CN103595656A - DVB_RCS satellite channel dynamic distribution method based on predicating of wavelet neural network - Google Patents

Abstract

The invention relates to a DVB_RCS satellite channel dynamic distribution method based on predicating of a wavelet neural network. In terms of the multiple-scale features of satellite network flow, firstly, a wavelet neural network flow predicating algorithm is used for predicating real-time arrived flow of user stations of the next cycle and sending the real-time arrived flow to a gateway station; secondly, the gateway station performs distribution as needed according to the real-time access data rate of all the user stations in each channel application distribution cycle. The access data rate in the next cycle predicated by all the user stations serves as weight allocation residual capacitance. The DVB_RCS satellite channel dynamic distribution method based on predicating of the wavelet neural network is characterized in that in terms of the multiple-scale features of satellite service flow such as long relevance, self similarity and the multiple fractal property, the flow of the next moment is predicated with the wavelet neural network, the predication accuracy of the flow is improved, service time slots are reasonably distributed, service time delay is effectively shortened, the service quality of the users is ensured and the channel resource utilization rate is improved.

Description

A kind of DVB_RCS satellite channel dynamic allocation method based on wavelet neural network prediction

Technical field

The invention belongs to satellite communication field, be specifically related to a kind of DVB_RCS satellite channel dynamic allocation method based on wavelet neural network prediction.

Background technology

Satellite bandwidth resource is nervous, and the main purpose of multiple access access is at utmost and most effectively to utilize satellite bandwidth resource.The mode of multiple access access mainly contains the methods of salary distribution such as fixed allocation, Random assignment, distribution according to need.Fixed allocation mode, when terminal quantity increases or business is not too balanced, this cut-in method is no longer applicable, especially more inapplicable to bursts of traffic.Random assignment access way, when traffic carrying capacity increases, can bump, and collision can cause the increase (especially synchronous satellite) of propagation delay time, continues to retransmit the reduction that has caused performance.

Demand assigned mode, is to send in advance reserve requests by ground based terminal, by satellite control center, according to the request of earth station, distributes bandwidth resources, with this, reaches and farthest utilizes bandwidth resources object.Compare reserved too much bandwidth and the collision in Random assignment and re-transmission in fixed allocation, distribution according to need can not cause the waste of bandwidth.In addition, control centre controls the distribution of bandwidth completely, therefore compares other strategies, and the robustness of network is very high, and fairness can be easy to realize.

Existing distribution according to need mode mostly is based on queue length or based on business model and predicts to distribute bandwidth, allocation algorithm based on queue length is a kind of method of pure reaction equation, only according to current queue length allocation bandwidth, distribute bandwidth capacity to equal queue length, do not consider the situation that flow arrives in real time; Method based on business model prediction is strong for business model dependence, does not have universality, does not consider the sudden of satellite network flow.

Since the people such as Leland have found in Ethernet since the self-similarity characteristics of Business Stream, confirmed that self-similar traffic is present in all kinds of packet switching networks widely, people have also adopted Self-similar models more when research wideband satellite communication net in recent years.The intrinsic dimensional properties of wavelet analysis becomes the effective ways that carry out multi-scale prediction very naturally, and under the selection of suitable small echo, the characteristic of network traffics under different scale can well be portrayed.

The object that is applied to the channel allocation algorithm based on wavelet neural network prediction of DVB_RCS broadband satellite is, improves volume forecasting accuracy rate, and reasonable distribution business time-slot, effectively shortens service delay, guarantees QoS of customer, and carries high channel resource use ratio.

Summary of the invention

Technical problem to be solved by this invention: for the multiple dimensioned characteristic of satellite business flow as long correlation, self-similarity and multi-fractal, on existing star, Forecasting Methodology is predicted not accurate enough problem, the invention provides a kind of method for channel allocation based on wavelet neural network prediction of the DVB_RCS of being applied to broadband satellite, can improve volume forecasting accuracy rate, reasonable distribution business time-slot, effectively shorten service delay, guarantee QoS of customer, carry high channel resource use ratio.

The method for channel allocation based on prediction that the invention provides a kind of DVB_RCS of being applied to broadband satellite, the method comprises:

The quantity of the flow bag arrival of link layer is recorded in A, user small station, for the multiple dimensioned characteristic of flow, uses wavelet neural network prediction algorithm, according to historical record data, predicts next periodic packets arriving amt;

In said method, steps A specifically comprises:

A1, subscriber station store the service traffics that arrive small station with the 10ms time cycle, using the flows of front 9 time cycles of storing as the training input of wavelet neural network;

In A2,9 input signals, after former 8 signal estimations 1, first carry out multi-scale wavelet transformation, 1 sequence transformation becomes 4 high and low frequency sequences, more respectively four sequences is carried out to neural network prediction training;

After A3, neural network prediction, carry out repeatedly inverse wavelet transform, 4 sequence transformations are returned to 1 sequence;

After A4, training result complete, with 8 up-to-date flow values, predict the flow arrival value of next cycle 10ms.

B, user small station during via satellite to the request of gateway station transmitted traffic time slot, piggyback next cycle flow timeslot number of prediction;

C, gateway station are when distributing time interval resource, first according to the real-time access data rate of each subscriber station, distribute according to need, the access data rate of take again in the next cycle that each subscriber station predicted is weight allocation residual capacity, more truly reasonably meets QoS of customer QoS.

In said method, step C specifically comprises:

C1, in each channel request assignment period, first distribute according to need according to the real-time access data rate of each subscriber station multiple access access control unit;

C2, the access data rate of take in the next cycle that each subscriber station predicted are again weight allocation residual capacity.Therefore the capacity that k subscriber station obtains within N+1 cycle is

$A_k(N+1)=R_k\left(N\right)+\frac{{\mathrm{Free}}_{\mathrm{Cap}}}{{\mathrm{\Σ}}_{k=1}^K{\mathrm{PR}}_k(N+1)}{\mathrm{PR}}_k(N+1)$

D, gateway station to user small station sending time slots allocation table, complete whole dynamic allocation procedure via satellite.

