Resource determination method and device, electronic equipment and storage medium

Abstract

The invention provides a resource determination method and device, electronic equipment and a storage medium, and particularly relates to the technical field of wireless resources. The method is used for determining a reasonable resource quantity so as to ensure the reliability of service transmission, and comprises the following steps: acquiring a time sensitive communication auxiliary information TSCAI parameter and a network environment parameter at a current moment; generating a real-time network state feature vector based on the TSCAI parameter and the network environment parameter; determining N historical network state feature vectors similar to the real-time network state feature vector, and determining respective historical resource scheduling strategies of the N historical network state feature vectors; and calculating a historical meta-probability index based on a service transmission result and an allowable failure rate corresponding to the historical resource scheduling strategy, and determining the number of redundant resources required by current service transmission based on the historical meta-probability index, the burst time and the survival time.

Description

Technical Field

[0001] This application relates to the field of data transmission technology, and in particular to a resource determination method, apparatus, electronic device, and storage medium. Background Technology

[0002] In 5G systems, ultra-reliable low-latency communication (URLLC) is a core application scenario, requiring transmission reliability of at least 99.999% with millisecond-level end-to-end latency. However, existing wireless networks are mostly optimized based on average performance metrics, making it difficult to adapt to the stringent requirements of URLLC regarding latency tail behavior and low-probability anomalies. This has become a major technical bottleneck for 5G to achieve URLLC functionality. Existing systems use statically reserved redundant resources to meet high reliability requirements, but this strategy lacks dynamic adaptability. When channel conditions or network environment change (such as sudden interference), it cannot respond in time, which can easily lead to a significant decrease in resource utilization or a sharp deterioration in instantaneous reliability, making it difficult to continuously meet reliability indicators of over 99.999%.

[0003] Therefore, determining a reasonable amount of resources to ensure the reliability of service transmission has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a resource determination method, apparatus, electronic device, and storage medium for determining a reasonable amount of resources to ensure the reliability of service transmission.

[0005] In a first aspect, this application provides a resource determination method, which includes: acquiring Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment; the TSCAI parameters include allowable failure rate, burst time, and lifetime; generating a real-time network state feature vector based on the TSCAI parameters and network environment parameters; the real-time network state feature vector is used to characterize the instantaneous state of the cell; determining N historical network state feature vectors similar to the real-time network state feature vector, and determining the historical resource scheduling strategy for each of the N historical network state feature vectors; calculating a historical meta-probability index based on the service transmission results and allowable failure rate corresponding to the historical resource scheduling strategy, the historical meta-probability index being used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current service among the N historical resource scheduling strategies; and determining the amount of redundant resources required for the current service transmission based on the historical meta-probability index, burst time, and lifetime.

[0006] The technical solution provided in this application brings at least the following benefits: by generating a real-time network state feature vector from the acquired TSCAI parameters and network environment parameters, it provides accurate input for subsequent analysis; thereby matching similar historical feature vectors and corresponding scheduling strategies, it narrows the decision-making scope with the help of historical experience, and then calculates historical meta-probability indicators by combining service transmission results and allowable failure rates, quantifying the proportion of strategies that meet reliability requirements, and providing a basis for resource decision-making; finally, it determines the number of redundant resources by combining this indicator, burst time, and survival time. Each step is progressive, fully combining real-time status and historical experience, and finally determines a reasonable number of resources to ensure the reliability of service transmission.

[0007] In one possible implementation, the TSCAI parameters also include the target signal-to-interference-plus-noise ratio (SINR); network environment parameters include network interference values; based on historical meta-probability indices, burst times, and lifetimes, the amount of redundant resources required for the current service transmission is determined, including: calculating a tail risk factor based on burst times, target SINR, and network interference values; the tail risk factor is used to characterize the probability of extreme abnormal events occurring; a lifetime weighting factor is determined based on lifetimes; the lifetime weighting factor is used to characterize the urgency of the current service; a current meta-probability index is obtained based on historical meta-probability indices, tail risk factors, and lifetime weighting factors; the current meta-probability index is used to characterize the estimated probability that the current service transmission meets reliability requirements; if the current meta-probability index is less than a dynamic threshold, the amount of redundant resources required for the current service transmission is calculated based on the current TSCAI parameters and resource adjustment coefficients.

[0008] Based on this, this application obtains the current meta-probability index by calculating the tail risk factor and the survival time weight factor, and combines the dynamic threshold to judge and calculate redundant resources, thereby further improving the accuracy and adaptability of resource decision-making.

[0009] In one possible implementation, the method further includes: when the current meta-probability index is greater than or equal to the dynamic threshold, taking the resource quantity in the historical resource scheduling strategy corresponding to the historical network state feature vector with the smallest feature distance as the resource quantity required for the current service transmission.

[0010] Based on this, when the current meta-probability index is met, this application directly adopts the resource quantity of the most similar historical strategy, which simplifies the decision-making process, improves the efficiency of resource determination, and ensures the rationality of the decision.

[0011] In one possible implementation, the TSCAI parameter at the current moment also includes the signal-to-interference-plus-noise ratio (SIR) at the current moment; based on the TSCAI parameter at the current moment and the resource adjustment coefficient, the amount of redundant resources required for the current service transmission is calculated, including: determining the SIR difference based on the target SIR and the SIR at the current moment; and multiplying the SIR difference by the resource adjustment coefficient as the amount of redundant resources required by the current network.

