base station, η swmax and η swmin They represent the maximum switching efficiency and the most switching efficiency that can be achieved by all feasible high-frequency base station cooperation sets, and η semax ,η semin ,η eemax ,η eemin ,η swmax and η swminThe estimated values ​​can be obtained by analyzing the received preliminary perception data. d1, d2, and d3 represent the weight parameters of the three parts respectively, and d1+d2+d3=1. The specific values ​​of the weight parameters can be determined according to actual needs. For example, d3 can be positively correlated with the target movement speed. When the target movement speed is faster, the base station switches more frequently, and the switching efficiency ratio increases. For another example, d1 can be increased when spectrum resources are scarce, and d2 can be increased when electric power is limited, so that a high-frequency base station can be selected based on energy saving considerations. It should be noted that here, η se ,η ee and η sw All of these should be understood as the spectrum efficiency, energy efficiency, and switching efficiency of the entire high-frequency base station cooperative set. For example, η can be calculated separately for each base station in the high-frequency base station cooperative set according to the calculation method described below. se ,η ee and η sw , and then perform weighted summation on the entire cooperation set to obtain the spectrum efficiency, energy efficiency and switching efficiency of the high-frequency base station cooperation set.

[0125] For example, the spectral efficiency can be calculated as follows

[0126] Among them, R is the bit rate of the communication signal, B is the bandwidth of the communication channel, and W is the bandwidth of the communication signal. In particular, when using OFDM technology, W represents the bandwidth of the subcarrier. At this time, the entire channel is divided into multiple mutually orthogonal subcarriers, and each subcarrier has its own bandwidth; B represents the overall signal bandwidth or can be called the baseband signal transmission rate, SNR com The signal-to-noise ratio of the communication signal can be calculated using any applicable method, which will not be described in detail here.

[0127] For example, the energy efficiency can be calculated as follows

[0128] Where W is the bandwidth of the communication signal, p is the transmission power of the high-frequency base station, and p c =p s +εR represents the high-frequency base station circuit consumption, p s is the static circuit loss during transmission, and ε is a constant that represents the energy consumption per unit of data transmitted.

[0129] Since high-frequency base station switching may occur during the sensing / communication process, the base station switching efficiency can also be considered when calculating the system efficiency. For example, the base station switching efficiency can be calculated as follows:

[0130] Among them, τ stayFor the target user (e.g., sensing target), base station x in the high-frequency base station cooperation set j The residence time within the effective coverage range can be uniformly determined as the base station coverage range divided by the target running speed roughly estimated during the initial perception, P sw Indicates that from base station x j Switch to the base station x that has just joined the cooperation set j Z's power consumption.

[0131] Any suitable method can be used to solve the objective function P. Preferably, the objective function P can be viewed as a knapsack problem within a combinatorial optimization problem. Therefore, a simulated annealing algorithm incorporating an interior point penalty function can be used to solve the objective function P to determine one or more high-frequency base stations to participate in secondary sensing. Figure 4 schematically illustrates the algorithm process.

[0132] The simulated annealing algorithm 40 shown in FIG. 4 starts at S402 .

[0133] In S404, a high frequency base station cooperation set including one or more high frequency base stations is randomly generated. In S406, the initial temperature T involved in the simulated annealing algorithm is b , cooling rate r, and inferior solution acceptance probability s1 are initialized. The initial values ​​of these parameters can be determined based on the actual needs of the synaesthesia integration scenario. For example, the selection of the initial temperature will determine the number of iterations and the optimization effect. A higher initial temperature will improve the strategy optimization effect, but will also increase the computation time.

[0134] In S408, constraints C1-C9 are added to the objective function as penalty functions, and appropriate penalty coefficients are set so that the new solution generated in the annealing iteration process meets the constraint requirements. In S410, the objective function of the introduction function is designed based on the above objective function P.

[0135] At S412, a random small perturbation is performed on the current high-frequency base station cooperation set to generate a channel high-frequency base station cooperation set. The random small perturbation refers to randomly changing one or two elements in the base station cooperation set (i.e., a binary array [0, 1, 1, 0, 1, 0, ...]), that is, attempting to add or exclude a small number of high-frequency base stations and then retry.

[0136] At S414, the objective function corresponding to the new high-frequency base station cooperation set is calculated, and the difference dF between the objective function and the objective function corresponding to the original high-frequency base station cooperation set is calculated. At S416, a determination is made as to whether this difference dF is greater than 0. If so, at S418, the current high-frequency base station cooperation set is accepted. If not, at S420, a determination is made as to whether exp(s1*dF / T)>rand() is true, according to the Metropolis criterion, where T is the current temperature and rand() represents a randomly generated number between 0 and 1. If so, the current high-frequency base station cooperation set is accepted; otherwise, the high-frequency base station cooperation set is rejected.

[0137] In S422, a cooling operation is performed to update the current temperature to T=T*r.

[0138] In S424, determine whether the termination condition is met. Generally, when the temperature drop values ​​of several consecutive cycles are less than a threshold, it is considered that the temperature is basically stable and the algorithm tends to converge, that is, the current high-frequency base station selection scheme is very close to the optimal scheme. When the termination condition is met, in S426, the algorithm is terminated and the selected high-frequency base station is output. The selection of the threshold involves a balance between optimization effect and execution time. If the threshold takes a smaller value, the algorithm is more difficult to converge, and more calculations and calculation time are required, but the result obtained is more similar to the optimal solution. Otherwise, if the termination condition has not been met, repeat the steps starting from S412.

[0139] The algorithm ends at S428.

[0140] Compared with traversing all high-frequency base station collaboration sets one by one, using the simulated annealing algorithm with the introduction of an interior point penalty function can result in fewer iterations and higher computational efficiency, thus enabling a better high-frequency base station selection plan to be made in a short time in an environment where high-frequency base stations are densely deployed.

[0141] The process of determining one or more high-frequency base stations to participate in secondary perception has been described in detail. Advantageously, based on perception effectiveness and system efficiency, and additionally based on communication effectiveness, the most appropriate one or more high-frequency base stations can be selected to participate in subsequent perception and / or communication, thereby enabling low-frequency base stations to efficiently collaborate with high-frequency base stations and improving the overall performance of the integrated synaesthesia system. Preferably, one or more high-frequency base stations to participate in secondary perception can be periodically determined, and the policy formulation data can be updated accordingly and the updated policy formulation data can be sent to the second electronic device.

[0142] According to the present disclosure, in addition to information indicating one or more high-frequency base stations determined to participate in secondary sensing as detailed above, policy-making data may also include at least one of data related to inference of communication user profiles, data related to network performance and status, and data assisting in secondary sensing. The process of determining this policy-making data is described in detail below.

[0143] According to the present disclosure, policy formulation data can be determined based on at least one or more of the perception data obtained through preliminary perception by one or more low-frequency base stations, information related to user communications, and information related to the network. For example, information related to user communications and / or information related to the network can be received from one or more network elements. For example, as shown in Figure 5, when the first electronic device acts as the NWDAF, the intermediate layer DCCF can be notified through the Ndccf interface to apply to the UDM for authentication information on whether the NWDAF is allowed to obtain and process user information. If the authentication is passed, the DCCF continues to collect information related to user communications and / or information related to the network from network elements such as OAM, AMF and / or SF, and feeds this information back to the NWDAF.

