A resource allocation system, method, device, and medium
By applying multi-dimensional spectrum data and dynamic transition probability matrices, a multi-objective optimization model was established, which solved the problem of incomplete channel assessment of RedCap terminals in vehicle scenarios, and achieved comprehensive optimization of channel status and improvement of stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA MOBILE GROUP SICHUAN
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-16
Smart Images
Figure CN122227402A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of wireless communication technology, and in particular to a resource allocation system, method, device and medium. Background Technology
[0002] Dynamic channel perception, resource scheduling, and access control optimization technologies for 5G network-enabled reduced-capability (RedCap) terminals are particularly suitable for vehicle-to-everything (V2X) scenarios, autonomous driving assistance, in-vehicle IoT devices (such as in-vehicle sensors and in-vehicle entertainment terminals), and vehicle-road cooperation.
[0003] The existing RedCap terminal architecture relies on a fixed strategy for allocating resources based on static parameter thresholds. When a RedCap terminal initiates an access request, the base station collects single-dimensional communication parameters reported by the RedCap terminal through the Uu interface. The main parameters are signal-to-noise ratio (SNR), received signal power (RSRP), or channel occupancy rate. Some schemes only add packet loss rate (PER) as an auxiliary criterion. The base station uses a simple threshold comparison method or mean filtering to process the data. For example, it sets SNR ≥ 15dB as "available channel" and SNR < 15dB as "unavailable channel," or it calculates the mean RSRP through a sliding window (window size N=10) and allocates fixed bandwidth resources (such as 10MHz / 20MHz) based on the mean result.
[0004] However, existing technologies only consider single-dimensional communication parameters and allocate resources based on the current state, failing to cover the core influencing factors in vehicle scenarios. This results in the evaluation results failing to reflect the full picture of the channel under high-speed movement, leading to passive switching situations when the channel quality suddenly changes or deteriorates, affecting the stability of vehicle data transmission, and causing vehicle data transmission lag and delay. Summary of the Invention
[0005] This invention provides a resource allocation system, method, device, and medium to achieve synergistic optimization of the stability of vehicle terminal access and communication efficiency.
[0006] According to a first aspect of the present invention, a resource allocation system is provided, including an in-vehicle terminal, a roadside edge computing node, and a base station;
[0007] The vehicle-mounted terminal is used to acquire multi-dimensional spectrum status data in the vehicle environment and upload it to the roadside edge computing node;
[0008] The roadside edge computing node is used to determine the dynamic transition probability matrix based on the multi-dimensional spectrum state data and upload it to the base station;
[0009] The base station is used to construct and solve a multi-objective optimization model based on the dynamic transfer matrix and the base station's global resource information, with the optimization objectives being spectrum resource utilization, handover cost, and communication quality in the vehicle scenario, in order to determine the channel allocation scheme for the vehicle terminal.
[0010] According to a second aspect of the present invention, a resource allocation method is provided, applied to a resource allocation system, the system including an on-board terminal, a roadside edge computing node, and a base station, the method comprising:
[0011] The vehicle-mounted terminal acquires multi-dimensional spectrum status data in the vehicle environment and uploads it to the roadside edge computing node.
[0012] The roadside edge computing node determines the dynamic transition probability matrix based on the multi-dimensional spectrum state data and uploads it to the base station;
[0013] The base station constructs and solves a multi-objective optimization model based on the dynamic transfer matrix and global resource information of the base station, with the optimization objectives being spectrum resource utilization, handover cost, and communication quality in the vehicle scenario, in order to determine the channel allocation scheme of the vehicle terminal.
[0014] According to a third aspect of the present invention, an electronic device is provided, which serves as a vehicle-mounted terminal, roadside edge computing node, or base station as described in any embodiment of the present invention, comprising:
[0015] At least one processor; and
[0016] A memory communicatively connected to the at least one processor; wherein,
[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the resource allocation method according to any embodiment of the present invention.
[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the resource allocation method described in any embodiment of the present invention.
[0019] The technical solution of this invention involves an in-vehicle terminal acquiring multi-dimensional spectrum status data in the in-vehicle environment and uploading it to a roadside edge computing node. The roadside edge computing node determines a dynamic transition probability matrix based on the multi-dimensional spectrum status data and uploads it to the base station. The base station, based on the dynamic transition matrix and its global resource information, constructs and solves a multi-objective optimization model with spectrum resource utilization, handover cost, and in-vehicle communication quality as optimization objectives, thereby determining the channel allocation scheme for the in-vehicle terminal. This solves the problem of one-sided evaluation using a single parameter, achieving a comprehensive characterization of channel status in in-vehicle scenarios. A multi-objective optimization channel allocation mathematical model for in-vehicle scenarios is established, taking into account the resource competition of multiple vehicles accessing concurrently, low latency for autonomous driving, and high reliability of data transmission. This improves resource utilization while reducing handover costs, achieving coordinated optimization of in-vehicle RedCap terminal access stability and communication efficiency.
