CN111629443A - Optimization method and system for dynamic spectrum slicing frame in super 5G vehicle networking - Google Patents

Optimization method and system for dynamic spectrum slicing frame in super 5G vehicle networking Download PDF

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CN111629443A
CN111629443A CN202010523949.5A CN202010523949A CN111629443A CN 111629443 A CN111629443 A CN 111629443A CN 202010523949 A CN202010523949 A CN 202010523949A CN 111629443 A CN111629443 A CN 111629443A
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CN111629443B (en
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吕丰
任炬
段思婧
张永敏
张尧学
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Central South University
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    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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Abstract

The invention discloses an optimization method of a dynamic spectrum slice frame in an ultra-5G vehicle networking, which comprises the following steps: for a particular type of service request, scheduling service requests arriving within a time slot into different downlink resource planes to deliver the service; maintaining a queue for each type of service request by each resource plane in a resource slicing mode; determining the number of unmanned aerial vehicles to be scheduled in each time slot to meet the dynamic packet processing requirements from all service requests according to the unmanned aerial vehicle packet processing capacity of each resource plane in each time slot; packet processing capabilities for each service request within a time slot according to each resource plane; and decoupling optimization parameters based on the Lyapunov optimization technology to determine optimal control parameters by taking the minimization of service supply cost and the maximization of service utility of all resource planes as optimization targets. The invention effectively improves the service performance and the resource utilization rate of the user.

Description

Optimization method and system for dynamic spectrum slicing frame in super 5G vehicle networking
Technical Field
The invention relates to the technical field of super-5G vehicle networking, in particular to an optimization method and system for a dynamic spectrum slice frame in the super-5G vehicle networking.
Background
To achieve extensive on-board service, modern interconnected vehicles require a robust network that simultaneously guarantees ultra-reliable and low-latency communications for assisted/autonomous driving, provides high-bandwidth on-board entertainment services, supports communication requirements for intensive task offloading, etc. Since different applications share the same spectrum resources and their traffic consumption patterns are different, they have significant impact on each other, which makes it difficult to achieve satisfactory quality of service. For example, high bandwidth services are highly susceptible to channel blockage, resulting in packet loss or transmission delays for lightweight, ultra-reliable and low-latency services. Recently, network virtualization and slicing technology is considered as an indispensable component in next generation networks, and network slicing refers to running multiple private networks on a shared physical network infrastructure, wherein slices are completely isolated from each other, and each slice can customize service-oriented functions or can be combined to meet different service requirements.
On the other hand, air-ground integrated car networking (i.e., integrated satellite network, drone network, and terrestrial cellular network) is a promising architecture of ultra-5G car networking (B5G, 6G oriented car networking introducing drone and satellite network). In the air-ground integrated vehicle networking, besides the ground cellular network, other heterogeneous networks exist, wherein a satellite network can provide ubiquitous wireless coverage, and an unmanned aerial vehicle network can assist the ground network in improving the flexibility of the ground network. However, implementing efficient slicing functions in such networks is a difficult task. Firstly, the space-air-ground integrated Internet of vehicles spectrum resources are multidimensional and heterogeneous, and access technologies of the space-air-ground integrated Internet of vehicles spectrum resources are different, so that the space-air-ground integrated Internet of vehicles spectrum resources are difficult to manage and slice; secondly, the network topology changes violently due to the rapid movement of the vehicle, so that the service request is unpredictable; third, how to achieve a balance between system performance and drone scheduling cost is also critical.
The current technology has limited research on the network slice of the air-space-ground integrated Internet of vehicles. There are some related researches to roughly discuss the car networking slicing technology, but no specific implementation details are given, while other researches are to perform network slicing in different scenes and mainly focus on content pushing and caching slicing resources. However, the vehicle-mounted network environment is more widely existed in the real environment, the spectrum resource slice is more complex, and the research on the spectrum resource slice and the management optimization technology used in the air-space-ground integrated vehicle networking has better application prospect and use value.
Disclosure of Invention
The invention provides an optimization method and system for a dynamic spectrum slicing frame in an ultra-5G vehicle networking system, which are used for solving the technical problem that efficient spectrum slicing is difficult to realize in an air-space-ground integrated vehicle networking system.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an optimization method for a dynamic spectrum slicing framework in an ultra-5G Internet of vehicles comprises the following steps:
for a particular type of service request, scheduling service requests arriving within a time slot into different downlink resource planes to deliver the service; maintaining a queue for each type of service request by each resource plane in a resource slicing mode;
determining the number of unmanned aerial vehicles to be scheduled in each time slot to meet the dynamic packet processing requirements from all service requests according to the unmanned aerial vehicle packet processing capacity of each resource plane in each time slot;
packet processing capabilities for each service request within a time slot according to each resource plane; the method comprises the following steps of taking minimization of service supply cost and maximization of service effectiveness of all resource planes as optimization targets, decoupling optimization parameters based on a Lyapunov optimization technology to determine optimal control parameters, wherein the control parameters comprise: request quantity a of ith service admittance in time slot ti(t) mixing ai(t) scheduling of request amounts toRequested quantity a in jth downlink resource planeij(t) dynamically scheduling the processing capacity C (t) provided by the drone and the packet processing capacity b of the jth resource plane for the ith service request in the time slot tij(t)。
As a further improvement of the process of the invention:
preferably, the control parameters satisfy the following constraints:
Figure BDA0002533063260000021
ai(t)＝∑j∈Jaij(t)
C(t)∈[0,Cmax]
Figure BDA0002533063260000022
Figure BDA0002533063260000023
wherein, I ═ 1, 2.. multidata, I } represents a group of services, I ∈ I, J ═ 0, 1.. multidata, J } represents a group of downlink resources, downlink resource plane T ═ 0,1, 2.. multidata, T.. multidata, Z represents a time slot in which the network operates, Z represents a time slot in which the network operates, and I represents a set of servicesj(t) (J ∈ J) is the packet throughput of the jth resource plane in the tth slot, CmaxThe number of unmanned aerial vehicles which can be actually dispatched; bmaxA maximum number of processing requests within a time slot; a. thei(t) denotes a certain type of service volume arriving within a time slot t, and Ai(t) are independently and identically distributed in different time slots and independent of other arriving service types.