[0144] For example, information related to user communications may include parameter information related to user communication behavior, such as application usage habits, traffic requirements, quality of service requirements, and user mobility parameters. Here, "user" can be the user that is the perception target, and if the perception target has communication requirements, "user" can also be the communication user that is the communication target. For example, information related to the network may include parameter information related to network performance and network status, such as latency, reliability, stability, network slicing, traffic, and so on.

[0145] According to the present disclosure, data related to communication user profile inference may include at least one or more of the following: user movement trajectory, user habits, and user quality of service requirements. For example, user communication habits can be determined using perception data based on preliminary perception and / or the aforementioned data received from one or more network elements. Furthermore, the determined user communication habits can be analyzed, and optionally, a user communication habit analysis and prediction model can be used to evaluate and analyze the communication user, thereby determining the communication user's movement trajectory, service usage habits, and service quality requirements, and thus constructing a user profile. For example, the user profile may also preferably include predictions of user behavior.

[0146] According to the present disclosure, data related to network performance and status may include at least one or more of the following: statistics and / or predictions of network traffic volume, statistics and / or predictions of network resource requirements, and statistics and / or predictions of user experience of network services. Here, "network" should be understood to broadly refer to any network structure, such as a communication network, a perception network, and a network slice. For example, information such as resource usage, traffic volume, and user experience of a network slice can be collected based on initially perceived perception data and / or the aforementioned data received from one or more network elements. Furthermore, by analyzing the collected information and optionally using a slice resource analysis and prediction model, statistics and predictions of network slice traffic volume, resource requirements, and slice user experience can be achieved, thereby constructing a network slice profile. For example, a network slice profile may include how network slices are divided, which types of users each network slice is suitable for, and so on. As another example, information such as network performance, traffic load in a specific area, and user experience can be collected based on initially perceived perception data and / or the aforementioned data received from one or more network elements. Furthermore, by analyzing the collected information and optionally using network performance analysis and prediction models, different types of services can be evaluated and analyzed to determine the intrinsic correlation between factors such as user QoE, service path, QoS, etc., and then build a network service profile.

[0147] According to the present disclosure, data assisting secondary perception may include at least one or more of the following items: the angle of arrival and angle of departure, time delay, Doppler information, and radar cross-section of the perception signal. For example, parameters such as the angle of arrival, time delay, Doppler information, and radar cross-section of the reflected echo of the perception signal (e.g., the synaesthesia integrated signal) can be obtained based on the perception data of the initial perception. Furthermore, by analyzing the above-obtained parameters and optionally using a perception target analysis and prediction model, the size, position, and operating status of the perception target can be evaluated and analyzed, thereby constructing a perception target portrait.

[0148] According to a specific embodiment, as shown in Figure 5, when the first electronic device acts as the NWDAF, the NWDAF can be divided into a data analysis logical network element (AnLF) and a model training logical network element (MTLF), responsible for generating inference results and model training, respectively. The NWDAF can extract features from at least a portion of the received data. For example, this at least a portion of the data can include preliminary perception data, information related to user communications, and / or network-related information, particularly data related to user connection management, mobility management, session management, accessed services, and data related to network performance and status. The extracted features can then be used by the MTLF for model training. Due to the high real-time requirements of communication and perception services, and the high demands on model size and simplicity, the MTLF can prune and quantize the model. After model training is complete, the AnLF uses the model to dynamically infer communication user / service / slice profiles. For network performance and status information, the AnLF uses a neural network to fit and predict network quality and traffic load.

[0149] Furthermore, NWDAF can also extract features from another portion of the received data. For example, this data may include parameter data that can support secondary perception and high-frequency base station communications, such as angle of arrival and departure, multipath parameters, and channel state information. NWDAF can then process this feature data through clustering, classification, and regression, generating results relevant to developing strategies for beamforming, base station subchannel allocation, and other aspects of the integrated synaesthesia signal.

[0150] The resulting policy-making data and other data used to determine the policy-making data can be cached in the ADRF for AnLF and network administrators to retrieve historical data and analyze the results. The data in the ADRF can be updated regularly to ensure that the cache does not overflow.

[0151] In particular, under the structural division of the NWDAF shown in FIG5 , the calculations related to determining one or more high-frequency base stations participating in the secondary sensing described above may also be completed by the AnLF.

[0152] According to the present disclosure, the first electronic device may autonomously initiate determination of policy-making data, for example, at a predetermined period or after receiving new data for determining policy-making data. Additionally or alternatively, the first electronic device may also receive a request for policy-making data from a second electronic device and determine the policy-making data in response to the request.

[0153] As described above, the first electronic device according to the present disclosure can also receive secondary perception data related to the secondary perception measurement results obtained by collaborative perception of one or more low-frequency base stations and one or more high-frequency base stations, and perform fusion processing on the secondary perception data to obtain a perception result.

[0154] Specifically, during collaborative sensing, high-frequency base stations can use multiple angles and multiple frequency bands with low-frequency base stations to sense the same sensing target. Since low-frequency signals have longer wavelengths, lower dielectric energy loss, stronger penetration, and slower attenuation, using low-frequency base stations to assist in secondary sensing can improve sensing performance when buildings block the view and high-frequency base stations have poor sensing effects. Furthermore, thanks to the wider sensing beams of low-frequency base stations, beam tracking can be more easily achieved when the target moves faster, thereby ensuring that the beam direction has the most basic coverage of the sensing target.

[0155] For example, raw perception measurement results from one or more high-frequency base stations and one or more low-frequency base stations may be processed. Component information used to determine a final perception result (such as, but not limited to, the shape, position, and movement speed of the perceived target) is then calculated based on the processed perception measurement results. The component information is then clustered and weighted to obtain a final perception result.

[0156] According to the present disclosure, processing the raw sensory measurement data, calculating component information based on the sensory measurement results, and clustering and weighting each component information can be performed entirely by the first electronic device, or can be performed collaboratively by the first electronic device and other electronic devices. For example, the method for determining the final sensory result can be dynamically selected based on the real-time computing power and transmission resources of the first electronic device and other electronic devices.

[0157] For example, when the computing power resources of other electronic devices are limited, and the transmission resources and computing resources of the first electronic device are abundant, the first electronic device can receive secondary perception measurement results from one or more low-frequency base stations and one or more high-frequency base stations (that is, the original measurement results when each base station does not perform any processing on the data obtained by its measurement). Then, the first electronic device can process the received secondary perception measurement results, calculate component information based on the processed perception measurement results, cluster and weight the component information to obtain the final perception result, and then cluster and weight to obtain the final perception result. For example, the first electronic device can perform the above calculations, especially clustering and weighted calculations, with the assistance of a neural network by means of a hardware computing environment with AI capabilities deployed thereon. This method of independently completing the fusion processing of secondary perception data by the first electronic device to obtain a perception result can be called soft fusion.

