CN-117425179-B - Space-space cooperative scheduling method for guaranteeing task priority and system throughput

CN117425179BCN 117425179 BCN117425179 BCN 117425179BCN-117425179-B

Abstract

The invention discloses an air-space-ground cooperative scheduling method for guaranteeing task priority and system throughput, and belongs to the technical field of wireless communication. Aiming at the problems of mismatching of task resources and changeable priority in space-to-earth emergency communication, the multi-layer task collaborative scheduling method is provided. Aiming at the problem of changeable task priority, a time-varying task priority is defined according to the timeliness of disaster data and the user priority to quantify task dynamic benefits, aiming at the problem of mismatching task resources, task priority service is realized and transmission rate is maximized by cooperatively designing unmanned plane tracks, user-associated scheduling and dynamic allocation of feedback bandwidth, and aiming at the problem of limited computing capacity of an edge server, a particle swarm algorithm based on limited secondary neighborhood search and a partial unloading strategy based on priority ordering are provided to improve system throughput. And finally, constructing a task scheduling model based on the problems, and obtaining an optimal space-to-ground cooperative scheduling scheme by using a block coordinate descent algorithm to alternately and iteratively solve.

Inventors

DAI CUIQIN
DU TAO
WANG LIANG
LIAO MINGXIA
TANG HONG

Assignees

重庆邮电大学

Dates

Publication Date: 20260508
Application Date: 20230823

Claims (10)

