CN115276765B - ATDM satellite communication scheduling method for service priority - Google Patents
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Abstract
The invention discloses a service priority-oriented ATDM satellite communication scheduling method, which simultaneously considers service priority, user priority and system throughput according to a system model, establishes an objective function, determines constraint conditions and provides an improved ant colony algorithm for solving the model. The invention must adopt proper resource scheduling algorithm in the central station to ensure the system performance. And accurately solving the optimal solution within an acceptable duration range. The scheduling service comprises real-time IP service, voice service, IP non-real-time service and the like, the real-time service and the voice service are transmitted preferentially, the user priority and the system resource throughput are considered, the requirements of multiple users and multiple services are met, and the service quality of the service is improved to a certain extent.
Description
Technical Field
The invention relates to an ATDM satellite communication scheduling method facing service priority, belonging to the technical field of satellite communication.
Background
Satellite communication is very critical in the fields of emergency communication, global positioning, military, airborne, shipping and the like, because compared with the traditional ground network, the satellite network has the advantages that networking is not limited by geographic factors, coverage area is free from blind areas and the like. In early days, the user terminal is mainly used for providing specific services, such as voice services and data services, and with the development of multimedia services and satellite communication technologies, satellite communication can process various services, such as audio and video, in real time, networking satellites are increased and task requests are increased, satellite communication resource allocation becomes more and more complex, and the complexity of allocation is represented by the fact that, besides selecting resources for tasks, optimization combination of a plurality of tasks needs to be considered, and the scale of a problem solving space is greatly enlarged with the increase of tasks and satellite numbers.
At present, the demands of various industries on satellite communication are also increasing, the space traffic is increased in a burst mode, the number of access terminals is continuously increased, the satellite communication capacity is enabled to approach to a saturated state, and the pressure faced by a satellite communication system is huge. The satellite resources are limited and the satellite communication cost is high, and the resource allocation not only needs to consider the utilization rate of the resources, but also fully considers the service quality (Quality of Service, qoS) requirements of different services. Therefore, limited satellite resources need to be reasonably scheduled while meeting user requirements.
The satellite scheduling problem (Satellite Scheduling Problem, SSP) refers to a process of allocating required resources for satellite tasks under the condition of meeting the resource constraint and the scheduling time window requirement in a certain scheduling time period, and dynamic scheduling is usually an NP hard problem. The satellite resource scheduling problem belongs to a typical resource scheduling problem, is widely studied in the fields of system engineering and management science, and three types of methods which are currently used are a deterministic optimization method, a constraint-based scheduling method and an intelligent scheduling method respectively.
The deterministic optimization method is also called an accurate algorithm, comprises methods of branch delimitation, dynamic planning, integer planning and the like, and is mostly a method for solving an approximate solution within a limited time by using an incomplete algorithm, so that the computational complexity is high, the application range is small, and the engineering practicability is lacked. The other part is an algorithm for solving an accurate solution according to constraint conditions, but as the number of scheduling resources and terminals increases, the incredible operand is finally achieved, and the solution is difficult to solve within a limited time. For example, spain's scholars Vazquez, rafael, etc., devised an integer linear programming program that allocates antenna slot resources to satellite users based on the cost function of the requesting client taking into account user priority.
Constraint-based scheduling is a method for obtaining optimal scheduling by enumeration of partial feasible scheduling solutions, and is essentially an enumeration optimization method, wherein an algorithm is low in implementation threshold, low in efficiency and incapable of playing a role in the problem above a medium scale. For example, frank and duncan use Constraint optimization to describe scheduling problems of satellite imaging, a CBI framework (Constraint-Based Interval) is adopted to identify satellite imaging scheduling, and then a greedy algorithm Based on random search is adopted for solving, and although a specific algorithm implementation method is not provided, the scheduling problem description is characterized in that mathematical modeling is carried out on data downloading and using conditions of a memory, and task grade problems are considered. The intelligent scheduling algorithm is the key point of research of most scholars in recent years, is essentially a scheduling method based on statistical optimization, mainly reflects intelligent system behaviors through numerical calculation, has certain enumeration characteristics, has optimal solution convergence time in a receivable range, and is mainly represented by the following algorithm: immune genetic algorithm, simulated annealing algorithm, ant colony algorithm, etc. For example, indian scholars Rao and Soma studied ISTRAC (ISRO telemetry Tracking and Comm and Network) in india and proposed to resolve satellite ground station time window overlap using a collision resolution method and schedule based on genetic algorithm.
The three methods have certain limitations, and as the traffic in the satellite resource scheduling model increases, the first two methods can obtain more accurate optimal solutions, but the calculated amount is huge, and the optimal solutions are often not obtained for a specified time length; in the third method, the solution of the objective function can be converged to a certain value in a specified time period, and when the traffic volume is increased, the optimal solution probability is reduced, so that the result is not ideal as the former two methods.