[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 This is a schematic diagram of a resource allocation system according to Embodiment 1 of the present invention;
[0023] Figure 2 This is a flowchart of a resource allocation method provided according to Embodiment 2 of the present invention;
[0024] Figure 3 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation
[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0027] Example 1
[0028] Figure 1 This is a schematic diagram of a resource allocation system provided in Embodiment 1 of the present invention. This embodiment is applicable to resource allocation in vehicle-mounted RedCap terminals. The resource allocation system can be implemented in hardware and / or software and can be configured in electronic devices. Figure 1 As shown, the system includes: vehicle-mounted terminal 11, roadside edge computing node 12, and base station 13.
[0029] In this embodiment, the vehicle-mounted terminal 11, also known as the vehicle-mounted RedCap terminal, is a lightweight 5G terminal adapted to the vehicle environment, integrating vehicle-grade RF sensors, speed sensors, a low-power MCU, and a 5G communication module. The roadside edge computing node 12 can be understood as an edge computing device deployed on the roadside (such as near roadside poles or traffic lights). The base station 13 can be understood as the core infrastructure of the 5G network, possessing global spectrum resource management, standard 5G protocol adaptation, and multi-objective optimization scheduling capabilities. It integrates a server-grade CPU and a dedicated vehicle-mounted scheduling chip, serving as the final decision-making and execution unit for resource allocation.
[0030] The vehicle-mounted terminal 11 is used to acquire multi-dimensional spectrum status data in the vehicle environment and upload it to the roadside edge computing node 12; the roadside edge computing node 12 is used to determine the dynamic transition probability matrix based on the multi-dimensional spectrum status data and upload it to the base station 13; the base station 13 is used to construct and solve a multi-objective optimization model based on the dynamic transition matrix and the global resource information of the base station 13, with spectrum resource utilization, handover cost and vehicle scenario communication quality as optimization objectives, and determine the channel allocation scheme of the vehicle-mounted terminal 11.
[0031] In this embodiment, multi-dimensional spectrum state data can be understood as data used to characterize the vehicle's spectrum state across multiple dimensions. For example, it may include vehicle-specific spectrum information in seven core dimensions: signal-to-noise ratio (SNR, dB), interference power (P_int, dBm), bandwidth utilization (η, values ∈ [0,1]), signal delay (τ, ms), packet loss rate (P_er, values ∈ [0,1]), Doppler frequency offset (f_d, Hz), and vehicle speed (v, km / h), comprehensively depicting the characteristics of the vehicle channel. The dynamic transition probability matrix can be understood as the probability of transitions between different channel states at each moment. The global resource information of base station 13 can be understood as the core 5G network resources within the area controlled by base station 13, including total available bandwidth (e.g., 200MHz), the number of available channels, and the maximum transmit power limit (23dBm). The multi-objective optimization model can be understood as a resource scheduling mathematical model designed for vehicle-mounted scenarios. The core optimization objectives include maximizing spectrum resource utilization, minimizing handover costs, and optimizing communication quality (low latency and high reliability). The channel allocation scheme can be understood as the optimal resource configuration result obtained by base station 13 through solving the multi-objective optimization model, including the channel number, bandwidth, transmit power parameters, and whether a channel handover command needs to be triggered in advance for vehicle terminal 11.
[0032] Specifically, the vehicle-mounted terminal 11 can use built-in sensors and a 5G communication module to collect multi-dimensional spectrum data at an adaptive sampling frequency (200Hz for basic sampling, increasing to 300Hz for vehicle speed ≥ 100km / h, and decreasing to 100Hz for battery ≤ 20%), and add high-precision timestamps (synchronization error ≤ 0.5ms) for time synchronization with the base station 13, for example, by setting the upload period. The data is uploaded to the roadside edge computing node 12 via a dedicated control channel using the 5GUu interface within 30ms to avoid data transmission delays affecting the real-time performance of the vehicle-mounted scenario. The roadside edge computing node 12 can preprocess the multi-dimensional spectrum data, perform state adaptive classification on the preprocessed data, and calculate the transition probability and construct a dynamic matrix based on the classification results, obtaining a real-time dynamic transition probability matrix which is then uploaded to the base station 13. Based on the dynamic transition probability matrix and global resource information, the base station 13 constructs a multi-objective optimization model aiming to maximize resource utilization, minimize handover costs, and optimize communication quality. Multiple constraints are set, and the multi-objective optimization model is solved using appropriate algorithms to obtain the output channel allocation scheme.
[0033] The technical solution of this invention addresses the problem of one-sided evaluation using a single parameter, achieving a comprehensive characterization of channel status in vehicular scenarios. It establishes a multi-objective optimized channel allocation mathematical model for vehicular scenarios, taking into account the resource contention of multiple vehicles accessing concurrently, low latency for autonomous driving, and high reliability of data transmission. This improves resource utilization while reducing switching costs, achieving coordinated optimization of access stability and communication efficiency for vehicular RedCap terminals.
[0034] Furthermore, based on the above embodiments, the roadside edge computing node 12 includes:
[0035] The information extraction module is used to preprocess multi-dimensional spectrum state data to obtain a standardized spectrum state information matrix.
[0036] Specifically, the information extraction module receives raw multi-dimensional spectrum state data uploaded by the vehicle-mounted RedCap terminal via a dedicated control channel of the 5GUu interface. To eliminate noise and outliers in the raw data caused by electromagnetic interference and vibration in the vehicle environment and to ensure feature validity, the information extraction module can preprocess the multi-dimensional spectrum state data. This preprocessing can include noise suppression, outlier removal, and data normalization to obtain a standardized spectrum state information matrix. The information extraction module then transmits the completed standardized spectrum state information matrix to the state classification module of the roadside edge computing node 12 in real time, providing high-quality input data for subsequent adaptive classification of vehicle channel states based on K-means++, ensuring a classification accuracy of ≥92%.
