CN111211830B - Satellite uplink bandwidth resource allocation method based on Markov prediction - Google Patents

Satellite uplink bandwidth resource allocation method based on Markov prediction Download PDF

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CN111211830B
CN111211830B CN202010030550.3A CN202010030550A CN111211830B CN 111211830 B CN111211830 B CN 111211830B CN 202010030550 A CN202010030550 A CN 202010030550A CN 111211830 B CN111211830 B CN 111211830B
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satellite
service
traffic
resource allocation
request
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CN111211830A (en
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廖丹
韩钧
王文滔
李慧
张明
陈雪
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1853—Satellite systems for providing telephony service to a mobile station, i.e. mobile satellite service
    • H04B7/18539—Arrangements for managing radio, resources, i.e. for establishing or releasing a connection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1853—Satellite systems for providing telephony service to a mobile station, i.e. mobile satellite service
    • H04B7/18569—Arrangements for system physical machines management, i.e. for construction operations control, administration, maintenance
    • H04B7/18573—Arrangements for system physical machines management, i.e. for construction operations control, administration, maintenance for operations control, administration or maintenance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2425—Traffic characterised by specific attributes, e.g. priority or QoS for supporting services specification, e.g. SLA
    • H04L47/2433—Allocation of priorities to traffic types

Abstract

The invention provides a satellite uplink bandwidth resource allocation method based on Markov prediction, which comprises the following steps: sending a resource allocation request to the satellite; establishing a Markov-based state transition probability matrix and predicting the traffic in the next period; classifying the service requests according to the service flow predicted in the next period to obtain an optimal distribution mode; and feeding back the optimal allocation mode to the satellite and accessing to complete the bandwidth resource allocation of the satellite uplink. The invention solves the problem of high delay of the traditional satellite communication framework by introducing the Markov theory; the services are enqueued and dequeued according to the FIFO principle, and are divided into different priorities, so that resource allocation of various services is realized, QoS (quality of service) of different types of terminals is guaranteed, and fairness of the same rating service is realized. The invention effectively reduces the time delay from the service request to the NCC and reduces the computing resource using the NCC.

Description

Satellite uplink bandwidth resource allocation method based on Markov prediction
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a satellite uplink bandwidth resource allocation method based on Markov prediction.
Background
With the development of communication networks, the demand of users for satellite communication is increasing, and the satellite communication becomes an indispensable part of the whole communication network. Compared with the traditional ground network, the satellite communication has the advantages of wide coverage area, high communication quality and high reliability in remote and offshore areas and the like. Because the satellite bandwidth resources are limited, the calculation and allocation of the satellite bandwidth resources are very research and challenging problems, on one hand, the reasonable and fair allocation of the bandwidth resources is ensured, and on the other hand, the problem of delay of the satellite requesting the bandwidth resources is solved. In a future network, more and more internet of things devices need to communicate and transmit, and the types and requirements of the devices are diversified, so that the satellite needs to ensure the service quality and coordinate the requirements of each terminal.
The current satellite bandwidth resource allocation scheme has two kinds, namely a static allocation scheme and a dynamic allocation scheme, and the main idea of the static allocation scheme is that once traffic reaches a satellite network, a control center calculates bandwidth resources and allocates bandwidth, and the scheme can ensure low delay, but the satellite resource utilization rate is not high because of the instant allocation idea, no matter which type of traffic, only access transmission is needed, resources are immediately allocated, and burst traffic cannot be effectively processed. In another scheme, resources are dynamically allocated, and a network control center allocates bandwidth resources according to requirements, instead of allocating the bandwidth resources instantly, one link requirement and one-time resource allocation are adopted, so that the resources can be effectively allocated according to the actual flow change of a satellite network system, the resource utilization rate is improved, and the delay is relatively high. A conventional satellite communication system architecture is shown in fig. 1, and mainly includes a satellite, a network control center, a gateway, and a user terminal. The user terminal predicts the user bandwidth demand of the user terminal, a request is sent to the network control center through the satellite, the network control center distributes the satellite uplink bandwidth to the terminal according to a specific scheme after calculation according to the request of each terminal, the gateway mainly has the function of information exchange between the terminal and the ground network, on one hand, the ground user terminal needs to regularly send bandwidth request information to the network control center, so that the request delay from the terminal to the network control center is increased, on the other hand, the network control center needs to coordinate the resource distribution among the terminals after receiving the request, so that the service quality requirement of each terminal cannot be ensured, and the calculation delay is also increased.