From the research problem, the research starts from specific constraint, a resource scheduling model is built based on different application scenes, and a deterministic optimization algorithm or an intelligent scheduling algorithm is provided to solve the resource scheduling problem, but the algorithms are only applicable to specific network scenes and cannot be well applicable to resource scheduling of a broadband satellite communication system. In addition, aiming at the problem of resource allocation of a broadband satellite communication system, a part of researches are carried out on forward links and backward links based on a TDM mechanism, the forward links and the backward links are optimized by taking bandwidth and power as optimization targets, and the matching degree of the resource allocation priority is not high; still another part of the research considers service priority and user priority; the satellite communication forward link resource scheduling problem based on the ATDM mechanism is rarely studied.
The core of the resource scheduling is a resource scheduling algorithm, wherein the intelligent scheduling algorithm is the key point of research of most students in recent years, and a genetic algorithm, an ant colony optimization algorithm, a particle swarm optimization algorithm and a combination thereof are commonly used, and can achieve a good optimization effect on general resource scheduling, but under the condition of an ATDM satellite communication system, the complexity of the algorithm is higher, and the convergence speed and the optimization effect of the algorithm are required to be improved, so that consideration needs to be carried out between the optimization performance and the complexity of the algorithm.
Therefore, how to design a resource allocation method so that all services reasonably complete resource allocation in the shortest time is a problem to be solved.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides an ATDM satellite communication scheduling method facing to service priority.
The technical scheme is as follows: in order to solve the technical problems, the invention adopts the following technical scheme:
an ATDM satellite communication scheduling method facing service priority comprises the following steps:
step 1: receiving P services according to the priority G of the services x And user priority A u And calculating a priority weight y, sequencing the services in the sub-queues according to the size of y, and numbering the sequenced services according to 1-P.
Step 2: initializing the following parameters, κ=0, supplus=0, v 0 ＝V p Subframe=0; kappa is the total number of services in the buffer queue, surplus is the residual amount of services which do not occupy the whole subframe, V 0 For the transmission rate of the traffic in the subframe, subframe is the number of subframes occupied by the scheduled traffic of the record buffer queue, V p For traffic p transmission rate into the buffer queue.
Step 3: and according to the sub-queue service arrangement, sequentially scheduling the services p into a buffer queue, and ordering the kappa services of the buffer queue according to the size of the modulation coding mode m.
Step 4: judging transmission rate V of service w in kappa services w Whether or not to be less than V 0 If less than or equal to V is updated 0 ＝V w Otherwise, not updating, transmitting the k services of the buffer queue in turn, and filling frames.
Step 5: when the service w fills frames, the transmission rate V of the service w is identified w And traffic d w 。
Step 6: according to
Calculating the number z of subframes occupied by the service w, wherein T is the subframe duration, and recording the residual data quantity of the service; judging whether z is greater than or equal to 1, if so, counting the total subframe number subframes of the buffer queue, and entering a step 7; otherwise, p=p+1, go to step 3.
Step 7: when the total subframe number of the buffer queue is subframe<S, continuing to schedule, entering a step 3, wherein S is the total number of subframes; subframe(s)>S, entering a step 8; subframe=s, the sequence is an initial solution i= (Φ, K) of a multiframe, K represents the number of transmission services in the multiframe, Φ is a solution vector [ Φ ] 1 ,φ 2 ,...,φ K ]Phi epsilon X represents the sequence of K services transmitted in the multiframe, and step 9 is entered.
Step 8: and placing the service w in the buffer queue to the final buffer queue, and selecting the service w+1 to enter the step 4, wherein the ordering of other services is unchanged.
Step 9: taking I as an initial solution set of an ant colony algorithm, and enabling time t=0 and cycle number N to be c =0, setting the maximum number of cycles G, placing Ω ants on κ services, initializing pheromone τ for each edge (i, j) ij (t) =c, where c represents a constant and the initial time Δτ ij (0)＝0。
Step 10: number of cycles N c ＝N c +1, tabu table index number k=1 for ants, number k=k+1 for ants.
Step 11: ant individuals select service J and advance according to the probability calculated by the state transition probability, J epsilon { J k (i)}，J k (i) Representing the set of services that ant k next allows to select after transmission of service i.
Step 12: modifying the tabu list pointer, moving ants to new business, and moving the business to the tabu list of the ants.
Step 13: if the business in the aggregate phi is not traversed, and k < omega, jumping to the step 10; otherwise, step 14 is performed.
Step 14: recording the optimal route of the iteration, and updating the information quantity on each path according to a pheromone updating formula.
Step 15: if the cycle number N c More than or equal to G, the cycle is ended and the optimization result I is output k And (3) completing the encapsulation of 1 multiframe, and if the scheduling of Q multiframes is completed, completing the scheduling of the service of the round: otherwise, returning to the step 3, and continuing to schedule to Q multiframes; if the cycle number N c And < G, clearing the tabu list and jumping to step 10.
Preferably, the calculation formula of the priority weight y is as follows:
wherein ,Au Indicating user u priority, G x Representing the class of service of the x services, A max Representing the user's highest preference level.
Preferably, the state transition probability calculation formula is as follows:
wherein alpha is the weight of the heuristic value of the pheromone, eta ij Selecting the expected degree of transmission of the service j after the service i is transmitted; beta is the weight of the expected heuristic value, tau ij Selecting the pheromone concentration of j when transmitting service i in multiframe, tau is Selecting the pheromone concentration when transmitting the service s after transmitting the service i in the multiframe; η (eta) is Selecting a service after i for representing a transmission services the desired degree of transmission; s epsilon J k (i)，J k (i) Representing the set of services that ant k next allows to select after transmission of service i.