[0012] Based on this, redundant resources are calculated using the difference between the target and the current signal-to-interference-plus-noise ratio (SIR) and the resource adjustment coefficient. This accurately quantifies the resource requirements caused by the SIR difference, making the allocation of redundant resources more in line with the actual network conditions.

[0013] In one possible implementation, the dynamic threshold is obtained by: using the ratio of survival time to the time of the emergency as the survival urgency factor; if the survival urgency factor is less than a preset threshold, adding the initial threshold to the threshold adjustment coefficient to obtain the dynamic threshold; or, if the survival urgency factor is greater than the preset threshold, using the initial threshold as the dynamic threshold.

[0014] Based on this, the threshold is dynamically adjusted according to the survival urgency factor, and different threshold standards are adapted according to the business urgency to ensure that the threshold setting is more reasonable and improve the flexibility of resource decision-making.

[0015] In one possible implementation, the survival time weighting factor is determined based on the survival time, including: obtaining the exponential decay factor corresponding to the survival time, wherein the exponential decay factor is negatively correlated with the survival time; determining the decay weight component based on the exponential decay factor and a preset weight coefficient; and calculating the survival time weighting factor based on the difference between the base weight value and the decay weight component.

[0016] Based on this, the survival time weight factor is determined by combining the exponential decay factor. The decay mechanism reflects the impact of survival time on the weight, making the weight setting more in line with the differences in the urgency of business.

[0017] In one possible implementation, determining N historical network state feature vectors that are similar to the real-time network state feature vector includes: calculating the feature distance between the real-time network state feature vector and multiple historical network state feature vectors; and selecting the N most similar historical network state feature vectors based on the feature distance.

[0018] Based on this, feature distance is calculated to filter similar historical vectors, and similar historical states are accurately matched in a quantitative way, providing a reliable basis for subsequent resource strategy reference.

[0019] In one possible implementation, the similarity between the real-time network state feature vector and multiple historical network state feature vectors is negatively correlated with the feature distance.

[0020] Based on this, the negative correlation between feature distance and similarity is clarified, providing a clear standard for judging feature vector similarity and ensuring the accuracy of screening similar historical vectors.

[0021] In one possible implementation, a historical meta-probability index is calculated based on the service transmission results and allowed failure rate corresponding to the historical resource scheduling strategy. This includes: determining the reliability requirements of the current service based on the allowed failure rate; determining the number of historical resource scheduling strategies that meet the reliability requirements among N historical resource scheduling strategies; and using the ratio of the number to N as the historical meta-probability index.

[0022] Based on this, reliability requirements are determined according to the allowable failure rate, and historical meta-probability indicators are calculated by the proportion of strategies that meet the requirements, making the indicator calculation more targeted and reliable.

[0023] In one possible implementation, a real-time network state feature vector is generated based on TSCAI parameters and network environment parameters, including: performing feature transformation on the TSCAI parameters to obtain a TSCAI feature vector; and combining the TSCAI feature vector with the network environment parameters to obtain a real-time network state feature vector.

[0024] Based on this, the TSCAI parameters are first converted into feature vectors and then combined with network environment parameters to standardize the feature vector generation process and ensure that they can comprehensively and accurately represent the real-time network status.

[0025] In one possible implementation, the TSCAI parameters are transformed to obtain a TSCAI feature vector, including: mapping the allowable failure rate to a reliability strength feature; the reliability strength feature is negatively correlated with the allowable failure rate; mapping the difference between burst time and lifetime to a lifetime margin feature; the lifetime margin feature is used to characterize the urgency of the time required for the service to complete transmission; mapping burst time to a time sensitivity feature; the time sensitivity feature is used to characterize the service's tolerance for latency; and generating the TSCAI feature vector based on the reliability strength feature, lifetime margin feature, and time sensitivity feature.

[0026] In one possible implementation, the network environment parameters include at least one of base station density, average interference value, network interference value, and terminal speed.

[0027] Secondly, this application provides a resource determination apparatus, comprising: a processing unit and an acquisition unit; the acquisition unit is used to acquire Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment; the TSCAI parameters include allowable failure rate, burst time, and lifetime; the processing unit is used to generate a real-time network state feature vector based on the TSCAI parameters and network environment parameters; the real-time network state feature vector is used to characterize the instantaneous state of the cell; the processing unit is further used to determine N historical network state feature vectors similar to the real-time network state feature vector, and to determine the historical resource scheduling strategy for each of the N historical network state feature vectors; the processing unit is further used to calculate a historical meta-probability index based on the service transmission results and allowable failure rate corresponding to the historical resource scheduling strategy, the historical meta-probability index being used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current service among the N historical resource scheduling strategies; the processing unit is further used to determine the amount of redundant resources required for the current service transmission based on the historical meta-probability index, burst time, and lifetime.

[0028] Thirdly, this application provides an electronic device comprising a processor and a memory. The memory stores processor-executable instructions, and when the processor is configured to execute the instructions, the electronic device implements the method described in the first aspect above.

[0029] Fourthly, this application provides a readable storage medium comprising software instructions. When the software instructions are executed in an electronic device, they cause the electronic device to implement the method described in the first aspect above.

[0030] Fifthly, this application provides a computer program product comprising computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the method described in the first aspect.

[0031] The beneficial effects of the second to fifth aspects mentioned above can be referred to the first aspect, and will not be repeated here. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A schematic diagram illustrating a wireless resource allocation scenario provided in an embodiment of this application; Figure 2A schematic diagram illustrating another scenario of wireless resource allocation provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a resource determination system provided in an embodiment of this application; Figure 4 A flowchart illustrating a resource determination method provided in an embodiment of this application; Figure 5 A flowchart illustrating another resource determination method provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a resource determination device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0035] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0036] To facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with essentially the same function and effect. Those skilled in the art can understand that the terms "first" and "second" are not intended to limit the quantity or execution order.