[0158] For another example, when other electronic devices have certain computing power resources and transmission resources are limited, the other electronic devices may first receive secondary perception measurement results (i.e., original measurement results) from one or more low-frequency base stations and one or more high-frequency base stations, and process the received secondary perception measurement results to obtain processed perception measurement results. Subsequently, the other electronic devices may send the processed perception measurement results to the first electronic device. The first electronic device may then calculate component information based on the received processed perception measurement results and cluster and weight the component information to obtain the final perception result. This method of processing the original measurement results by other electronic devices, and then completing the calculation of the component information and the clustering and weighting of the component information by the first electronic device, thereby finally completing the fusion processing of the secondary perception data to obtain the perception result, can be called neutral fusion.

[0159] For another example, when the computing power resources of the first electronic device are in short supply and the computing power resources of other electronic devices are sufficient, deeper processing can be completed by other electronic devices. For example, the received secondary perception measurement results are first processed by other electronic devices. Subsequently, other electronic devices continue to calculate the component information based on the processed perception measurement results (here, the component information can also be used as a form of processed perception measurement results). Next, the other electronic devices can send the component information to the first electronic device, and the first electronic device then clusters and weights the quantity information to obtain the final perception result. This way of completing the fusion processing of the secondary perception data to obtain the perception result can be called hard fusion. This hard fusion can reduce the computing pressure of the first electronic device.

[0160] According to the present disclosure, when the first electronic device acts as an NWDAF, the above-mentioned other devices may be SFs. In particular, the above-mentioned other devices may be SFs deployed at edge servers. Specifically, depending on the design of the network and the operator's strategy, SFs may be deployed at different locations in the network. For example, SFs may be arranged in the core network to centrally process perception data from the entire network, and work in conjunction with other network analysis functions such as NWDAF to perform deeper analysis using data collected by the core network. For another example, SFs may also be deployed in base stations or other wireless access network elements to directly collect data at the wireless layer. For another example, SFs may also be deployed on edge servers at locations such as base stations and urban data centers to quickly process and analyze perception data. In particular, SFs may also be arranged in a distributed manner on the core network, base stations or other wireless access network elements and / or edge servers.

[0161] 3 to 5 , the first electronic device according to the present disclosure has been described. Now, the second electronic device according to the present disclosure will be described.

[0162] First, the conceptual structure of the second electronic device 60 according to an embodiment of the present disclosure will be described with reference to Figure 6. The electronic device 60 shown in Figure 6 may include various units to implement the corresponding operations according to the embodiment of the present disclosure. In this example, the electronic device 60 includes a communication unit 602 and a computing unit 604. According to the present disclosure, the electronic device 60 may be the network element device described above. In one embodiment, the electronic device 60 is implemented as the network element device itself or a part thereof, or is implemented as a device for controlling the network element device or otherwise being related thereto or a part of the device. In particular, the electronic device may act as a PCF. The various operations described below in conjunction with the second electronic device may be implemented by units 602, 604 or other possible units of the electronic device 60.

[0163] As shown in FIG6 , the electronic device 60 may include a communication unit 602. The communication unit 602 may be configured to receive data from other electronic devices and to send data to other electronic devices. For example, the data received from the other devices may be policy-making data determined by the first electronic device, and the data sent to the other devices may be data indicating a communication policy and / or perception policy formulated by the electronic device 60 (e.g., the policy-making unit 604). More generally, the communication unit 202 may be configured to receive any appropriate data from or send any appropriate data to the other electronic devices, such as request information requesting the first electronic device to determine policy-making data, and the like.

[0164] The electronic device 20 may further include a policy formulation unit 604. The policy formulation unit 604 may be configured to formulate a communication policy and / or a perception policy based on the policy formulation data. In addition, the policy formulation unit 604 may also be configured to formulate any other desired policies, such as a charging policy, a session policy, and the like.

[0165] It should be noted that the above-mentioned units are only logical modules divided according to the specific functions implemented by them, rather than being used to limit specific implementation methods, for example, they can be implemented in software, hardware or a combination of software and hardware. The functions of the units disclosed herein can be implemented using circuits or processing circuits. Processing circuits can refer to various implementations of digital circuit systems, analog circuit systems or mixed signal (a combination of analog and digital) circuit systems that perform functions in a computing system. Processing circuits can include, for example, circuits such as integrated circuits (ICs), application specific integrated circuits (ASICs), parts or circuits of separate processor cores, entire processor cores, separate processors, programmable hardware devices such as field programmable gate arrays (FPGAs), and / or systems including multiple processors. Processors are considered to be processing circuits or circuits because they include transistors and other circuits therein.

[0166] In the present disclosure, a circuit, unit, device or apparatus is hardware that performs or is programmed to perform the function. The hardware can be any hardware disclosed herein or otherwise known to be programmed or configured to perform the function. When the hardware is a processor that can be considered as a type of circuit, the circuit, device or unit is a combination of hardware and software, and the software is used to configure the hardware and / or processor. In the implementation of hardware, the hardware can be programmed or configured to perform the function. In the implementation of software or a combination of software and hardware, the software can be used to configure the hardware and / or processor. In actual implementation, the above-mentioned various units can be implemented as independent physical entities, or can also be implemented by a single entity (for example, a processor (CPU or DSP, etc.), an integrated circuit, etc.).

[0167] Hereinafter, various operations implemented by the second electronic device 70 will be described in detail with reference to the conceptual operation flow 70 of the transmitting end shown in FIG. 7 .

[0168] The operation of the second electronic device starts at S702.

[0169] At S704, the second electronic device receives policy-making data from the first electronic device. For example, the policy-making data may be determined by the first electronic device as described above. As described in detail above, the policy-making data perception data may at least include information indicating one or more high-frequency base stations to participate in secondary perception, and may further include data related to communication user profile inference, data related to network performance and status, and data related to auxiliary secondary perception.

[0170] Next, in S706, the second electronic device may specify a communication strategy and / or a perception strategy based on the policy formulation data. For example, the communication strategy may include at least one of the following: an access / frequency selection priority RFSP index, a slice partitioning strategy, a user equipment routing selection strategy URSP, and a communication collaboration method between the one or more high-frequency base stations and the one or more low-frequency base stations. For example, the perception strategy may include at least a collaboration method between the one or more high-frequency base stations and the one or more low-frequency base stations corresponding to different perception requirements, and / or a perception signal beamforming strategy.

[0171] For example, when the sensing target has a communication demand, the second electronic device may determine a communication strategy.