As a preferred embodiment of the present invention,
wherein ,Amax 、G max The maximum user priority and the maximum service priority are respectively; a is that i Indicating user priority of i service, A j User priority of j service is represented; g i Representing the priority of service i, G j Indicating the priority of service j.
Preferably, the pheromone update formula is calculated as follows:
τ ij (t+n)＝(1-ρ)·τ ij (t)+Δτ ij
where n represents the number of iterations, ρ represents the evaporation coefficient of the pheromone on the path, Δτ ij Representing the increment of the pheromone on the service ij in the iteration.
As a preferred embodiment of the present invention,
wherein ,
represents the amount of pheromone that the kth ant leaves on the edge ij in the present iteration, and Ω refers to the total number of ants.
As a preferred embodiment of the present invention,
wherein ,
the average weight of the business i and j is C is a normal number, F k Represents the size of the path that the kth ant walks in the current round trip, L NC The maximum value of the path iterated to NC times is shown.
As a preferred embodiment of the present invention,
wherein ,Amax Is the maximum user priority; a is that i Indicating user priority of i service, A j User priority of j service is represented; g i Representing the priority of service i, G j Indicating the priority of service j.
As a preferred embodiment of the present invention,
wherein ,
x services of U users representing the path of the kth ant in the current round trip are transmitted in the multiframe, U represents the users, U represents the total number of the users, X represents the services, X represents the total number of the services, A u Indicating user u priority, A max Representing the highest user preference, G x Representing the class of service of the x-service.
As a preferred scheme, solve F k When the following constraint conditions are to be satisfied:
wherein S represents the S-th subframe, S ε [1,2, … …, S]The method comprises the steps of carrying out a first treatment on the surface of the S represents the total number of subframes in one multiframe; u user identification, which represents the U user, U e [1,2, …, U]The method comprises the steps of carrying out a first treatment on the surface of the U is the total number of users; x is the number of services, and represents the xth service, X is [1,2, … …, X ]]The method comprises the steps of carrying out a first treatment on the surface of the X is the total number of services; a is that u Indicating user u priority, A u ∈[1,2,……,A max ]；G x Representing the class of service of the x services, G x ∈[1,2,……,G max ]；d u,x Representing the traffic of the x service of the u user transmitted in the multiframe;
representing the proportion of the transmission time of the x service of the user u in the s sub-frames to the total transmission time of the sub-frames; v (m) represents the rate of the mth modulation coding mode; />
Representing modulation coding mode identification in a subframe, if s subframes are selectedM modulation coding mode used,/->
Otherwise the first set of parameters is selected,
m represents the mth modulation coding mode, +.>
Representing the highest modulation coding mode that u user can support; t represents the total duration of one subframe; />
The number of bits to be sent of the x service of the user u at the beginning of the round of scheduling period is represented; />
Indicating that user u occupies subframe identity, +.>
Indicating that user u occupies s subframes, +.>
Indicating that user u does not occupy s subframes, alpha s,u Representing the duty ratio of the user u in the s sub-frame, the value is 0 to 1, sgn (delta) represents a sign function, and the method comprises the following steps of
Representing the modulation coding mode of the s+1st subframe; v (V) as Representing the entire forward ATDM carrier information rate; y represents the weight of the service x and the user priority; h is a set of all services in S, including services not all transmitted, B represents the rest service set of the current scheduling, H n B= [1,2, and X]。
The beneficial effects are that: the invention provides a service priority-oriented ATDM satellite communication scheduling method, which simultaneously considers service priority, user priority and system throughput according to a system model, establishes an objective function, determines constraint conditions and provides an improved ant colony algorithm for solving the model. The characteristics of the ant colony algorithm such as too slow searching speed, weak local searching capability and the like caused by the lack of pheromone in the initial stage of the ant colony algorithm severely restrict the application of the algorithm in the satellite scheduling process with strong real-time and high-efficiency requirements, so the ant colony algorithm is improved, and an improved ant colony optimization algorithm with an initial solution set structure and enhanced global searching as cores is provided.
The invention must adopt proper resource scheduling algorithm in the central station to ensure the system performance. And accurately solving the optimal solution within an acceptable duration range. The scheduling service comprises real-time IP service, voice service, IP non-real-time service and the like, the real-time service and the voice service are transmitted preferentially, the user priority and the system resource throughput are considered, the requirements of multiple users and multiple services are met, and the service quality of the service is improved to a certain extent.
Drawings
Fig. 1 is a system overview, satellite and terrestrial communication environments.
Fig. 2 is a block diagram of a demonstration system, the system operation of a ground central station, the present invention being used for an optimization algorithm at a resource scheduling facility.
Fig. 3 is a forward frame structure diagram, where S subframes are multiplexed into one multiframe, and Q multiframes are multiplexed into one schedule.
Fig. 4 is a business queue model.
Fig. 5 is a flow chart of a service priority resource scheduling algorithm.
Fig. 6 is a graph comparing intra-frame data with the exhaustive results for three algorithms (10 services).