Preferably, the control parameters satisfy the following criteria:
queue length Q for ith service request in time slot tij(t) having:
Qij(t+1)＝max[Qij(t)-bij(t),0]+aij(t), (3)
wherein
Figure BDA0002533063260000024
Satisfies the following conditions:
time-averaged queue stability criterion:
Figure BDA0002533063260000031
wherein
Figure BDA0002533063260000032
Is the time-averaged queue length when
Figure BDA0002533063260000033
The system is stable;
for the drone resource plane, its packet processing capability depends on the real-time scheduling of the drone, i.e.:
Figure BDA0002533063260000034
wherein Z isj(t) where j is 0 denotes the drone resource plane, Z0(t) indicates available drone packet processing capacity within time slot t; bi0(t) packet processing capability of the unmanned plane resource plane for the ith service request in time slot t;
long term time averaged equivalence:
Figure BDA0002533063260000035
where θ is the desired unmanned aerial vehicle processing capability across time slices, Z0(0) Can be initialized to any non-negative value.
Preferably, with the minimization of service supply cost and maximization of service utility for all resource planes as optimization objectives, the optimization problem is as follows:
Figure BDA0002533063260000036
Figure BDA0002533063260000037
wherein the system utility function:
Figure BDA0002533063260000038
wherein λiA positive real number representing the ith service weight,
Figure BDA0002533063260000039
is the long-term time-averaged admission request volume for the ith service; the long-term average scheduling cost of the unmanned aerial vehicle is
Figure BDA00025330632600000310
β is a unit cost representing the ability to schedule a drone for processing, and is a weight constant.
Preferably, with the minimization of service supply cost and maximization of service utility for all resource planes as optimization objectives, the optimization problem is as follows:
Figure BDA00025330632600000311
Figure BDA00025330632600000312
Figure BDA00025330632600000313
wherein the system utility function:
Figure BDA0002533063260000041
wherein λiA positive real number representing the ith service weight,
Figure BDA0002533063260000042
is the ithLong-term time-averaged admission request volume for a service; the long-term average scheduling cost of the unmanned aerial vehicle is
Figure BDA0002533063260000043
β is a unit cost representing the processing power of the unmanned aerial vehicle, and is a weight constant, gammai(t) is ai(t) non-negative auxiliary variables.
Preferably, when decoupling optimization parameters based on the lyapunov optimization technology, a lyapunov drift plus penalty minimization algorithm is adopted, and the conditional lyapunov drift plus penalty of a single time slot is as follows:
Figure BDA0002533063260000044
wherein
Figure BDA0002533063260000045
Is the conditional lyapunov offset for a single slot, and V is a weight constant, used to trade off between system revenue and queue stability; deltaV(t) indicates more stable queue length and higher system revenue;
Figure BDA0002533063260000046
a connection vector representing the instantaneous queue size of all queues at the end of the tth slot; queue
Figure BDA0002533063260000047
The virtual queues in the set of (1) are:
Figure BDA0002533063260000048
wherein
Figure BDA0002533063260000049
Is a set of the lengths of the queues,
Figure BDA00025330632600000410
packet processing energy for available UAV within time slot tThe set of forces.
Preferably, ΔVThe upper limit of (t) is:
Figure BDA00025330632600000411
wherein B is defined by the upper limit Amax,bmax,CmaxDetermined bymaxUpper bound for admission of all services, bmaxIs an upper limit for processing requests within a time slot, CmaxIs the upper limit of the unmanned aerial vehicle which can be dispatched practically.
Preferably, at each time slot t, the status Θ (t) of all queues is observed based on the following five control phases;
auxiliary variable decision making: minimization
Figure BDA0002533063260000051
Request control and scheduling: maximization
Figure BDA0002533063260000052
Scheduling an unmanned aerial vehicle: minimization
Figure BDA0002533063260000053
Resource slicing: for each resource plane, sequencing the queues according to the descending order of the weights of the resource planes, and segmenting resources in sequence as long as a resource constraint formula (2) is met;
and (3) queue updating: queue state Q after all control parameters are obtainedij(t)，Hi(t) and Z0(t) is updated based on equations (3), (11) and (5), respectively, for making a decision in the next time slot.
The invention also provides a computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the computer program.