In summary, the present invention predicts traffic based on Markov, queues service in priority queue based on the prediction, and performs different resource request allocation algorithms.
Disclosure of Invention
Aiming at the defects in the prior art, the satellite uplink bandwidth resource allocation method based on Markov prediction solves the problem of service flow access in the traditional satellite network and the problem of satellite uplink bandwidth resource allocation.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a satellite uplink bandwidth resource allocation method based on Markov prediction, which comprises the following steps:
s1, sending a resource allocation request to the satellite;
s2, establishing a Markov-based state transition probability matrix according to the resource allocation request, and predicting the traffic in the next period according to the state transition probability matrix;
s3, classifying the service requests according to the service flow in the next period, and arranging the service types and the service requests through a Network Control Center (NCC) to obtain an optimal distribution mode;
and S4, feeding back the optimal allocation mode to the satellite, and accessing to complete the bandwidth resource allocation of the satellite uplink.
Further, the step S2 includes the following steps:
s201, initializing historical satellite traffic data U;
s202, dividing a state space E according to the resource allocation request;
s203, acquiring the frequency transfer matrix f according to the data interval of each state space E and the satellite traffic in each period of timeij；
S204, according to the frequency transfer matrix fijCalculating to obtain a state transition probability matrix;
s205, calculating to obtain predicted traffic x' (t +1) at the next moment according to the state transition probability matrix and the traffic x (t) at the current moment;
s206, calculating to obtain a prediction error p according to the predicted traffic x' (t +1) at the next moment and the real-time traffic x (t +1) at the next momentr；
S207, judging the prediction error prIf the predicted traffic x '(t +1) is smaller than the preset threshold, feeding the predicted traffic x' (t +1) at the next moment back to the network control center NCC and going to step S3, otherwise, feeding the real-time traffic x (t +1) at the next moment back to the network control center NCC and going to step S3, thereby completing the prediction of the traffic in the next period.
Still further, the expression of the state transition probability matrix is as follows:
Figure BDA0002364146830000031
wherein p isijElements representing the ith row and jth column in the state transition probability matrix, fijIndicating the number of times the state transition from i to j occurs.
Still further, the step S3 includes the steps of:
s301, judging whether a predicted traffic is adopted or not according to the traffic in the next period, if so, entering a step S303, and if not, entering a step S302;
s302, adopting actual business volume and performing enqueuing operation according to a first-in first-out (FIFO) principle;
s303, receiving a service request S, and obtaining the minimum requirement min (S) of each service according to the service request S;
s304, judging whether the satellite resource quantity B supports each service requirement, if so, entering a step S308, otherwise, entering a step S305;
s305, judging whether the satellite resource quantity B meets the minimum requirement min (S) of each service, if so, entering a step S306, and otherwise, entering a step S307;
s306, distributing the minimum bandwidth to each service, and distributing the residual bandwidth according to the weighted proportion through a Network Control Center (NCC);
s307, satisfying the queue resource request with high priority, and distributing the residual bandwidth according to the weighting proportion through a Network Control Center (NCC);
and S308, distributing the data according to needs through a Network Control Center (NCC).
Still further, the expression of the service request S in step S303 is as follows:
Figure BDA0002364146830000041
where N denotes the number of priority traffic queues, Cmij denotes the m-th queue in the j-th stageiA service request, j 1,2, N, i 1,2i1,2.., m (i), i denotes the number of requests in a queue, and m (i) denotes the total service request.