[0037] For example, noise suppression can employ an enhanced moving average filtering method to denoise the data across various dimensions, with the moving window size... (Adapting to the high-frequency fluctuation characteristics of vehicle data), the formula is as follows:
[0038]
[0039] in, For vehicle terminal 11 No. Dimensional data at time The denoising results For vehicle terminal 11 history The raw data at each moment.
[0040] Outlier removal can be based on the 3σ criterion (adapted to the abrupt changes in vehicle data) to identify outliers. satisfy If the value is not found, it is considered an outlier and replaced using weighted linear interpolation (to improve the accuracy of processing mutation data).
[0041]
[0042] in, For the first The terminal The mean of the dimensional data. The standard deviation is denoted as .
[0043] Data normalization can be achieved by using min-max normalization to map the data to... The interval, the formula is as follows:
[0044]
[0045] in, , The first The 11th vehicle-mounted terminal Minimum and maximum values of dimensional data These are the normalized eigenvalues.
[0046] The final output is a standardized spectrum state information matrix. Each row corresponds to a vector of spectral state information at a given time. .
[0047] The state classification module is used to classify the spectral state information matrix into vehicle-mounted channel states based on an improved clustering algorithm to obtain a channel state sequence.
[0048] In this embodiment, the improved clustering algorithm can be understood as a K-means++ algorithm optimized for the characteristics of vehicle-mounted scenario data (high-speed fluctuations, multi-dimensionality, and multi-terminal concurrency). Core improvements include dynamically determining the optimal number of clusters by improving the elbow rule, optimizing the cluster center initialization strategy, and setting convergence thresholds and maximum iterations adapted to vehicle-mounted requirements, thus solving the problems of low classification accuracy and poor adaptability of traditional clustering algorithms. The channel state sequence can be understood as a set of vehicle-mounted channel states arranged in chronological order.
[0049] Specifically, the state classification module can use an improved clustering algorithm to determine the optimal number of channel state categories and optimize the initialization of cluster centers. Through iterative updates and convergence determination of clustering, it can classify the vehicle-mounted channel state of the spectrum state information matrix, determine the unique channel state category corresponding to the spectrum state information at each time point, and sort the channels in chronological order to obtain the channel state sequence.
[0050] The probability calculation module is used to determine the dynamic transition probability matrix based on the channel state sequence and upload it to base station 13.
[0051] In this embodiment, the dynamic transition probability matrix can be understood as a model used to quantify the evolution of the vehicle channel state.
[0052] Specifically, the probability calculation module can calculate the dynamic transition probability matrix based on the channel state sequence through maximum likelihood estimation, and continuously provide the latest transition probability calculation results within the window for matrix updates based on the update parameters.
[0053] Specifically, the state classification module is used for:
[0054] Based on the elbow rule and the spectral state information matrix, the cluster sum of squares function under different optimal numbers of clusters is determined; the cluster centers are initialized based on the improved clustering algorithm to obtain the initial cluster centers; the initial cluster centers are iteratively updated and classified by minimizing the cluster sum of squares function until the termination condition is met, and the iteration stops to obtain the channel state sequence.
[0055] In this embodiment, the Elbow Method is a quantitative analysis method used to determine the optimal number of categories for channel states. The optimal number of categories can be understood as the optimal number of categories (C≥4) for classifying vehicle channel states determined by the Elbow Method. In practical applications, it is usually set to 5 (Excellent S1, Good S2, Average S3, Poor S4, Extremely Poor S5) to ensure that the classification is both precise and non-redundant, comprehensively characterizing the differences in vehicle channel quality. The WCSS function can be understood as the core objective function used to quantify the clustering effect. The cluster centers can be understood as the mean of the feature vectors of each channel state category. The initial cluster centers can be understood as the initial category core vectors optimized and selected using the K-means++ algorithm. The termination condition can be understood as the criteria for stopping the clustering iteration, which may include the change in cluster centers reaching the accuracy requirements of the vehicle data, or the number of iterations reaching a set maximum value.
[0056] Specifically, the state classification module can use the elbow rule to calculate the sum of squares within clusters for different numbers of categories, optimize the initial cluster center selection by improving the clustering algorithm to avoid local optima, and iteratively update the cluster centers by minimizing the sum of squares within clusters until the termination condition is met to stop the iteration and obtain the channel state sequence.
[0057] For example, the Elbow Method is used to calculate the number of different optimal categories. ( The corresponding cluster inner sum of squares (WCSS) function is shown in the following formula:
[0058]
[0059] in, For the first Cluster center vector of vehicle-mounted channel state, This is an indicator function (it takes a value of 1 if the condition is met, and 0 otherwise), and its definition remains the same. Let be the Euclidean distance. When the rate of decrease of WCSS(C) changes abruptly, the corresponding... This represents the optimal number of categories, adapting to the multi-state fluctuations of the vehicle channel.