The invention has the following beneficial effects:
1. the optimization method and the system for the dynamic spectrum slicing frame in the ultra-5G vehicle networking are used for dynamically slicing resources of the ultra-5G vehicle networking (air-space-ground integrated vehicle-mounted network) and managing and optimizing the spectrum resources of the ultra-5G vehicle networking so as to realize isolation of service resource supply. In order to provide service isolation, the system can independently establish a service queue for each type of network service and supply spectrum resources, the dynamic spectrum slice framework comprises user request control, request scheduling, unmanned plane scheduling and resource slicing, and the optimization aim is to maximize the long-term benefit of the network system under the condition that the service queue is stable. Based on the Lyapunov optimization technology, the dynamic slice optimization problem is decoupled into a plurality of independent sub-problems and solved. A large number of simulation results show that the method has good effects on the aspects of improving the system throughput, efficiently using the unmanned aerial vehicle, balancing contradictions between the system benefit and the stability and the like.
2. In a preferred scheme, the optimization method and the optimization system for the dynamic spectrum slice frame in the super-5G vehicle networking maintain the stability of the queue, can flexibly balance the stability and the benefits of the system, realize the balance between the throughput of the system and the unmanned aerial vehicle by adjusting control parameters, and effectively improve the service performance and the resource utilization rate of users.
In addition to the objects, features and advantages described above, other objects, features and advantages of the present invention are also provided. The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a system model diagram of an embodiment of the present invention;
fig. 2 is a schematic diagram of a dynamic slicing workflow according to an embodiment of the present invention, including: request control, request scheduling, unmanned aerial vehicle scheduling and resource slicing;
FIG. 3 is a schematic diagram of a simulation experiment scenario according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating the size and stability of a simulated service queue according to an embodiment of the present invention;
FIG. 5 is a schematic diagram illustrating a length of a time-averaged service queue under different values of a weight coefficient V in a simulation experiment according to an embodiment of the present invention;
fig. 6 is a schematic diagram of time-averaged unmanned aerial vehicle resource conditions under different values of weight coefficient V in a simulation experiment according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of time-averaged profit variation under different values of the weight coefficient V in a simulation experiment according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating a variation of a queue length averaged over time in a simulation experiment according to an embodiment of the present invention under different values of the weighting factor V;
FIG. 9 is a diagram illustrating a time-averaged throughput of a system in a simulation experiment in accordance with an embodiment of the present invention;
fig. 10 is a schematic diagram of the time-averaged queue length in the simulation experiment according to the embodiment of the present invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Fig. 1 is a typical model of the air-space-ground integrated internet of vehicles system in this embodiment, in which a fixed ground network is formed at a ground base station to provide cellular network connection, highly flexible drones are dynamically scheduled to provide an on-demand aeronautical network, and an orbiting satellite synchronized with the equator is provided to serve a complete wireless coverage area of interest. Assuming that each vehicle has three network interfaces, namely a cellular network, an unmanned aerial vehicle and satellite communication, since the vehicle network service mainly depends on the performance of a downlink, the downlink service scenario of the air-ground integrated vehicle networking is mainly considered in the embodiment.
On the basis of the system model, the invention adopts a dynamic spectrum slicing frame to dynamically slice the air-space-ground integrated vehicle-mounted network resources and manage and optimize the spectrum resources so as to realize the isolation of service resource supply. By software and slicing of spectrum resources, requests for each type of service can be processed and serviced in a separate queue. The dynamic spectrum slicing framework of the invention considers that an online decision is made under each time slot, and comprises the following contents:
(a) controlling how many different service requests the system should access;
(b) how to schedule resources of the satellite, drone and terrestrial cellular networks to process for each type of request;
(c) if needed, determining how many drones should be scheduled to supplement spectrum resources;
(d) how to slice the spectrum to reduce the queue backlog for the service queues on each resource plane;
moreover, the invention mainly considers three core requirements of the vehicle network system:
(e) the system should access and process as many service requests as possible;
(f) after the system performance is ensured, the scheduling cost of the unmanned aerial vehicle is reduced as much as possible;
(g) the backlog of queues should be stabilized within a certain range for each type of service.
Under the above decision and demand conditions, the optimization method for the dynamic spectrum slice frame in the super 5G vehicle networking of the invention comprises the following steps:
for a particular type of service request, scheduling service requests arriving within a time slot into different downlink resource planes to deliver the service; maintaining a queue for each type of service request by each resource plane in a resource slicing mode;
determining the number of unmanned aerial vehicles to be scheduled in each time slot to meet the dynamic packet processing requirements from all service requests according to the unmanned aerial vehicle packet processing capacity of each resource plane in each time slot;
requesting each service within a time slot according to each resource planeThe packet processing capability of (a); decoupling optimization parameters based on a Lyapunov optimization technology to determine optimal control parameters by taking minimization of service supply cost and maximization of service utility of all resource planes as optimization targets, wherein the control parameters comprise: request quantity a of ith service admittance in time slot ti(t) mixing ai(t) scheduling of the requested amount into the requested amount a in the jth downlink resource planeij(t) dynamically scheduling the processing capacity C (t) provided by the drone and the packet processing capacity b of the jth resource plane for the ith service request in the time slot tij(t)。
In practical application, the invention can be optimized on the basis of the steps, and the following steps are exemplified:
the definition I ═ {1, 2., I } denotes a set of services, and J ═ 0, 1., J } denotes a set of downlink resources. The network operates in time slots T ═ 0,1, 2. Fig. 2 shows a workflow of dynamic slicing in this embodiment, which includes the following steps:
s1. request control for a certain type of service I ∈ I, Ai(t) represents the amount of service arrived in time slot t, assume Ai(t) are independently and identically distributed in different time slots and independent of other arriving service types. Not all requests can arrive due to the limited downstream capacity of the entire network. Definition ai(t) is the amount of requests for the ith service admission in time slot t, thus:
Figure BDA0002533063260000071
s2, request scheduling: next, the admitted requests need to be scheduled into different downlinks to deliver the service. a isij(t) represents ai(t) scheduling the requested amount into a jth downlink resource plane. Then there is ai(t)＝∑j∈Jaij(t) of (d). By way of resource slicing, each resource plane maintains a dedicated queue for each type of service request, as shown in FIG. 1.