Fig. 7 is a plot of fitness function change.
Fig. 8 is a graph of f-value contrast of the objective function in 16 multiframes of four algorithms.
Fig. 9 is a graph of traffic in 16 multiframes for four algorithms.
Fig. 10 is a graph of residual traffic over 16 multiframes for four algorithms.
Fig. 11 is a comparison diagram of the priority weights y of the service compatible users in 16 multiframes of four algorithms.
Fig. 12 is a graph of 16 multiframe scheduling data versus four algorithms.
Detailed Description
The invention will be further described with reference to specific examples.
As shown in fig. 1, the ATDM satellite communication system is a satellite communication system composed of a communication satellite, a central station and a plurality of earth stations (ground small stations), the central station is a whole network core, all services land at the central station, and the central station switches to a public telephone switching network, a public land mobile network, a public network or a private network. The system backward traffic channel adopts FDMA system, and the forward channel adopts ATDM system. The backward direction operates on the spot beam and the forward direction operates on the global beam.
An ATDM satellite communication scheduling method facing service priority comprises the following steps:
step one, a mathematical model is built for satellite resource scheduling problems, and an objective function is determined.
Based on the service priority resource scheduling algorithm which preferentially transmits real-time service and simultaneously gives consideration to user priority and system resource utilization rate, the optimized objective function is as follows:
wherein ,du,x The X service of U users is represented by the service quantity transmitted in the multiframe, U represents users, U represents the total number of users, X represents the service, X represents the total number of services, A u Indicating user u priority, A max Representing the highest user preference, G x Representing the class of service of the x-service.
And step two, determining the constraint condition of the objective function.
The algorithm has the following constraint conditions:
constraint condition description:
the formula (2) shows that the x service bit number of the user u distributed to all subframes is not more than the total number of the service x waiting bits of the user u at the beginning of the round of scheduling period; equation (3) indicates that each subframe is occupied and there is no free slot; equation (4) shows that only one modulation coding mode is used for each subframe; equation (5) indicates that the modulation and coding scheme for each subframe is set to the lowest value of the upper limits of the user modulation and coding schemes in the subframe; equation (6) indicates that the modulation and coding pattern level of the s-th subframe is equal to or less than the modulation and coding pattern level of the s+1th subframe; equation (7) indicates that the forward transmission rate of the system is required to meet the speed limit, and the traffic transmission rate of all subframes cannot exceed V as (in Mbps). And the formula (8) shows that the priority weight y of any service in S after the user is considered is not less than y of the rest non-transmitted service, so that the strict priority transmission of the service with high y is ensured.
Wherein S in formula (2) represents the S-th subframe, s.epsilon.1, 2, … …, S]The method comprises the steps of carrying out a first treatment on the surface of the S represents the total number of subframes in one multiframe; u user identification, indicating the u-th userUser U e [1,2, …, U]The method comprises the steps of carrying out a first treatment on the surface of the U is the total number of users; x is the number of services, and represents the xth service, X is [1,2, … …, X ]]The method comprises the steps of carrying out a first treatment on the surface of the X is the total number of services; a is that u Indicating user u priority, A u ∈[1,2,……,A max ]；G x Representing the class of service of the x services, G x ∈[1,2,……,G max ]；d u,x Representing the traffic of the x service of the u user transmitted in the multiframe;
representing the proportion of the transmission time of the x service of the user u in the s sub-frames to the total transmission time of the sub-frames; v (m) represents the rate of the mth modulation coding mode; />
Representing the modulation coding mode identification in a subframe, if the m modulation coding mode selected for s subframes,/for s subframes is selected>
Otherwise the first set of parameters is selected,
m represents the mth modulation coding mode, +.>
Representing the highest modulation coding mode that u user can support; t represents the total duration of one subframe; />
The number of bits to be sent for the x traffic of user u at the beginning of this round of scheduling period is indicated.
In (5)
Indicating that user u occupies subframe identity, +.>
Indicating that user u occupies s subframes, +.>
Indicating that user u does not occupy s subframes, alpha s,u Representing the duty ratio of the user u in the s sub-frame, the value is 0 to 1, sgn (delta) represents a sign function, and the method comprises the following steps of
In (6)
Representing the modulation coding pattern of the s+1th subframe.
V in (7) as Indicating the overall forward ATDM carrier information rate.
In the formula (8), y represents a weight after the service x and the user priority; h is a set of services in S, including services for which all transmissions are not completed, B represents the remaining set of services of this scheduling, H n b= [1,2, ], X ]. And thirdly, constructing an improved ant colony optimization algorithm.
For time t, let τ be ij After the service i is transmitted in the multiframe, selecting the pheromone concentration when the service j is transmitted, and then the state transition probability formula is as follows:
the alpha is a pheromone heuristic value weight, which is used for describing the influence degree of the pheromone concentration on a scheduling task, and the larger the value is, the larger the probability of ants on the walking path selection is; η (eta) ij The heuristic factor is used for representing the expected degree of selecting the transmission of the service j after the transmission of the service i; beta is a desired heuristic weight, and the choice of alpha and beta determines the local search capability of the algorithm. τ is Selecting the pheromone concentration when transmitting the service s after transmitting the service i in the multiframe; η (eta) is Is heuristic factor, representing the transmission of the selected service s after transmission of service iA desired degree; s epsilon J k (i)，J k (i) Representing the set of services that ant k next allows to select, each ant independently selects the next service based on the amount of pheromone remaining on the path and heuristic information.