[0060] The K-means++ algorithm is used to optimize the selection of initial cluster centers and avoid local optima. The steps are as follows: randomly select one feature vector of the vehicle scene as the initial center. Calculate the distance from all feature vectors to the selected center. According to probability Select the next center, prioritizing feature vectors with larger fluctuations in vehicle data; repeat until all centers are selected. One center.
[0061] The clustering iterative update steps are as follows: First, sample allocation is performed, assigning the standardized feature vector X'_k(t) at each time step to the category of the nearest initial cluster center to form the initial classification result. Then, by minimizing the WCSS(C) objective function, the cluster centers and sample classifications are iteratively updated. The iterative formula is as follows:
[0062]
[0063] in, For the number of iterations, For the first During the nth iteration The number of samples in each class For the first During the nth iteration A sample collection of classes. When ( To achieve a convergence threshold (adapting to the accuracy requirements of in-vehicle data) or to reach a certain number of iterations. Stop iterating when the time is right.
[0064] Classification result output: Standardized spectral state information vector at each time step All were assigned to a unique channel state category Output vehicle terminal 11 Channel state sequence ,in .
[0065] Specifically, the probability calculation module is used for:
[0066] The state transition probability is determined using the maximum likelihood estimation method and the channel state sequence. An initial transition probability matrix is constructed based on the state transition probability. The initial transition probability matrix is updated according to the preset window length, update period, and preset smoothing coefficient to obtain an updated matrix. The validity of the updated matrix is verified to obtain the verification result. If the verification result is valid, it is used as the dynamic transition probability matrix and uploaded to base station 13. If the verification result is invalid, an emergency update is triggered to obtain a new updated matrix and the validity is verified again.
[0067] In this embodiment, the maximum likelihood estimation method can be understood as a mathematical method for estimating probability based on the statistical frequency of actual observation data. In this invention, it is used to quantify the probability of the vehicle channel transitioning from one state to another by using the number of state transitions in the channel state sequence. The state transition probability is used to represent the probability of the vehicle channel transitioning from one state to another. The initial transition probability matrix can be understood as the basic matrix for constructing the state transition probabilities calculated for the first time. Each element in the matrix corresponds to a set of state transition probabilities and is the initial basis for dynamic updates. The preset window length can be understood as the time range of the latest vehicle channel state data selected each time the matrix is updated. For example, the preset window length Tw=0.8s corresponds to 240 sets of data, ensuring that the probability calculation has sufficient latest data to support it, balancing calculation accuracy and real-time performance. The update period can be understood as the time interval for recalculating and updating the transition probability matrix. For example, it can be set to Tupd=150ms, which ensures that the matrix can keep up with the rapid fluctuations of the vehicle channel and reflect the evolution of the channel state in a timely manner. The preset smoothing coefficient is used to balance the stability of the historical transition probability matrix with the real-time performance of the newly calculated matrix in the current window during matrix updates, reducing the weight of historical data and adapting to the characteristics of rapid transfer in vehicle channels. For example, it can be set to α∈[0.2,0.5]. The updated matrix can be understood as the matrix obtained by dynamically adjusting the initial transition probability matrix according to the preset window length, update period, and smoothing coefficient.
[0068] Specifically, the probability calculation module can statistically analyze the state across the entire global scope (all vehicle terminals 11 and at all times). Transition to state Total number of times and status Total number of occurrences The formula is as follows:
[0069]
[0070]
[0071] According to the principle of maximum likelihood estimation, the state arrive transition probability The ratio of the number of transitions to the total number of times a state occurs is given by the following formula:
[0072]
[0073] Satisfy probability constraints: ( That is, the sum of the elements in each row is 1.
[0074] Based on the above transition probabilities Construct the initial transition probability matrix The matrix form is as follows:
[0075]
[0076] An adaptive sliding time window update strategy is adopted to adapt to rapid changes in the vehicle channel, and a preset window length is set. (correspond (Group data), updated every interval Recalculated based on the latest vehicle data within the window and The update matrix is obtained. The updated formula is as follows:
[0077]
[0078] Among them, the preset smoothing coefficient (Adapting to rapid switching of vehicle channels, reducing the weight of historical data), balancing the stability of historical data with the sensitivity of real-time vehicle data. Calculate the new transition probability matrix within the current window.
[0079] Matrix validity verification: If the updated matrix satisfies ( If the fluctuation threshold is set to adapt to the vehicle scenario, the vehicle channel state is determined to be stable, and the current matrix is maintained; otherwise, an emergency update is triggered to accelerate the convergence speed and cope with channel mutations.
[0080] Furthermore, based on the dynamic transfer matrix and the global resource information of base station 13, a multi-objective optimization model is constructed and solved, with spectrum resource utilization, handover cost, and communication quality in the vehicle scenario as optimization objectives, to determine the channel allocation scheme of vehicle terminal 11, including:
[0081] Based on the bandwidth utilization of the channel of the vehicle terminal 11 and the usage scenario of the vehicle terminal 11, a first optimization objective function corresponding to the spectrum resource utilization is constructed; based on the scenario handover cost coefficient and indicator function of the vehicle terminal 11, a second optimization objective function corresponding to the handover cost is constructed; based on the packet loss rate and signal delay of the vehicle terminal 11, a third optimization objective function corresponding to the vehicle scenario communication quality is constructed; based on the resource threshold set, a set of constraints is constructed; based on the first optimization objective function, the second optimization objective function, the third optimization objective function, and the set of constraints, a multi-objective optimization model is determined; based on the dynamic transfer matrix and the global resource information of the base station 13, the multi-objective optimization model is solved to determine the channel allocation scheme of the vehicle terminal 11.