Accompanying drawing explanation

Fig. 1 is the method for channel allocation structural representation that the present invention is based on wavelet neural network prediction.Being 1. wherein user small station, is 2. satellite, is 3. gateway station.

Embodiment

Refer to Fig. 1, the invention provides a kind of method for channel allocation based on prediction of the DVB_RCS of being applied to broadband satellite, the method comprises:

The quantity of the flow bag arrival of link layer is recorded in A, user small station, for the multiple dimensioned characteristic of flow, uses wavelet neural network prediction algorithm, according to historical record data, predicts next periodic packets arriving amt;

B, user small station be during via satellite to the request of gateway station transmitted traffic time slot, and next that piggybacks prediction be flow timeslot number constantly;

C, gateway station are when distributing time interval resource, first according to the real-time access data rate of each subscriber station, distribute according to need, the access data rate of take again in the next cycle that each subscriber station predicted is weight allocation residual capacity, more truly reasonably meets QoS of customer QoS.

D, gateway station to user small station sending time slots allocation table, complete whole dynamic allocation procedure via satellite.

In said method, utilize wavelet neural network prediction algorithm to predict next periodic packets arriving amt described in steps A according to historical record data, its concrete steps comprise:

A1, subscriber station store the service traffics that arrive small station with the 10ms time cycle, using the flows of front 9 time cycles of storing as the training input of wavelet neural network;

In A2,9 input signals, after former 8 signal estimations 1, first carry out multi-scale wavelet transformation, 1 sequence transformation becomes 4 high and low frequency sequences, more respectively four sequences is carried out to neural network prediction training;

After A3, neural network prediction, carry out repeatedly inverse wavelet transform, 4 sequence transformations are returned to 1 sequence;

After A4, training result complete, with 8 up-to-date flow values, predict the flow arrival value of next cycle 10ms, finally will predict the outcome and join in request time slot, send to gateway station as channel allocation parameter.

In said method, distribute residual capacity described in step C according to access data rate in next cycle of prediction, its concrete steps comprise:

C1, in each channel request assignment period, first distribute according to need according to the real-time access data rate of each subscriber station multiple access access control unit;

C2, the access data rate of take in the next cycle that each subscriber station predicted are again weight allocation residual capacity.Therefore the capacity that k subscriber station obtains within N+1 cycle is

$A_k(N+1)=R_k\left(N\right)+\frac{{\mathrm{Free}}_{\mathrm{Cap}}}{{\mathrm{\Σ}}_{k=1}^K{\mathrm{PR}}_k(N+1)}{\mathrm{PR}}_k(N+1)$

C3, gateway station send to subscriber station by time slot allocation information via satellite, complete channel assignment scheme process.

Claims (3)

1. the method for channel allocation based on prediction that is applied to DVB_RCS broadband satellite, is characterized in that, the method comprises:

The quantity of the flow bag arrival of link layer is recorded in A, user small station, for the multiple dimensioned characteristic of flow, uses wavelet neural network prediction algorithm, according to historical record data, predicts next periodic packets arriving amt;

B, user small station during via satellite to the request of gateway station transmitted traffic time slot, piggyback next cycle flow timeslot number of prediction;

C, gateway station are when distributing time interval resource, first according to the real-time access data rate of each subscriber station, distribute according to need, the access data rate of take again in the next cycle that each subscriber station predicted is weight allocation residual capacity, more truly reasonably meets QoS of customer QoS.

D, gateway station to user small station sending time slots allocation table, complete whole dynamic allocation procedure via satellite.

2. method according to claim 1, is characterized in that, utilizes wavelet neural network prediction algorithm to predict that the method for next periodic packets arriving amt comprises described in steps A according to historical record data:

A1, subscriber station store the service traffics that arrive small station with the 10ms time cycle, using the flows of front 9 time cycles of storing as the training input of wavelet neural network;

In A2,9 input signals, after former 8 signal estimations 1, first carry out multi-scale wavelet transformation, 1 sequence transformation becomes 4 high and low frequency sequences, more respectively four sequences is carried out to neural network prediction training;

After A3, neural network prediction, carry out repeatedly inverse wavelet transform, 4 sequence transformations are returned to 1 sequence;

After A4, training result complete, with 8 up-to-date flow values, predict the flow arrival value of next cycle 10ms, finally will predict the outcome and join in request time slot, send to gateway station as channel allocation parameter.

3. method according to claim 1, is characterized in that, distributes the method for residual capacity to comprise described in step C according to access data rate in next cycle of prediction:

C1, in each channel request assignment period, first distribute according to need according to the real-time access data rate of each subscriber station multiple access access control unit;

C2, the access data rate of take in the next cycle that each subscriber station predicted are again weight allocation residual capacity.Therefore the capacity that k subscriber station obtains within N+1 cycle is

$A_k(N+1)=R_k\left(N\right)+\frac{{\mathrm{Free}}_{\mathrm{Cap}}}{{\mathrm{\Σ}}_{k=1}^K{\mathrm{PR}}_k(N+1)}{\mathrm{PR}}_k(N+1)$

R in formula _k(N) be the capacity with the real-time access data rate equivalence of k subscriber station within N cycle, PR _k(N+1) be the access data rate of k subscriber station in N+1 cycle predicting, the quantity that K is subscriber station.

C3, gateway station send to subscriber station by time slot allocation information via satellite, complete channel assignment scheme process.

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