Method and system for minimizing slice resource configuration of mobile cell resources

Abstract

The application provides a mobile cell resource minimization slice resource configuration method and system, and belongs to the technical field of train communication. The method comprises the following steps: building a train mobile network model based on a relay system, and constructing a resource slice framework; considering train intelligent service downlink bearing demand, establishing a communication and service quality guarantee model for passenger on-demand services and over-the-horizon services; establishing an optimization problem with bandwidth allocation and terminal preemption grouping as target variables; simplifying the original problem by using convex optimization theory, and decomposing the original problem into a resource block allocation sub-problem and a mini-slot preemption sub-problem; and using a resource block allocation and preemption efficient algorithm based on block coordinate descent to iteratively solve the two sub-problems and obtain an optimal resource allocation strategy. The application guarantees the deterministic transmission demand of heterogeneous services under the non-ideal train-ground link transmission conditions caused by the high-speed moving characteristics of the train, and realizes the joint design of a preemption-based slice deployment strategy and a resource allocation algorithm.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of train communication, and particularly relates to a mobile cell resource minimization slice resource configuration method and system. BACKGROUND

[0002] High-speed railways are gradually developing towards intelligence, which not only needs to meet the safety and reliability of train operation, but also needs to meet the comfort of passenger travel. The rapid development of high-speed railways also puts forward higher requirements for railway wireless communication networks. In order to improve the intelligent level of high-speed railways, the railway communication network needs to carry various train services related to safe operation, such as new train state monitoring and auxiliary driving services. In addition, in order to meet the demand of passengers for high-quality travel experience, the communication network needs to provide large-capacity passenger video on-demand services. The above-mentioned various services have different Quality of Service (QoS) transmission requirements such as throughput, delay and connection capacity. In order to solve the problem that various differentiated service requirements cannot be met at the same time and there are hidden dangers in service isolation, the network slicing technology in the 5th Generation Mobile Communication Technology (5G) provides a feasible solution. The network slicing technology isolates multiple virtual networks to carry different services, which can meet the QoS requirements of various services. However, it is difficult to obtain accurate Channel State Information (CSI) in a high-speed mobile scenario. Under the condition of non-ideal CSI, the existing hybrid service carrying and slicing strategy cannot strictly meet the QoS requirements of different services, especially the train-mounted services related to train operation safety. Therefore, it is necessary to fully consider the particularity of the high-speed railway scene and carry out research on service carrying and slicing resource allocation in the high-speed railway scene to ensure the safe operation of trains and provide high-quality travel experience for passengers. In summary, under the premise of guaranteeing the QoS transmission requirements of train services, a solution is needed to carry heterogeneous services in a mobile cell under a non-ideal link. SUMMARY

[0003] In view of the above deficiencies in the prior art, the present application provides a mobile cell resource minimization slice resource configuration method and system for a non-ideal link, which solves the problem of heterogeneous service carrying and resource optimization configuration under a high-speed mobile cell network deployment.

[0004] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a mobile cell resource minimization slice resource configuration method, comprising the following steps:

[0005] A train mobile network model based on a relay system is built, and a resource slicing framework is constructed;

[0006] Based on the constructed resource slicing framework, when considering the train intelligent service downlink bearing demand, the passenger on-demand service and the over-the-horizon service are established communication and service quality guarantee model;

[0007] Based on the constructed communication and service quality guarantee model, an optimization problem is established with bandwidth allocation and terminal preemption grouping as target variables, wherein the target is to minimize the bandwidth resource;

[0008] Based on the optimization problem of the target variable, the original problem is simplified by using convex optimization theory, the optimal allocation strategy of the resource block allocation sub-problem is derived by constraint relaxation, and the mini-slot preemption sub-problem is converted into a target function, auxiliary variables are introduced, and is simplified into an easy-to-solve form;

[0009] The resource block allocation and preemption BCD-AP efficient algorithm based on block coordinate descent is used to iteratively solve the resource block allocation sub-problem and the mini-slot preemption sub-problem, and the optimal resource allocation strategy is obtained, and the mobile cell resource minimization slicing resource configuration is completed.

[0010] The beneficial effects of the present application are: the present application can bear two types of services of passenger on-demand and over-the-horizon with differentiated QoS requirements under a non-ideal train-ground mobile network, and the proposed resource block allocation and preemption BCD-AP algorithm can meet the QoS transmission requirements of heterogeneous services, minimize the system bandwidth resource, and obtain the optimal resource allocation strategy. The resource slicing strategy proposed in the present application can minimize the bandwidth resource under the deployment of the mobile cell network and meet the transmission requirements of the two types of heterogeneous services.

[0011] Further, the train mobile network model based on the relay system is built, and a resource slicing framework is constructed, which specifically comprises:

[0012] A link is established between the base station and the roof relay, wherein the roof relay communicates with the terminal through the train station.

[0013] Real-time video data is captured by a trackside camera, and the video data is transmitted to a ground data processing center for processing, and the processing result is sent to a vehicle-mounted DAS terminal, wherein the vehicle-mounted terminal comprises a VDS terminal and a DAS terminal.

[0014] The system bandwidth is divided into a group of resource blocks, denoted as k={1,2,...,K}, and the bandwidth of a single resource block RB is represented as B unit , which is defined as the minimum resource unit allocated to the VDS terminal in the time-frequency domain, wherein k represents the number of resource blocks, and K represents the number of resource blocks.

[0015] The time domain is divided into a millisecond time slot with a time length of T s =1, wherein the time length of a single mini-slot is represented as T ms =T s / Ns wherein, N s represents the number of mini-slots under a single millisecond slot, T ms represents the time length of a single mini-slot, T s represents the time length of a single millisecond slot;

[0016] The single resource block RB is allocated to the VDS terminal by using the standard slot, wherein the data packet arriving at the DAS terminal preoccupies the bandwidth resource allocated to the VDS terminal, the data is transmitted by selecting the available mini-slot, and the construction of the resource slicing framework is completed.

[0017] The beneficial effect of the further scheme is that the train mobile network resource slicing framework based on the relay system is constructed, and the small-scale fading characteristics of the train-ground link under the non-ideal CSI condition are accurately described in combination with the time correlation statistical distribution of the fast time-varying fading channel.