[0082] In this embodiment, bandwidth utilization refers to the proportion of channel bandwidth allocated to the vehicle terminal 11 that is actually used. Usage scenario refers to the actual application scenario of the vehicle terminal 11, which is mainly divided into autonomous driving-related scenarios and in-vehicle entertainment / IoT scenarios, with different priority weights for different scenarios. Spectrum resource utilization refers to the overall efficiency of 5G spectrum resources within a region being effectively utilized by all vehicle terminals 11, and is one of the core objectives of multi-objective optimization, aiming to maximize it. The first optimization objective function can be understood as a mathematical function with maximizing spectrum resource utilization as its core. The scenario switching cost coefficient can be understood as a quantitative coefficient of switching cost set to suit the vehicle scenario. The indicator function is used to determine whether a channel state switch has occurred; if the current channel state of the terminal is different from the previous moment, it takes 1; otherwise, it takes 0, which is the key judgment basis for calculating the switching cost. The second optimization objective function is a mathematical function with minimizing the switching cost as its core. Packet loss rate refers to the proportion of data packets lost during data transmission by the vehicle terminal 11 to the total number of data packets. Signal latency refers to the time delay from data transmission to reception by the vehicle terminal 11. Vehicle scenario communication quality refers to a quantitative indicator that comprehensively considers the low latency and high reliability characteristics of vehicle data transmission. The third optimization objective function is a mathematical function centered on maximizing communication quality in vehicular scenarios. The resource threshold set can be understood as the set of upper limits and constraint thresholds for various resources used by base station 13 for vehicular terminal 11's access to the 5G network. It is the core basis for constructing constraints and includes thresholds for channel capacity, transmit power, state matching, and multi-vehicle concurrency. The constraint set can be understood as the set of all hard rules that vehicular terminal 11 must satisfy for resource allocation, constructed based on the resource threshold set. The global resource information of base station 13 can be understood as the core resource information of the 5G network within the area controlled by base station 13, including total available bandwidth, maximum transmit power, and the number of available channels. It is the basic resource data for constructing constraints and solving the model.
[0083] Specifically, according to each vehicle terminal 11 Channel bandwidth utilization Based on the priority weight corresponding to its use case (e.g., autonomous driving related terminals) In-vehicle entertainment terminal To adapt to the differentiated needs of in-vehicle scenarios, the first optimization objective function is constructed by weighted summation, and the formula is as follows: M represents the total number of connected vehicle terminals 11, enabling differentiated and efficient utilization of spectrum resources.
[0084] Specifically, based on the switching cost coefficient for in-vehicle scenarios ( =0.4), combined with indicator function (Take 1 for channel state switching, otherwise take 0), construct the second optimization objective function by summing, the formula is: ,in This represents the channel state of the terminal at time t, to avoid frequent switching that could degrade the stability of vehicle communication.
[0085] Specifically, based on the packet loss rate of each vehicle terminal 11 and signal delay The third optimization objective function is constructed by comprehensively quantifying the packet loss rate to reflect communication reliability and highlighting the low latency weight through signal delay. The formula is as follows: It is adapted to the low latency requirement of ≤50ms in automotive scenarios and increases the latency weight.
[0086] Specifically, based on the resource threshold set of base station 13, a condition set containing four types of core constraints is constructed, including channel capacity constraints, power constraints, state matching constraints, and multi-vehicle concurrency constraints. The constructed first, second, and third optimization objective functions are fused with the constraint set to form a complete multi-objective optimization model for the vehicular scenario. The core of the model is to maximize spectrum resource utilization, minimize handover costs, and maximize communication quality in the vehicular scenario while satisfying all constraints. The dynamic transition matrix uploaded by the roadside edge computing node 12 (providing channel state transition probabilities and prediction basis) and the global resource information of base station 13 itself (providing total available bandwidth, transmit power upper limit, and other resource data) are imported into the multi-objective optimization model as core input data. Solving the multi-objective optimization model yields the comprehensive optimal solution, generating a channel allocation scheme for each vehicular terminal 11, specifying the allocated channel number, allocated bandwidth, and transmit power parameters for each terminal.
[0087] Based on the above embodiments, the step of constructing a set of constraint conditions according to the resource threshold set can be refined as follows:
[0088] Based on the total available bandwidth threshold of base station 13 in the resource threshold set, the first constraint condition for channel capacity is determined; based on the power threshold in the resource threshold set, the second constraint condition for power is determined; based on the minimum transition probability threshold in the resource threshold set, the third constraint condition for channel state matching is determined; based on the maximum concurrency threshold in the resource threshold set, the fourth constraint condition for multi-vehicle concurrency is determined; and the first, second, third, and fourth constraint conditions constitute a constraint set.