[0172] As one of the communication strategies, the second electronic device can determine an RFSP index. In actual implementation, the RFSP index can be used to determine the user access method. For example, the RFSP index can not only determine whether the user prioritizes accessing a nearby high-frequency or low-frequency base station for duplex communication, but also determine which subcarrier of the base station the user uses for communication after determining the base station to access. For example, the policy-making data received by the second electronic device may include data related to network performance and status, and in particular may include prediction information indicating future frequency resources and spectrum requirements (for example, this prediction information may be determined by the first electronic device based on data regarding allocated frequency resources, available frequency bands, user spectrum requirements, etc.). The second electronic device can update the RFSP index based on this prediction information. For example, assume that two slices are maintained in the current operator network, one is slice A for general eMBB, and the other is slice B for delay-sensitive services. If the policy-making data indicates that the target user should use slice A, the second electronic device can adjust the RFSP index so that the target user prioritizes accessing the high-frequency base station. If the policy-making data indicates that the target user should use slice B, the second electronic device can adjust the RFSP index so that the target user prioritizes accessing the low-frequency base station. For another example, if the policy-making data indicates that the target user wants to use a service with ultra-high bandwidth requirements (such as augmented reality services, virtual display services, etc.), the second electronic device can adjust the RFSP index so that the target user preferentially accesses a high-frequency base station to ensure user experience. If the policy-making data indicates that the target user wants to use a service that is sensitive to latency, the second electronic device can adjust the RFSP index so that the target user preferentially accesses a low-frequency base station to ensure low latency and stability.

[0173] As one of the communication strategies, the second electronic device can determine the slice division strategy. For example, the policy formulation data received by the second electronic device may include data related to communication user portrait reasoning and data related to network performance and status (especially data related to slice portrait reasoning). The second electronic device can classify users based on these data, for example, into users with high service quality requirements, users who are sensitive to delays, and so on. The second electronic device can determine the slice division strategy based on the classification, so that users with high service quality requirements can access slices with richer resources and stronger service capabilities as much as possible, while users who are sensitive to delays can access slices with deterministic delays as much as possible.

[0174] As one of the communication strategies, the second electronic device can determine the URSP. The URSP can describe the correspondence between the target user's application and the slice. For example, the target user (specifically, its user device) can select the slice that should be used first for the application according to the URSP rules, thereby achieving flexible and dynamic optimization allocation of network resources for different users. For example, the second electronic device can determine the service quality, latency, and other requirements of different applications of the user based on the data related to the communication user profile reasoning in the policy formulation data, and determine the URSP accordingly.

[0175] As one of the communication strategies, the second electronic device can determine a communication collaboration method between one or more high-frequency base stations and one or more low-frequency base stations. For example, the second electronic device can formulate a strategy so that the target user accesses one or more high-frequency base stations with the highest signal strength and the lowest communication signal-to-noise ratio. When accessing multiple high-frequency base stations, CoMP transmission technology can be adopted, and the JT mode can be selected. Corresponding control and data information can be exchanged between the base stations of the joint transmission, so that multiple coordinated base stations jointly process the same data information to provide services to the user. In particular, when the second electronic device determines based on data related to the communication user profile inference that all high-frequency base stations in the area are far away from the target user or there is a lot of obstruction, the second electronic device can formulate a strategy so that the low-frequency base station assists the high-frequency base station to meet the most basic communication needs, while using the carrier aggregation function to improve the user's speed and coverage experience. For example, the second electronic device can formulate a strategy so that the control information of the high-frequency base station can be sent on the low frequency, and the high frequency is used as a subcarrier and the low frequency is used as the main carrier, and the high frequency resources are scheduled by the low frequency through cross scheduling.

[0176] In actual implementation, the communication strategy is not limited to the above strategies, but may also include any strategy for communication. For example, the policy formulation data may include information indicating whether the current slice service experience exceeds the user's requirements. The second electronic device can determine whether to reduce the network resource configuration of the corresponding slice based on this information. In this example, for example, the information can be determined by the first electronic device using a linear regression machine learning algorithm to characterize the service model as follows: h(x) = w0x0 + w1x1 + w2x2 + w3x3 + w d x d +w5x5…

[0177] Where h(x) represents the service experience, including delay, jitter, packet loss rate, etc. i Represents network data, w i The weight of each network data in affecting the service experience, i is a positive integer.

[0178] For another example, the policy-making data may also include information indicating user classifications related to regional signal strength, such as "users in weak signal areas" or "users in strong signal areas." The second electronic device can formulate a policy based on this information, so that interference management can be performed by changing the beamforming strategy and / or appropriately increasing the transmit power and / or reducing the transmit power of adjacent cell base stations, thereby providing more stable and reliable services. In this example, for example, the information can be obtained by the first electronic device using a support vector machine for classification based on collected indicators such as RSSI, CQI, BLER, etc. that measure channel quality and transmission performance.

[0179] As one of the sensing strategies, the second electronic device may determine a collaboration mode between the one or more high-frequency base stations and the one or more low-frequency base stations corresponding to different sensing requirements. For example, the sensing requirements of the sensing target may be determined based on at least the strategy formulation data.

[0180] Perception requirements may include environmental detection requirements, such as detecting the location of various objects in the environment surrounding the perception target, detecting the radio environment around the perception target, and so on. For such perception requirements, the second electronic device can formulate a perception strategy so that each high-frequency base station serves users in a local hotspot area (e.g., one or more perception targets) and establishes a local radio signature map. Leveraging the wide coverage and reliable link quality of low-frequency base stations, the second electronic device can formulate a perception strategy so that the low-frequency base station is responsible for uploading / downloading radio signature maps and network control. For example, the perception strategy can be formulated so that the local radio signature map established by the high-frequency base station is transmitted to the low-frequency base station, thereby using the user's precise location and movement trajectory obtained by the high-frequency base station to assist the low-frequency base station and VA in positioning, where VA represents the base station's mirror image relative to the perception target scatterer. The low-frequency base station can generate a global radio signature map by fusing the local maps and distribute the appropriate radio signature to each user. Advantageously, using this perception strategy, when a user enters the service area of ​​the high- and low-frequency network, the low-frequency base station can quickly establish a communication link and distribute the local radio signature map to the user. The user can quickly access the high-frequency base station by matching their rough location obtained from GPS with the downloaded radio signature map. In addition, only the appropriate radio characteristics of the local map relative to the user's location can be sent to reduce the transmission overhead at the sender. If the user successfully accesses the high-frequency base station, subsequent map downloads can be completed through the communication with the high-frequency base station.

[0181] Perception needs may also include positioning needs, such as locating one or more perception targets in the environment. For this type of perception need, the second electronic device can formulate a perception strategy so that the low-frequency base station first uses a wide beam to scan the approximate position of the perception target, and then the high-frequency base station uses a narrow beam for precise positioning, thereby achieving low-overhead high-precision perception. The multiple collaborating base stations receive the reflected signal of the target at different angles and distances relative to the same perception target and perform perception data fusion, which makes it possible to eliminate the influence of random errors that exist when a single receiving node obtains measurement values ​​such as arrival angle, distance, and Doppler frequency. In addition, the use of multi-base station collaboration can utilize multi-dimensional target measurement values ​​for positioning, which helps to improve the accuracy of target positioning.

[0182] It should be understood that the perception requirements are not limited to the above-mentioned environmental detection requirements and positioning requirements, and the second electronic device can determine the corresponding cooperation mode between the high and low frequency base stations according to the specific characteristics of the perception requirements.