[0037] In 5G systems, Ultra-Reliable Low-Latency Communication (URLLC) is a key application scenario, requiring transmission reliability of at least 99.999% under millisecond-level end-to-end latency constraints. However, existing wireless network designs are mostly optimized based on average performance metrics, making it difficult to effectively address the stringent requirements of URLLC services for extreme statistical characteristics, such as delay distribution tail behavior and low-probability anomalies. This poses a major technical challenge to implementing URLLC functionality in 5G systems.

[0038] Currently, most common scheduling and resource allocation schemes adopt static fixed resource allocation or preemptive strategies, which have the following significant drawbacks: First, fixed-granularity resource allocation (e.g., using a fixed number of resource blocks, RBs) easily leads to spectrum fragmentation. When the number of remaining resource blocks after allocation for enhanced mobile broadband (eMBB) services is insufficient to meet the minimum resource unit required for URLLC transmission, this portion of resources cannot be effectively utilized by URLLC, resulting in decreased spectrum efficiency and resource waste. Figure 1 As shown, the total system bandwidth is 100 PRB, 32 PRB (eMBB occupied by user 1), 48 PRB (eMBB occupied by user 2), and the remaining resources are 4 PRB (free fragments) and 3 PRB (free fragments).

[0039] Secondly, if a simple and crude puncturing mechanism is adopted—that is, forcibly inserting URLLC transmissions into resources already allocated to eMBB services—it will cause the transmission of eMBB service data on the corresponding resource blocks to be interrupted. The affected eMBB data needs to be recovered by retransmitting the entire encoded block (which may span multiple resource blocks), not only reducing eMBB user throughput by 30%–50%, but also increasing network load due to retransmission requests, potentially triggering a cascading delay effect and affecting the overall system stability. Figure 2 As shown, the original eMBB resource allocation includes PRB1, PRB2, PRB3, PRB4, PRB5, ..., PRBn, PRBn+1; due to the arrival of a sudden URLLC service, PRB3 and PRB4 are preempted (i.e., PRB3 and PRB4 are preempted by the URLLC).

[0040] Furthermore, the traditional hybrid automatic repeat request (HARQ) mechanism struggles to quickly compensate for interrupted services under the extremely tight delay constraints required by URLLC, and it cannot provide sufficient reliability guarantees, especially in the face of sudden interference.

[0041] Existing systems typically use statically reserved redundant resources to meet high reliability requirements, but this strategy lacks dynamic adaptability. When channel conditions or network environment change (such as the occurrence of sudden interference), the fixed redundancy strategy cannot respond in time, which may lead to a significant decrease in resource utilization or a sharp deterioration in instantaneous reliability, making it difficult to continuously guarantee a reliability index of over 99.999%.

[0042] Therefore, determining a reasonable amount of resources to ensure the reliability of service transmission is a problem that needs to be studied.

[0043] Based on this, this application provides a resource determination method. This method generates a real-time network state feature vector by acquiring TSCAI parameters and network environment parameters, providing accurate input for subsequent analysis. It then matches similar historical feature vectors and corresponding scheduling strategies, narrowing the decision-making scope with historical experience. Next, it calculates historical meta-probability indicators by combining service transmission results and allowable failure rates, quantifying the proportion of strategies that meet reliability requirements, and providing a basis for resource decision-making. Finally, it determines the number of redundant resources by combining this indicator, burst time, and survival time. Each step is progressive, fully combining real-time status and historical experience, and finally determines a reasonable number of resources to ensure the reliability of service transmission.

[0044] The resource determination method provided in this embodiment will be described below, starting with an introduction to related technologies.

[0045] like Figure 3 As shown, Figure 3 This is a schematic diagram of a resource determination system 300 provided in an embodiment of this application. The resource determination system is located on the base station side and includes an interface unit 110, a meta-probability unit 120, and a scheduling unit 130. The interface unit 110, the meta-probability unit 120, and the scheduling unit 130 are communicatively connected.

[0046] In some embodiments, interface unit 110 is configured to perform the following operations: receive Time-Sensitive Communication Auxiliary Information (TSCAI) parameters at the current time from an upper-layer industrial application or Network Exposure Function (NEF). Additionally, interface unit 110 is also configured to acquire network environment parameters at the current time.

[0047] The TSCAI parameters include, but are not limited to, at least one of the following: service flow transmission direction, allowable failure rate, service cycle, burst time, lifetime, and target signal-to-interference-to-noise ratio.

[0048] For example, the interface unit 110 further encapsulates the TSCAI parameters into scheduling reference information that can be recognized by the radio access network (RAN).

[0049] The encapsulation process includes mapping TSCAI parameters to specific 5G Quality of Service identifiers (5QIs) so that the scheduling unit 130 can make resource pre-allocation and scheduling decisions based on the quality of service characteristics associated with the 5QI.

[0050] In other words, the interface unit 110 performs feature transformation on the received TSCAI parameters to obtain the TSCAI feature vector, and combines the TSCAI feature vector with the network environment parameters to obtain the real-time network state feature vector, which is used to characterize the instantaneous state of the cell.

[0051] Through the above processing, the interface unit 110 outputs a structured TSC service description parameter set (the TSCAI parameters and network environment parameters at the current moment) to the meta-probability unit 120 and the scheduling unit 130, enabling the scheduling unit 130 to predict the arrival patterns and reliability requirements of periodic or deterministic service flows, thereby providing prior information support for achieving high-reliability, low-latency communication.