Still further, the expression of the minimum service requirement min (S) in step S303 is as follows:
Figure BDA0002364146830000042
where N denotes the number of priority traffic queues, Cmijmin represents the m-th queue in the j-th queueiA minimum bandwidth traffic request, j 1,2, and N, i 1,2i1,2.., m (i), i denotes the number of requests in a queue, and m (i) denotes the total service request.
The invention has the beneficial effects that:
(1) aiming at the problem of service flow access in a traditional satellite network, a novel algorithm for predicting service flow is provided by combining a Markov state transition prediction model, wherein the Markov process has the characteristics of no typical aftereffect, state independence and randomness, and the state at the future moment only depends on the state at the current moment, so that the characteristic of satellite service flow can be perfectly conformed, on one hand, the pressure of mass data on a satellite can be prevented, on the other hand, the delay and the calculation delay from a service submission request to the satellite can be saved, and the problem of satellite service flow prediction can be realized;
(2) in order to solve the problem of the distribution of the uplink bandwidth resources of the satellite, the invention provides a resource distribution algorithm for ensuring the QoS (quality of service) based on service priority queuing, and by queuing different service flows, a network control center combines the priority weighting of different services according to different resource requests and residual resources in the current state, so as to realize the effective division of the satellite link resources and ensure that the information of each terminal is transmitted efficiently;
(3) the invention carries out deep research and analysis on the resource dynamic allocation scheme of the traditional satellite communication architecture, places the resource strategy on the satellite for processing, can effectively reduce the time delay from the service request to the network control center NCC, and simultaneously reduces the computing resource using the network control center NCC;
(4) the invention carries out enqueue and dequeue operation on the service by the first-in first-out FIFO principle, divides the service into different priorities, and realizes resource allocation of various services by evaluating the total satellite resource amount and the service request, thereby not only ensuring the QoS of different types of terminals, but also realizing the fairness of the same rating service.
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Fig. 1 is a diagram of a conventional satellite communication system architecture in the background art.
Fig. 2 is a conventional dynamic allocation of resources DRA procedure.
FIG. 3 is a flow chart of the method of the present invention.
FIG. 4 is a block diagram of the dynamic allocation algorithm of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Examples
The invention applies the Markov theory to the satellite Internet of things, predicts the traffic in the next phase period of the satellite through the non-aftereffect of the Markov process, constructs a novel predictable satellite Internet of things access algorithm, and realizes the low time delay and high efficiency of the satellite uplink access process. The satellite uplink resource allocation not only solves the problem of access request, but also comprises the problem of resource strategy after request, the resource allocation in the current research mainly takes dynamic resource allocation as the main, and the invention also optimizes the dynamic resource allocation algorithm to ensure the service quality and high-efficiency transmission of each service. After the terminal accesses the satellite, a resource request needs to be sent out, the satellite receives the request of the terminal user, the request is uniformly collected to a network control center, the network control center solves the problem according to the current network state and the service weighting, and finally the satellite broadcasts the problem to each terminal.
Dynamic resource allocation is a satellite uplink bandwidth resource allocation scheme that is distinguished from a static allocation scheme. The static allocation scheme is that after a terminal and a satellite establish connection, a network control center makes a decision to allocate resources for each successfully established terminal for use, and when the connection exists all the time, the allocated bandwidth resources are also owned by the terminal all the time and cannot be released. A dynamic resource DRA allocation process is shown in fig. 2, which represents a process in which a terminal completes satellite uplink resource allocation through an NCC, time t0 represents that a terminal user sends a connection request to a satellite to start a communication process, time t1 represents that the terminal sends the request to the satellite, and the satellite has no data processing capability due to limited resources, so that the satellite uploads the resource request to a network control center NCC at time t2, the network control center NCC completes a resource allocation task at time t3 according to different resource allocation models, and the satellite broadcasts the resource allocation task to each terminal at time t4 to finally complete an access allocation request of the terminal. The whole dynamic allocation scheme process is analyzed, on one hand, the flexibility of the scheme is obviously improved, the defects of the static allocation scheme can be greatly overcome, and the problem of resources of all services, particularly burst services, is solved; on the other hand, the dynamic allocation scheme comprises five steps, wherein a terminal submits a request to a satellite, the satellite submits the request to a network control center NCC, the network control center NCC processes the request, the network control center NCC issues a strategy to the satellite, and the satellite broadcasts the strategy to the terminal. It takes a relatively long time from the submission of a request to the return, which is fatal to the delay-sensitive traffic, resulting in a long time required for the traffic information to reach the destination.
