Network slice resource allocation method with synergy of communication and sensing and computer equipment

Abstract

The invention is applicable to the field of communication, and provides a network slice resource allocation method with synergy of communication and sensing and computer equipment. The method comprises the following steps: constructing a channel model based on a 3GPP standard based on set simulation parameters and service type parameters, calculating basic path loss, shadow fading and a signal-to-noise ratio according to the simulation parameters and the service type parameters, and constructing a TSN synchronization error model and a model for calculating a sensing target distance; calculating the service weight of the equipment according to the set service type parameter, and correcting the channel quality; carrying out priority enhancement on services, calculating normalized weights, carrying out K-means clustering on weighted channel gains, calculating a proportion of perception services, and designing a power distribution rule; in a resource allocation stage, an application scene slice bandwidth and a sensing service slice bandwidth are calculated and dynamically allocated, and resource allocation, precision, time delay and a resource reuse rate are calculated. The method can significantly improve the utilization efficiency of network resources, reduce the time delay, and improve the sensing precision.

Description

Technical Field

[0001] This application belongs to the field of communications, and in particular relates to a network slicing resource allocation method and computer equipment based on sensory collaboration. Background Technology

[0002] With the booming development of emerging applications such as the Industrial Internet and autonomous driving, 5G-Advanced, an enhanced version of fifth-generation mobile communication technology, has emerged, placing unprecedented demands on integrated sensing and communication (ISAC). In the field of intelligent manufacturing, industrial robots urgently need both precise environmental perception capabilities and efficient real-time control capabilities to automate and intelligentize complex production processes. In vehicle-to-everything (V2X) scenarios, autonomous vehicles require both high-precision environmental perception to acquire real-time road condition information and reliable communication guarantees to ensure smooth and accurate information exchange between vehicles and between vehicles and infrastructure. These emerging application scenarios pose extremely challenging requirements on network performance, prompting us to explore dynamic resource allocation strategies for 5G-Advanced integrated sensing and communication networks to meet the collaborative optimization needs of communication and sensing tasks, providing solid network support for the development of a future intelligent society.

[0003] The existing technology has the following shortcomings:

[0004] (1) Insufficient dynamism in resource allocation strategies: Although traditional 5G network slicing technology can achieve multi-service resource isolation, its static resource allocation mode is difficult to adapt to the dynamic needs of integrated communication and sensing scenarios. In practical applications, the needs of communication and sensing tasks change dynamically with time and environment, while most existing resource allocation strategies are based on static assumptions and cannot adjust the resource allocation scheme in real time, resulting in low resource utilization and failure to fully realize the performance potential of the network.

[0005] (2) Difficulty in co-optimizing communication and sensing tasks: Communication services require low latency and high reliability, while sensing tasks require high precision and large bandwidth. The two compete for resources. Existing technologies usually treat communication and sensing separately, lacking an effective co-optimization mechanism, resulting in low spectrum efficiency and difficulty in achieving efficient coordination between communication and sensing tasks.

[0006] (3) Real-time requirements in dynamic environments are difficult to meet: In dynamic environments, channel states and service demands change in real time, placing extremely high demands on the real-time performance of resource allocation strategies. Existing technologies often neglect the computational complexity and real-time performance of dynamic resource allocation algorithms, making it difficult for these algorithms to meet the real-time requirements of 5G-A networks for dynamic resource management in practical applications. Summary of the Invention

[0007] The application aims to provide a network slice resource allocation method and computer equipment for sensing and coordination, aiming to solve the problems of insufficient dynamic of resource allocation strategy, difficulty of communication and sensing task coordination optimization, and difficulty of meeting real-time requirements in dynamic environment.