[0089] In this embodiment, the total available bandwidth threshold of base station 13 can be understood as the upper limit of the total 5G spectrum bandwidth (e.g., 200MHz) that base station 13 can allocate to vehicle-mounted RedCap terminals within a specified area. The first constraint on channel capacity can be understood as a bandwidth allocation rule built based on the total available bandwidth threshold of base station 13, limiting the sum of bandwidth allocated to all vehicle-mounted terminals 11 to not exceed the total available bandwidth of base station 13, thus avoiding spectrum resource overload. The second constraint on power can be understood as a transmit power usage rule built based on a power threshold, limiting the actual transmit power of a single vehicle-mounted terminal 11 to not exceed a preset power threshold, ensuring compliant terminal operation and low power consumption characteristics. The third constraint on channel state matching can be understood as a channel allocation rule built based on a minimum transition probability threshold, limiting the transition probability from the current channel state to the allocated channel state of vehicle-mounted terminal 11 to not be lower than this threshold, thus avoiding communication quality degradation due to low state transition feasibility. The fourth constraint on multi-vehicle concurrency can be understood as a channel concurrency usage rule built based on a maximum concurrency threshold, limiting the number of vehicle-mounted terminals 11 allocated to a single channel to not exceed this threshold, adapting to the needs of multi-vehicle concurrent access vehicle scenarios.
[0090] Specifically, from a preset set of vehicle-mounted scenario resource thresholds, the specific value of the total available bandwidth of base station 13 (e.g., 200MHz) is retrieved. This threshold is the maximum upper limit for channel capacity allocation. The sum of the bandwidth allocated to all accessing vehicle-mounted RedCap terminals must be less than or equal to the total available bandwidth threshold of base station 13, forming the first constraint condition to avoid overloading of spectrum resources. The formula for the first constraint condition can be expressed as:
[0091]
[0092] in For vehicle terminal 11 allocated bandwidth The total available bandwidth for base station 13 (e.g., 200MHz, to accommodate concurrent access by multiple vehicles).
[0093] Specifically, the maximum transmit power threshold (23dBm) of the vehicle-mounted RedCap terminal is retrieved from the resource threshold set. This threshold takes into account both the 5G protocol standard and the power consumption deployment requirements of the vehicle-mounted terminal 11. The actual operating transmit power of a single vehicle-mounted terminal 11 must be less than or equal to the preset power threshold, forming a second constraint to ensure compliant operation of the terminal hardware and control vehicle power consumption. The second constraint can be expressed as:
[0094]
[0095] in For the transmission power of vehicle-mounted terminal 11, (RedCap terminal standard upper limit), while also meeting the power consumption limit of vehicle terminal 11.
[0096] Specifically, from the resource threshold set, a minimum state transition probability threshold (e.g., 0.65) is retrieved to ensure stable vehicle-mounted communication. This threshold is adapted to the rapid fluctuations of the vehicle-mounted channel. Based on the extracted minimum transition probability threshold, a channel allocation matching rule is formulated: the transition probability from the current channel state of the vehicle terminal 11 to the proposed channel state must be greater than or equal to this threshold, forming a third constraint to ensure that the allocated channel has high state transition feasibility. Vehicle terminal 11 Assigned channel state category The third constraint must be satisfied: ,in The minimum transition probability threshold is set (higher than in normal scenarios to ensure the stability of vehicle communication).
[0097] Specifically, the number of vehicle-mounted RedCap terminals allocated to any 5G channel must be less than or equal to the maximum concurrent connection threshold, forming the fourth constraint: the number of vehicle-mounted terminals allocated to the same channel. ( To avoid interference caused by concurrent access from multiple vehicles, the first, second, third, and fourth constraints are integrated to form a complete set of constraints for vehicular 5G access resource scheduling. This set serves as the hard boundary for base station 13 to solve the multi-objective optimization model, ensuring that the subsequent channel allocation schemes all meet the resource and communication requirements of the vehicular scenario.
[0098] Based on the above embodiments, the steps for solving the multi-objective optimization model based on the dynamic transfer matrix and the global resource information of base station 13 to determine the channel allocation scheme of vehicle terminal 11 can be refined as follows:
[0099] By improving the second-generation non-dominated sorting genetic algorithm based on the dynamic transition matrix and global resource information of base station 13, the multi-objective optimization model is solved to obtain the Pareto optimal solution set. The comprehensive optimal scheme is selected from the Pareto optimal solution set by the entropy weight method as the initial channel allocation scheme of vehicle terminal 11. The predicted state at the next moment is predicted according to the dynamic transition probability matrix. If the predicted state meets the channel early switching condition, the channel switching time of the initial channel allocation scheme is adjusted to obtain the channel allocation scheme of vehicle terminal 11; otherwise, the initial channel allocation scheme is used as the channel allocation scheme.