A max 、G max The maximum user priority and the maximum service priority are respectively; a is that i Indicating user priority of i service, A j User priority of j service is represented; g i Representing the priority of service i, G j Indicating the priority of service j. Fitness function F:
F＝f (11)
in addition, taboo table tabu k The traffic currently transmitted by ant k is recorded. When all services are added to taboo table tabu k When ant k walks around once, the traffic sequence transmitted by ant k is a feasible solution of F, and F represents the fitness function, which can be regarded as a mapping on the combination optimization problem (I, F):
I←F (12)
i is a solution element, i= (Φ, K), where K represents the number of transmission services in the multiframe; phi is the solution vector [ phi ] 1 ,φ 2 ,...,φ K ]Phi epsilon X represents the sequence of K services transmitted in the multiframe.
In the ant colony algorithm, for each solution element I k For = (Φ, K), there is a pheromone concentration τ ij Corresponding to this. After one round of trip is completed, the pheromone concentration on each path is updated as follows:
τ ij (t+n)＝(1-ρ)τ ij (t)+Δτ ij (13)
wherein: n represents the iteration number, ρ (0 < ρ < 1) represents the evaporation coefficient of the pheromone on the path, 1- ρ represents the persistence coefficient of the pheromone, ρ is too small, and the possibility that the previously searched path is selected again is too large, which affects the randomness and global searching capability of the algorithm; Δτ ij Representing the increment of the pheromone on the service ij in the current iteration,
represents the amount of pheromone left on the edge ij by the kth ant in the current iteration, and Ω is the total number of ants.
With the increase of the number of services, the ant colony algorithm is difficult to converge in a certain time, the number of ants needs to be increased along with the number of services, the calculated amount is increased, and the acquired pheromones tend to be average and are easy to fall into a local optimal solution. The three ant colony algorithm models proposed by dorigo have poor optimization effect,
the updating mechanism needs to be improved, the ratio of the feasible solution to the optimal path of each iteration is added on the basis of the ant-quality model, the pheromone quantity on the path can be updated better by utilizing the global information, and the local optimal can be jumped out.
wherein ,
for the average weight of service ij, C is the normal number, F k Representing the size of the path of the kth ant in the current circumferential stream, namely the size of the fitness function of the kth ant circumferential stream, L NC The maximum value of the path iterated to NC times is shown. />
And x service of u users, which represents the path of the kth ant in the current round trip, is transmitted in the multiframe.
And step four, adopting an algorithm to meet constraint conditions, and solving an optimal solution of the objective function. I is a solution element, i= (Φ, K), where K represents the number of transmission services in the multiframe; phi is the solution vector [ phi ] 1 ,φ 2 ,...,φ K ]Phi epsilon (1-X) represents the sequence of K services transmitted in the multiframe.
And fifthly, simulating different algorithms, and analyzing results. The highest speed of each spot beam backward channel of the ATDM satellite communication system can reach V sp (in Mbps) the overall forward ATDM carrier information rate is V as (in Mbps). Proper resource scheduling algorithm must be adopted in the central station to ensure the system performance.
A block diagram of the presentation system is shown in fig. 2. The central station receiving box receives service data, network management data and the like sent by a user terminal side through a split path, and forms UDP packets to be sent to the switching unit, and the floor service is directly handed over to a network access or local related server; all other messages which need to be sent to the small station are sent to the resource scheduling equipment, and the resource scheduling equipment generates a frame plan of the forward frame according to the optimization target.
The forward direction is an ATDM broadcasting mode, and is multiplexed with data of multiple types, multiple users, multiple services, and multiple modulation coding modes, as shown in fig. 3. The physical layer requires the link layer to multiplex the modulation coding modes into subframes in order from low to high, and the modulation coding mode in each subframe is unchanged. Different modulation coding modes can multiplex different numbers of bits.
For the ground station, all data can be received as long as the modulation and coding scheme is appropriate. The network manager tells the resource scheduling device the highest modulation coding mode of each ground station. The resource scheduling device considers the impact of the modulation coding pattern when forming a forward set of multiframes.
For the resource scheduling device, the input conditions are various UDP data packets (including services and network management) of a network layer, a receiving modulation coding mode of each ground small station, a network management channel modulation coding mode, user priority, service priority and the like, and the queue scheduling algorithm performs service priority resource scheduling according to the input conditions, preferentially transmits real-time IP services and simultaneously gives consideration to the user priority and the system resource utilization rate. The priority of the real-time service is highest, and the non-real-time service is ranked according to the priority according to the formula (8), the services are sequentially scheduled, enter a buffer queue, the services are ranked according to a modulation coding mode in the buffer queue, one multiframe is completed, and the scheduling is continued until Q multiframes are multiplexed to complete one scheduling. The Q multiframes are the services which are transmitted based on the priority of the service, are transmitted through the central station case, and the forward channel adopts an ATDM system, is transmitted to the satellite for transfer, and then returns to the target ground small station. And completing the service transmission of the ground small station to the ground small station.