[0052] In some embodiments, the meta-probability unit 120 is used to receive a real-time network state feature vector from the interface unit 110 and determine the current meta-probability index based on the real-time network state feature vector.

[0053] Among them, the current meta-probability index is used to characterize the estimated probability that the current service transmission meets the reliability requirements.

[0054] In some embodiments, the scheduling unit 130 is configured to: receive TSCAI parameters from the interface unit 110 and the current meta-probability index from the meta-probability unit 120, and dynamically adjust the resource allocation strategy at the physical resource block level based on the service latency and reliability requirements represented by the TSCAI parameters and the comparison result between the current meta-probability index and the dynamic threshold.

[0055] Specifically, when the TSCAI parameter indicates that there is an upcoming periodic service traffic, the scheduling unit 130 can pre-allocate physical resource blocks based on this periodicity to avoid scheduling delays and ensure service latency requirements.

[0056] Meanwhile, if the current meta-probability index is lower than the dynamic threshold, it indicates that the estimated reliability of the service transmission does not meet the requirements, and the scheduler module dynamically triggers at least one of the following reliability enhancement mechanisms: 1. Increase the number of redundant physical resource blocks allocated for business flows; 2. Activate a multi-transmission receiving point cooperative transmission mechanism to improve transmission reliability through spatial diversity.

[0057] Through the above mechanism, the scheduling unit 130 achieves simultaneous optimization of latency and reliability performance during resource allocation to meet the stringent quality of service requirements of time-sensitive communication services.

[0058] Figure 4 This is a flowchart illustrating a resource determination method provided in an embodiment of this application, as shown below. Figure 4 As shown, the method includes the following steps: S101. Obtain the Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment.

[0059] In some embodiments, TSCAI parameters include, but are not limited to, allowed failure rate, burst time, lifetime, transmission direction, target signal-to-interference ratio (SIR), and current SIR.

[0060] Network environment parameters include, but are not limited to, base station density, average interference value, network interference value, and terminal speed.

[0061] For example, the Time Sensitive Communication Auxiliary Information (TSCAI) parameter for the current moment is received from an upper-layer industrial application or NEF.

[0062] S102. Based on TSCAI parameters and network environment parameters, generate real-time network state feature vectors.

[0063] Among them, the real-time network state feature vector is used to characterize the instantaneous state of the cell.

[0064] As one possible implementation of S102 above, the TSCAI parameters are transformed to obtain the TSCAI feature vector; the TSCAI feature vector is combined with the network environment parameters to obtain the real-time network state feature vector.

[0065] For example, the allowable failure rate is mapped to a reliability strength characteristic. Formula 1 for calculating the reliability strength characteristic is as follows: Lε=log10(1 / ε) formula 1 Where ε is the allowable failure rate.

[0066] For example, for a service with an allowable failure rate ε=0.001, if its reliability strength is calculated as Lε=log10(1 / 0.001)=3, it means that the service requires high reliability assurance.

[0067] For example, the difference between the time of the outbreak and the survival time is mapped to a survival margin feature. For instance, for the time of the outbreak T... b =10ms, Survival time T r For a service with a duration of 30ms, calculate the survival margin T. l =30-10=20ms, indicating that there is ample time leeway for the service to complete the transmission.

[0068] For example, burst times can be mapped to time sensitivity features. For instance, a burst time Tb = 2ms indicates that the service is highly sensitive to latency.

[0069] For example, a TSCAI feature vector is generated based on reliability strength characteristics, survival margin characteristics, and time sensitivity characteristics.

[0070] Among them, the reliability strength feature is negatively correlated with the allowable failure rate; the survival margin feature is used to characterize the urgency of the time required for the service to complete the transmission; and the time sensitivity feature is used to characterize the service's tolerance for latency.

[0071] Furthermore, the TSCAI feature vector is combined with network environment parameters (including base station density d_bs, average interference value (average interference I_avg), network interference value (95% interference I_95), and terminal speed v) to form a 7-dimensional real-time network state feature vector θ=[Lε, Tr, Tb, d_bs, I_avg, I_95, v).

[0072] S103. Determine N historical network state feature vectors that are similar to the real-time network state feature vectors.

[0073] As one possible implementation of S103 above, the feature distance between the real-time network state feature vector and multiple historical network state feature vectors is calculated; based on the feature distance, the N most similar historical network state feature vectors are selected.

[0074] Among them, the similarity between the real-time network state feature vector and multiple historical network state feature vectors is negatively correlated with the feature distance.

[0075] For example, the feature distance d is calculated using the following formula 2.

[0076] Formula 2 in, This represents the k-th component of the real-time feature vector. This represents the k-th component of the i-th historical feature vector. This represents the standard deviation of the k-th feature. This indicates the feature weight (e.g., setting the weight of the time sensitivity feature to 0.3, the weight of the reliability strength feature to 0.2, etc.).

[0077] Furthermore, multiple feature distances are calculated, and the N=10 historical feature vectors with the smallest distances are selected.

[0078] S104. Determine the historical resource scheduling strategy for each of the N historical network state feature vectors.

[0079] It should be understood that a historical network state feature vector corresponds to a historical resource scheduling strategy.

[0080] For example, historical resource scheduling strategies may include specific scheduling decisions such as the number of allocated physical resource blocks (PRBs), the modulation and coding scheme (MCS) level, and whether to use coordinated multi-point (CoMP) technology.