[0018] Further, the communication and service quality guarantee model for the passenger on-demand service and the over-the-horizon service is established, and the model is specifically as follows:

[0019] Considering the downlink bearing demand of the train intelligent service, the linear model is used to describe the relationship between the rate loss of the passenger on-demand service and the number of mini-slots preoccupied by the auxiliary driving service, wherein the transmission data rate of the VDS terminal m is:

[0020]

[0021]

[0022] wherein, r m represents the transmission data rate of the VDS terminal m, B unit represents the bandwidth of a single resource block RB, x m represents the total number of single resource blocks RB allocated to the VDS terminal m, z m represents the number of mini-slots preoccupied by the DAS terminal n to the VDS terminal m, γ m represents the signal-to-noise ratio of the VDS terminal m, M represents a set of multiple VDS terminals in the system, a m represents the large-scale channel gain, P m represents the transmission power of the VDS terminal m, h m represents the channel state information, σ 2 represents the additive white Gaussian noise power;

[0023] Based on the described relationship, the statistical probability distribution of the actual CSI |h j | 2 is constructed by using the time correlation in the fast time-varying fading channel, wherein the expression of the conditional probability density function is as follows:

[0024]

[0025] ρ[τ]=J0(2πf D τ)

[0026] f D =vf c / v c

[0027] Where f() represents the conditional probability density function, h j Let f represent the true small-scale fading of the j-th terminal, ρ[τ]∈[0,1] represent the channel estimation error coefficients, J0() represent the zeroth-order Bessel function of the first kind, and f D ρ[τ] represents the maximum Doppler frequency shift, ρ[τ] represents the channel estimation error coefficient, τ represents the channel feedback delay, v represents the train speed, and f represents the maximum Doppler frequency shift. c Indicates the carrier frequency, v c Represents the speed of light. This represents the estimated small-scale fading of the j-th terminal. Let I0 represent the additive white Gaussian noise power, and let I0() represent the zeroth-order modified Bessel function of the first kind. j The value represents the estimation error, M represents the set of multiple VDS terminals in the system, and n represents that there is only one DAS terminal in the system.

[0028] Using |h m | 2 The conditional probability density function is used to obtain the average data rate of VDS terminal m on all individual resource blocks RB, thus completing the establishment of the communication and service quality assurance model for passenger on-demand services:

[0029]

[0030] in, Let f() represent the average data rate of VDS terminal m across all individual resource blocks (RBs), and let f() represent the conditional probability density function. Let d represent the estimated small-scale fading of the m-th VDS terminal, and d() represent the integration operation;

[0031] The average data rate of DAS terminal n is obtained by calculating the data rate of the DAS terminal.

[0032] The data transmission delay of the DAS terminal is obtained based on the average data rate of the DAS terminal n.

[0033] Based on the data transmission delay of the DAS terminal, the tail characteristics of the DAS transmission delay are captured using conditional risk values ​​to obtain φ. a (D n The δ) function is used to establish a service quality assurance model for beyond-line-of-sight business communication.

[0034] The beneficial effect of the above further solution is that by introducing the small-scale fading statistical distribution of the time-varying fading channel and the channel estimation error model, the average data rate of the VDS terminal is calculated by using |h j | 2 The probability density function is derived to obtain the average data rate of the VDS terminal.

[0035] Further, the expression of the data transmission delay of the DAS terminal is as follows:

[0036]

[0037] wherein D n represents the data transmission delay of the DAS terminal, F n represents the data packet length of the DAS terminal, represents the average data rate of the DAS terminal n, r n represents the data rate of the DAS terminal, f() represents the conditional probability density function, h n represents the real small-scale fading of the DAS terminal n, represents the estimated small-scale fading of the DAS terminal n, γ n represents the received signal-to-noise ratio of the terminal n, a n represents the large-scale channel gain, P n represents the transmission power of the DAS terminal n.

[0038] The beneficial effect of the above further solution is that based on the average rate of the VDS terminal, the average rate of the DAS terminal can be further calculated.

[0039] Further, the expression of the φ a (D n , δ) function is as follows:

[0040] φ a (D n , δ): = δ + (1 - a) -1 E[(D n - δ) +]

[0041] wherein φ a (D n , δ) represents the C-VaR function, δ represents the delay threshold variable, a represents the delay threshold, E represents the expected value, and C-VaR represents the average value of potential delay exceeding the risk value.

[0042] The beneficial effect of the above further solution is that the tail characteristics of the DAS transmission delay are captured by using the conditional risk value method, the jitter risk level is quantified, and reliable quality of service is provided for the DAS with dual QoS requirements of delay jitter.

[0043] Further, the expression of the bandwidth resource minimization problem is as follows:

[0044]

[0045] φ a (D n ,δ)≤θ max ,

[0046]

[0047] wherein F represents a target function, x and z are both sets of optimization variables, x = {x m} represents a set of allocated resource block numbers, z = {z m} represents a set of allocated mini-slots numbers, δ represents a delay threshold variable, x m represents the total number of single resource blocks RB allocated to the VDS terminal m, M represents a set of multiple VDS terminals in the system, B unit represents the bandwidth of a single resource block RB, r m represents the transmission data rate of the VDS terminal m, represents the minimum throughput QoS requirement of the VDS terminal m, φ a (D n ,δ) represents a C-VaR function, D n represents the data transmission delay of the DAS terminal, θ max represents the maximum risk threshold of C-VaR of the DAS terminal, C-VaR represents the average value of potential delays exceeding the risk value, represents a set of positive integers, z m represents the number of mini-slots of the DAS terminal n pre-empting the VDS terminal m, N s represents the number of mini-slots under a single millisecond slot.

[0048] The beneficial effects of the above further scheme are: a mathematical optimization model of the bandwidth resource minimization problem is established, the resource block allocation variable and the mini-slot pre-emption variable are jointly optimized, and the throughput QoS requirement of the VDS terminal and the delay and jitter QoS requirement of the DAS terminal are satisfied.

[0049] Further, the expression of the resource block allocation sub-problem is as follows:

[0050]

[0051] wherein F represents a target function, x represents a set of optimization variables, x = {x m} represents a set of allocated resource block numbers, r m represents the transmission data rate of the VDS terminal m, x mdenotes the total number of individual resource blocks RB allocated to VDS terminal m, denotes a set of positive integers, denotes the optimal resource block allocation for terminal m, denotes the minimum throughput QoS requirement of VDS terminal m, B unit denotes the bandwidth of an individual resource block RB, R m denotes the transmission rate of VDS terminal m, z m denotes the number of mini-slots that DAS terminal n preempts from VDS terminal m, N s denotes the number of mini-slots in an individual millisecond slot, γ m denotes the signal-to-noise ratio of VDS terminal m, f() denotes the conditional probability density function, h m denotes the channel state information, ρ[τ] denotes the channel estimation error coefficient, denotes the estimated small-scale fading of the mth VDS terminal, τ denotes the channel feedback delay, d() denotes the integral operation.