[0183] As one of the perception strategies, the second electronic device can determine the perception signal beamforming strategy. For example, the second electronic device can determine the reflection and scattering of the perception signal by the perception target based on the auxiliary secondary perception data included in the strategy formulation data (i.e., for example, one or more of the arrival angle and departure angle, time delay, Doppler information and radar scattering cross-section of the perception signal), and then determine the transmission and reception beamforming vectors of the perception signal to be used for secondary perception. Thus, a narrow beam with good beamforming can be used to achieve more accurate coverage of the perception target. In some cases, the reflection and scattering of the perception signal by the perception target can also be determined by the first electronic device and included in the strategy formulation data. For example, the first electronic device can further determine the position and target type of the perception target based on the reflection and scattering of the perception signal by the perception target, and include the information indicating the position and type in the strategy formulation data, so as to facilitate the second electronic device to determine the beamforming strategy of the perception signal.

[0184] In actual implementation, the perception strategy is not limited to the above strategies, and may also include any strategy for perception. The second electronic device may formulate a corresponding perception strategy based on the strategy formulation data according to specific circumstances.

[0185] Continuing to refer to FIG7 , the operation of the second electronic device ends at S310.

[0186] It should be noted that the operation steps of the second electronic device shown in Figure 7 are merely illustrative. In practice, the operation of the second electronic device may also include some additional or alternative steps. For example, before S706, the second electronic device may also send a request message for policy formulation data to the first electronic device. For another example, after S706, the second electronic device may also send the formulated policy to other electronic devices, such as the electronic devices acting as network elements SF and AMF as described below with reference to the information interaction of Figures 8A and 8B.

[0187] The first electronic device and the second electronic device according to the present disclosure have been described in detail above. As explained above, the first electronic device according to the present disclosure can function as an NWDAF, and the second electronic device according to the present disclosure can function as a PCF. Below, with reference to Figures 8A and 8B, an exemplary interaction between various devices in a network is described in which the first electronic device functions as an NWDAF and the second electronic device functions as a PCF.

[0188] FIG8A schematically illustrates exemplary interactions between devices in a network when sensing is triggered by AF.

[0189] The AF may trigger sensing in response to sensing requirements of certain applications in the core network. As shown in Figure 8A, in step 1, the AF may send a sensing request to the NEF. The sensing request may include sensing service request information (e.g., including information indicating sensing service type, sensing accuracy requirement, sensing target area, etc.) and designated sensing node information (e.g., sensing node may include UE to participate in sensing).

[0190] Then, in step 2, the NEF performs an authorization check on the AF's service perception request to determine whether the AF is allowed to obtain the perception data it wishes to acquire. For example, the NEF may request authorization verification from the UDM and obtain privacy check information from the UDM. If the privacy check information indicates that the perception data requested by the AF is not allowed to be acquired, the NEF may reject the perception request sent by the AF.

[0191] If the perception request sent by the AF is allowed, the NEF may interact with different network elements depending on whether the perception request specifies the UE to participate in the perception. Specifically, for the case where the AF specifies the UE to participate in the perception, in step 3, the NEF may select the AMF serving the area based on the location information of the UE specified in the AF's request, and forward the perception request to the AMF. Subsequently, in step 4, the AMF may select a SF with suitable capabilities to serve the perception service based on the type of perception service, demand information, area restriction information, etc. In step 5, the AMF may forward the perception request to the selected SF. For example, the AMF may send the perception request to the control plane SF-C of the SF.

[0192] For requests from AFs that do not specify UEs participating in sensing, as shown in the dotted box, the base station has already registered the SF's sensing capabilities with the NRF through the AMF and SF. In step 3', the NEF can notify the NRF to search for a suitable SF, and the NRF will feedback the selected SF to the NEF. In step 4', the NEF can forward the sensing request to the SF (e.g., SF-C). Subsequently, in step 5', the SF (e.g., SF-C) can notify the NRF to search for a suitable AMF, and the SF (e.g., SF-C) will feedback the selected AMF to the NRF.

[0193] In step 6, the SF-C determines the sensing method to be used for preliminary sensing of the low-frequency base station based on the sensing requirement information carried in the sensing request, and uses the determined sensing method. As described above, the sensing method may include: a low-frequency base station sending a sensing signal (e.g., a synaesthesia signal) to another low-frequency base station and obtaining sensing data based on the received sensing signal; a low-frequency base station sending a sensing signal to a sensing target and obtaining sensing data based on the echo signal of the received sensing signal; and a UE sending a sensing signal to a low-frequency base station and obtaining sensing data based on the received sensing signal. The SF-C may select one or more low-frequency base stations to participate in the preliminary sensing based on the specific sensing method. For example, one or more low-frequency base stations that are closest to the sensing target and whose signal strength is greater than a threshold may be selected. The SF-C may send a sensing control request to the selected low-frequency base station via the AMF. The sensing control request may also carry the IP and port number of the user plane of the SF (i.e., SF-U) to receive sensing measurement data.

[0194] In step 7, one or more low-frequency base stations participating in the preliminary sensing process may enable sensing functions and obtain preliminary sensing data. Then, in step 8, these one or more low-frequency base stations may report the sensing data to the SF-U. In step 9, the SF-U may report receipt of the sensing data to the SF-C. In step 10, the SF-C may send a notification to the PCF indicating that preliminary sensing has been completed.

[0195] In step 11, the PCF may send a request for policy formulation data (e.g., a policy analysis request) to the NWDAF. In step 12, the NWDAF may first perform authentication verification to determine whether the NDWAF has the authority to receive and process the perception data of the preliminary perception. If the authentication is passed, the NWDAF may collect other data used to determine the policy formulation data, such as the approved RFSP index from the PCF, the RFSP index currently being used by the user from the AMF, information related to RFSP, etc. on the base station from the OAM, authentication verification information from the NRF, session-related information from the SMF, and the first perception information from the SF-U (i.e., preliminary perception data).

[0196] At step 13, the NWDAF determines policy-making data based on the data it collected, as described in detail above. At step 14, the NWDAF opens (eg, sends) the determined policy-making data to the PCF.

[0197] In step 15, the PCF determines / updates the perception policy and / or communication policy based on the policy formulation data received from the NWDAF as detailed above, and sends the policies associated with the SF and AMF to the SF (e.g., SF-C) and AMF, respectively, so that the SF and AMF perform policy updates.

[0198] In step 16, the SF-C sends a sensing control request to the high-frequency base station specified in the received policy via the AMF. The sensing control request may carry both the IP and port number of the SF-U and the IP addresses of one or more low-frequency base stations for collaborative sensing (i.e., one or more low-frequency base stations participating in the initial sensing).

[0199] In step 17, one or more high-frequency base stations that receive the perception control request activate the perception function, and simultaneously transmit and receive integrated perception signals in full duplex mode to collaboratively complete user access and perception services. In step 18, one or more high-frequency base stations report the secondary perception measurement results to the SF-U. For example, the high-frequency base station may receive the perception measurement results of the low-frequency base station from one or more low-frequency base stations participating in collaborative perception, and report the perception measurement results of the low-frequency base station together with the high-frequency base station's own perception measurement results as secondary perception measurement results to the SF-U. In some cases, the high-frequency base station may perform perception based on the perception measurement results of the low-frequency base station. In this case, the high-frequency base station may also only report its own perception measurement results to the SF-U as secondary perception measurement results.