[0081] S105. Based on the service transmission results and allowed failure rate corresponding to the historical resource scheduling strategy, the historical meta-probability index is calculated.

[0082] Among them, the historical probability index is used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current business among N historical resource scheduling strategies.

[0083] As one possible implementation of S105 above, the reliability requirements of the current service are determined based on the allowable failure rate; the number of historical resource scheduling strategies that meet the reliability requirements among N historical resource scheduling strategies is determined; and the ratio of the number to N is used as the historical probability index.

[0084] For example, for a service with ε=0.001, the reliability requirement is a transmission success rate of no less than 99.9%; in 10 historical schemes, 7 schemes achieved a transmission success rate of over 99.9%, which is a historical probability index. It can be obtained through the following formula 3: Formula 3 Right now =7 / 10=0.7, which means that the reliability estimate based on historical experience is 70%.

[0085] S106. Based on historical meta-probability indicators, burst time, and survival time, determine the amount of redundant resources required for the current service transmission.

[0086] As a possible implementation of the above S106, the tail risk factor is calculated based on the burst time, target signal-to-interference-plus-noise ratio, and network interference value; the survival time weight factor is determined based on the survival time; the current meta-probability index is obtained based on the historical meta-probability index, the tail risk factor, and the survival time weight factor; if the current meta-probability index is less than the dynamic threshold, the amount of redundant resources required for the current service transmission is calculated based on the TSCAI parameter and resource adjustment coefficient at the current moment.

[0087] Among them, the tail risk factor is used to characterize the risk probability of extreme abnormal events occurring; the survival time weight factor is used to characterize the urgency of the current business; and the current meta-probability index is used to characterize the estimated probability that the current business transmission meets the reliability requirements.

[0088] For example, tail risk factors It can be obtained through the following formula 4: Formula 4 Where φ=0.5v+0.1d_bs represents the event occurrence rate (v is the terminal speed, d_bs is the base station density), ε and σ are the shape and scale parameters of the generalized Pareto distribution (GDP), I_95 represents the 95% interference value, and γ0 represents the target signal-to-interference-plus-noise ratio.

[0089] In some embodiments, the survival time weight factor is obtained by: obtaining the exponential decay factor corresponding to the survival time; determining the decay weight component based on the exponential decay factor and a preset weight coefficient; and calculating the survival time weight factor based on the difference between the base weight value and the decay weight component.

[0090] Among them, the exponential decay factor is negatively correlated with survival time, that is, the smaller the survival margin Tr is, the smaller the exponential decay factor is, and the lower the survival time weight factor is.

[0091] For example, survival time weighting factor It can be obtained through the following formula 5: Formula 5 in, The exponential decay factor is 0.1, which is the preset weight coefficient, and 1 is the base weight value.

[0092] For example, when Tr=2ms, ω_life=1-0.1(1-e^(-1))=0.9-0.1×0.632=0.8368, reflecting the reliability discount caused by time urgency.

[0093] Furthermore, the current meta-probability index It can be obtained through the following formula 6: Formula 6 For example, assuming the calculated tail risk factor τ = 0.1, the survival time weighting factor... =0.95, then the current probability index is... =0.7×(1-0.1)×0.95=0.5985.

[0094] It should be noted that if the current probability index is less than the dynamic threshold, indicating a risk level of three (critical), then the resource allocation needs to be modified using the above scheme to determine the amount of redundant resources required for the current service transmission.

[0095] In addition, regarding risk levels The determination can be made using the following formula 7.

[0096] Formula 7 In the current meta-probability index Less than the dynamic threshold If the risk level is determined to be level three, the amount of redundant resources required for the current service transmission needs to be calculated based on the TSCAI parameters and resource adjustment coefficients at the current moment.

[0097] In some embodiments, the signal-to-interference-plus-noise ratio (SIR) difference is determined based on the target SIR and the current SIR; the product of the SIR difference and the resource adjustment coefficient is used as the amount of redundant resources required by the current network.

[0098] For example, if the target SINR γ0 = 20dB and the current worst SINR is 15dB, then the signal-to-interference-plus-noise ratio gap G = 20 - 15 = 5dB.

[0099] Assuming the resource adjustment coefficient k=1.2, the redundant resource requirement is N_RB=1.2×5 / 3=2 resource blocks, or N_TRP=1.2×5 / 2=3 transmission points.

[0100] Specifically, the redundancy scheme is selected based on cost: the cost of the transmit-receive point (TRP) scheme = 0.3 × 3 = 0.9.

[0101] The cost of the resource block (RB) scheme is 1.0 × 2 = 2.0.

[0102] In summary, since 0.9 < 2.0, the TRP scheme is selected.

[0103] Furthermore, three TRPs (Transmission Points) are activated to form a cooperative MIMO array, and the terminal device is notified through the DCI field.

[0104] For example, the resource adjustment coefficient k mentioned above can be determined by the following formula 8.

[0105] Formula 8 In some embodiments, the dynamic threshold is obtained by using the ratio of survival time to the time of the outbreak as a survival urgency factor; when the survival urgency factor is less than a preset threshold, the initial threshold is added to the threshold adjustment coefficient to obtain the dynamic threshold.

[0106] For example, the survival urgency factor u is obtained by the following formula 9.

[0107] Formula 9 Where u is the survival urgency factor.

[0108] For example, It takes 15ms. If the time limit is 10ms, then the survival urgency factor u = 15 / 10 = 1.5.

[0109] Furthermore, assuming a preset threshold of 2, when the survival urgency factor 1.5 is less than the preset threshold of 2, the initial threshold (e.g., β=0.6) is added to the threshold adjustment coefficient (e.g., 0.00005) to obtain the dynamic threshold. =0.60005.