1. An air-space cooperative scheduling method for guaranteeing task priority and system throughput is characterized by comprising the following steps: s1, constructing a task model in an aerospace-ground emergency communication network, and defining a time-varying task priority to quantify task dynamic benefits; S2, modeling a task scheduling process in space-to-ground emergency communication by cooperatively considering unmanned plane track design, user association, high-altitude platform bandwidth allocation, uplink connection selection and calculation unloading decision; S3, calculating the transmission rate and the system throughput of communication links among the nodes; s4, constructing a high-altitude platform queue model, and calculating the length and the storage capacity of the queue; S5, constructing a task scheduling problem in the space-to-ground emergency communication network into a collaborative task scheduling model which maximizes the product weighted sum of task priority and system throughput by collaborative design of unmanned aerial vehicle user association, track design, high-altitude platform bandwidth allocation, optimization of uplink selection and calculation unloading between a high-altitude platform and a satellite; s6, decomposing the cooperative task scheduling model into a data collection scheduling model for maximizing the weighted rate sum of priority and a calculation unloading scheduling model for maximizing the throughput of the system by applying a block coordinate descent method; S7, for a data collection scheduling model, designing an unmanned aerial vehicle user association scheme by adopting a method of combining linear relaxation with CVX solution; S8, for a calculation unloading scheduling model, optimizing uplink connection between a satellite and an aerial platform by a particle swarm algorithm based on limited secondary neighborhood search, and optimizing unloading decisions by partial unloading strategies based on priority ordering; And S9, alternately solving a data collection scheduling model and a calculation unloading scheduling model by using a block coordinate descent algorithm, and reconstructing a non-feasible solution to generate an optimal space-earth collaborative scheduling scheme.
2. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein a time-varying task priority is defined in step S1 to quantify task dynamic benefits, as follows: Wherein the task model is modeled as a quadruple to characterize task requirements, i.e ; Types representing users who generate tasks, including four kinds of emergency and regular tasks from user equipment and rescue equipment in an emergency scene, respectively; refers to tasks A required service time window; refers to tasks Is a priority of (3); refers to tasks Is a data amount of (a); initial priority for the user; To be in time slot The time-dependent weighting factor of the data of the task being requested, To be in time slot The horizontal coordinates of the disaster center within the container, For time slots Distance between the inner user and the disaster center.
3. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein the step S2 specifically comprises the following steps: (1) Modeling a track variable of the unmanned aerial vehicle; Within any time slot, the UAV position can be considered stationary, so the unmanned aerial vehicle trajectory uses N two-dimensional sequences To approximate, N is the number of time slots, And Representing sub-regions respectively Well unmanned aerial vehicle Is a horizontal axis and a vertical axis coordinate of (1); (2) Modeling of unmanned aerial vehicle user-related variables; In one task period The same frequency band is shared when all unmanned aerial vehicles and ground rescue equipment communicate, and each unmanned aerial vehicle provides service for associated users in a time division multiple access mode If in time slot In, unmanned aerial vehicle Rescue equipment Providing data collection service, then Otherwise ; (3) Modeling a bandwidth allocation variable of the high-altitude platform; The high altitude platform adopts frequency division multiple access to dynamically allocate the back transmission bandwidth between all the ground user equipment and the unmanned aerial vehicle in the area, namely the high altitude platform receives the data of a plurality of ground user equipment and unmanned aerial vehicle in one time slot Is shown in time slot Distribution to ground user equipment And unmanned aerial vehicle Bandwidth ratio of (2); (4) Modeling uplink transmission connection variables; for uplink data transmission between a high-altitude platform and LEO satellites, the frequency bandwidth of an uplink of the high-altitude platform-satellite is divided into a group of orthogonal subchannels by using an orthogonal frequency division multiplexing technology, different high-altitude platforms are connected to the same LEO satellite by using different orthogonal subchannels in the same time slot, and a binary variable is defined Representing a high altitude platform And LEO satellite Time-varying connection relationship between time slots Inner high-altitude platform And LEO satellite A data transmission link exists between them, then Otherwise ; (5) Calculating unloading scheduling variable modeling; Designing an offloading decision binary variable To describe at time slots Inner high-altitude platform Locally or offloaded to LEO satellites Task data are calculated on a satellite-borne edge calculation server when a high-altitude platform is in a time slot Is a calculated queue length of (1) Greater than a calculated threshold Unloading the excess data to LEO satellite edge server for processing, i.e Otherwise The following formula is shown: 。
4. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein the step S3 specifically comprises the following steps: (1) Calculating the transmission rate of the data collection stage; for any subarea in disaster area, the ground user layer is provided with Personal user equipment The rescue equipment respectively transmits the data to the high-altitude platform and the unmanned aerial vehicle in the air layer, and the channel power gain between the high-altitude platform and the user equipment can be expressed as: Wherein, the Is a reference distance of The power of the channel at the time of the channel, Is shown in time slot Time user equipment And a high-altitude platform in the area The distance between the unmanned aerial vehicle and the high-altitude platform and the channel power gain between the unmanned aerial vehicle and the rescue equipment are respectively calculated as And ; At high altitude platform In the rescue equipment To unmanned aerial vehicle When transmitting data, the interference signal-to-noise ratio of the receiving end can be expressed as: Wherein, the The transmission power is specified for the rescue equipment, Representing time slots And co-channel interference caused when all other unmanned aerial vehicles transmit data, For AWGN power at the receiving end of the unmanned aerial vehicle, thus, at the time slot Interior unmanned aerial vehicle The achievable received data rates are: By using Representing a high altitude platform And assuming a terrestrial user equipment At a prescribed transmission power Unmanned aerial vehicle At a prescribed transmission power Upload its data to the high-altitude platform, thus, when And When data is transmitted to the high-altitude platform, the instantaneous reachable data rates are respectively expressed as follows: Wherein, the received signal-to-noise ratio at the high altitude platform is respectively expressed as 、 , Is AWGN power spectral density; (2) Calculating the transmission rate of the data unloading stage; Will be high altitude platform And LEO satellite The channel fading factor between the two is modeled as a circularly symmetric complex Gaussian random variable, namely , wherein, , Each high-altitude platform is allocated to a different sub-channel with a Ka band bandwidth W for accessing LEO satellites, thus, in time slots Inner subregion The data rates achievable when transmitting data are: Wherein, the Is a high-altitude platform The transmission power of the data is set to a predetermined value, Is the square of the channel gain for the "high altitude platform-LEO satellite", Is a high-altitude platform And LEO satellite The distance between the two plates is set to be equal, In order to be a path loss index, AWGN power at the satellite receiving end; And Indicating the altitude of the LEO satellite and the high altitude platform respectively, And Respectively representing horizontal coordinates of the LEO satellite and the high-altitude platform; High-altitude platform by integrating uplink selection variables and offloading decisions In time slot The calculation rate at this point is: Wherein, the The rate is calculated locally for the high altitude platform edge server, Respectively represent deployment on high-altitude platforms The edge at the point calculates the CPU cycle frequency of the server and the number of CPU cycles required to calculate the unit bit data.