[0200] In step 19, as described in detail above, SF can be integrated with NWDAF in an appropriate manner to complete the calculation of the perception results.

[0201] In step 20, the SF-U may open (eg, send) the sensing result to the AF via the NEF.

[0202] FIG8B schematically illustrates exemplary interactions between devices in a network when perception is triggered by a UE.

[0203] The UE may trigger perception in response to its own needs. As shown in Figure 8B, in step 1, the UE may send a perception request to the AMF via a low-frequency base station. For example, the UE may implement AMF selection by providing NSSAI or a temporary ID (Temp ID) in a registration request (e.g., a NAS message), and initiate a perception request to the selected AMF. For example, the perception request may include perception service request information (perception service type, perception accuracy requirements, the area where the perception target is located, etc.) and designated perception node information. In step 2, the AMF queries the NRF that has registered the perception information based on the type of perception service, demand information, area restriction information, etc., and selects a perception network element SF with suitable capabilities to serve the perception service. In step 3, the AMF may send a request to the selected SF (e.g., SF-C).

[0204] The subsequent interaction process is similar to the process from step 6 to step 19 in Figure 8A. In step 18 in Figure 8A, the SF-U opens (e.g., sends) the sensing result to the UE via the AMF.

[0205] In particular, in the processes of Figures 8A and 8B , the SF-C may also periodically reselect the low-frequency base stations participating in sensing. This is particularly useful, for example, when the sensing target is rapidly moving, so that the SF-C can periodically update one or more low-frequency base stations that are closest to the sensing target and have signal strengths greater than a threshold. In both of these cases, base station switching may be necessary. The following describes the base station switching process.

[0206] The source low-frequency base station determines whether to switch to a low-frequency base station based on the sensing control request received from the service provider (SF). During the handover between two low-frequency base stations, the two low-frequency base stations can be considered macro base stations, and the high-frequency base station can be considered a micro base station. Furthermore, a high-frequency base station acting as a micro base station can be introduced as an auxiliary base station between the overlapping coverage areas of the two low-frequency base stations, acting as macro base stations, to perform transition functions. During the handover process, the SF maintains a connection with the auxiliary micro base station, improving the success rate of base station handovers and ensuring the reliability of communication and sensing transmission. In scenarios where a UE participates in sensing, the UE may need to report some sensing measurement data to the low-frequency base station, which then receives the data and sends it to the SF. In this case, the UE can first send the sensing measurement data to the auxiliary base station or the target base station, which is then sent to the SF by the receiving base station. The SF can then make the sensing data available to the AF via the networked energy management (NEF). In scenarios where base stations generate sensing data, this only involves the handover of sensing services between multiple base stations. The SF obtains continuous sensing measurement data for the sensing target based on the sensing data from the source base station, auxiliary base station, and target base station.

[0207] The embodiments of the present disclosure have been described in detail. According to the present disclosure, it is advantageous that high and low frequency base stations can work together to efficiently perform communication and / or perception in the synergy integration system. In particular, the first electronic device can comprehensively determine the policy formulation data based on the various information available in the network, thereby assisting the second electronic device to determine the optimal communication strategy and / or perception strategy for the synergy integration network. The second electronic device can achieve flexible and efficient management and scheduling of wireless resources such as carriers, bandwidths, and slices, improve network utilization and reduce energy consumption, and enable secondary perception to achieve optimal performance to obtain more accurate perception results by formulating appropriate communication strategies and / or perception strategies.

[0208] It should be understood that the machine-executable instructions in the machine-readable storage medium or program product according to the embodiments of the present disclosure can be configured to perform operations corresponding to the above-mentioned device and method embodiments. When referring to the above-mentioned device and method embodiments, the embodiments of the machine-readable storage medium or program product are clear to those skilled in the art and are therefore not described again. Machine-readable storage media and program products for carrying or including the above-mentioned machine-executable instructions also fall within the scope of the present disclosure. Such storage media may include, but are not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.

[0209] In addition, it should be understood that the above series of processes and devices can also be implemented by software and / or firmware. In the case of being implemented by software and / or firmware, the program constituting the software is installed from a storage medium or a network to a computer with a dedicated hardware structure, such as the general-purpose computer / computer system 1300 shown in Figure 9. When various programs are installed, the computer can perform various functions, etc. Figure 9 is a block diagram showing an example structure of a computer / computer system that can be used in an embodiment of the present disclosure. Although shown as a single structural block diagram, the functions of the computer / computer system 1300 can be implemented as a distributed system. For example, while one processor can be used to perform some processes, other remote processors can be used to perform other processes. Other elements of the computer / computer system 1300 can also be distributed similarly. In addition, the functions disclosed herein can be implemented on separate servers or devices that can be coupled together via a network. In addition, one or more components of the system 1300 may not be included.

[0210] In some embodiments, the computer / computer system 1300 can be used as a whole to implement the electronic device 20 shown in Figure 2 or the electronic device 60 shown in Figure 6. In some embodiments, the electronic device 20 shown in Figure 2 or the electronic device 60 shown in Figure 6 can be implemented by multiple computers / computer systems 1300 in a distributed manner.

[0211] 9 , a central processing unit (CPU) 1301 executes various processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage section 1308 to a random access memory (RAM) 1303. In the RAM 1303, data required when the CPU 1301 executes various processes and the like is also stored as needed.

[0212] The CPU 1301, the ROM 1302, and the RAM 1303 are connected to one another via a bus 1304. An input / output interface 1305 is also connected to the bus 1304.

[0213] The following components are connected to the input / output interface 1305: an input section 1306 including a keyboard, a mouse, etc.; an output section 1307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1308 including a hard disk, etc.; and a communication section 1309 including a network interface card such as a LAN card, a modem, etc. The communication section 1309 performs communication processing via a network such as the Internet.

[0214] A drive 1310 is also connected to the input / output interface 1305 as needed. A removable medium 1311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 1310 as needed so that a computer program read therefrom is installed in the storage section 1308 as needed.

[0215] In the case of realizing the above-described series of processing by software, a program constituting the software is installed from a network such as the Internet or a storage medium such as the removable medium 1311 .

[0216] Those skilled in the art will appreciate that such storage media are not limited to the removable medium 1311 shown in FIG9 , which stores the program and is distributed separately from the device to provide the program to the user. Examples of the removable medium 1311 include magnetic disks (including floppy disks (registered trademark)), optical disks (including compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), magneto-optical disks (including minidiscs (MDs) (registered trademark)), and semiconductor memories. Alternatively, the storage medium may be the ROM 1302, a hard disk included in the storage section 1308, or the like, in which the program is stored and distributed to the user together with the device containing the program.