[0110] It should be understood that embodiments of this application can enhance sensitivity to reliability risks by increasing the dynamic threshold.

[0111] In some embodiments, the dynamic threshold can also be obtained by using the ratio of survival time to outbreak time as a survival urgency factor, and using an initial threshold as a dynamic threshold when the survival urgency factor is greater than a preset threshold.

[0112] For example, It takes 30ms. If the time frame is 10ms, then the survival urgency factor u = 0 / 10 = 3. Furthermore, assuming a preset threshold of 2, if the survival urgency factor 1.5 is less than the preset threshold 2, the initial threshold (e.g., β = 0.6) is used as the dynamic threshold. =0.6.

[0113] It should be understood that when there is sufficient time leeway, the original threshold should be maintained to avoid overly conservative resource allocation.

[0114] For example, in an actual deployment at a car factory, this system reduced the failure rate of robotic arm control command transmission from 0.1% to 0.001%, while simultaneously reducing wireless resource costs by 45%. When a sudden movement of the robotic arm causes the terminal speed v to change from 0 to 20 km / h, the system uses a survival time weighting factor... Dynamically adjust redundancy levels to avoid production line downtime caused by channel mutations.

[0115] The technical solution provided in this application provides at least the following beneficial effects: by generating a real-time network state feature vector from the acquired TSCAI parameters and network environment parameters, accurate input is provided for subsequent analysis; thereby matching similar historical feature vectors and corresponding scheduling strategies, the decision-making scope is narrowed by leveraging historical experience; then, by combining service transmission results and allowable failure rates, historical meta-probability indicators are calculated to quantify the proportion of strategies that meet reliability requirements, providing a basis for resource decision-making; finally, the number of redundant resources is determined by comprehensively considering this indicator, burst time, and survival time. Each step is progressive, fully combining real-time status and historical experience, and finally determining a reasonable number of resources to ensure the reliability of service transmission.

[0116] Figure 5 A flowchart illustrating another resource determination method provided in this application embodiment is shown below. Figure 5 As shown, the method also includes the following steps: S201. When the current meta-probability index is greater than or equal to the dynamic threshold, the resource quantity in the historical resource scheduling strategy corresponding to the historical network state feature vector with the smallest feature distance is taken as the resource quantity required for the current service transmission.

[0117] It should be understood that, according to Formula 7 above, when the current probability index is greater than or equal to the dynamic threshold, it indicates that the risk level is low and the resource quantity in the historical resource scheduling strategy can be adopted.

[0118] For example, in the current meta-probability index Less than the dynamic threshold And greater than or equal to the dynamic threshold In the case of a risk level of 2, the historical network state feature vector corresponding to the minimum feature distance can be determined. Then, the historical resource scheduling strategy associated with the historical network state feature vector can be determined, thereby obtaining the number of resources in the historical resource scheduling strategy, and using the number of resources as the number of resources required for the current service transmission.

[0119] Another example is the current meta-probability index. Greater than the dynamic threshold In the case of a risk level of 1, the historical network state feature vector corresponding to the minimum feature distance can be determined. Then, the historical resource scheduling strategy associated with the historical network state feature vector can be determined, thereby obtaining the number of resources in the historical resource scheduling strategy, and using the number of resources as the number of resources required for the current service transmission.

[0120] The technical solution provided in this application has at least the following beneficial effects: when the current meta-probability index meets the standard, the resource quantity with the most similar historical strategy can be selected directly, simplifying the decision-making process, improving the efficiency of resource determination, and ensuring the rationality of the decision.

[0121] In summary, this application's embodiments, by introducing statistically based reliability metrics and real-time network awareness information, can dynamically adjust resource allocation strategies according to service requirements and channel environment, achieving synergistic optimization of link layer reliability and resource utilization efficiency. Specifically, it achieves dynamic adjustment of resource allocation length, avoiding... Figure 1 This addresses the fragmentation issue while ensuring that URLLC retransmission does not preempt the entire PRB, thus avoiding impacting eMBB resource allocation.

[0122] In an exemplary embodiment, this application also provides a resource determination device, which can be applied to the first traffic transmission control device of the data receiving end described above. Figure 6 This is a schematic diagram of a resource determination device provided in an embodiment of this application. Figure 6 As shown, the resource determination device 1000 includes a processing unit 1001 and an acquisition unit 1002.

[0123] In some embodiments, the acquisition unit 1002 is used to acquire the Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment; the TSCAI parameters include the allowed failure rate, burst time, and survival time.

[0124] In some embodiments, the processing unit 1001 is used to generate a real-time network state feature vector based on TSCAI parameters and network environment parameters; the real-time network state feature vector is used to characterize the instantaneous state of the cell.

[0125] In some embodiments, the processing unit 1001 is further configured to determine N historical network state feature vectors that are similar to the real-time network state feature vectors, and to determine the historical resource scheduling strategy of each of the N historical network state feature vectors.

[0126] In some embodiments, the processing unit 1001 is further configured to calculate a historical element probability index based on the service transmission results and allowed failure rate corresponding to the historical resource scheduling strategy. The historical element probability index is used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current service among N historical resource scheduling strategies.

[0127] In some embodiments, the processing unit 1001 is further configured to determine the amount of redundant resources required for the current service transmission based on historical meta-probability indicators, burst time, and lifetime.