As shown in the detailed scheduling algorithm flow in fig. 4, according to different users of service sources and different types, all services are given different priority levels according to formula 8, and are arranged according to y size of the services, and are put into sub-queues, and X services are all scheduled into a buffer queue in turn from service 1, and are arranged from large to small according to a modulation coding mode, and service ordering numbers are given in turn.
As shown in fig. 5, step 1: the resource scheduling device receives the service transmitted by the chassis, and P services are all available to obtain the priority G of the service x User priority A corresponding to service u And updating the business to be scheduled of the sub-queues according to a formula 8, sequencing the business according to the size y, numbering P, wherein the size is (1-P), and P is the business number entering the buffer queue.
Step 2: initializing, wherein kappa=0; surplus=0, v 0 ＝V p Subframe=0; kappa is the total number of services in the buffer queue, surplus is the residual amount of services which do not occupy the whole subframe, V 0 V for data transmission rate in subframe p For the transmission rate of traffic p into the buffer queue, subframe is denoted asRecording the number of subframes occupied by the scheduled service of the buffer queue.
Step 3: and according to the sub-queue service arrangement, sequentially scheduling the services p into a buffer queue, and ordering the kappa services of the buffer queue according to the size of the modulation coding mode m.
Step 4: sequentially transmitting k services of the buffer queue, filling frames, and transmitting the transmission rate V of the service w w Whether or not to be less than V 0 If less than or equal to V is updated 0 ＝V w Otherwise, not updating. The objective is to arrange the actual modem mode within the S subframe from high to low in order to satisfy the constraint of equation (6). The service transmission rate in the following subframe cannot be greater than the service transmission rate in the preceding subframe, that is, the actual modulation coding mode is from large to small, if the situation of frame splicing occurs (a plurality of services exist in one frame), the modulation coding mode of the frame is selected to be smaller, and the ordering of the modulation coding modes of the S subframes is maintained from large to small.
Step 5: identifying transmission rate V of traffic w w And traffic.
Step 6: according to
Formula calculates the number of subframes z, d occupied by service w w Representing traffic volume of traffic w, V w And recording the residual data quantity of the service, wherein T is the subframe duration, and the transmission rate corresponds to the service w. Judging whether z is greater than or equal to 1, if so, counting the total subframe number subframes of the buffer queue, and entering a step 7; otherwise, p=p+1, step 3 is performed,
step 7: judging whether all S subframes are completely encapsulated, continuously scheduling the total subframe number subframes < S of the buffer queue, and entering a step 3; subframe > S, go to step 8; subframe=s, then the sequence is an initial solution i= (Φ, K) for one multiframe, and step 9 is entered.
Step 8: and finally, placing the service number w in the buffer queue, and ensuring that other service sequences are unchanged, ensuring the priority transmission of the high-priority weight service, and selecting the service w+1 to enter the step 4.
Step 9: finishing a multi-frame scheduling, taking the result I as the initial of the ant colony algorithmSolution set, let time t=0 and number of cycles N c =0, setting the maximum number of cycles G, placing Ω ants on κ elements (traffic), initializing pheromone τ for each side (i, j) ij (t) =c, where c represents a constant and the initial time Δτ ij (0)＝0。
Step 10: number of cycles N c ＝N c +1, taboo table index number k=1 for ants. Number of ants k=k+1.
Step 11: the ant individual selects the element J and advances according to the probability calculated by the state transition probability formula 9, J epsilon { J k (i)}。
Step 12: the tabu table pointer is modified, i.e. after selection, the ant is moved to a new element and the element is moved to the individual tabu table of the ant.
Step 13: if the elements in the set phi are not traversed, namely k < omega, jumping to the 10 th step; otherwise, step 14 is performed.
Step 14: recording the optimal route of the iteration, and updating the information quantity on each path according to a pheromone updating formula.
Step 15: if the end condition is satisfied, i.e. if the number of loops N c If not less than G, the cycle is ended and the program optimization result I is output k The method comprises the steps of carrying out a first treatment on the surface of the And (3) completing the encapsulation of 1 multiframe, and if the scheduling of Q multiframes is completed, completing the scheduling of the service of the round: otherwise, returning to the step 3, and continuing to schedule to Q multiframes. If the cycle number N c And < G, the tabu list is cleared and the process jumps to step 10.