[0217] The exemplary embodiments of the present disclosure are described above with reference to the accompanying drawings, but the present disclosure is certainly not limited to the above examples. Those skilled in the art may obtain various changes and modifications within the scope of the appended claims, and it should be understood that these changes and modifications will naturally fall within the technical scope of the present disclosure.

[0218] It should be understood that the machine-executable instructions in the machine-readable storage medium or program product according to the embodiments of the present disclosure can be configured to perform operations corresponding to the above-mentioned device and method embodiments. When referring to the above-mentioned device and method embodiments, the embodiments of the machine-readable storage medium or program product are clear to those skilled in the art and are therefore not described again. Machine-readable storage media and program products for carrying or including the above-mentioned machine-executable instructions also fall within the scope of the present disclosure. Such storage media may include, but are not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, and the like.

[0219] In addition, it should be understood that the above series of processes and devices can also be implemented by software and / or firmware. In the case of implementation by software and / or firmware, the storage medium of the relevant device stores the corresponding program constituting the corresponding software, and when the program is executed, various functions can be performed.

[0220] For example, a plurality of functions included in one unit in the above embodiments may be implemented by separate devices. Alternatively, a plurality of functions implemented by a plurality of units in the above embodiments may be implemented by separate devices, respectively. In addition, one of the above functions may be implemented by a plurality of units. Needless to say, such a configuration is included in the technical scope of the present disclosure.

[0221] In this specification, the steps described in the flowchart include not only processing executed in time series in the order described, but also processing executed in parallel or individually rather than necessarily in time series. In addition, even in the steps processed in time series, it goes without saying that the order can be changed as appropriate.

[0222] Although the present disclosure and its advantages have been described in detail, it should be understood that various changes, substitutions and transformations can be made without departing from the spirit and scope of the present disclosure as defined by the appended claims. Moreover, the terms "comprises," "comprising," or any other variations thereof in the embodiments of the present disclosure are intended to cover non-exclusive inclusions, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or device. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0223] In addition, the present disclosure may also have the following configurations:

[0224] (1) A first electronic device for a communication and perception integrated system, comprising:

[0225] at least one processor; and

[0226] at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the first electronic device to:

[0227] receiving sensing data, wherein the sensing data is obtained by performing preliminary sensing by one or more low-frequency base stations;

[0228] determining policy development data for use in developing a communications strategy and / or an awareness strategy; and

[0229] sending the policy formulation data to a second electronic device,

[0230] The strategy formulation data at least includes information indicating one or more high-frequency base stations to participate in the secondary sensing.

[0231] (2) The first electronic device according to (1), wherein the at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to:

[0232] receiving secondary sensing data related to secondary sensing measurement results obtained by performing collaborative sensing on the one or more low-frequency base stations and the one or more high-frequency base stations; and

[0233] The secondary perception data is fused to obtain a perception result.

[0234] (3) The first electronic device according to (2), wherein the secondary perception data is one of the following:

[0235] Secondary sensing measurement results from the one or more low-frequency base stations and the one or more high-frequency base stations,

[0236] Processed sensing measurements are received from edge servers associated with the one or more low-frequency base stations and the one or more high-frequency base stations.

[0237] (4) The first electronic device according to any one of (1) to (3), wherein the at least one memory and the computer program code are further configured to cause the first electronic device to determine, through the at least one processor, the one or more high-frequency base stations based at least on perceived effectiveness and system efficiency,

[0238] The sensing performance is determined based on a combined signal-to-noise ratio of sensing signals sent by one or more high-frequency base stations, and

[0239] The system efficiency is determined based on the spectrum efficiency, energy efficiency and base station switching efficiency of one or more high-frequency base stations.

[0240] (5) The first electronic device as described in (4), wherein the at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to further determine the one or more high-frequency base stations based on communication performance,

[0241] The communication performance is determined based on a combined signal-to-interference-and-noise ratio and a total received signal received power of communication signals sent by one or more high-frequency base stations.

[0242] (6) The first electronic device as described in (5), wherein the at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to determine the one or more high-frequency base stations based on one or more of the following constraints:

[0243] The minimum rated transmit power of the base station, the minimum required signal-to-interference-and-noise ratio for communication, the minimum required signal-to-noise ratio for sensing, the minimum required received signal strength for communication, the maximum required Cramer-Rao bound for sensing, the minimum required range accuracy for sensing, the minimum required angle accuracy for sensing, the minimum required velocity accuracy for sensing, and the minimum maximum access capability of the base station.

[0244] (7) The first electronic device as described in (6), wherein the at least one memory and the computer program code are further configured to enable the first electronic device to determine the one or more high-frequency base stations using a simulated annealing algorithm that introduces an interior point penalty function through the at least one processor.

[0245] (8) The first electronic device as described in (4), wherein the at least one memory and the computer program code are further configured to enable the first electronic device to periodically determine the one or more high-frequency base stations and update the policy formulation data through the at least one processor.

[0246] (9) The first electronic device as described in any one of (1) to (3), wherein the at least one memory and the computer program code are further configured to cause the first electronic device to further receive information related to user communications and / or network-related information from one or more core network elements through the at least one processor, and

[0247] The policy formulation data is determined based on at least one or more of the perception data, the information related to user communication, and the information related to the network.

[0248] (10) A first electronic device as described in any one of (1) to (3), wherein the at least one memory and the computer program code are further configured to, through the at least one processor, enable the first electronic device to receive a request for policy-making data from a second electronic device before determining the policy-making data.

[0249] (11) The first electronic device according to any one of (1) to (3), wherein:

[0250] The policy formulation data also includes at least one of data related to communication user portrait reasoning, data related to network performance and status, and data related to auxiliary secondary perception.

[0251] (12) The first electronic device according to (11), wherein:

[0252] The data related to communication user profile reasoning includes at least one or more of the following items: user movement trajectory, user habits, and user requirements for service quality;

[0253] The data related to network performance and status includes at least one or more of the following items: statistics and / or predictions of network traffic, statistics and / or predictions of network resource requirements, and statistics and / or predictions of user experience of network services; and

[0254] The data assisting the secondary perception includes at least one or more of the following items: arrival angle and departure angle of the perception signal, time delay, Doppler information, and radar cross-section.

[0255] (13) The first electronic device as described in any one of (1) to (3), wherein the first electronic device functions as a network data analysis function NWDAF, and the second electronic device functions as a policy control function PCF.

[0256] (14) The first electronic device according to any one of (1) to (3), wherein the low-frequency base station is a sub-6 GHz base station, and the high-frequency base station is a millimeter wave base station.

[0257] (15) A second electronic device for a communication and sensing integrated system, comprising at least one processor; and

[0258] at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the second electronic device to:

[0259] receiving the policy development data from a first electronic device as described in any one of (1)-(14); and

[0260] A communication policy and / or an awareness policy is formulated based on the policy formulation data.

[0261] (16) The second electronic device according to (15), wherein

[0262] The communication strategy includes at least one of the following: an access / frequency selection priority (RFSP) index, a slice partitioning strategy, a user equipment routing strategy (URSP), and a communication collaboration method between the one or more high-frequency base stations and the one or more low-frequency base stations; and

[0263] The sensing strategy includes at least a collaboration mode between the one or more high-frequency base stations and the one or more low-frequency base stations corresponding to different sensing requirements, and / or a sensing signal beamforming strategy.