In summary, the present invention analyzes the advantages and disadvantages of two resource allocation schemes, and mainly solves the problems of service delay, congestion and quality of service. Because a long time is needed from the resource request to the reply, a Markov service prediction model is provided, the service volume request in the next period is predicted through the service volume in the current period, and when the service volume in the next period is determined, the network control center NCC can determine the uplink bandwidth resource allocation scheme in the next period according to the service volume, so that the request delay of the terminal is effectively reduced. In the scene of the satellite internet of things, the types and the number of services are infinite, real-time services, non-real-time services, video stream services, voice services and the like exist, and various services also have different QoS requirements on self transmission, such as low time delay, high accuracy and low packet loss. Aiming at the problem, the invention provides a service priority queuing algorithm, when a terminal is accessed to a satellite, the terminal queues in a service queue to which the terminal belongs, and a Network Control Center (NCC) distributes in real time according to the weight of each queue and the required bandwidth to ensure the QoS (quality of service) requests of various terminals. As shown in fig. 3, the implementation method is as follows:
a method for allocating uplink bandwidth resources of a satellite based on markov prediction, comprising the steps of:
s1, sending a resource allocation request to the satellite;
s2, establishing a Markov-based state transition probability matrix according to the resource allocation request, and predicting the traffic in the next period according to the state transition probability matrix, wherein the implementation method comprises the following steps:
s201, initializing historical satellite traffic data U;
s202, dividing a state space E according to the resource allocation request;
s203, acquiring the frequency transfer matrix f according to the data interval of each state space E and the satellite traffic in each period of timeij；
S204, according to the frequency transfer matrix fijCalculating to obtain a state transition probability matrix;
s205, calculating to obtain predicted traffic x' (t +1) at the next moment according to the state transition probability matrix and the traffic x (t) at the current moment;
s206, calculating to obtain a prediction error p according to the predicted traffic x' (t +1) at the next moment and the real-time traffic x (t +1) at the next momentr；
S207, judging the prediction error prIf the predicted traffic x '(t +1) is smaller than the preset threshold, feeding the predicted traffic x' (t +1) at the next moment back to the network control center NCC and going to step S3, otherwise, feeding the real-time traffic x (t +1) at the next moment back to the network control center NCC and going to step S3, thereby completing prediction of the traffic in the next period;
s3, classifying the service requests according to the service flow in the next period, and arranging the service types and the service requests through the NCC to obtain an optimal distribution mode, wherein the implementation method comprises the following steps:
s301, judging whether a predicted traffic is adopted or not according to the traffic in the next period, if so, entering a step S303, and if not, entering a step S302;
s302, adopting actual business volume and performing enqueuing operation according to a first-in first-out (FIFO) principle;
s303, receiving a service request S, and obtaining the minimum requirement min (S) of each service according to the service request S;
the expression of the service request S is as follows:
Figure BDA0002364146830000091
where N denotes the number of priority traffic queues, Cmij denotes the m-th queue in the j-th stageiA service request, j 1,2, N, i 1,2i1,2., m (i), i denotes the number of requests of a certain queue, and m (i) denotes the total service request;
the expression of the service minimum requirement min(s) is as follows:
Figure BDA0002364146830000092
where N denotes the number of priority traffic queues, Cmijmin represents the m-th queue in the j-th queueiA minimum bandwidth traffic request, j 1,2, and N, i 1,2i1,2., m (i), i denotes the number of requests of a certain queue, and m (i) denotes the total service request;
s304, judging whether the satellite resource quantity B supports each service requirement, if so, entering a step S308, otherwise, entering a step S305;
s305, judging whether the satellite resource quantity B meets the minimum requirement min (S) of each service, if so, entering a step S306, and otherwise, entering a step S307;
s306, distributing the minimum bandwidth to each service, and distributing the residual bandwidth according to the weighted proportion through a Network Control Center (NCC);
s307, satisfying the queue resource request with high priority, and distributing the residual bandwidth according to the weighting proportion through a Network Control Center (NCC);
s308, distributing the data according to needs through a Network Control Center (NCC);
and S4, feeding back the optimal allocation mode to the satellite terminal, and accessing to complete the bandwidth resource allocation of the satellite uplink.