[0100] In this embodiment, the improved second-generation non-dominated sorting genetic algorithm, namely the improved NSGA-II algorithm, is a multi-objective optimization algorithm optimized for vehicle-mounted scenarios. By optimizing population initialization, iteration strategies, and optimal solution selection logic, it adapts to the rapid fluctuations and low-latency solution requirements of vehicle-mounted channels, efficiently solving multi-objective optimization models and outputting a Pareto optimal solution set. The Pareto optimal solution set can be understood as the set of solutions to a multi-objective optimization problem, where each solution cannot improve the performance of one objective without degrading the performance of others; it represents the candidate set of optimal solutions for vehicle-mounted channel allocation. The entropy weight method can be understood as an objectively weighted multi-index decision-making method. By calculating the information entropy of each optimization objective to determine the weights, it selects the scheme with the best overall performance from the Pareto optimal solution set, avoiding bias caused by subjective weights and adapting to the differentiated needs of vehicle-mounted scenarios. The initial channel allocation scheme can be understood as the comprehensive optimal resource allocation scheme selected from the Pareto optimal solution set. The predicted state can be understood as the most likely state of the vehicle-mounted channel at the next moment, predicted based on the dynamic transition probability matrix and the maximum probability criterion, which can predict the trend of channel quality changes. The channel early switching condition can be understood as a preset switching trigger rule for the vehicle scenario, i.e., the predicted channel state is poor (s4) or extremely poor (s5). When this condition is met, the channel needs to be adjusted in advance to avoid communication quality degradation. The channel switching time can be understood as the time node when the vehicle terminal 11 performs channel switching. When the early switching condition is met, the switching time is advanced by 40ms (which can be set according to requirements) to adapt to the low latency and high reliability communication requirements of the vehicle scenario.
[0101] Specifically, the channel state transition probabilities provided by the dynamic transition matrix and the network resource data provided by the global resource information of base station 13 are used as core inputs into the vehicle-mounted multi-objective optimization model. The model is solved by improving the NSGA-II algorithm. This algorithm embeds four constraints of the vehicle-mounted scenario in the population initialization stage to reduce invalid solutions, dynamically adjusts the number of iterations to adapt to channel fluctuations, and finally outputs a Pareto optimal solution set that satisfies all constraints. The information entropy and objective weights of the three objectives of "spectrum resource utilization, handover cost, and communication quality" in the multi-objective optimization model are calculated using the entropy weight method. The comprehensive performance score of each candidate scheme in the Pareto optimal solution set is calculated based on the weights. The scheme with the highest score is selected as the initial channel allocation scheme, and the core configuration parameters such as the allocated channel number, allocated bandwidth, and transmit power of each vehicle-mounted terminal 11 are clarified. The real-time dynamic transition probability matrix uploaded by the roadside edge computing node 12 is called. Based on the current vehicle-mounted channel state s(t), the predicted state spred(t+1) of the vehicle-mounted channel at the next moment is calculated and determined by the maximum probability criterion argmaxjps(t)→j(t), and the trend of channel quality change is predicted. The predicted state is compared with the preset channel early switching conditions for the vehicle scenario to determine whether the predicted state is poor (s4) or extremely poor (s5). If the conditions are met, the channel early switching mechanism is triggered, and the channel switching time is advanced by 40ms based on the initial channel allocation scheme to avoid increased packet loss rate and latency caused by passive switching after channel quality deterioration. The final channel allocation scheme is obtained after adjustment. If the conditions are not met, it means that the channel state will remain stable in the next moment, and there is no need to adjust the switching time. The initial channel allocation scheme is directly used as the final channel allocation scheme for the vehicle terminal 11. The base station 13 distributes the final determined channel allocation scheme to each vehicle RedCap terminal and roadside edge computing node 12 to guide the terminals to complete 5G channel access and resource occupation, and realize precise resource scheduling in the vehicle scenario.
[0102] Example 2
[0103] Figure 2 This is a flowchart illustrating a resource allocation method provided in Embodiment 2 of the present invention. This embodiment is applicable to resource allocation for vehicle-mounted RedCap terminals. The method can be executed by a resource allocation system, which includes the vehicle-mounted terminal, roadside edge computing nodes, and base stations. This resource allocation system can be implemented in hardware and / or software and can be configured in electronic devices. Figure 2 As shown, the method includes:
[0104] S210. Obtain multi-dimensional spectrum status data in the vehicle environment through the vehicle terminal and upload it to the roadside edge computing node.
[0105] S220: The roadside edge computing node determines the dynamic transition probability matrix based on multi-dimensional spectrum status data and uploads it to the base station.
[0106] S230. Based on the dynamic transfer matrix and global resource information of the base station, a multi-objective optimization model is constructed and solved to determine the channel allocation scheme of the vehicle terminal.
[0107] The technical solution of this invention addresses the problem of one-sided evaluation using a single parameter, achieving a comprehensive characterization of channel status in vehicular scenarios. It establishes a multi-objective optimized channel allocation mathematical model for vehicular scenarios, taking into account the resource contention of multiple vehicles accessing concurrently, low latency for autonomous driving, and high reliability of data transmission. This improves resource utilization while reducing switching costs, achieving coordinated optimization of access stability and communication efficiency for vehicular RedCap terminals.
[0108] Example 3
[0109] Figure 3 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0110] like Figure 3 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.
[0111] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0112] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as resource allocation methods.
[0113] In some embodiments, the resource allocation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the resource allocation method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the resource allocation method by any other suitable means (e.g., by means of firmware).
[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0115] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0116] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0119] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0120] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the resource allocation method of any embodiment of the present invention.
[0121] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0122] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0123] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A resource allocation system, characterized in that, This includes vehicle-mounted terminals, roadside edge computing nodes, and base stations; The vehicle-mounted terminal is used to acquire multi-dimensional spectrum status data in the vehicle environment and upload it to the roadside edge computing node; The roadside edge computing node is used to determine the dynamic transition probability matrix based on the multi-dimensional spectrum state data and upload it to the base station; The base station is used to construct and solve a multi-objective optimization model based on the dynamic transfer matrix and the base station's global resource information, with the optimization objectives being spectrum resource utilization, handover cost, and communication quality in the vehicle scenario, in order to determine the channel allocation scheme for the vehicle terminal.