[0128] In some embodiments, the TSCAI parameters further include the target signal-to-interference-plus-noise ratio (SINR); network environment parameters include network interference values; the processing unit 1001 is specifically used to calculate the tail risk factor based on the burst time, the target SINR, and the network interference value; the tail risk factor is used to characterize the probability of extreme abnormal events occurring; a survival time weighting factor is determined based on the survival time; the survival time weighting factor is used to characterize the urgency of the current service; a current meta-probability index is obtained based on the historical meta-probability index, the tail risk factor, and the survival time weighting factor; the current meta-probability index is used to characterize the estimated probability that the current service transmission meets the reliability requirements; when the current meta-probability index is less than a dynamic threshold, the amount of redundant resources required for the current service transmission is calculated based on the TSCAI parameters and resource adjustment coefficients at the current time.

[0129] In some embodiments, the processing unit 1001 is further configured to, when the current meta-probability index is greater than or equal to the dynamic threshold, use the number of resources in the historical resource scheduling strategy corresponding to the historical network state feature vector with the smallest feature distance as the number of resources required for the current service transmission.

[0130] In some embodiments, the TSCAI parameter at the current moment also includes the signal-to-interference-plus-noise ratio (SIR) at the current moment; the processing unit 1001 is specifically used to determine the SIR difference based on the target SIR and the SIR at the current moment; and to use the product of the SIR difference and the resource adjustment coefficient as the amount of redundant resources required by the current network.

[0131] In some embodiments, the dynamic threshold is obtained in the following manner: the processing unit 1001 is specifically used to take the ratio of survival time to sudden time as the survival urgency factor; when the survival urgency factor is less than a preset threshold, the initial threshold is added to the threshold adjustment coefficient to obtain the dynamic threshold; or, when the survival urgency factor is greater than the preset threshold, the initial threshold is used as the dynamic threshold.

[0132] In some embodiments, the processing unit 1001 is specifically used to obtain the exponential decay factor corresponding to the survival time, wherein the exponential decay factor is negatively correlated with the survival time; determine the decay weight component based on the exponential decay factor and a preset weight coefficient; and calculate the survival time weight factor based on the difference between the basic weight value and the decay weight component.

[0133] In some embodiments, the processing unit 1001 is specifically used to calculate the feature distance between the real-time network state feature vector and multiple historical network state feature vectors; and to select the N most similar historical network state feature vectors based on the feature distance.

[0134] In some embodiments, the similarity between real-time network state feature vectors and multiple historical network state feature vectors is negatively correlated with feature distance.

[0135] In some embodiments, the processing unit 1001 is specifically configured to determine the reliability requirements of the current service based on the allowable failure rate; determine the number of historical resource scheduling strategies that meet the reliability requirements among N historical resource scheduling strategies; and use the ratio of the number to N as a historical probability index.

[0136] In some embodiments, the processing unit 1001 is specifically used to perform feature transformation on the TSCAI parameters to obtain the TSCAI feature vector; and to combine the TSCAI feature vector with the network environment parameters to obtain the real-time network state feature vector.

[0137] In some embodiments, the processing unit 1001 is specifically configured to map the allowable failure rate to a reliability strength feature; the reliability strength feature is negatively correlated with the allowable failure rate; map the difference between burst time and lifetime to a lifetime margin feature; the lifetime margin feature is used to characterize the urgency of the time required for the service to complete transmission; map the burst time to a time sensitivity feature; the time sensitivity feature is used to characterize the service's tolerance for latency; and generate a TSCAI feature vector based on the reliability strength feature, lifetime margin feature, and time sensitivity feature.

[0138] Of course, the resource determination device 1000 includes, but is not limited to, the unit modules listed above. Furthermore, the specific functions that the aforementioned functional units can implement include, but are not limited to, the functions corresponding to the method steps in the above embodiments. For detailed descriptions of other modules of the resource determination device 1000, please refer to the detailed descriptions of their corresponding method steps; these descriptions will not be repeated here.

[0139] In an exemplary embodiment, this application also provides a computer program product that, when run on a computer, causes the computer to execute the aforementioned related method steps to implement the resource determination method in the above embodiments.

[0140] In an exemplary embodiment, this application also provides an electronic device. Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device may include a processor 1101 and a memory 1102; the memory 1102 stores instructions executable by the processor 1101; when the processor 1101 is configured to execute the instructions, the electronic device implements the method as described in the foregoing method embodiments.

[0141] In an exemplary embodiment, this application also provides a computer-readable storage medium storing computer program instructions thereon; when the computer program instructions are executed by an electronic device, the electronic device performs the method as described in the foregoing embodiments. The computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0142] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer-executable instructions. When these computer-executable instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer-executable instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0143] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0144] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0145] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining resources, characterized in that, The method includes: Obtain the Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment; the TSCAI parameters include the allowable failure rate, burst time, and lifetime. Based on the TSCAI parameters and the network environment parameters, a real-time network state feature vector is generated; the real-time network state feature vector is used to characterize the instantaneous state of the cell. Determine N historical network state feature vectors that are similar to the real-time network state feature vector, and determine the historical resource scheduling strategy for each of the N historical network state feature vectors; Based on the service transmission results corresponding to the historical resource scheduling strategy and the allowed failure rate, a historical element probability index is calculated. The historical element probability index is used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current service among the N historical resource scheduling strategies. Based on the historical probability index, the burst time, and the survival time, the amount of redundant resources required for the current service transmission is determined.