In fig. 6, the optimal solution obtained by the algorithm in a multi-frame is compared with the optimal solution obtained by the exhaustion method, and after 1000 times of simulation, 992 times of results are consistent, the probability of obtaining the optimal solution by the algorithm reaches 99.2%, which is superior to 92.7% of the queue scheduling algorithm and 93.9% of the ant colony algorithm. The convergence of the algorithm is an important index for measuring the ant colony optimization algorithm, the convergence rate of the algorithm is greatly improved by constructing an initial solution set and enhancing a global pheromone, and compared with the traditional ant colony optimization algorithm, the algorithm can converge to a target value more quickly. As shown in fig. 7, the proposed algorithm completes convergence after 83 iterations, but the conventional ant colony optimization algorithm needs 187 iterations, and when the number of iterations is fixed, the fitness function value of the proposed algorithm after iteration is always higher than that of the conventional ant colony optimization algorithm, so that the optimization performance of the proposed algorithm is improved to a certain extent. In addition, the invention compares data such as S multiframe traffic, waiting traffic residual, objective function f, service considering user priority y with the queue algorithm and ant colony algorithm, and the like, and it can be seen from fig. 8-12 that the algorithm can transmit more traffic in the same multiframe, the priority corresponding to the transmitted service is higher, and the user priority and the system resource throughput are considered. Fig. 1 is a general diagram of a system, from which it can be seen that a communication environment is composed of satellites and the ground. Fig. 2 is a block diagram of a demonstration system depicting the system operation of a ground hub station, the present invention being used for an optimization algorithm at a resource scheduling facility. Fig. 3 is a forward frame structure diagram, where S subframes are multiplexed into one multiframe, and Q multiframes are multiplexed into one schedule. Fig. 4 is a flow chart of a service queue model, where an initial solution set is generated by a queue scheduling policy, services are sequentially arranged according to the priority sizes, peer services are further ordered according to the user priority sizes, and if both services are the same, the services are further ordered according to the corresponding modulation and coding mode sizes of the services, so as to ensure that the priority of the scheduled services is as high as possible. And transmitting the data to a multi-frame in sequence, arranging the data in the multi-frame according to the modulation coding mode corresponding to the service until the multi-frame is filled, and completing initial deconstructing, namely, the service entering the multi-frame at the tail still counts into initial deconstructing even if the service entering the multi-frame at the tail is not transmitted completely, so that the service quantity is ensured to be enough to fill the multi-frame. And obtaining an optimal solution from the initial solution through an ant colony algorithm, and continuously incorporating the rest of the service of the multiframe into a waiting service queue to wait for the next scheduling. Fig. 5 is a business flow of a queue scheduling algorithm. Fig. 6 is a graph comparing intra-frame data with the exhaustive results for three algorithms (10 services). Fig. 7 shows the change curves of fitness functions of the ant colony algorithm and the improved algorithm, and as can be seen from the figures, the convergence performance of the invention is better, and fig. 8-12 show simulation result graphs obtained by applying four algorithms to the model, wherein the four algorithms are four data comparisons of the fitness functions in 16 multiframes, the accumulated value of the priority weight of the service considering user, the transmission traffic and the residual traffic.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (10)
1. A service priority-oriented ATDM satellite communication scheduling method is characterized in that: the method comprises the following steps:
step 1: receiving P services according to the priority G of the services x And user priority A u Calculating a priority weight y, sequencing the services in the sub-queues according to the size of y, and numbering the sequenced services according to 1-P;
step 2: initializing the following parameters, κ' =0, surplus=0, v 0 ＝V p Subframe=0; kappa' is the total number of services in the buffer queue, surplus is the remaining amount of services that do not occupy the entire subframe, V 0 For the transmission rate of the traffic in the subframe, subframe is the number of subframes occupied by the scheduled traffic of the record buffer queue, V p The transmission rate of the service p entering the buffer queue;
step 3: according to the sub-queue service arrangement, sequentially scheduling the services p into a buffer queue, and ordering kappa' services of the buffer queue according to the size of a modulation coding mode m;
step 4: judging transmission rate V of service w in kappa' services w Whether or not to be less than V 0 If less than or equal to V is updated 0 ＝V w If not, the buffer queue k' services are transmitted in turn without updating, and the frames are filled;
step 5: when the service w fills frames, the transmission rate V of the service w is identified w And traffic d w ；
Step 6: according to
Calculating the number z of subframes occupied by the service w, wherein T is the subframe duration, and recording the residual data quantity of the service; judging whether z is greater than or equal to 1, if so, counting into a buffer queueA total subframe number subframe, and step 7 is entered; otherwise, p=p+1, go to step 3;
step 7: when the total subframe number of the buffer queue is subframe<S, continuing to schedule, entering a step 3, wherein S is the total number of subframes; subframe(s)>S, entering a step 8; subframe=s, the sequence is an initial solution i= (Φ, K) of a multiframe, K represents the number of transmission services in the multiframe, Φ is a solution vector [ Φ ] 1 ,φ 2 ,...,φ K ]Phi epsilon X represents the sequence of K services transmitted in the multiframe, and step 9 is entered;
step 8: placing the service w in the buffer queue to the last of the buffer queue, and selecting the service w+1 to enter a step 4, wherein the ordering of other services is unchanged;
step 9: taking I as an initial solution set of an ant colony algorithm, and enabling time t=0 and cycle number N to be c =0, setting maximum number of cycles G, placing Ω ants on K services, initializing pheromone τ of each side (i, j) ij (t) =c, where c represents a constant and the initial time Δτ ij (0)＝0，Δτ ij Representing the increment of the pheromone on the service ij in the iteration;
step 10: number of cycles N c ＝N c +1, tabu table index number of ants k=1, number of ants k=k+1;
step 11: ant individuals select service J and advance according to the probability calculated by the state transition probability, J epsilon { J k (i)}，J k (i) Representing the service set that ant k allows to select next after transmitting service i;
step 12: modifying the tabu list pointer, moving ants to new service, and moving the service to the tabu list of the ants;
step 13: if the business in the aggregate phi is not traversed, and k < omega, jumping to the step 10; otherwise, go to step 14;
step 14: recording the optimal route of the iteration, and updating the information quantity on each path according to a pheromone updating formula;
step 15: if the cycle number N c More than or equal to G, the cycle is ended and the optimization result I is output k Completing the encapsulation of 1 multiframe, if Q multiframe modulationAnd (3) completing the degree, namely completing the service scheduling of the round: otherwise, returning to the step 3, and continuing to schedule to Q multiframes; if the cycle number N c And < G, clearing the tabu list and jumping to step 10.