S3, unmanned aerial vehicle scheduling: definition of Zj(t) (J ∈ J) is the jth resource planePacket processing capability in the t-th slot. Consider that in a high-density vehicle scenario, where network resources are insufficient to meet a busy service arrival, unmanned aerial vehicle scheduling is used on demand. Denote the drone resource plane by j ═ 0, Z0Since the number of dispatchable drones is limited in practice, C (t) satisfies the bounded hypothesis C (t) ∈ [0, C (t)max]. Within each time slot t, the system needs to determine the number of drones scheduled to meet the dynamic packet processing requirements from all services.
S4, resource slicing: definition bij(t) packet processing capability for the ith service request in time slot t for the jth resource plane. Thus:
Figure BDA0002533063260000081
in the present embodiment, only a limited number of requests can be processed in one time slot for each type of service request, i.e., the following bounded assumption holds,
Figure BDA0002533063260000082
to perform the above four steps, it is necessary to determine the following control parameters, i.e., a, in each sloti(t)，aij(t)，C(t)，bij(t) of (d). In order to make these control decisions, it is first necessary to describe the impact of these parameters on the system performance. By Qij(t) indicates the queue length of the ith service request in time slot t, and there are:
Qij(t+1)＝max[Qij(t)-bij(t),0]+aij(t), (3)
wherein
Figure BDA0002533063260000083
In order to achieve a feasible iteration, the stability of all queues must be guaranteed, so a time-averaged queue stability criterion is adopted, defined as follows:
Figure BDA0002533063260000084
wherein
Figure BDA0002533063260000085
Is the time-averaged queue length when
Figure BDA0002533063260000086
The system is stable.
For the drone resource plane, its packet processing capability depends on the real-time scheduling of the drone, i.e.:
Figure BDA0002533063260000087
in the embodiment, the time delay of scheduling and running of the unmanned aerial vehicle is considered, and in practical application, the available processing capacity of the unmanned aerial vehicle should be kept at a certain level so as to ensure the continuity of service. It can translate into the following long-term time-averaged equivalents:
Figure BDA0002533063260000088
where θ is the desired unmanned aerial vehicle processing capability across time slices, Z0(0) Can be initialized to any non-negative value.
Problem construction:
in this embodiment, different services typically have different quality of service, which also provide different levels of utility to the network service provider. To achieve fair service provisioning, the following system utility functions are defined:
Figure BDA0002533063260000089
wherein λiA positive real number representing the ith service weight,
Figure BDA00025330632600000810
is the long-term time-averaged admission request volume for the ith service. In particular, due to utility function
Figure BDA00025330632600000811
Are all convex functions, equation (7) guarantees a diminishing return, i.e. the service provider cannot guarantee a continuous increase of its overall utility by increasing only admission requests for services.
At the same time, in order to maximize revenue, the service provisioning cost for all resource planes should be minimized. Since the processing power of cellular networks and satellites is static, network service providers need to minimize the cost of drone dispatch. Defining the long-term average scheduling cost of the unmanned plane as
Figure BDA0002533063260000091
In fact, it is difficult to simultaneously consider drone scheduling cost minimization and service utility maximization. Thus, in the next step, a weighted sum of the two objectives will be taken and form the following optimization problem:
Figure BDA0002533063260000092
Figure BDA0002533063260000093
where β is a weight constant, and refers to the unit cost of scheduling the processing power of the drones.
In this embodiment, it is challenging to directly solve the above optimization problem for the following reasons, first, the number of request arrivals for different services is time-varying and a priori unknown, so it is difficult to satisfy the offline optimal decision of the constraint condition (1); second, since the processing power of the drone dispatched from the t slot is only available from the t +1 slot, a set of decision parameters that satisfy constraint (2) may not be able to serve all admission requests in the next slot, resulting in potential request accumulation and long-term queue instability. Therefore, there is a continuing need to use the lyapunov optimization framework to decouple the correlation between the optimization parameters and provide efficient control decisions across each slot of a time slice, collectively solving the optimization problem described above.