[0008] In a first aspect, the application provides a network slice resource allocation method for sensing and coordination, comprising the following steps:

[0009] S101, setting simulation parameters and service type parameters;

[0010] S102, constructing a channel model SINR based on 3GPP standards based on the simulation parameters and service type parameters, calculating the basic path loss, shadow fading and signal-to-noise ratio according to the simulation parameters and service type parameters, building a time sensitive network TSN synchronization error model and a model for calculating the sensing target distance;

[0011] S103, calculating the service weight of the device according to the set service type parameter, and correcting the channel quality;

[0012] S104, strengthening the priority of the service, calculating the normalized weight, K-means clustering the weighted channel gain, calculating the proportion of the sensing service, and designing the power allocation rule;

[0013] S105, in the resource allocation stage, calculating and dynamically allocating the application scenario slice bandwidth and the sensing service slice bandwidth, calculating the resource allocation, precision, delay and resource multiplexing rate.

[0014] In a second aspect, the application provides a computer equipment, comprising:

[0015] One or more processors;

[0016] Memory; and

[0017] One or more computer programs, the processor and the memory are connected through the bus, wherein the one or more computer programs are stored in the memory and are configured to be executed by the one or more processors, and the processor executes the computer program to realize the steps of the network slice resource allocation method for sensing and coordination as described above.

[0018] In the present application, since the channel model SINR based on the 3GPP standard is constructed based on the simulation parameters and the service type parameters, the base path loss, shadow fading and signal-to-noise ratio are calculated according to the simulation parameters and the service type parameters, the time sensitive network TSN synchronization error model is built and the model for calculating the perception target distance, the actual communication environment is more comprehensively simulated, and more accurate basis is provided for subsequent accurate resource allocation. Again, since the service weight of the device is calculated according to the set service type parameter, and the channel quality is corrected, the resource weight of the device can be dynamically adjusted according to the requirements of the service such as delay, reliability, perception accuracy and channel quality, so that the resource allocation can more reasonably and accurately meet the needs of different services. Again, since the service is strengthened priority, the normalized weight is calculated, the weighted channel gain is K-means clustered, the proportion of perception service is calculated, and the power allocation rule is designed, so that more refined resource allocation is realized, and the resource demand of key services and the energy efficiency of the overall network can be better balanced. Again, in the resource allocation stage, the application scenario slice bandwidth and the perception service slice bandwidth are calculated and dynamically allocated, the resource allocation, accuracy, delay and resource multiplexing rate are calculated, so that the resource allocation can be dynamically adjusted according to the real-time demand of the service, the resource utilization rate is improved, and the performance requirements of the service are guaranteed.

[0019] The present application is verified by simulation to be effective, which can significantly improve the utilization efficiency of network resources, reduce the delay, improve the perception accuracy, and is suitable for industrial internet, automatic driving and other scenes with high requirements for delay and accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of the network slice resource allocation method for sensing and communication cooperation provided by an embodiment of the present application.

[0021] Figure 2 is a simulation effect diagram of the network slice resource allocation method for sensing and communication cooperation provided by an embodiment of the present application.

[0022] Figure 3 is a specific structure block diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and beneficial effects of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0024] In order to illustrate the technical scheme described in the present application, the following specific embodiments are used to illustrate the technical scheme.

[0025] Please refer to Figure 1FIG. 1 is a flowchart of a network slice resource allocation method provided by an embodiment of the present application, wherein the network slice resource allocation method is a method for network slice resource allocation based on communication and sensing synergy. The communication and sensing synergy refers to the fusion of communication and sensing functions, and the realization of information transmission and environment sensing through the same wireless signal. This technology can significantly improve the efficiency and reliability of the system, and is suitable for a variety of application scenarios.

[0026] The network slice resource allocation method based on communication and sensing synergy provided by an embodiment of the present application comprises the following steps:

[0027] S101, setting simulation parameters and service type parameters.