[0052] The above further scheme has the beneficial effect that the resource block allocation sub-problem expression is established, and the optimal resource block allocation closed-form solution is obtained, with low computational complexity.

[0053] Further, the expression of the mini-slot preemption sub-problem is as follows:

[0054]

[0055] s.t δ+(1-a) -1 κ≤θ max ,

[0056] κ≥E[D n ]-δ,

[0057] κ≥0,

[0058]

[0059]

[0060] wherein F1 denotes the sub-problem objective function, z is the optimization variable set, z = {z m} denotes the allocated mini-slot number set, δ denotes the delay threshold variable, κ denotes the auxiliary variable, a denotes the delay threshold, θ max denotes the C-VaR maximum risk threshold of the DAS terminal, C-VaR denotes the average of potential delays that exceed the risk value, E[D n ] denotes the expectation of D n , E denotes the expected value, D n denotes the data transmission delay of the DAS terminal, x mN represents the total number of single resource blocks RB allocated to VDS terminal m s M represents the number of mini-slots in a single millisecond slot, M represents a set of multiple VDS terminals in the system, q m Z represents the number of mini-slots that can be preempted by the DAS terminal without affecting the transmission rate of the VDS m Z represents the number of mini-slots that can be preempted by the DAS terminal without affecting the transmission rate of the VDS B represents the minimum throughput QoS requirement of VDS terminal m unit R represents the bandwidth of a single resource block RB m F represents the transmission rate of VDS terminal m n Y represents the packet length of the DAS terminal n H represents the signal-to-noise ratio of DAS terminal n n P represents the real small-scale fading of DAS terminal n, represents the channel estimation error coefficient, and d() represents the integral operation P represents the estimated small-scale fading of DAS terminal n, and represents the channel feedback delay.

[0061] The beneficial effects of the above further scheme are: a mini-slot preemption sub-problem is established, the VDS throughput loss caused by DAS preemption is minimized under the premise of strictly meeting the DAS quality of service requirement, and the system bandwidth resource is saved.

[0062] The application also provides a mobile cell resource minimization slice resource configuration system, comprising:

[0063] The first processing module is used for building a train mobile network model based on a relay system and constructing a resource slice framework;

[0064] The second processing module is used for establishing a communication and service quality guarantee model for passenger on-demand services and beyond line of sight services based on the constructed resource slice framework and considering train intelligent service downlink bearing requirements;

[0065] The third processing module is used for establishing an optimization problem with bandwidth allocation and terminal preemption grouping as target variables based on the constructed communication and service quality guarantee model, wherein the target is to minimize bandwidth resources;

[0066] The fourth processing module is used for simplifying the original problem by using convex optimization theory based on the optimization problem of the target variable, deriving an optimal allocation strategy for the resource block allocation sub-problem through constraint relaxation, and converting the target function of the mini-slot preemption sub-problem, introducing auxiliary variables, and simplifying it into an easy-to-solve form;

[0067] The fifth processing module is used for solving the resource block allocation sub-problem and the mini-slot preemption sub-problem iteratively by using the resource block allocation and preemption BCD-AP efficient algorithm based on block coordinate descent, so as to obtain an optimal resource allocation strategy and complete the mobile cell resource minimization slice resource configuration.

[0068] The present application has the advantages that the present application can carry two types of services, namely, passenger on-demand service and over-the-horizon service, with differentiated QoS requirements in a non-ideal train-ground mobile network, and the proposed resource block allocation and preemption BCD-AP algorithm can meet the QoS transmission requirements of heterogeneous services, minimize the system bandwidth resources, and obtain an optimal resource allocation strategy. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 The present application has the advantages that the present application can carry two types of services, namely, passenger on-demand service and over-the-horizon service, with differentiated QoS requirements in a non-ideal train-ground mobile network, and the proposed resource block allocation and preemption BCD-AP algorithm can meet the QoS transmission requirements of heterogeneous services, minimize the system bandwidth resources, and obtain an optimal resource allocation strategy.

[0070] Figure 2 The present application has the advantages that the present application can carry two types of services, namely, passenger on-demand service and over-the-horizon service, with differentiated QoS requirements in a non-ideal train-ground mobile network, and the proposed resource block allocation and preemption BCD-AP algorithm can meet the QoS transmission requirements of heterogeneous services, minimize the system bandwidth resources, and obtain an optimal resource allocation strategy.

[0071] Figure 3 The present application has the advantages that the present application can carry two types of services, namely, passenger on-demand service and over-the-horizon service, with differentiated QoS requirements in a non-ideal train-ground mobile network, and the proposed resource block allocation and preemption BCD-AP algorithm can meet the QoS transmission requirements of heterogeneous services, minimize the system bandwidth resources, and obtain an optimal resource allocation strategy. DETAILED DESCRIPTION

[0072] The specific embodiments of the present application are described below to facilitate the understanding of the present application by those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and any changes that are obvious to those skilled in the art within the spirit and scope of the present application as defined in the appended claims are included in the protection of the present application.

[0073] Example 1

[0074] As shown in Figure 1 and Figure 2 The present application provides a mobile cell resource minimization slice resource configuration method for a non-ideal link, and the implementation method is as follows:

[0075] (I) A train mobile network model based on a relay system is built, and a resource slicing framework is constructed, which is specifically as follows:

[0076] The base station establishes a link with the roof relay, wherein the roof relay communicates with the terminal through the train station; real-time video data is captured by a trackside camera and transmitted to a ground data processing center for processing, and the processing result is sent to the vehicle-mounted DAS terminal, wherein the vehicle-mounted terminal includes a VDS terminal and a DAS terminal; the system bandwidth is divided into a group of resource blocks, denoted as k = {1, 2,..., K}, and the bandwidth of a single resource block RB is denoted as B unit , which is defined as the minimum resource unit allocated to the VDS terminal in the time-frequency domain, wherein k represents the number of resource blocks, and K represents the number of resource blocks; the time domain is divided into millisecond slots with a time length of T s = 1, wherein the time length of a single mini-slot is denoted as T ms = T s / N s , wherein N s represents the number of mini-slots in a single millisecond slot, T ms represents the time length of a single mini-slot, and T s represents the time length of a single millisecond slot; a standard slot is used to allocate a single resource block RB to the VDS terminal, wherein the data packet arriving at the DAS terminal preoccupies the bandwidth resource allocated to the VDS terminal, and the available mini-slot is selected to transmit data, thereby completing the construction of the resource slicing framework.