2. The service priority oriented ATDM satellite communication scheduling method according to claim 1, wherein: the calculation formula of the priority weight y is as follows:
wherein ,Au Indicating user u priority, G x Representing the class of service of the x services, A max Indicating the highest priority of the user.
3. The service priority oriented ATDM satellite communication scheduling method according to claim 1, wherein: the state transition probability calculation formula is as follows:
wherein alpha is the weight of the heuristic value of the pheromone, eta ij Selecting the expected degree of transmission of the service j after the service i is transmitted; beta is the weight of the expected heuristic value, tau ij Selecting the pheromone concentration of j when transmitting service i in multiframe, tau is Selecting the pheromone concentration when transmitting the service s after transmitting the service i in the multiframe; η (eta) is Selecting the expected degree of transmission of the service s after the service i is transmitted; s epsilon J k (i)，J k (i) Representing the set of services that ant k next allows to select after transmission of service i.
4. The service priority oriented ATDM satellite communication scheduling method according to claim 3, wherein:
wherein ,Amax 、G max The maximum user priority and the maximum service priority are respectively; a is that i Indicating user priority of i service, A j User priority of j service is represented; g i Representing the priority of service i, G j Indicating the priority of service j.
5. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 4, wherein: the pheromone update formula is calculated as follows:
τ ij (t+n)＝(1-ρ)·τ ij (t)+Δτ ij
where n represents the number of iterations, ρ represents the evaporation coefficient of the pheromone on the path, Δτ ij Representing the increment of the pheromone on the traffic i j in the current iteration.
6. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 5, wherein:
wherein ,/>
Represents the amount of pheromone that the kth ant leaves on the edge ij in the present iteration, and Ω refers to the total number of ants.
7. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 6, wherein:
expressed as:
wherein ,
the average weight of the business i and j is C is a normal number, F k Represents the size of the path that the kth ant walks in the current round trip, L NC The maximum value of the path iterated to NC times is shown.
8. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 7, wherein:
wherein ,Amax Is the maximum user priority; a is that i Indicating user priority of i service, A j User priority of j service is represented; g i Representing the priority of service i, G j Indicating the priority of service j.
9. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 7, wherein:
wherein ,
x services of U users representing the path of the kth ant in the current round trip are transmitted in the multiframe, U represents the users, U represents the total number of the users, X represents the services, X represents the total number of the services, A u Indicating user u priority, A max Indicating the highest priority of the user, G x Representing the class of service of the x-service.
10. The service priority oriented ATDM satellite communication scheduling method as set forth in claim 9, wherein: solving F k When it is full ofThe following constraints apply:
wherein S represents the S-th subframe, S ε [1,2, … …, S]The method comprises the steps of carrying out a first treatment on the surface of the S represents the total number of subframes in one multiframe; u user identification, which represents the U user, U e [1,2, …, U]The method comprises the steps of carrying out a first treatment on the surface of the U is the total number of users; x is the number of services, and represents the xth service, X is [1,2, … …, X ]]The method comprises the steps of carrying out a first treatment on the surface of the X is the total number of services; a is that u Indicating user u priority, A u ∈[1,2,……,A max ]；G x Representing the class of service of the x services, G x ∈[1,2,……,G max ]；d u,x Representing the traffic of the x service of the u user transmitted in the multiframe;
representing the proportion of the transmission time of the x service of the user u in the s sub-frames to the total transmission time of the sub-frames; v (m) represents the rate of the mth modulation coding mode; />
Representing the modulation coding mode identification in a subframe, if the m modulation coding mode selected for s subframes,/for s subframes is selected>
Otherwise the first set of parameters is selected,
m represents the mth modulation coding mode, +.>
Representing the highest modulation coding mode that u user can support; t represents the total duration of one subframe; />
The number of bits to be sent of the x service of the user u at the beginning of the round of scheduling period is represented; />
Indicating that user u occupies subframe identity, +.>
Indicating that user u occupies s subframes, +.>
Indicating that user u does not occupy s subframes, alpha s,u Representing the duty ratio of the user u in the s sub-frame, the value is 0 to 1, sgn (delta) represents the sign function, and there is +.>
Representing the modulation coding mode of the s+1st subframe; v (V) as Representing the entire forward ATDM carrier information rate; y represents the weight of the service x and the user priority; h is the set of all the services in S, including the services which are not all transmitted, B represents the rest service set of the current scheduling, H n B= [1,2, ], X]。/>
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