[0028] In the embodiment of the present application, the simulation parameters can be set as follows: the base station coverage radius in the simulation parameters is set to 500 meters, the carrier frequency is set to 3.5 GHz (i.e. the medium frequency band, which can balance the coverage capability and bandwidth advantage), the total system bandwidth is set to 100 MHz (to meet the demand for concurrent services), the maximum transmission power is set to 30 dBm (to ensure reliable access of edge users), the noise power spectral density is set to -174 dBm / Hz (i.e. the normal temperature thermal noise, which provides a reference for receiver sensitivity calculation), the total number of computing cores is set to 100, the total system memory is set to 100 GB (which can support real-time signal processing in a dense device access scenario), the initial number of devices is set to 200 (to simulate the device density in a typical industrial internet scenario), the basic perception resolution is set to 0.5 meters (to meet the requirements of automatic driving environment reconstruction), the TSN time synchronization error is set to ≤0.6 ms (to ensure the determinism of time-sensitive services), and the perception task triggering interval is set to 0.5 seconds.

[0029] In the embodiment of the present application, the service type parameters can be set as follows: the latency requirement of URLLC (ultra-reliable & low-latency communication) service in the service type is set to 1 ms, and the reliability requirement is set to 99.9% (to simulate industrial control command transmission), the latency requirement of eMBB (Enhanced Mobile Broadband) service is set to 10 ms (the latency requirement corresponds to a 4K video transmission scenario), the latency requirement of mMTC (massive Machine-Type Communication) service is set to 100 ms (to adapt to periodic reporting of sensors), and the latency requirement of SENSING (sensing) service is set to 0.5 ms.

[0030] The simulation parameters and service type parameters can refer to Table 1 and Table 2.

[0031] Table 1: Simulation parameters

[0032]

[0033] Table II: Service type parameters

[0034]

[0035] S102, based on the simulation parameters and service type parameters, a channel model SINR based on 3GPP standard is constructed, the base path loss, shadow fading and signal-to-noise ratio are calculated according to the simulation parameters and service type parameters, a TSN (Time Sensitive Network) synchronization error model is built and a model for calculating the perception target distance is built.

[0036] In the embodiment of the application, the channel model SINR based on 3GPP standard is specifically constructed based on the simulation parameters and service type parameters:

[0037] The parameter values of the channel model SINR based on 3GPP standard are based on 3GPP TR 38.901 UMa model, the free space path loss base attenuation constant a = 32.4, the distance attenuation factor b = 21 (the value of the simplified model), and the frequency attenuation factor c = 20.

[0038] In the embodiment of the application, the base path loss can be specifically calculated as:

[0039]

[0040] Wherein, PL(d,f) represents the base path loss, d represents the distance between the device and the base station (unit: meter), d≥1, f = 3.5GHz represents the carrier frequency parameter.

[0041] In the embodiment of the application, the total channel loss, i.e. the shadow fading L total , can be specifically calculated as:

[0042] L total = PL(d,f) + X σ + F

[0043] Wherein, σ is the standard deviation, σ = 8dB, F = 10log 10 (r), r is the Rayleigh fading (subject to Rayleigh distribution, scale parameter σ r = 1).

[0044] In the embodiment of the application, the TSN synchronization error model can be specifically built as:

[0045] T err = 600ns

[0046] Wherein, T err represents the maximum allowable synchronization error limit value, and Δt is a TSN synchronization error, in units of ns, in order to adapt to the edge computing scene with high real-time requirement, linear approximation is realized through Taylor expansion in mathematical equivalence proof, and the derivation steps are as follows:

[0047] Let Then G eff =G total -20log 10 (1+x)

[0048] Geff represents an effective channel gain (in units of dB), x represents a nonlinear distortion factor, and the value range x is [0, 0.2], G total =-L total represents a total channel gain, and a first-order Taylor expansion is performed on log 10 (1+x) at x=0:

[0049]

[0050] When x << 1, that is, the time error is much smaller than the synchronization period, it is simplified as:

[0051]

[0052] Substitute the constant ln10≈2.302585, then G eff ≈G total -8.6859·x.