[0077] In this embodiment, the present application considers a train mobile network architecture based on a relay system, as shown in Figure 2 . To reduce the body penetration loss and ensure reliable connection, the trackside base station communicates with the train-mounted terminal through a two-hop transmission link. The base station first establishes a link with the roof relay, and then the relay communicates with the terminal through the train station. Two types of train services are considered, including a passenger on-demand service (VDS) and an auxiliary driving service (DAS). The passenger on-demand service can provide passengers with customized high-quality media video, and the QoS requirement of this type of service is high throughput requirement to ensure smooth video playback and improve passenger travel experience. The auxiliary driving service is responsible for transmitting trackside monitoring video containing danger warnings to provide driving guidance information for the driver. The trackside camera captures real-time video data and transmits the data to the ground data processing center for intelligent identification and data processing, and then sends the related video and data to the vehicle-mounted DAS terminal to provide the driver with key information outside the line of sight. The DAS has strict delay and jitter requirements. Therefore, the vehicle-mounted terminal is divided into a VDS terminal and a DAS terminal, and the VDS terminal set is set as m = {1, 2,..., M}. In addition, it is assumed that there is one DAS terminal deployed in the train cab, denoted as n.

[0078] To meet the heterogeneous QoS requirements of the above two types of services, the present application adopts a sliced mini-slot preemption scheme, as shown in Figure 2The system bandwidth is partitioned into a set of resource blocks, denoted as k = {1, 2, …, K}, with the bandwidth of a single RB (resource block) denoted as B unit , which is defined as the minimum resource unit allocated to a VDS terminal in the time-frequency domain. The time domain is partitioned into slots with a time duration of T s = 1 ms, and each slot contains N s = 7 mini-slots, and the time duration of a single mini-slot is denoted as T ms = T s / N s , where N s represents the number of mini-slots in a single millisecond slot, T ms represents the time duration of a single mini-slot, and T s represents the time duration of a single millisecond slot. To effectively meet the high throughput requirement, RBs are allocated to VDS terminals in standard slots. In addition, since DAS data arrives randomly and has strict latency and jitter requirements, the arriving DAS data packet preempts the bandwidth resources allocated to the VDS terminal, and the available mini-slots are selected to transmit data.

[0079] Considering that the high-speed movement of the train causes significant Doppler shift, it is assumed that the base station side cannot obtain the ideal channel state information (CSI) of the on-board terminal. Therefore, the base station can only obtain the estimated small-scale fading where τ represents the channel feedback delay. The relationship between the current CSI h j and the estimated feedback is expressed as follows:

[0080]

[0081] where ρ[τ] ∈ [0, 1] represents the channel estimation error coefficient, i.e., ρ[τ] = J0(2πf D τ), and J0() is the first-order zero Bessel function. The maximum Doppler shift is f D = vf c / v c , where v represents the train speed, f c represents the carrier frequency, v c represents the speed of light, and the estimation error is independent of .

[0082] To handle the non-ideal CSI, the time correlation in the fast time-varying fading channel is utilized to construct the statistical probability distribution of the actual , and its conditional probability density function can be derived as:

[0083]

[0084] where I0() represents the first kind of zero-order modified Bessel function, f() represents a conditional probability density function, h j represents the real small-scale fading of the jth terminal, represents the estimated small-scale fading of the jth terminal, represents the power of an additive white Gaussian noise, M represents a set of multiple VDS terminals in the system, and n represents only one DAS terminal in the system.

[0085] (ii) Based on the constructed resource slice framework, when considering the downlink bearing demand of the train intelligent service, a communication and service quality guarantee model is established for the passenger on-demand service and the over-the-horizon service, wherein the communication and service quality guarantee model for the passenger on-demand service and the over-the-horizon service is specifically:

[0086] Considering the downlink bearing demand of the train intelligent service, a linear model is used to describe the relationship between the rate loss of the passenger on-demand service and the number of mini-slots occupied by the auxiliary driving service; based on the described relationship, the time correlation in the fast time-varying fading channel is used to construct the statistical probability distribution of the actual CSI|h j | 2 ; the conditional probability density function of|h m | 2 is used to obtain the average data rate of the VDS terminal m on all single resource blocks RB, and the establishment of the communication and service quality guarantee model for the passenger on-demand service is completed;

[0087] The average data rate of the DAS terminal n is obtained by calculating the data rate of the DAS terminal; the data transmission delay of the DAS terminal is obtained according to the average data rate of the DAS terminal n; and the φ a (D n ,δ) function is obtained by using the conditional risk value to capture the tail characteristics of the DAS transmission delay, and the establishment of the communication and service quality guarantee model for the over-the-horizon service is completed.

[0088] In this embodiment, the application uses a linear model to describe the relationship between the rate loss of the passenger on-demand service terminal and the number of mini-slots occupied by the auxiliary driving service. It is assumed that the DAS data packet is transmitted through the slice occupation scheme in each time slot, and in each transmission period, the system allocates a corresponding number of RBs to the VDS terminal, and the integer allocation index represents the total number of RBs allocated to the VDS terminal m. Let z m represent the number of mini-slots occupied by the DAS terminal n to the VDS terminal m. Therefore, the transmission data rate of the VDS terminal m is:

[0089]

[0090] Where, γ m The signal-to-noise ratio of the VDS terminal m is:

[0091]

[0092] Where, r m B represents the data transmission rate of VDS terminal m. unit x represents the bandwidth of a single resource block (RB). m z represents the total number of individual resource blocks (RBs) allocated to VDS terminal m. m γ represents the number of mini-time slots that DAS terminal n preempts from VDS terminal m. m a represents the signal-to-noise ratio of the VDS terminal m. m P represents the large-scale channel gain. m h represents the transmission power of VDS terminal m. m Represents channel state information, σ 2 This represents the power of additive white Gaussian noise.

[0093] Because the base station cannot obtain ideal channel state information h m Using |h m | 2 The probability density function, the average data rate of VDS terminal m on all RBs is:

[0094]

[0095] in, Let f(t) represent the average data rate of VDS terminal m across all individual resource blocks (RBs), f(t) represent the conditional probability density function, and ρ[τ] represent the channel estimation error coefficient. Let τ represent the estimated small-scale fading of the m-th VDS terminal, τ represent the channel feedback delay, d() represent the integration operation, and B unit This indicates the bandwidth of resource block RB.