In the embodiment, in order to improve the service quality of the guaranteed service and improve the utilization rate of the satellite bandwidth resources, the invention provides a bandwidth resource allocation method based on Markov prediction, a request is sent from a terminal to a Network Control Center (NCC) to issue a strategy, and the invention relates to a prediction method and an allocation method. A flow chart of a specific prediction method and allocation method is shown in fig. 4.
In this embodiment, it is assumed that each terminal has N types of priority service queues, and each queue has m (i) (1, 2.., N) service requests, and then the bandwidth resource of a certain service request is Cmij(j＝1,2,...,N)，i＝1,2,...,N，mi＝1,2,..,M(i)，Cmij denotes the m-th queue in the j-th queueiBandwidth of individual service requests, Cmijmin represents the minimum bandwidth required for the service. The total amount of the uplink bandwidth resources of the satellite is B, and the total amount of the resources required by all services is:
Figure BDA0002364146830000101
wherein N denotes the kind of priority traffic queue, Cmij denotes the m-th queue in the j-th stageiA service request, j 1,2, N, i 1,2i＝1,2,...,M(i)。
In this embodiment, as shown in fig. 4, the satellite terminal receives the resource allocation request, and analyzes the state transition probability by establishing a markov state transition chain, so as to obtain the predicted traffic flow in the next period. And classifying the service requests, wherein each type of service has different priority queues and different resource requirements, the network control center NCC exists in the identity of a strategy maker, and makes an optimal allocation scheme by sorting and analyzing the service types and the service requests, and then feeds the optimal allocation scheme back to each terminal for access, thereby completing the resource allocation process of the satellite uplink.
In this embodiment, let { xnN is 1,2.. n } is the markov chain in question, and the state space E is {1,2.. m }, then the conditional probability is:
Figure BDA0002364146830000102
for Markov chains { xnN 1,2.., k } transition probabilities in k steps at time m. In particular, when k is 1, then:
Figure BDA0002364146830000103
the above formula is one-step transition probability, which is referred to as transition probability for short. The k-step transition probability of the mahalanobis chain is: and (5) under the condition that the time m is in the state i, the probability that the time m is in the state j after k steps. The matrix composed of transition probabilities is called a transition matrix, and since the markov chain does not usually satisfy the time alignment condition in the actual situation, the invention only adopts one-step transition probability for research, and the one-step transition probability matrix can be expressed as:
P(m)＝pij(m)
pij(m)≥0,∑j∈Epij(m)＝1
the above equation shows that the markov chain starts from state i at time m and the next time must transition to one of the states in the state space. By studying the state transition matrix, we can predict the trend of future states in the system. When the Markov chain is a homogeneous Markov chain, namely the one-step transition probability is irrelevant to the initial time m, the k-step transition matrix of the Markov chain is also irrelevant to the time starting point, namely the k-th power of the one-step transition probability matrix, and the predicted value at the future time is determined.