[0053] In the embodiment of the application, the model for calculating the distance of the perceived target can be built as follows:

[0054]

[0055] Wherein, d sensing represents the distance of the perceived target, represents a dynamically updated set of perceived targets, the perception accuracy is positively correlated with the distance, p device represents a geographic position vector of the device, and p t represents a geographic position vector of the perceived target.

[0056] In the embodiment of the application, the signal-to-noise ratio can be calculated as follows:

[0057]

[0058] Wherein, SINR represents a signal-to-noise ratio, the base station transmit power P tx =30dBm, the receiver noise correction coefficient B represents an allocated bandwidth, which is determined by dynamic slicing; P txTake 30dBm base station transmission power level in line with 3GPP TS 38.101-1, considering the transmission power interval of macro base station (43-46) and micro base station (23-30), so take the maximum value of micro base station, non-fixed, can be adjusted according to the use scene (such as outdoor base station or indoor base station wireless communication environment). The fixed value of N0 is the theoretical limit value determined by Boltzmann constant k-physical nature, which is used for receiver noise correction.

[0059] S103, according to the set service type parameter, the service weight of the device is calculated, and the channel quality is corrected.

[0060] The service weight calculation is used to determine the priority of different service types in resource allocation. In the embodiment of the application, the formula for calculating the service weight of the device according to the set service type parameter is:

[0061]

[0062] Wherein, w i represents the service weight of device i, W D represents the delay weight coefficient, W R represents the reliability weight coefficient, D i represents the delay requirement, R i represents the reliability requirement, which considers the delay and reliability requirements of the service. The higher the delay requirement, the smaller the D i , the greater the weight, and the higher the priority. The higher the reliability requirement, the greater the weight, and the higher the priority. W D and W R are empirical values set by man.

[0063] For URLLC service, W D = 0.6, W R = 0.4, reflecting that the delay is slightly more important than the reliability in the current scene. If the reliability is more important in the actual scene (such as industrial control), W D = 0.4, W R = 0.6. The formula can change different values to adapt to different service scenarios, such as eMBB, mMTC, etc.

[0064] In the embodiment of the application, the formula for calculating the perceived service weight of the device according to the set service type parameter is:

[0065] Wherein, represents the perceived target distance, which comprehensively considers the perceived accuracy and reliability requirement of the perceived service. The closer the perceived target distance, the greater the weight, and the higher the priority. The higher the reliability requirement, the greater the weight, and the higher the priority.

[0066] In the embodiments of the present application, the correction of the channel quality is specifically:

[0067]

[0068] wherein w i represents the initial resource weight of the device i, represents the effective channel gain of the device i, and the formula is is a process of converting the channel gain from decibel (dB) to linear scale; ensuring that the resource allocation can reflect the actual communication conditions, and used for adjusting the device resource weight to reflect the influence of the channel conditions.

[0069] S104, reinforcing the priority of the service, calculating the normalized weight, K-means clustering the weighted channel gain, calculating the proportion of the sensing service, and designing the power allocation rule.

[0070] In the embodiments of the present application, S104 specifically includes:

[0071] For the TSN service, w i ←1.5w i , the physical meaning is that the weight of the delay-sensitive service is increased by 50%, corresponding to the URLLC scene (such as industrial control); for the sensing service: w i ←2.0w i , the physical meaning is that the weight of the sensing service is doubled, guaranteeing the environmental sensing capability in the millimeter wave communication, using normalization and clustering to classify the devices according to the weighted channel gain , so as to perform more effective resource allocation The weight is compressed to the interval [0, 1], satisfying

[0072] The proportion of the sensing service is calculated on the dynamic power allocation wherein N sensing represents the number of sensing service devices; N total represents the maximum number of service devices supported by the system, the base value 0.3: ensuring that 30% of resources are still reserved for channel detection and other basic functions when there is no sensing service, and the incremental coefficient 0.3 represents that for every 1% increase in sensing devices, the resource proportion is increased by 0.3 percentage points, and the upper limit is 0.6, in order to avoid the squeezing of communication resources by the sensing service (in line with the definition of 3GPP TS 38.314);