[0096] Since the bandwidth resources allocated to DAS terminal n depend on all VDS terminals, the data rate of the DAS terminal can be expressed as:

[0097]

[0098] Where, γ n The received signal-to-noise ratio of terminal n can be expressed as:

[0099]

[0100] Among them, P n This represents the transmission power of DAS terminal n. Therefore, the average data rate of DAS terminal n can be expressed as:

[0101]

[0102] Since DAS data packets transmit data by immediately preempting the RB resources of the VDS terminal, we do not consider queuing delays, but only the DAS data transmission delay. The data transmission delay of the DAS terminal is:

[0103]

[0104] Among them, D n F represents the data transmission latency of the DAS terminal. n Indicates the packet length of the DAS terminal. r represents the average data rate of DAS terminal n. n Indicates the data rate a of the DAS terminal n h represents the large-scale channel gain. n This represents the true small-scale fading of DAS terminal n. This represents the estimated small-scale fading of DAS terminal n.

[0105] To characterize the QoS requirements of DAS, the Conditional Value-at-Risk (CVaR) function is used to capture the tail characteristics of DAS transmission delay. CVaR represents the average potential delay exceeding the risk value. The parameter a-VaR is defined as representing the probability that the average delay of the DAS terminal does not exceed a certain minimum value, which is greater than or equal to a, and can be expressed as:

[0106]

[0107] Where P() represents the probability density function, δ represents the delay threshold variable, and CVaR function represents the time in D n ≥VaR a (D n Under the condition D n The expected value, representing the average data latency under violation conditions, can be expressed as:

[0108] CVaR a (D n )=Ε[D n |D n ≥VaR a (D n )]

[0109] Therefore, we can obtain:

[0110] φ a (D n ,δ):=δ+(1-a) -1 E[(D n -δ)+]

[0111] wherein φ a (D n ,δ) represents a C-VaR function, C-VaR represents the average of potential delay exceeding a risk value, δ represents a delay threshold variable, a represents a delay threshold, and E represents an expectation value.

[0112] (Three) based on the constructed communication and service quality guarantee model, an optimization problem is established with bandwidth allocation and terminal preemption grouping as target variables, wherein the target is to minimize the bandwidth resource, and the constraints are the delay and jitter requirements of the over-the-horizon service, the passenger service data throughput requirements and the resource limitation, etc.

[0113] In this embodiment, in order to minimize the system bandwidth resource consumption, a utility function U is defined as follows: The parameter vector is defined as x = {x m} and z = {z m}. Therefore, the bandwidth resource minimization problem can be expressed as follows:

[0114]

[0115] φ a (D n ,δ) ≤ θ max ,

[0116]

[0117] wherein F represents a target function, x and z are both sets of optimization variables, x = {x m} represents a set of allocated resource block numbers, z = {z m} represents a set of allocated mini-slots, δ represents a delay threshold variable, x m represents the total number of single resource blocks RB allocated to VDS terminal m, M represents a set of multiple VDS terminals in the system, B unit represents the bandwidth of a single resource block RB, r m represents the transmission data rate of VDS terminal m, represents the minimum throughput QoS requirement of VDS terminal m, φ a (D n ,δ) represents a C-VaR function, D n represents the data transmission delay of a DAS terminal, θ max represents the maximum risk threshold of C-VaR of a DAS terminal, C-VaR represents the average of potential delay exceeding a risk value, represents a set of positive integers, z m represents the number of mini-slots pre-empted by DAS terminal n from VDS terminal m, N s represents the number of mini-slots in a single millisecond slot.

[0118] To ensure that the arrived DAS data packets complete transmission within a single time slot, δ max = 1 ms is set as the upper limit of δ, i.e., δ ≤ δ max Since there is an integer optimization variable, the problem is a mixed integer nonlinear programming problem, δ max = 1 ms represents an upper limit value.

[0119] (Four) Based on the optimization problem of the target variable, the original problem is simplified by using convex optimization theory, the resource block allocation sub-problem is derived by constraint relaxation to obtain an optimal allocation strategy, and the mini-slot preemption sub-problem is converted into a target function, an auxiliary variable is introduced, and is simplified into an easy-to-solve form;

[0120] In the embodiment, to solve the above optimization problem, the application proposes a resource block allocation and preemption algorithm based on block coordinate descent (BCD-AP), and obtains a suboptimal feasible solution. The original problem is decomposed into two sub-problems: (1) RB allocation problem of passenger on-demand service; (2) mini-slot preemption problem of auxiliary driving service.

[0121] In the RB allocation sub-problem of passenger on-demand service, the DAS preemption variable z m is fixed, and only the RB allocation variable x m is optimized. Therefore, the sub-problem can be expressed as:

[0122]

[0123] It can be seen that the RB allocation problem of VDS is an integer linear programming problem. To simplify the problem, first relax the integer constraint condition. Therefore, the optimal RB allocation closed-form solution of terminal m is as follows:

[0124]

[0125] Wherein, the auxiliary variable

[0126] Wherein, represents the optimal resource block allocation of terminal m, represents the minimum throughput QoS requirement of VDS terminal m, B unit represents the bandwidth of a single resource block RB, R m represents the transmission rate of VDS terminal m, z m represents the number of mini-slots preoccupied by DAS terminal n to VDS terminal m, N s represents the number of mini-slots in a single millisecond time slot, γ m represents the signal-to-noise ratio of VDS terminal m, f() represents the conditional probability density function, h m represents the channel state information, |ρ[τ] represents the channel estimation error coefficient, represents the estimated small-scale fading of the mth VDS terminal, and τ represents the channel feedback delay.

[0127] In the mini-slot preemption problem of the assisted driving service, for a given RB allocation strategy x of the VDS, the original problem can be transformed into the following DAS preemption problem:

[0128]

[0129] δ+(1-a) -1 E[(D n -δ) + ]≤θ max ,

[0130]

[0131] where F represents the objective function, z is the set of optimization variables, and z = {z m} represents the set of allocated mini-slot numbers.