In this embodiment, when a terminal initiates a request to a satellite and uploads the request, in order to predict traffic change at a future time, a state transition probability matrix needs to be calculated first. The satellite traffic at the next moment is predicted by acquiring traffic data generated by a satellite in a previous period of time and then calculating a state transition matrix of a Markov chain through state division. Because the predicted value and the actual value have difference, a threshold value is set, when the error between the predicted value and the actual value is smaller than the threshold value, the predicted result is considered to be accurate and credible, the predicted result can be directly distributed according to a resource distribution algorithm which is set in advance, and if the predicted error is large, a new resource distribution scheme is uniformly recalculated by the satellite terminal, so that the effective transmission of services is ensured.
In this embodiment, a prediction algorithm is described: firstly, initializing satellite traffic data U in a period of time, wherein the data are equal interval data of the same satellite in different periods of time, secondly, dividing states according to the satellite data, in general practical application, a state space E is five states, and after dividing the states, determining the state of the satellite traffic in each period of time through the data interval of each state, so that the frequency transfer matrix f can be determinedij. The frequency transition matrix is the frequency needed to transition from one state to another, i.e. the i-th row and j-th column element f of the transition frequency matrixijDividing the sum of the rows by the value to obtain a transition probability matrix, and calculating to obtain a state transition probability matrix pijFinally, the predicted traffic x' (t +1) obtained by the state transition matrix is compared with the actual traffic x (t + 1). If the error of the predicted traffic is less than the threshold ThAnd then, the predicted traffic is directly fed back to the control center, the control center formulates a scheme according to the requests of all parties, otherwise, the real-time traffic needs to be submitted, so that the reliability of a predicted traffic algorithm is ensured on one hand, and the flexibility of the satellite system is reflected on the other hand.
In this embodiment, the sum of the minimum bandwidth resources required by all services obtained above is SminAnd the satellite fixed resource quantity B, and different strategies can be adopted for allocation by comparing the relative sizes of the satellite fixed resource quantity B and the satellite fixed resource quantity B, and the desirability degree of the satellite fixed resource quantity B to the resources is also different due to different service types. With the development of the satellite internet of things, voice, data, video services and the like are more and more, obviously, the real-time requirement of voice type services is higher, and when the satellite resources are insufficient, resources of other services with lower requirements on time delay can be occupied, so that the services have higher priority. And therewithMeanwhile, due to the characteristics of numerous service types and mass data of the internet of things, the satellite system cannot provide resources for all service requests, and therefore the services are added into the waiting queue until available resources are allocated. And the queue is divided according to the service priority and the request duration to determine the service of preferentially distributing resources. For the queuing method, a FIFO (first in first out) strategy is adopted, so that the priority of the service in the queue is well ensured, the algorithm complexity is relatively low, and the satellite system resource allocation service can be better realized.
In this embodiment, the generation process of the resource allocation scheme is as follows: first we receive a service request S, containing a resource type and a resource request CmijAnd minimum resource request Cmijmin, initialize total resource amount B, predict traffic x' (t +1) and actual traffic x (t + 1). Before resource allocation, the traffic at the current time needs to be evaluated. When the actual traffic is adopted, the service needs to be queued and reported. In order to solve the problem of numerous service types in practical application, the service types are divided, and minimum resources for ensuring basic information of the services are provided. Based on the above information, when the satellite resources are sufficient, we fully ensure that each service request is satisfied. Assuming that the total amount of satellite resources is lower than the minimum bandwidth required by the satellite request, we preferentially guarantee the service requirement at the front of the queue and have the residual bandwidth resource R1If the service request exists, the service requests in the rest queue are distributed according to the weighting proportion, so that on one hand, the successful transmission of the service with high priority is ensured, and the fairness of the service with the same level is also ensured. Assuming the total amount of satellite resources is lower than the satellite requested bandwidth, we can allocate a minimum bandwidth to each service, while at the same time the remaining bandwidth R2The method can supply services sensitive to delay and high in resource requirement, and has the advantages that service information of the Internet of things is achieved, and high-priority services can be transmitted better and faster. When the satellite request source continuously arrives, the network control center NCC can directly distribute according to the resource distribution scheme, thereby reducing the delay and realizing the advantage of high efficiency.