Problem transformation: the lyapunov drift plus penalty minimization algorithm is very effective in optimizing the long-term time-averaged objective. Applying the Qisheng inequality to a set of convex effects functions, problem
Figure BDA0002533063260000094
The transformation may be done by introducing a set of auxiliary variables. Definition of gammai(t) is ai(t), then the following reasoning holds:
introduction 1: problem of optimization
Figure BDA0002533063260000095
Is equivalent to:
Figure BDA0002533063260000096
Figure BDA0002533063260000097
Figure BDA0002533063260000098
in this example, the problem after transformation
Figure BDA0002533063260000099
Belonging to the class of the lyapunov optimization framework, it can be solved by constructing a virtual queue corresponding to the auxiliary variable constraint in (10). Specifically, for each service type, a virtual queue is defined:
Figure BDA00025330632600000910
as long as the queue
Figure BDA00025330632600000911
Is stable, the constraint (10) can be satisfied. Meanwhile, in order to further illustrate the constraint of resource slicing in the condition (2), a large-flow scene is also considered, namely, the processing capacity of the satellite and the cellular network is assumed to be fully utilized. Then in each time slot, there is
Figure BDA00025330632600000912
And is
Figure BDA0002533063260000101
Consider a queue
Figure BDA0002533063260000102
And
Figure BDA0002533063260000103
as a function of time slice, wherein
Figure BDA0002533063260000104
Is a set of the lengths of the queues,
Figure BDA0002533063260000105
is the set of available drone packet processing capabilities within time slot t. Problem(s)
Figure BDA0002533063260000106
Can be defined as:
Figure BDA0002533063260000107
the above definition represents one scalar measure of all queue sizes, typically non-negative, and l (t) is 0 if and only if all queue sizes are 0. Order to
Figure BDA0002533063260000108
A connection vector representing the instantaneous queue size of all queues at the end of the t-th slot. The conditional lyapunov drift plus penalty for a single slot is defined as:
Figure BDA0002533063260000109
wherein
Figure BDA00025330632600001010
Is the conditional lyapunov offset for a single slot, and V is a weight constant that trades off the trade-off between system gain and queue stability. A smaller ΔV(t) indicates a more stable queue length and higher system revenue. DeltaVThe upper limit of (t) can be derived from the following lemma.
2, leading: for all possible queue states and control actions, ΔVThe upper limit of (t) is:
Figure BDA00025330632600001011
wherein B is defined by the upper limit Amax,bmax,CmaxDetermined bymaxUpper bound for admission of all services, bmaxIs an upper limit for processing requests within a time slot, CmaxIs the upper limit of the unmanned aerial vehicle which can be dispatched practically.
Decoupling of the control parameters to the right of the drift plus penalty upper bound (14) enables us to align gammai(t),ai(t),aij(t),C(t),bij(t) making independent and sequential decisions. Next, an attempt is made to minimize four independent condition terms in the boundary (14).
Dynamic slicing algorithm: at each time slot t, first, the states Θ (t) of all queues are observed based on the following five control phases.
P1. auxiliary variable decision: i.e. state H of a given observation queuei(t), minimization
Figure BDA0002533063260000111
. The optimization problem is a feasible set gamma for solving the three univariate optimization problemsiAnd they all use a function effectively
Figure BDA0002533063260000112
Closed-form solution of (1).
P2. request control and scheduling: given queue State Hi(t) and Qij(t) of (d). Due to coupling constraints
Figure BDA0002533063260000113
Determining a group ai(t) and aij(t) to maximize per service
Figure BDA0002533063260000114
It is difficult, therefore, to use a connection-based shortest queue first heuristic to determine a sequentiallyi(t) and aij(t) of (d). For the ith type of service, a simple scheduling policy is to put all allowed requests ai(t) sending to the resource plane with the shortest queue, i.e. aij(t)＝ai(t) of (d). If it is not
Figure BDA0002533063260000115
Then aij(t) is 0. Then, it reduces to maximize
Figure BDA0002533063260000116
At aiAnd (t) is easier to solve in a feasible interval.
And P3, unmanned plane scheduling: based on instantaneous queue size Z0(t), predefined parameters β, θ, V and feasible intervals C (t) ∈ [0, Cmax]. By minimizing
Figure BDA0002533063260000117
The optimal unmanned aerial vehicle dispatch capacity can be easily found.
P4. resource slicing it is sufficient to make a separate allocation decision on each resource plane in order to maximize the last term in equation (14) for the satellite and cellular planes (j ∈ {1,2}), to bij(t) assigned a weight of Qij(t)+ZjAnd the weight assigned to the drone resource plane (j ═ 0) is Qi0(t)+Z0(t) - θ. An effective solution is toAt each resource plane, the queues are first sorted according to the order of decreasing their weights, and then the resources are partitioned in order as long as the resource constraint (2) is satisfied. This stage does not consider queues with negative weights, and b is the correspondingij(t) is set to 0.
P5. queue update: queue state Q after all control parameters are obtainedij(t)，Hi(t) and Z0(t) can be used to make a decision in the next slot based on the (3), (11) and (5) updates, respectively.
In conclusion, the embodiment adopts the lyapunov optimization technology to maximize the long-term benefit of the system. Specifically, firstly, the time-averaged queue backlog of all services is considered, a quadratic Lyapunov function is constructed, and a system yield function combining the system average throughput and the unmanned aerial vehicle scheduling cost is provided. In order to maximize the system gain, a reverse penalty function is also constructed. Furthermore, in order to stabilize the system while minimizing the time-averaged penalty, the drift plus penalty should be limited to a minimum. And decomposing the control problem into deterministic subproblems such as auxiliary variable setting, request control, request scheduling, unmanned aerial vehicle scheduling, resource slicing and the like based on a derived drift plus penalty theory.
Simulation experiment:
the technical scheme of the invention is demonstrated by simulation experiments.
As shown in fig. 3, the simulation experiment defines a bidirectional 8-lane highway scene, and uses the simulation tool SUMO to generate a real vehicle track. The entrance of each passage and the arrival of the vehicle are considered to be a poisson distribution lambdaVOf arrival, λV0.25. There is a cellular base station in the center of the road with coordinates (R2000 m, D100 m, H50 m), and if a drone is derived, it hovers at coordinates (x, 0m, 5 m) (e.g., H5 m).