[0132] However, the objective function of the above problem remains unchanged in the solving process of the given RB allocation strategy. To obtain the optimal DAS slice preemption strategy, the objective of the sub-problem is redefined to be consistent with the original problem, i.e., to minimize the system bandwidth resources and reduce the network deployment cost. After the DAS preempts the bandwidth resources of the VDS, the algorithm needs to supplement additional RB resources for the VDS in subsequent iterations to guarantee the quality of service requirements of the VDS. Therefore, it is crucial to minimize the impact of DAS preemption on the RB resource allocation of the VDS terminal. Considering that the two services have different resource allocation dimensions, i.e., the VDS is allocated according to the time slot, and the DAS is allocated according to the mini-slot, we design a scheme that allows the DAS to preferentially use the excess mini-slots of the VDS terminal without affecting the VDS traffic, thereby minimizing the number of additional RBs allocated to the VDS in subsequent iterations. This scheme effectively minimizes the number of system RBs occupied. We define the number of remaining mini-slots of the VDS terminal m as q m , which represents the available mini-slot resources of the DAS terminal without affecting the QoS of the VDS, and is denoted as:

[0133]

[0134] The utility function is defined as Meanwhile, to remove the non-convex term (·) + , an auxiliary variable κ is introduced to replace E[(D n -δ) + ]. Therefore, the original sub-problem can be re-expressed as:

[0135]

[0136] s.t. δ + (1 - a) < 0 -1 κ < θ max ,

[0137] κ ≥ E[D n ] - δ,

[0138] κ ≥ 0,

[0139]

[0140] where the average delay of DAS data packets is approximately:

[0141]

[0142] where F1represents the sub-problem objective function, z is the optimization variable set, z = {z m} represents the allocated mini-slot number set, δ represents the delay threshold variable, κ represents the auxiliary variable, a represents the delay threshold, θ max represents the maximum risk threshold of C-VaR of DAS terminals, C-VaR represents the average value of potential delay exceeding the risk value, E[D n ] represents the expectation of D n , E represents the expectation value, D n represents the data transmission delay of DAS terminals, x m represents the total number of single resource blocks RB allocated to VDS terminal m, N s represents the number of mini-slots under a single millisecond slot, M represents the set of multiple VDS terminals in the system, q m represents the number of mini-slots that can be preempted by DAS terminals without affecting the transmission rate of VDS, z m represents the number of mini-slots preempted by DAS terminal n from VDS terminal m, represents the minimum throughput QoS requirement of VDS terminal m, B unit represents the bandwidth of a single resource block RB, R m represents the transmission rate of VDS terminal m, F n represents the packet length of DAS terminals, γ n represents the signal-to-noise ratio of DAS terminal n, h n represents the real small-scale fading of DAS terminal n, ρ[τ] represents the channel estimation error coefficient, d() represents the integral operation, represents the estimated small-scale fading of DAS terminal n, τ represents the channel feedback delay.

[0143] (five) An efficient algorithm BCD-AP based on block coordinate descent is used to iteratively solve the resource block allocation sub-problem and the mini-slot preemption sub-problem, to obtain the optimal resource allocation strategy, and to complete the mobile cell resource minimization slice resource configuration.

[0144] In this embodiment, due to the objective function F1 and the integer variable z, the above problem is still a non-convex problem. To solve the mini-slot preemption sub-problem of the auxiliary driving service, the application proposes a mini-slot preemption algorithm based on a genetic algorithm to realize an optimal DAS slicing strategy. According to the above design, a BCD-AP efficient algorithm based on block coordinate descent is proposed to allocate RB and mini-slot resources to VDS terminals and DAS terminals, and a feasible slicing preemption strategy is designed. The proposed slicing strategy can minimize the bandwidth resources under the deployment of a mobile cell network and simultaneously meet the transmission requirements of the two types of heterogeneous services.

[0145] In summary, the application first establishes a train mobile network model based on a relay system to represent the mobility characteristics of the train-ground communication link. Considering the downlink bearing requirements of train intelligent services, the QoS guarantee requirements of passenger services and over-the-horizon services, a service communication and service quality guarantee model is established. Considering that the over-the-horizon service belongs to a delay-sensitive service, the application designs a slicing scheme for real-time preemption of passenger service resource blocks by over-the-horizon services. Secondly, the application takes minimizing bandwidth resource consumption as the target, takes the delay and jitter requirements of over-the-horizon services, the data throughput requirements of passenger services, and resource constraints as constraints, and establishes an optimization problem with bandwidth allocation and terminal preemption grouping as target variables. The application proposes a BCD-AP slicing resource configuration algorithm based on block coordinate descent, and designs a feasible slicing preemption strategy. The application effectively completes resource optimization configuration, and the proposed slicing strategy can minimize the bandwidth resources under the deployment of a mobile cell network and simultaneously meet the transmission requirements of the two types of heterogeneous services. The problem of heterogeneous service bearing and resource optimization configuration under the deployment of a high-speed mobile cell network is solved.

[0146] Embodiment 2

[0147] As shown in Figure 3 The application provides a mobile cell resource minimization slicing resource configuration system for performing embodiment 1, which comprises:

[0148] A first processing module is configured to build a train mobile network model based on a relay system and construct a resource slicing framework.

[0149] A second processing module is configured to, based on the constructed resource slicing framework, establish a communication and service quality guarantee model for passenger on-demand services and over-the-horizon services when considering the downlink bearing requirements of train intelligent services.

[0150] A third processing module is configured to, based on the constructed communication and service quality guarantee model, establish an optimization problem with bandwidth allocation and terminal preemption grouping as target variables, wherein the target is to minimize the bandwidth resources.

[0151] The fourth processing module is configured to simplify the original problem based on the optimization problem of the target variable by using convex optimization theory, derive an optimal allocation strategy for the resource block allocation sub-problem by constraint relaxation, and convert the mini-slot preemption sub-problem into a target function, introduce an auxiliary variable, and simplify it into an easily solvable form.

[0152] The fifth processing module is configured to use a resource block allocation and preemption BCD-AP efficient algorithm based on block coordinate descent to iteratively solve the resource block allocation sub-problem and the mini-slot preemption sub-problem, obtain an optimal resource allocation strategy, and complete the mobile cell resource minimization slice resource configuration.

[0153] In this embodiment, the mobile cell resource minimization slice resource configuration method can be used to divide the functional units. For example, each function can be divided into a functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of units in the present application is illustrative and is only a logical division. Actual implementation can have another division method.

[0154] In this embodiment, the mobile cell resource minimization slice resource configuration system includes hardware structures and / or software modules for performing each function to achieve the principles and beneficial effects of the method described in Embodiment 1. Those skilled in the art should easily realize that, in combination with the description of each schematic unit and algorithm steps, the present application can be implemented in the form of hardware and / or a combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or computer software depends on the specific application and design constraints of the technical solution. Different methods can be used to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