[0073] According to the device type and service demand, the power allocation rule is designed, and the rule is as follows: for different clustering clusters, the sensing devices are divided into different priority groups, the resource allocation strategy is adjusted, and the transmission power P i of the sensing device is allocated, Pi = max (0.4P max , aP max ), P max represents the maximum transmit power, and the power allocation is between 0.4 and 0.9 times the maximum transmit power for the sensing device, ensuring the high accuracy requirement of the sensing task;

[0074] The transmit power P i of the TSN device is allocated as P i = max (0.3P max , 0.7aP max ), and the power allocation is between 0.3 and 0.63 times the maximum transmit power for the TSN device, meeting the communication requirements of low latency and high reliability. The power allocation rule ensures that critical services (such as TSN and sensing services) can obtain sufficient resources, while optimizing the energy efficiency of the overall network.

[0075] In the resource allocation stage, the application scenario slice bandwidth and the sensing service slice bandwidth are calculated and dynamically allocated, and the resource allocation, accuracy, latency, and resource reuse rate are calculated.

[0076] In an embodiment of the present application, the slice bandwidth of the URLLC service in the application scenario can be calculated as B URLLC = 0.25B + 0.15η urgent B + 0.05η sensing B

[0077] where B URLLC is the slice bandwidth of the URLLC service, B is the total bandwidth, η urgent and η sensing represent the urgency factors of the URLLC service and the SENSING service, respectively. In the demand for different services in other application scenarios, the numbers can be adjusted, such as changing 0.25B to 0.50B, which represents 50% of the total bandwidth, to ensure the processing and emergency priority of related services.

[0078] In an embodiment of the present application, the sensing service slice bandwidth can be calculated as B SENSING = 0.15B (1 + η sensing )

[0079] where B SENSING is the sensing service slice bandwidth, N sensing represents the number of sensing service devices; N URLLC represents the number of URLLC emergency service devices, and N total represents the total access device capacity of the system.

[0080] In the embodiment of the present application, the computing resource allocation can be specifically C SENSING = 0.1C total (1+η sensing ), wherein C SENSING represents the computing resource allocated to the sensing service, C total represents the total computing resource available to the system.

[0081] In the embodiment of the present application, the computing precision can be specifically ∈ represents the precision, d sensing represents the sensing distance, in meters.

[0082] In the embodiment of the present application, the computing time delay can be specifically wherein N active represents the number of active devices, and τ sensing represents the time delay.

[0083] In the embodiment of the present application, the computing resource multiplexing rate can be specifically wherein ρ represents the resource multiplexing rate, ∑ i represents the actual occupied bandwidth, and ∑ k B k represents the sum of all slice bandwidths.

[0084] Please refer to Figure 2 , Figure 2 is a simulation effect diagram of the network slice resource allocation method in the sensing coordination provided by an embodiment of the present application. The effectiveness of the network slice resource allocation method in the sensing coordination provided by an embodiment of the present application is verified through simulation, which can dynamically adjust resource allocation according to real-time demand of the service, improve resource utilization, and at the same time guarantee the performance requirements of the service.

[0085] Figure 3 The specific structure block diagram of the computer device provided by an embodiment of the present application is shown, a computer device 100 includes one or more processors 101, a memory 102, and one or more computer programs, wherein the processor 101 and the memory 102 are connected through a bus, the one or more computer programs are stored in the memory 102 and are configured to be executed by the one or more processors 101, and the processor 101 implements the steps of the network slice resource allocation method in the sensing coordination provided by an embodiment of the present application when executing the computer program. The computer device includes servers and terminals, etc. The computer device can be a desktop computer, a mobile terminal or a vehicle-mounted device, and the mobile terminal includes at least one of a mobile phone, a tablet computer, a personal digital assistant or a wearable device, etc.