Cellular parameters: respectively setting total bandwidth WBIs 20MHz, and has a transmission power pB20W, background noise density n0Is 1 × 10- 15W/Hz, and in addition, the gain of the channel power is
Figure BDA0002533063260000121
Wherein sigmaBiIs the belief fading and is assumed to follow an exponential distribution of unity mean.
Figure BDA0002533063260000122
Is the distance between the vehicle and the base station, α is the path loss factor and α -3. in addition, there are 100 orthogonal sub-channels, K-100, that can be allocated to the user, each with a bandwidth of 0.2mhz
Figure BDA0002533063260000123
In the long term, the data rate of a subchannel can be expressed as
Figure BDA0002533063260000124
Figure BDA0002533063260000125
Where y is the distance the vehicle travels from road entry point a to road center point B, so the initial transport capacity of the cellular resource plane can be obtained.
Unmanned aerial vehicle communication parameters: the method comprises the steps of adopting a WiFi technology to realize unmanned aerial vehicle communication, and adopting a model based on a drive-thru area to realize unmanned aerial vehicle throughput acquisition, wherein continuous communication areas exist, and each area has different data rates. In particular, an 802.11n protocol is employed, where the bandwidth operates at 2.4GHz and the total bandwidth is set to 40 MHz. The length of each region and the associated data rate are calculated under a free space path loss model according to the WiFi data table.
Satellite communication parameters: the attenuation of the satellite-link is modeled based on the Weibull channel model, considering that the vehicle can always use satellite connections. Specifically, assuming 20 subchannels, each subchannel providing a data rate of 0.5Mbps, the transmission capacity of the satellite resource plane is therefore
Figure BDA0002533063260000126
Vehicle service activities: in the slicing framework described above, three typical vehicle network services are considered. Specifically, an ultra-reliable low-latency service is initiated for each vehicle, where each vehicle downloads one packet per time slot. In order to simulate automatic high-precision map downloading service or assist calculation-intensive task unloading, the embodiment provides a streaming media service for each vehicle, and requires to maintain a relatively stable throughput T during network connectionstrm(set to 0.5 Mbps). For the vehicle network, the user may randomly initiate a download service. In particular, a user may initiate a download request during a Poisson process at a rate of 0.2, and the amount of download may be from the set {10 }5,106,...,1010Are randomly selected.
Fig. 4 shows the stability and size (in units of packets) of the service queues of three vehicle-mounted applications in the simulation experiment of the present invention, where the queue of each application service is very stable and the queue length is positively correlated to the request requirement.
Fig. 5 shows the average time length of the service queue under different values of the weighting factor V in the simulation experiment of the present invention. The average queue may also stabilize well when the request is far beyond the processing power of the system. When the time slot reaches 4000, the increase in the time-averaged queue size tends to be smooth. Furthermore, the time-averaged queue increases as V increases. (larger V indicates that the system is more concerned with revenue rather than queue stability)
Fig. 6 shows the time-averaged unmanned aerial vehicle resource conditions under different weight coefficient V values in the simulation experiment of the present invention. After initial adjustment, the unmanned aerial vehicle resources can be well stabilized by scheduling the unmanned aerial vehicle, but the larger V results in lower stability of the unmanned aerial vehicle resources.
FIG. 7 shows the time-averaged profit variation under different values of the weighting factor V in the simulation experiment of the present invention. As the value of V increases, the time-averaged benefit increases significantly, but to satisfy the stability constraint, the benefit is bounded above regardless of how the value of V is increased. Specifically, when V ranges from 0 to 106, the time-averaged benefit increases to 23:4, and even if V is too high (e.g., 1010), the benefit is around 23: 4.
FIG. 8 shows the variation of time-averaged queue size at different values of V in the simulation of the present invention, the average queue size increases with increasing value of V, however, as V is varied from 5 × 105To 5.5 × 105There is a sudden rise, as can be seen from figure 5, the value of V is less than 5 × 105When the unmanned aerial vehicle resource cost is reduced, the V value is 5.5 × 105No additional drone resources are provisioned, resulting in a sudden increase in time-averaged revenue and queue size.
Fig. 9 and 10 show the time-averaged throughput and the time-averaged queue size of the system in the simulation experiment of the present invention. The following conclusions can be drawn from the figure: first, compared with the dynamic slicing scheme, the fixed slicing scheme cannot keep up with the vehicle state due to the rapid decrease of the system throughput and the rapid increase of the queue size, and the resource management efficiency is low. For example, a fixed slice provides a throughput of approximately 1750 packets per slot, while a dynamic slice can increase this to 2200 packets/slot, an improvement of approximately 26%. Secondly, the upgraded dynamic slicing scheme can achieve the same level of throughput performance and queue stability as dynamic slicing. Therefore, the proposed dynamic slicing not only can improve the overall service performance of the system, but also can support differentiated service provisioning. Furthermore, with the upgraded dynamic slice, ultra-high reliability low latency communication services can be guaranteed with negligible degradation in throughput performance.
The invention also provides a computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of any of the above embodiments being carried out when the computer program is executed by the processor.