Claims

1. A method for configuring a mobile cell resource minimization slice resource, characterized in that, The method comprises the following steps: A train mobile network model based on a relay system is built, and a resource slicing framework is constructed; The train mobile network model based on the relay system is built, and the resource slicing framework is constructed, specifically as follows: A link is established between the base station and the roof relay, wherein the roof relay communicates with the terminal through the train station; Real-time video data is captured by a trackside camera, and the video data is transmitted to a ground data processing center for processing, and the processing result is sent to the vehicle-mounted DAS terminal, wherein the vehicle-mounted terminal includes a VDS terminal and a DAS terminal; The system bandwidth is divided into a set of resource blocks, denoted by The bandwidth of a single resource block RB is denoted by , defined as the minimum resource unit allocated to a VDS terminal in the time-frequency domain, where k denotes the number of resource blocks, K denotes the number of resource blocks; The time domain is divided into millisecond slots with a time length of , wherein the time length of a single mini-slot is represented as , wherein represents the number of mini-slots under a single millisecond slot, represents the time length of a single mini-slot, represents the time length of a single millisecond slot; A single resource block RB is allocated to the VDS terminal using a standard time slot, wherein the data packet arriving at the DAS terminal occupies the bandwidth resource already allocated to the VDS terminal, and the available mini time slot is selected to transmit data, thereby completing the construction of the resource slicing framework; Based on the constructed resource slicing framework, a communication and service quality guarantee model is established for the passenger on-demand service and the over-the-horizon service when considering the downlink bearing demand of the train intelligent service: The communication and service quality guarantee model for the passenger on-demand service and the over-the-horizon service is specifically as follows: Considering the train intelligent service downlink bearer demand, a linear model is used to describe the relationship between the rate loss of the passenger on-demand service and the number of mini-slots occupied by the auxiliary driving service, wherein the transmission data rate of the VDS terminal m is: wherein denotes a transmission data rate of the VDS terminal m , denotes a bandwidth of a single resource block RB, denotes a total number of single resource blocks RB allocated to the VDS terminal m , denotes a number of mini-slots preempted by the DAS terminal n from the VDS terminal m , denotes a signal-to-noise ratio of the VDS terminal m , denotes a set of multiple VDS terminals in the system, denotes a large-scale channel gain, denotes a transmission power of the VDS terminal m , denotes a channel state information, denotes an additive white Gaussian noise power; Based on the described relationship, the time correlation in fast time-varying fading channels is utilized to construct the actual statistical probability distribution, wherein the expression of the conditional probability density function is as follows: wherein, denotes a conditional probability density function, denotes the real small-scale fading of the j th terminal, denotes a channel estimation error coefficient, denotes a first kind zeroth order Bessel function, denotes the maximum Doppler shift, denotes a channel estimation error coefficient, denotes a channel feedback delay, denotes a train speed, denotes a carrier frequency, denotes the speed of light, denotes the estimated small-scale fading of the j th terminal, denotes an additive white Gaussian noise power, denotes a first kind zeroth order modified Bessel function, denotes an estimation error, denotes a set of VDS terminals in the system, denotes only one DAS terminal in the system; Utilizing the conditional probability density function of the VDS terminal m The average data rate on all individual resource blocks RB, complete the establishment of the passenger on-demand service communication and quality of service model: wherein denotes a VDS terminal m average data rate over all single resource blocks RB, denotes a conditional probability density function, denotes the estimated small-scale fading of the m VDS terminal, denotes an integration operation; By calculating the data rate of the DAS terminal, the average data rate of the DAS terminal n is obtained. The average data rate of the DAS terminal n The data transmission delay of the DAS terminal is obtained according to the average data rate According to the data transmission delay of the DAS terminal, tail characteristics of the DAS transmission delay are captured by using a conditional risk value to obtain The function is used for completing establishment of the over-the-horizon service communication and the quality of service guarantee model, wherein The C-VaR function is represented as The delay threshold variable is represented as C-VaR, and the C-VaR represents an average value of potential delays exceeding the risk value The data transmission delay of the DAS terminal is represented as Based on the constructed communication and service quality guarantee model, an optimization problem is established with bandwidth allocation and terminal preemption grouping as target variables, wherein the target is to minimize the bandwidth resource; Based on the optimization problem of the target variable, the original problem is simplified by using the convex optimization theory, the resource block allocation sub-problem is derived to obtain the optimal allocation strategy by constraint relaxation, and the mini time slot preemption sub-problem is converted into a target function, an auxiliary variable is introduced, and is simplified into an easy-to-solve form; An efficient BCD-AP algorithm based on block coordinate descent is used to iteratively solve the resource block allocation sub-problem and the mini time slot preemption sub-problem, so as to obtain the optimal resource allocation strategy and complete the minimum slicing resource configuration of the mobile cell resource. 2.The mobile cell resource minimization slice resource configuration method according to claim 1, characterized in that, The expression of the data transmission delay of the DAS terminal is as follows: wherein denotes the data transmission delay of a DAS terminal, denotes the data packet length of a DAS terminal, denotes the average data rate of a DAS terminal n denotes the data rate of a DAS terminal, denotes the conditional probability density function, denotes the true small-scale fading of a DAS terminal n denotes the estimated small-scale fading of a DAS terminal n denotes the received signal-to-noise ratio of a terminal n denotes the large-scale channel gain, denotes the transmission power of a DAS terminal n ​​​​​ 3.The mobile cell resource minimization slice resource configuration method according to claim 2, characterized in that, The The expression of the function is as follows: wherein, represents a C-VaR function, represents a delay threshold variable, represents a latency threshold, represents an expected value, C-VaR represents an average value of potential latency exceeding a risk value. 4.The mobile cell resource minimization slice resource configuration method of claim 1, wherein, The expression of the bandwidth resource minimization problem is as follows: in, Describe the objective function. x and z All are sets of optimization variables. x ={ } represents the set of resource blocks to be allocated. z ={ } represents the set of mini-slots to be allocated. Represents the delay threshold variable. Indicates allocation to VDS terminal m The total number of RBs in a single resource block. This represents a set of multiple VDS terminals in the system. This represents the bandwidth of a single resource block (RB). Indicates VDS terminal m Data transmission rate Indicates VDS terminal m Minimum throughput QoS requirements, Represents the C-VaR function. This indicates the data transmission latency of the DAS terminal. This indicates the maximum risk threshold C-VaR for the DAS terminal. C-VaR represents the average potential latency exceeding the risk value. Represents the set of positive integers. Indicates DAS terminal n Seize the VDS terminal m The number of mini-slots, This indicates the number of mini-slots within a single millisecond slot. 5.The mobile cell resource minimization slice resource configuration method of claim 1, wherein, The expression of the resource block allocation sub-problem is as follows: wherein, denotes an objective function, x denotes a set of optimization variables, x = { } denotes a set of allocated resource block quantities, denotes a transmission data rate of a VDS terminal m , denotes a total number of allocated single resource blocks RBs to a VDS terminal m , denotes a set of positive integers, denotes an optimal resource block allocation of a terminal m , denotes a minimum throughput QoS requirement of a VDS terminal m , denotes a bandwidth of a single resource block RB, denotes a transmission rate of a VDS terminal m, denotes a number of mini-slots of a DAS terminal n pre-empting a VDS terminal m , denotes a number of mini-slots under a single millisecond slot, denotes a signal-to-noise ratio of a VDS terminal m , denotes a conditional probability density function, denotes a channel state information, denotes a channel estimation error coefficient, denotes an estimated small-scale fading of the m th VDS terminal, denotes a channel feedback delay, denotes an integral operation. 6.The mobile cell resource minimization slice resource configuration method according to claim 1, characterized in that, The expression of the mini time slot preemption sub-problem is as follows: in, Describe the objective function of the subproblem. z To optimize the set of variables, z ={ } represents the set of mini-slots to be allocated. Represents the delay threshold variable. Represents auxiliary variables. Indicates the delay threshold. This indicates the maximum risk threshold C-VaR for the DAS terminal. C-VaR represents the average potential latency exceeding the risk value. Indicates to Seeking expectations, Indicates the expected value. This indicates the data transmission latency of the DAS terminal. Indicates allocation to VDS terminal m The total number of RBs in a single resource block. This indicates the number of mini-slots within a single millisecond time slot. This represents a set of multiple VDS terminals in the system. This indicates the number of mini-time slots that a DAS terminal can preempt without affecting the VDS transmission rate. Indicates DAS terminal n Seize the VDS terminal m The number of mini-slots, Indicates VDS terminal m Minimum throughput QoS requirements, This represents the bandwidth of a single resource block (RB). Indicates VDS terminal m transmission rate, Indicates the packet length of the DAS terminal. Indicates DAS terminal n signal-to-noise ratio, Indicates DAS terminal n True small-scale decay, This represents the channel estimation error coefficient. This represents integration. Indicates DAS terminal n The estimated small-scale fading, This indicates the channel feedback delay.