[0086] In the present application, since the channel model SINR based on the 3GPP standard is constructed based on the simulation parameters and the service type parameters, the base path loss, shadow fading and signal-to-noise ratio are calculated according to the simulation parameters and the service type parameters, the time sensitive network TSN synchronization error model is built and the model for calculating the perception target distance, the actual communication environment is more comprehensively simulated, and more accurate basis is provided for subsequent accurate resource allocation. Again, because the service weight of the device is calculated according to the set service type parameter, and the channel quality is corrected, the resource weight of the device can be dynamically adjusted according to the requirements of the service delay, reliability, perception accuracy and other factors, and the channel quality and other factors, so that the resource allocation is more reasonable and accurate to meet the needs of different services. Again, because the service is strengthened priority, the normalized weight is calculated, the weighted channel gain is K-means clustered, the proportion of perception service is calculated, and the power allocation rule is designed, more refined resource allocation is realized, and the resource demand of key services and the energy efficiency of the overall network can be better balanced. Again, because the application scenario slice bandwidth and the perception service slice bandwidth are calculated and dynamically allocated in the resource allocation stage, the resource allocation, accuracy, delay and resource multiplexing rate are calculated, the resource allocation can be dynamically adjusted according to the real-time demand of the service, the resource utilization rate is improved, and the performance requirements of the service are guaranteed.

[0087] The present application is verified by simulation to be effective, which can significantly improve the utilization efficiency of network resources, reduce the delay, improve the perception accuracy, and is suitable for industrial internet, automatic driving and other scenes with high requirements for delay and accuracy.

[0088] It should be understood that the steps in each embodiment of the present application are not necessarily executed in the order indicated by the step numbers. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least a part of other steps or sub-steps or stages of other steps.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in each embodiment provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0090] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0091] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the present application patent should be subject to the appended claims.

Claims

1. A method for allocating network slicing resources in a sensory-coordinated manner, characterized in that, Includes the following steps: S101. Set simulation parameters and service type parameters; S102. Construct a channel model SINR based on the 3GPP standard based on the simulation parameters and service type parameters. Calculate the basic path loss, shadow fading, and signal-to-noise ratio according to the simulation parameters and service type parameters. Build a time-sensitive network (TSN) synchronization error model and a model for calculating the distance to the sensing target. S103. Calculate the service weight of the device according to the set service type parameters, and correct the channel quality. S104. Prioritize services, calculate normalized weights, perform K-means clustering on the weighted channel gain, calculate the proportion of sensed services, and design power allocation rules. S105. During the resource allocation phase, calculate and dynamically allocate application scenario slice bandwidth and perception service slice bandwidth, and calculate resource allocation, accuracy, latency and resource reuse rate.

2. The method as described in claim 1, characterized in that, The specific steps for constructing the 3GPP standard-based channel model SINR based on the simulation parameters and service type parameters are as follows: The parameters of the channel model SINR based on the 3GPP standard are determined according to the 3GPP TR 38.901UMa model, with the free space path loss basic attenuation constant a = 32.4, the distance attenuation factor b = 21, and the frequency attenuation factor c = 20. The calculation of the basic path loss is specifically as follows: Where PL(d,f) represents the basic path loss, d represents the distance between the device and the base station, and f = 3.5GHz represents the carrier frequency parameter; The calculation of total channel loss, i.e., shadow fading L total Specifically: L total =PL(d,f)+X σ +F, where, σ is the standard deviation, σ = 8 dB, F = 10 log 10 (r), where r is Rayleigh decay.