In conclusion, the method and the device keep the stability of the queue, can flexibly balance the stability and the benefit of the system, realize the balance between the throughput of the system and the unmanned aerial vehicle by adjusting the control parameters, and effectively improve the service performance and the resource utilization rate of the user. A large number of simulation results show that the method has good effects in the aspects of improving the system throughput, efficiently using the unmanned aerial vehicle, balancing contradictions between the system benefit and the stability and the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for optimizing a dynamic spectrum slicing framework in an ultra-5G Internet of vehicles is characterized by comprising the following steps:
for a particular type of service request, scheduling service requests arriving within a time slot into different downlink resource planes to deliver the service; maintaining a queue for each type of service request by each resource plane in a resource slicing mode;
determining the number of unmanned aerial vehicles to be scheduled in each time slot to meet the dynamic packet processing requirements from all service requests according to the unmanned aerial vehicle packet processing capacity of each resource plane in each time slot;
packet processing capabilities for each service request within a time slot according to each resource plane; decoupling optimization parameters based on a Lyapunov optimization technology to determine optimal control parameters by taking minimization of service supply cost and maximization of service utility of all resource planes as optimization targets, wherein the control parameters comprise: request quantity a of ith service admittance in time slot ti(t) mixing ai(t) scheduling of the requested amount into the requested amount a in the jth downlink resource planeij(t) dynamically scheduling the processing capacity C (t) provided by the drone and the packet processing capacity b of the jth resource plane for the ith service request in the time slot tij(t)。
2. The optimization method for dynamic spectrum slicing framework in ultra-5G internet of vehicles according to claim 1, wherein the control parameters satisfy the following constraints:
Figure FDA0002533063250000011
ai(t)＝∑j∈Jaij(t)
C(t)∈[0,Cmax]
Figure FDA0002533063250000012
Figure FDA0002533063250000013
wherein, I ═ 1, 2.. multidata, I } represents a group of services, I ∈ I, J ═ 0, 1.. multidata, J } represents a group of downlink resources, downlink resource plane T ═ 0,1, 2.. multidata, T.. multidata, Z represents a time slot in which the network operates, Z represents a time slot in which the network operates, and I represents a set of servicesj(t) (J ∈ J) is the packet throughput of the jth resource plane in the tth slot, CmaxThe number of unmanned aerial vehicles which can be actually dispatched; bmaxAn upper limit for processing requests within a slot; a. thei(t) denotes a certain type of service volume arriving within a time slot t, and Ai(t) are independently and identically distributed in different time slots and independent of other arriving service types.
3. The optimization method for dynamic spectrum slicing framework in ultra-5G internet of vehicles according to claim 2, wherein the control parameters satisfy the following criteria:
queue length Q for ith service request in time slot tij(t) having:
Qij(t+1)＝max[Qij(t)-bij(t),0]+aij(t), (3)
wherein
Figure FDA0002533063250000021
Satisfies the following conditions:
time-averaged queue stability criterion:
Figure FDA0002533063250000022
wherein
Figure FDA0002533063250000023
Is the time-averaged queue length when
Figure FDA0002533063250000024
The system is stable;
for the drone resource plane, its packet processing capability depends on the real-time scheduling of the drone, i.e.:
Figure FDA0002533063250000025
wherein Z isj(t) where j is 0 denotes the drone resource plane, Z0(t) indicates available drone packet processing capacity within time slot t; bi0(t) packet processing capability of the unmanned plane resource plane for the ith service request in time slot t;
long term time averaged equivalence:
Figure FDA0002533063250000026
where θ is the desired unmanned aerial vehicle processing capability across time slices, Z0(0) Can be initialized to any non-negative value.
4. The optimization method for the dynamic spectrum slice framework in the ultra-5G vehicle networking according to claim 3, wherein the optimization objective is minimization of service supply cost of all resource planes and maximization of service utility, and the optimization problem is as follows:
Figure FDA0002533063250000027
Figure FDA0002533063250000028
wherein the system utility function:
Figure FDA0002533063250000029
wherein λiA positive real number representing the ith service weight,
Figure FDA00025330632500000210
is the long-term time-averaged admission request volume for the ith service; the long-term average scheduling cost of the unmanned aerial vehicle is
Figure FDA00025330632500000211
β is a unit cost representing the ability to schedule a drone for processing, and is a weight constant.
5. The optimization method for the dynamic spectrum slice framework in the ultra-5G vehicle networking according to claim 4, wherein the optimization objective is minimization of service supply cost of all resource planes and maximization of service utility, and the optimization problem is as follows:
Figure FDA0002533063250000031
Figure FDA0002533063250000032
Figure FDA0002533063250000033
wherein the system utility function:
Figure FDA0002533063250000034
wherein λiA positive real number representing the ith service weight,
Figure FDA0002533063250000035
is the long-term time-averaged admission request volume for the ith service; the long-term average scheduling cost of the unmanned aerial vehicle is
Figure FDA0002533063250000036
β is a unit cost representing the processing power of the unmanned aerial vehicle, and is a weight constant, gammai(t) is ai(t) non-negative auxiliary variables.