2. The method according to claim 1, characterized in that, The TSCAI parameters also include the target signal-to-interference-plus-noise ratio; the network environment parameters include network interference values. The determination of the amount of redundant resources required for the current service transmission based on the historical probability index, the burst time, and the lifetime includes: Based on the burst time, the target signal-to-interference-plus-noise ratio, and the network interference value, a tail risk factor is calculated; the tail risk factor is used to characterize the probability of extreme abnormal events occurring. Based on the survival time, a survival time weighting factor is determined; the survival time weighting factor is used to characterize the urgency of the current business. Based on the historical meta-probability index, the tail risk factor, and the survival time weight factor, the current meta-probability index is obtained; the current meta-probability index is used to characterize the estimated probability that the current service transmission meets the reliability requirements. If the current probability index is less than the dynamic threshold, the amount of redundant resources required for the current service transmission is calculated based on the TSCAI parameter and resource adjustment coefficient at the current time.

3. The method according to claim 2, characterized in that, The method further includes: When the current meta-probability index is greater than or equal to the dynamic threshold, the number of resources in the historical resource scheduling strategy corresponding to the historical network state feature vector with the smallest feature distance is taken as the number of resources required for the current service transmission.

4. The method according to claim 2, characterized in that, The TSCAI parameters at the current moment also include the signal-to-interference-plus-noise ratio at the current moment; The step of calculating the amount of redundant resources required for the current service transmission based on the TSCAI parameters and resource adjustment coefficient at the current moment includes: Based on the target signal-to-interference-plus-noise ratio (SIR) and the current SIR, the SIR difference is determined; The product of the signal-to-interference-plus-noise ratio difference and the resource adjustment coefficient is taken as the amount of redundant resources required by the current network.

5. The method according to any one of claims 2-4, characterized in that, The dynamic threshold is obtained in the following way: The ratio of the survival time to the sudden event time is used as the survival urgency factor; When the survival urgency factor is less than a preset threshold, the initial threshold is added to the threshold adjustment coefficient to obtain the dynamic threshold; or, If the survival urgency factor is greater than the preset threshold, the initial threshold is used as the dynamic threshold.

6. The method according to claim 2, characterized in that, The determination of the survival time weighting factor based on the survival time includes: Obtain the exponential decay factor corresponding to the survival time, wherein the exponential decay factor is negatively correlated with the survival time; Based on the exponential decay factor and the preset weight coefficient, the decay weight component is determined; The survival time weight factor is calculated based on the difference between the base weight value and the decay weight component.

7. The method according to claim 1, characterized in that, The step of determining N historical network state feature vectors similar to the real-time network state feature vector includes: Calculate the feature distance between the real-time network state feature vector and multiple historical network state feature vectors; Based on the feature distance, the N most similar historical network state feature vectors are selected.

8. The method according to claim 7, characterized in that, The similarity between the real-time network state feature vector and the multiple historical network state feature vectors is negatively correlated with the feature distance.

9. The method according to claim 1, characterized in that, The historical meta-probability index is calculated based on the service transmission results corresponding to the historical resource scheduling strategy and the allowed failure rate, including: Based on the allowable failure rate, determine the reliability requirements of the current service; Determine the number of historical resource scheduling strategies that meet the reliability requirements among the N historical resource scheduling strategies; The ratio of the quantity to N is used as the historical element probability index.

10. The method according to claim 1, characterized in that, The process of generating a real-time network state feature vector based on the TSCAI parameters and the network environment parameters includes: The TSCAI parameters are transformed to obtain the TSCAI feature vector; The TSCAI feature vector is combined with the network environment parameters to obtain the real-time network state feature vector.

11. The method according to claim 10, characterized in that, The step of performing feature transformation on the TSCAI parameters to obtain the TSCAI feature vector includes: The allowable failure rate is mapped to a reliability strength feature; the reliability strength feature is negatively correlated with the allowable failure rate. The difference between the burst time and the survival time is mapped to a survival margin feature; the survival margin feature is used to characterize the urgency of the time required for the service to complete transmission. The burst time is mapped to a time sensitivity feature; the time sensitivity feature is used to characterize the service's tolerance for latency; The TSCAI feature vector is generated based on the reliability strength feature, the survival margin feature, and the time sensitivity feature.

12. The method according to claim 1, characterized in that, The network environment parameters include at least one of the following: base station density, average interference value, network interference value, and terminal speed.

13. A resource determination device, characterized in that, The device includes: a processing unit and an acquisition unit; The acquisition unit is used to acquire the Time Sensitive Communication Auxiliary Information (TSCAI) parameters and network environment parameters at the current moment; the TSCAI parameters include the allowable failure rate, burst time, and survival time. The processing unit is used to generate a real-time network state feature vector based on the TSCAI parameters and the network environment parameters; the real-time network state feature vector is used to characterize the instantaneous state of the cell. The processing unit is further configured to determine N historical network state feature vectors that are similar to the real-time network state feature vector, and to determine the historical resource scheduling strategy of each of the N historical network state feature vectors. The processing unit is further configured to calculate a historical probability index based on the service transmission results corresponding to the historical resource scheduling strategy and the allowed failure rate. The historical probability index is used to characterize the proportion of historical resource scheduling strategies that meet the reliability requirements of the current service among the N historical resource scheduling strategies. The processing unit is further configured to determine the amount of redundant resources required for the current service transmission based on the historical probability index, the burst time, and the lifetime.

14. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory stores instructions that the processor can execute; When the processor is configured to execute the instructions, it causes the electronic device to implement the method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes: computer software instructions; When the computer software instructions are executed in an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1 to 12.

16. A computer program product comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed on a processing device, the method of any one of claims 1 to 12 is implemented.

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