7. A mobile cell resource minimization slice resource configuration system for performing the mobile cell resource minimization slice resource configuration method of any one of claims 1-6, characterized by, It comprises: The first processing module is configured to build a train mobile network model based on a relay system, and construct a resource slicing framework; The train mobile network model based on the relay system is built, and the resource slicing framework is constructed, specifically as follows: A link is established between the base station and the roof relay, wherein the roof relay communicates with the terminal through the train station; Real-time video data is captured by a trackside camera, and the video data is transmitted to a ground data processing center for processing, and the processing result is sent to the vehicle-mounted DAS terminal, wherein the vehicle-mounted terminal includes a VDS terminal and a DAS terminal; The system bandwidth is divided into a set of resource blocks, denoted by The bandwidth of a single resource block RB is denoted by is defined as the minimum resource unit allocated to a VDS terminal in the time-frequency domain, where k denotes the number of resource blocks, K denotes the number of resource blocks; The time domain is divided into millisecond slots with a time length of , wherein the time length of a single mini-slot is represented as , wherein represents the number of mini-slots under a single millisecond slot, represents the time length of a single mini-slot, represents the time length of a single millisecond slot; A single resource block RB is allocated to the VDS terminal using a standard time slot, wherein the data packet arriving at the DAS terminal occupies the bandwidth resource already allocated to the VDS terminal, and the available mini time slot is selected to transmit data, thereby completing the construction of the resource slicing framework; The second processing module is configured to, based on the constructed resource slicing framework, establish a communication and service quality guarantee model for the passenger on-demand service and the over-the-horizon service when considering the downlink bearing demand of the train intelligent service: The model for establishing communication and service quality guarantee for the passenger on-demand service and over-the-horizon service is specific to: Considering the train intelligent service downlink bearer demand, a linear model is used to describe the relationship between the rate loss of the passenger on-demand service and the number of mini-slots occupied by the auxiliary driving service, wherein the transmission data rate of the VDS terminal m is: wherein denotes the transmission data rate of a VDS terminal m , denotes the bandwidth of a single resource block RB, denotes the total number of single resource blocks RB allocated to a VDS terminal m , denotes the number of mini-slots a DAS terminal n pre-empts from a VDS terminal m , denotes the signal-to-noise ratio of a VDS terminal m , denotes a set of multiple VDS terminals in the system, denotes a large-scale channel gain, denotes the transmission power of a VDS terminal m , denotes a channel state information, denotes an additive white Gaussian noise power; Based on the described relationship, the time correlation in fast time-varying fading channels is utilized to construct the actual statistical probability distribution, wherein the expression of the conditional probability density function is as follows: wherein, denotes a conditional probability density function, denotes a real small-scale fading of the j th terminal, denotes a channel estimation error coefficient, denotes a first kind zeroth order Bessel function, denotes a maximum Doppler shift, denotes a channel estimation error coefficient, denotes a channel feedback delay, denotes a train speed, denotes a carrier frequency, denotes a speed of light, denotes an estimated small-scale fading of the j th terminal, denotes an additive white Gaussian noise power, denotes a first kind zeroth order modified Bessel function, denotes an estimation error, denotes a set of multiple VDS terminals in the system, denotes only one DAS terminal in the system; Utilizing the conditional probability density function of the VDS terminal m The average data rate on all individual resource blocks RB, complete the establishment of the passenger on-demand service communication and quality of service model: wherein denotes a VDS terminal m average data rate over all single resource blocks RB, denotes a conditional probability density function, denotes an estimate of the small-scale fading for the m VDS terminal, denotes an integration operation; By calculating the data rate of the DAS terminal, the average data rate of the DAS terminal n is obtained. The average data rate of the DAS terminal n The data transmission delay of the DAS terminal is obtained according to the average data rate of the DAS terminal According to the data transmission delay of the DAS terminal, tail features of the DAS transmission delay are captured by using conditional risk values to obtain The function is used to complete the establishment of the communication and service quality guarantee model for the over-the-horizon service, and a third processing module is used to establish an optimization problem with bandwidth allocation and terminal preemption grouping as target variables based on the established communication and service quality guarantee model, wherein the target is to minimize the bandwidth resources. The fourth processing module is used for simplifying the original problem by using the convex optimization theory based on the optimization problem of the target variable, deriving the optimal allocation strategy by constraint relaxation for the resource block allocation sub-problem, and converting the target function, introducing auxiliary variables, and simplifying the mini-slot preemption sub-problem into an easy-to-solve form; The fifth processing module is used for iteratively solving the resource block allocation sub-problem and the mini-slot preemption sub-problem by using the resource block allocation and preemption BCD-AP efficient algorithm based on the block coordinate descent, obtaining the optimal resource allocation strategy, and completing the mobile cell resource minimization slice resource configuration.

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