6. The optimization method for the dynamic spectrum slice frame in the super-5G vehicle networking according to claim 5, wherein when the optimization parameters are decoupled based on the Lyapunov optimization technology, a Lyapunov drift plus penalty minimization algorithm is adopted, and the conditional Lyapunov drift plus penalty of a single time slot is as follows:
Figure FDA0002533063250000037
wherein
Figure FDA0002533063250000038
Is the conditional lyapunov offset for a single slot, and V is a weight constant, used to trade off between system revenue and queue stability; deltaV(t) indicates more stable queue length and higher system revenue;
Figure FDA0002533063250000039
a connection vector representing the instantaneous queue size of all queues at the end of the tth slot; queue
Figure FDA00025330632500000310
The virtual queues in the set are:
Figure FDA00025330632500000311
wherein
Figure FDA00025330632500000312
Is a set of the lengths of the queues,
Figure FDA00025330632500000313
is the set of available drone packet processing capabilities within time slot t.
7. The method of claim 6, wherein the delta is a function of a time of day, a timeVThe upper limit of (t) is:
Figure FDA0002533063250000041
wherein B is defined by the upper limit Amax,bmax,CmaxDetermined bymaxUpper bound for admission of all services, bmaxIs an upper limit for processing requests within a time slot, CmaxIs the upper limit of the unmanned aerial vehicle which can be dispatched practically.
8. The optimization method for the dynamic spectrum slicing framework in the ultra-5G vehicle networking according to the claim 6, wherein, at each time slot t, the states Θ (t) of all queues are observed based on the following five control phases;
auxiliary variable decision making: minimization
Figure FDA0002533063250000042
Request control and scheduling: maximization
Figure FDA0002533063250000043
Scheduling an unmanned aerial vehicle: minimization
Figure FDA0002533063250000044
Resource slicing: for each resource plane, sequencing the queues according to the descending order of the weights of the resource planes, and segmenting resources in sequence as long as a resource constraint formula (2) is met;
and (3) queue updating: queue state Q after all control parameters are obtainedij(t)，Hi(t) and Z0(t) is updated based on equations (3), (11) and (5), respectively, for making a decision in the next time slot.
9. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1 to 8 are performed when the computer program is executed by the processor.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112565377A (en) * 2020-11-30 2021-03-26 北京邮电大学 Content grading optimization caching method for user service experience in Internet of vehicles
CN112866368A (en) * 2021-01-12 2021-05-28 北京邮电大学 Air-ground remote Internet of things design method and system
CN113296909A (en) * 2021-05-14 2021-08-24 上海交通大学 Optimized service deployment method in mobile edge computing
CN115397021A (en) * 2022-07-18 2022-11-25 北京邮电大学 Method and device for allocating dynamic resources of wireless access network in imperfect channel state
CN117082626A (en) * 2023-07-26 2023-11-17 北京交通大学 Service multiplexing slice resource allocation method and system for train wireless communication network

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107682135A (en) * 2017-09-30 2018-02-09 重庆邮电大学 A kind of network slice adaptive virtual resource allocation method based on NOMA
EP3297227A1 (en) * 2016-09-14 2018-03-21 Deutsche Telekom AG Method for routing in a communication network, communication network, program and computer program product
CN108965024A (en) * 2018-08-01 2018-12-07 重庆邮电大学 A kind of virtual network function dispatching method of the 5G network slice based on prediction
CN110366193A (en) * 2019-05-28 2019-10-22 中国电力科学研究院有限公司 A kind of splitting and disposing method and device of the network arrangements layer carrying of smart grid
CN110809261A (en) * 2019-10-31 2020-02-18 重庆邮电大学 Network slice dynamic resource scheduling method for joint congestion control and resource allocation in H-CRAN network
CN111258677A (en) * 2020-01-16 2020-06-09 重庆邮电大学 Task unloading method for heterogeneous network edge computing

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3297227A1 (en) * 2016-09-14 2018-03-21 Deutsche Telekom AG Method for routing in a communication network, communication network, program and computer program product
CN107682135A (en) * 2017-09-30 2018-02-09 重庆邮电大学 A kind of network slice adaptive virtual resource allocation method based on NOMA
CN108965024A (en) * 2018-08-01 2018-12-07 重庆邮电大学 A kind of virtual network function dispatching method of the 5G network slice based on prediction
CN110366193A (en) * 2019-05-28 2019-10-22 中国电力科学研究院有限公司 A kind of splitting and disposing method and device of the network arrangements layer carrying of smart grid
CN110809261A (en) * 2019-10-31 2020-02-18 重庆邮电大学 Network slice dynamic resource scheduling method for joint congestion control and resource allocation in H-CRAN network
CN111258677A (en) * 2020-01-16 2020-06-09 重庆邮电大学 Task unloading method for heterogeneous network edge computing

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
乔楚: "5G网络端到端切片技术研究", 《通信技术》 *
王文琪等: "基于QoS的5G切片间资源分配", 《光通信研究》 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112565377A (en) * 2020-11-30 2021-03-26 北京邮电大学 Content grading optimization caching method for user service experience in Internet of vehicles
CN112866368A (en) * 2021-01-12 2021-05-28 北京邮电大学 Air-ground remote Internet of things design method and system
CN113296909A (en) * 2021-05-14 2021-08-24 上海交通大学 Optimized service deployment method in mobile edge computing
CN115397021A (en) * 2022-07-18 2022-11-25 北京邮电大学 Method and device for allocating dynamic resources of wireless access network in imperfect channel state
CN117082626A (en) * 2023-07-26 2023-11-17 北京交通大学 Service multiplexing slice resource allocation method and system for train wireless communication network
CN117082626B (en) * 2023-07-26 2024-03-26 北京交通大学 Service multiplexing slice resource allocation method and system for train wireless communication network

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