3. The method as described in claim 1, characterized in that, The specific steps for constructing and calculating the TSN synchronization error model are as follows: T err = 600ns, where T err Let represent the maximum permissible synchronization error limit, and Δt be the TSN synchronization error. To adapt to edge computing scenarios with high real-time requirements, a linear approximation is achieved through Taylor expansion in the mathematical equivalence proof. The derivation steps are as follows: make Then G eff =G tota1 -20log 10 (1+x) Geff represents the effective channel gain, x represents the nonlinear distortion factor, and G... total =-L total Represents the total channel gain, relative to log 10 The first-order Taylor expansion of (1+x) at x=0 is: When x << 1, that is, when the time error is much smaller than the synchronization period, it simplifies to: Substituting the constant ln10≈2.302585, then G eff ≈G total -8.6859·x; The specific steps for building the model to calculate the distance to the perceived target are as follows: Where, d sensing Represents the perceived distance to the target. p represents a dynamically updated set of perceived targets, where perception accuracy is positively correlated with distance. device p represents the geographic location vector of the device. t A vector representing the geographic location of the perceived target; The calculation of the signal-to-noise ratio specifically involves: SINR represents the signal-to-noise ratio, and the base station transmit power P tx =30dBm, receiver noise correction factor B represents the allocated bandwidth.

4. The method as described in claim 1, characterized in that, The formula for calculating the service weight of the device based on the set service type parameters is expressed as follows: Among them, w i W represents the business weight of device i. D W represents the delay weighting coefficient. R D represents the reliability weighting coefficient. i Represents latency requirements, R i Represents reliability requirements; The formula for calculating the perceived service weight of the device based on the set service type parameters is expressed as follows: in, This represents the perceived distance to the target.

5. The method as described in claim 3, characterized in that, The specific steps for correcting channel quality are as follows: Among them, w i Represents the initial resource weight of device i. The effective channel gain of device i is represented by the formula. It is the process of converting the channel gain from decibels to a linear ratio.

6. The method as described in claim 5, characterized in that, S104 specifically includes: The device is classified according to the weighted channel gain using normalization and clustering. Classify the data to enable more efficient resource allocation and normalize the weights. Compress the weights to the interval [0, 1], satisfying... Calculate the proportion of sensing services in dynamic power allocation. Where N sensing N represents the number of sensing service devices; total This represents the maximum number of business devices supported by the system. Based on equipment type and business requirements, power allocation rules are designed as follows: For different clusters, sensing devices are divided into different priority groups, and resource allocation strategies are adjusted to allocate the transmission power P of the sensing devices. i P i =max(0.4P) max ,αP max ), P max Represents the maximum transmit power; allocates the transmit power P of the TSN device. i P i =max(0.3P) max ,0.7αP max ).

7. The method as described in claim 1, characterized in that, The specific slice bandwidth for the URLLC service in the aforementioned computing application scenario is: B URLLC =0.25B + 0.15η urgent B+0.05η sensing B, of which, B URLLC This refers to the slice bandwidth of the URLLC service, where B is the total bandwidth and η is the slicing bandwidth. urgent With η sensing These represent the urgency factors for URLLC and SENSING services, respectively. The bandwidth of the computational sensing service slice is specifically: B SENSING =0.15B(1+η) sensing ) Among them, B SENSING It is the perceived bandwidth of the service slice. N sensing N represents the number of sensing service devices; URLLC N represents the number of URLLC emergency service devices. total This indicates the total capacity of connected devices in the system.

8. The method as described in claim 7, characterized in that, The specific allocation of computing resources is as follows: C SENSING =0.1C total (1+η sensing ), where C SENSING C represents the computing resources allocated to the sensing service. total This represents the total computing resources available to the system.

9. The method as described in claim 1, characterized in that, The specific calculation precision is: ∈ = ∈ represents precision, d sensing Represents perceived distance; The calculation delay is specifically as follows: Where, N active Represents the number of active devices, τ sensing Represents latency; The computing resource reuse rate is specifically: Where ρ represents the resource reuse rate, ∑ i Represents the actual bandwidth used, ∑ k B k This represents the sum of the bandwidth of all slices.

10. A computer device, comprising: One or more processors; Memory; as well as One or more computer programs, wherein the processor and the memory are connected via a bus, wherein the one or more computer programs are stored in the memory and configured to be executed by the one or more processors, characterized in that, when the processor executes the computer programs, it implements the steps of the network slicing resource allocation method according to any one of claims 1 to 9.

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