2. The system according to claim 1, characterized in that, The step of determining the dynamic transition probability matrix based on the multi-dimensional spectrum state data and uploading it to the base station includes: The multi-dimensional spectrum state data is preprocessed to obtain a standardized spectrum state information matrix; The spectral state information matrix is classified into vehicle channel states using an improved clustering algorithm to obtain a channel state sequence. Based on the channel state sequence, a dynamic transition probability matrix is determined and uploaded to the base station.
3. The system according to claim 2, characterized in that, The process of classifying the spectral state information matrix into vehicle-mounted channel states using an improved clustering algorithm to obtain a channel state sequence includes: Based on the elbow rule and the aforementioned spectral state information matrix, determine the clustering inner square sum function for different optimal number of categories; The improved clustering algorithm is used to initialize the cluster centers to obtain the initial cluster centers; The initial cluster centers are iteratively updated and classified by minimizing the sum of squares within the cluster until the termination condition is met, thus obtaining the channel state sequence.
4. The system according to claim 2, characterized in that, The step of determining the dynamic transition probability matrix based on the channel state sequence and uploading it to the base station includes: The state transition probability is determined using the maximum likelihood estimation method and the channel state sequence. Construct an initial transition probability matrix based on the state transition probabilities; The initial transition probability matrix is updated according to the preset window length, update period, and preset smoothing coefficient to obtain the update matrix; The validity of the update matrix is verified to obtain the verification result. If the verification result is valid, it is used as the dynamic transition probability matrix and uploaded to the base station. If the verification result is invalid, an emergency update is triggered to obtain a new update matrix and perform validity verification again.
5. The system according to claim 1, characterized in that, The process involves constructing and solving a multi-objective optimization model based on the dynamic transfer matrix and base station global resource information, with the optimization objectives being spectrum resource utilization, handover cost, and communication quality in vehicular scenarios. This model determines the channel allocation scheme for the vehicular terminal, including: Based on the bandwidth utilization of the channel of the vehicle terminal and the usage scenario of the vehicle terminal, a first optimization objective function corresponding to the spectrum resource utilization is constructed. Based on the scene switching cost coefficient and indicator function of the vehicle terminal, a second optimization objective function corresponding to the switching cost is constructed; Based on the packet loss rate and signal delay of the vehicle terminal, a third optimization objective function corresponding to the communication quality in the vehicle scenario is constructed. Construct a set of constraints based on the resource threshold set; A multi-objective optimization model is determined based on the first optimization objective function, the second optimization objective function, the third optimization objective function, and the set of constraints. Based on the dynamic transfer matrix and the global resource information of the base station, the multi-objective optimization model is solved to determine the channel allocation scheme of the vehicle terminal.
6. The system according to claim 5, characterized in that, The step of constructing a set of constraints based on a set of resource thresholds includes: The first constraint condition for channel capacity is determined based on the total available bandwidth threshold of base stations in the resource threshold set. The second constraint condition for power is determined based on the power threshold in the resource threshold set; The third constraint condition under channel state matching is determined based on the minimum transition probability threshold in the resource threshold set. Based on the maximum concurrency threshold in the resource threshold set, determine the fourth constraint condition for multi-vehicle concurrency; The first constraint, the second constraint, the third constraint, and the fourth constraint constitute a set of constraints.
7. The system according to claim 5, characterized in that, The step of solving the multi-objective optimization model based on the dynamic transfer matrix and base station global resource information to determine the channel allocation scheme for the vehicle terminal includes: By improving the second-generation non-dominated sorting genetic algorithm based on the dynamic transition matrix and the global resource information of the base station, the multi-objective optimization model is solved to obtain the Pareto optimal solution set; The optimal solution is selected from the Pareto optimal solution set using the entropy weight method, and used as the initial channel allocation scheme for the vehicle terminal. Predict the predicted state at the next moment based on the dynamic transition probability matrix; If the predicted state meets the channel early switching condition, the channel switching time of the initial channel allocation scheme is adjusted to obtain the channel allocation scheme of the vehicle terminal. Otherwise, the initial channel allocation scheme shall be used as the channel allocation scheme.
8. A resource allocation method, characterized in that, The method is applied to a resource allocation system, the system including an on-board terminal, a roadside edge computing node, and a base station, and includes: The vehicle-mounted terminal acquires multi-dimensional spectrum status data in the vehicle environment and uploads it to the roadside edge computing node. The roadside edge computing node determines the dynamic transition probability matrix based on the multi-dimensional spectrum state data and uploads it to the base station; The base station constructs and solves a multi-objective optimization model based on the dynamic transfer matrix and global resource information of the base station, with the optimization objectives being spectrum resource utilization, handover cost, and communication quality in the vehicle scenario, in order to determine the channel allocation scheme of the vehicle terminal.
9. An electronic device, characterized in that, The electronic device serves as a vehicle-mounted terminal, roadside edge computing node, or base station as described in any one of claims 1-7, and the electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the resource allocation method according to any one of claims 8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the resource allocation method of any one of claims 8.