[0264] (17) A method for a first electronic device in a communication and perception integrated system, comprising:

[0265] receiving sensing data, wherein the sensing data is obtained by performing preliminary sensing by one or more low-frequency base stations;

[0266] determining policy development data for use in developing a communications strategy and / or an awareness strategy; and

[0267] sending the policy formulation data to a second electronic device,

[0268] The strategy formulation data at least includes information indicating one or more high-frequency base stations to participate in the secondary sensing.

[0269] (18) A method for a second electronic device in a communication and sensing integrated system, comprising:

[0270] receiving the policy development data from a first electronic device as described in any one of (1)-(14); and

[0271] A communication policy and / or an awareness policy is formulated based on the policy formulation data.

[0272] (19) A non-transitory computer-readable storage medium storing executable instructions, wherein the executable instructions, when executed, implement the method as described in (17) or (18).

[0273] (20) A computer program product comprising executable instructions, which, when executed, implement the method as described in (17) or (18).

Claims

1. A first electronic device for a communication and sensing integrated system, comprising: At least one processor; And At least one memory, including computer program code, wherein the at least one memory and the computer program code are configured to, through the at least one processor, cause the first electronic device to: Receive sensing data, wherein the sensing data is obtained by preliminary sensing by one or more low-frequency base stations; Determine policy-making data for formulating communication policies and / or sensing policies; and Send the policy-making data to a second electronic device, wherein the policy-making data at least includes information indicating one or more high-frequency base stations to participate in secondary sensing.

2. The first electronic device according to claim 1, wherein, The at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to: Receive secondary sensing data related to secondary sensing measurement results obtained by collaborative sensing with the one or more low-frequency base stations and the one or more high-frequency base stations; and Perform fusion processing on the secondary sensing data to obtain a sensing result.

3. The first electronic device according to claim 2, wherein, The secondary sensing data is one of the following: Secondary sensing measurement results from the one or more low-frequency base stations and the one or more high-frequency base stations, Processed sensing measurement results received from an edge server associated with the one or more low-frequency base stations and the one or more high-frequency base stations.

4. The first electronic device according to any one of claims 1-3, wherein, The at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to determine the one or more high-frequency base stations at least based on sensing efficiency and system efficiency, wherein the sensing efficiency is determined based on the combined signal-to-noise ratio of sensing signals sent by one or more high-frequency base stations, and wherein the system efficiency is determined based on the spectral efficiency, energy efficiency, and base station handover efficiency of one or more high-frequency base stations.

5. The first electronic device according to claim 4, wherein, The at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to further determine the one or more high-frequency base stations based on communication efficiency, wherein the communication efficiency is determined based on the combined signal-to-interference-plus-noise ratio and total received signal power of communication signals sent by one or more high-frequency base stations.

6. The first electronic device according to claim 5, wherein, The at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to further determine the one or more high-frequency base stations based on one or more of the following constraint conditions: The maximum rated transmission power of the base station, the minimum required signal-to-interference-plus-noise ratio for communication, the minimum required signal-to-noise ratio for sensing, the minimum required received signal strength for communication, the maximum required Cramér-Rao bound for sensing, the minimum required distance accuracy for sensing, the minimum required angle accuracy for sensing, the minimum required speed accuracy for sensing, and the minimum of the maximum access capacity of the base station.

7. The first electronic device according to claim 6, wherein, The at least one memory and the computer program code are further configured to, through the at least one processor, cause the first electronic device to use a simulated annealing algorithm introducing an interior point penalty function to determine the one or more high-frequency base stations.

8. The first electronic device according to claim 4, wherein, The at least one memory and the computer program code are further configured to, via the at least one processor, cause the first electronic device to periodically determine the one or more high-frequency base stations and update the policy-making data.

9. The first electronic device according to any one of claims 1-3, wherein, The at least one memory and the computer program code are further configured to, via the at least one processor, cause the first electronic device to also receive information related to user communication and / or network-related information from one or more core network elements, and wherein the policy-making data is determined based on at least one or more of the sensed data, the information related to user communication, and the network-related information.

10. The first electronic device according to any one of claims 1-3, wherein, The at least one memory and the computer program code are further configured to, via the at least one processor, cause the first electronic device to receive a request for the policy-making data from the second electronic device before determining the policy-making data.

11. The first electronic device according to any one of claims 1-3, wherein, The policy-making data further includes at least one of data related to communication user profile inference, data related to network performance and status, and data for assisting secondary sensing.

12. The first electronic device according to claim 11, wherein, The data related to communication user profile inference includes at least one or more of the following items: the user's movement trajectory, user habits, and the user's requirements for quality of service; The data related to network performance and status includes at least one or more of the following items: statistics and / or predictions of the traffic volume of the network, the resource requirements of the network, and / or predictions and statistics and / or predictions of the user experience of the network services; and The data for assisting secondary sensing includes at least one or more of the following items: the angle of arrival and departure of the sensing signal, time delay, Doppler information, and radar cross section.

13. The first electronic device according to any one of claims 1-3, wherein, The first electronic device acts as a network data analysis function NWDAF, and the second electronic device acts as a policy control function PCF.

14. The first electronic device according to any one of claims 1 to 3, wherein, The low-frequency base station is a sub-6GHz base station, and the high-frequency base station is a millimeter-wave base station.

15. A second electronic device for a communication and sensing integrated system, comprising at least one processor; and at least one memory, including computer program code, wherein, The at least one memory and the computer program code are configured to, via the at least one processor, cause the second electronic device to: Receive the policy-making data from the first electronic device according to any one of claims 1-14; and Formulate a communication policy and / or a sensing policy based on the policy-making data.

16. The second electronic device according to claim 15, wherein, The communication policy includes at least one of the following: access / frequency selection priority RFSP index, slice division policy, user equipment routing selection policy URSP, and the cooperation mode for communication between the one or more high-frequency base stations and the one or more low-frequency base stations; and The sensing policy includes at least the cooperation mode between the one or more high-frequency base stations and the one or more low-frequency base stations corresponding to different sensing requirements, and / or a sensing signal beamforming policy.

17. A method for a first electronic device in a communication-sensing integrated system, comprising: Receiving sensing data, where the sensing data is obtained by preliminary sensing performed by one or more low-frequency base stations; Determining policy-making data for formulating a communication policy and / or a sensing policy; and Sending the policy-making data to a second electronic device, where the policy-making data at least includes information indicating one or more high-frequency base stations to participate in secondary sensing.

18. A method for a second electronic device in a communication-sensing integrated system, comprising: Receiving the policy-making data from the first electronic device according to any one of claims 1-14; and Formulating a communication policy and / or a sensing policy based on the policy-making data.

19. A non-transitory computer-readable storage medium storing executable instructions, the executable instructions, when executed, implementing the method according to claim 17 or 18.

20. A computer program product comprising executable instructions, the executable instructions, when executed, implementing the method according to claim 17 or 18.

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