Claims (5)

1. A method for allocating uplink bandwidth resources of a satellite based on markov prediction, comprising the steps of:
s1, sending a resource allocation request to the satellite;
s2, establishing a Markov-based state transition probability matrix according to the resource allocation request, and predicting the traffic in the next period according to the state transition probability matrix;
the step S2 includes the steps of:
s201, initializing historical satellite traffic data U;
s202, dividing a state space E according to the resource allocation request;
s203, acquiring the frequency transfer matrix f according to the data interval of each state space E and the satellite traffic in each period of timeij；
S204, according to the frequency transfer matrix fijCalculating to obtain a state transition probability matrix;
s205, calculating to obtain predicted traffic x' (t +1) at the next moment according to the state transition probability matrix and the traffic x (t) at the current moment;
s206, calculating to obtain a prediction error p according to the predicted traffic x' (t +1) at the next moment and the real-time traffic x (t +1) at the next momentr；
S207, judging the prediction error prIf the predicted traffic x '(t +1) is smaller than the preset threshold, feeding the predicted traffic x' (t +1) at the next moment back to the network control center NCC and going to step S3, otherwise, feeding the real-time traffic x (t +1) at the next moment back to the network control center NCC and going to step S3, thereby completing prediction of the traffic in the next period;
s3, classifying the service requests according to the service flow in the next period, and arranging the service types and the service requests through a Network Control Center (NCC) to obtain an optimal distribution mode;
and S4, feeding back the optimal allocation mode to the satellite, and accessing to complete the bandwidth resource allocation of the satellite uplink.
2. The markov prediction-based satellite uplink bandwidth resource allocation method of claim 1, wherein the state transition probability matrix is expressed as follows:
Figure FDA0002688288030000021
wherein p isijElements representing the ith row and jth column in the state transition probability matrix, fijIndicating the number of times the state transition from i to j occurs.
3. The markov prediction-based satellite uplink bandwidth resource allocation method of claim 1, wherein the step S3 comprises the steps of:
s301, judging whether a predicted traffic is adopted or not according to the traffic in the next period, if so, entering a step S303, and if not, entering a step S302;
s302, adopting actual business volume and performing enqueuing operation according to a first-in first-out (FIFO) principle;
s303, receiving a service request S, and obtaining the minimum requirement min (S) of each service according to the service request S;
s304, judging whether the satellite resource quantity B supports each service requirement, if so, entering a step S308, otherwise, entering a step S305;
s305, judging whether the satellite resource quantity B meets the minimum requirement min (S) of each service, if so, entering a step S306, and otherwise, entering a step S307;
s306, distributing the minimum bandwidth to each service, distributing the residual bandwidth according to a weighted proportion through a Network Control Center (NCC), ending the process and obtaining an optimal distribution mode;
s307, satisfying the queue resource request with high priority, distributing the residual bandwidth according to the weighted proportion through a Network Control Center (NCC), ending the process and obtaining an optimal distribution mode;
and S308, distributing the data according to needs through the NCC, ending the process and obtaining an optimal distribution mode.
4. The markov prediction-based satellite uplink bandwidth resource allocation method of claim 3, wherein the expression of the service request S in step S303 is as follows:
Figure FDA0002688288030000031
where N denotes the number of priority traffic queues, Cmij denotes the m-th queue in the j-th stageiA service request, j 1,2, N, i 1,2i1,2.., m (i), i denotes the number of requests in a queue, and m (i) denotes the total service request.
5. The markov prediction-based satellite uplink bandwidth resource allocation method of claim 3, wherein the expression of the minimum traffic requirement min (S) in step S303 is as follows:
Figure FDA0002688288030000032
where N denotes the number of priority traffic queues, Cmijmin represents the m-th queue in the j-th queueiA minimum bandwidth traffic request, j 1,2, and N, i 1,2i1,2.., m (i), i denotes the number of requests in a queue, and m (i) denotes the total service request.
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