5. The space-time cooperative scheduling method for guaranteeing task priority and system throughput as set forth in claim 1, wherein the step S4 specifically includes representing a set of queues of all high-altitude platforms as For any subregion In time slot Initially, the length of the queue is calculated Can be expressed as: Wherein, the Representing a high altitude platform In time slot The amount of data received is determined by the data, For a single slot length of time, Representing the amount of data split after offloading decision by calculation The length of (c) can be expressed as: Wherein, the Representing the amount of data that the compute queue shunts to the transmit queue after computing the offload decision, Is shown in time slot Inner high-altitude platform The amount of data transmitted to the LEO satellite; In any time slot, high-altitude platform The length of the system queue should not exceed its storage capacity The method comprises the following steps: 。
6. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein the weighted sum of the product of task priority and transmission rate in the data collection stage in step S5 is expressed as: The system throughput for the compute offload phase may be expressed as: Order the , , , , The collaborative task scheduling model in step S5 may be expressed as: Wherein the objective function together with (C1) represents maximizing the minimum average achievable weighting rate of the system with the aim of finding a most conservative solution in the whole constraint space (C2) represents any unmanned aerial vehicle in the task scheduling period The starting position and the ending position in the system are the same to provide periodic data acquisition service, and (C3) and (C4) are the maximum speeds of the unmanned aerial vehicle respectively And minimum collision avoidance distance The system comprises a plurality of unmanned aerial vehicles, wherein (C5) - (C7) represent user association constraints of the unmanned aerial vehicles so as to ensure that each unmanned aerial vehicle can serve one rescue device at most in any time slot, each rescue device can serve one unmanned aerial vehicle at most, C8) and (C9) are bandwidth allocation limits, the proportion of bandwidths allocated to each user by a high-altitude platform is ensured to be not less than zero, the sum of bandwidths allocated to all users is ensured to be not more than total available bandwidth, C10) - (C12) represent connection constraints between the high-altitude platform and LEO satellites so as to ensure that the high-altitude platform transmits data to one LEO satellite at most in any time slot, and one LEO satellite can be connected to at most The method comprises the steps of (1) a high-altitude platform, (C13) a storage capacity constraint of any high-altitude platform, and (C14) a constraint of calculating an unloading decision variable.
7. The space-to-ground collaborative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein the step S6 is characterized in that an original collaborative task scheduling optimization model CTSM is decomposed into two sub-models, namely a data collection scheduling model and a calculation unloading scheduling model by applying a block coordinate descent method, wherein the former is used for collaborative optimization of high-altitude platform bandwidth allocation and unmanned aerial vehicle user association and track planning in a data collection stage by fixing high-altitude platform uplink selection variables and calculation unloading decision variables, and the latter is used for collaborative optimization of high-altitude platform uplink selection variables and calculation unloading decision variables in a data return stage by fixing high-altitude platform bandwidth allocation and unmanned aerial vehicle user association and track planning.
8. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 6, wherein the data collection scheduling model in step S7 uses a linear relaxation method to make binary variables in the user-associated sub-model For convex constraints in the trajectory optimization and bandwidth allocation submodel, a successive convex approximation method is adopted to relax the convex constraints, namely, in each iteration, an original function is approximated by a function which is easier to process at a given local point; In the step S7, an initial unmanned aerial vehicle track design based on circle coverage and an initial bandwidth allocation algorithm based on priority ordering are adopted to obtain a higher-quality initial track and bandwidth allocation scheme, the initial unmanned aerial vehicle track design algorithm based on circle coverage firstly clusters user nodes in a network and then searches an initial track with the shortest flight distance according to the maximum user coverage criterion, and the initial bandwidth allocation algorithm based on priority ordering takes the proportion of the priority of user equipment and unmanned aerial vehicles accounting for the total priority of all nodes as the bandwidth allocation proportion of a high-altitude platform so as to transmit more data with higher priority.
9. The space-to-ground collaborative scheduling method for guaranteeing task priority and system throughput according to claim 1, wherein in the step S8, a particle swarm algorithm based on limited secondary neighborhood search is provided to select a link with the maximum uplink rate for each high-altitude platform, and the fitness function is as follows: The algorithm is first randomly generated Individual particles And calculate fitness function value of each particle Then, in a first neighborhood operation, searching for a neighborhood solution by matrix element exchange for each particle And preferentially replacing the original solution set, and updating the neighborhood searching times at the same time, and then, in the second neighborhood operation, aiming at the current solution set Selecting any two solutions 、 Preferentially carrying out neighborhood search, repeating the search for U times, and selecting a neighborhood set of each solution after the neighborhood search is completed Neighborhood solution with maximum middle fitness function value Checking the neighborhood searching times of each solution, discarding if the neighborhood searching times are larger than a threshold value, otherwise updating the local optimal solution, and outputting the global optimal solution until the maximum iteration times are reached; in step S8, partial unloading strategy optimization calculation unloading decision based on priority ordering is provided, and first, each queue is provided with Prioritizing data packets Sequencing, if the data volume contained in the high-altitude platform queue Not exceeding the calculated threshold And if not, unloading the excess data quantity to the LEO satellite in sequence for processing, wherein the non-excess data quantity is still calculated locally.
10. The space-time cooperative scheduling method for guaranteeing task priority and system throughput according to claim 6, wherein the step S9 specifically includes: optimizing high-altitude platform bandwidth allocation, unmanned user association, and trajectory planning with block coordinate descent algorithm with fixed uplink selection and calculation of offloading scheduling decisions (B, C) (A, Q), then obtaining% In the case of A, Q), solving (B, C) by adopting an improved particle swarm algorithm and a partial unloading strategy based on priority ordering, and alternately executing two processes until the objective function value of the CTSM is changed within a specified threshold value; In order to ensure the feasibility of the solution, the step S9 reconstructs the possibly generated non-binary solution, and each length is as follows Is further divided into time slots Sub-slots, i.e. total number of sub-slots 。

Description

Space-space cooperative scheduling method for guaranteeing task priority and system throughput Technical Field The invention belongs to the technical field of wireless communication. In particular to an air-space cooperative scheduling method for guaranteeing task priority and system throughput. Background The emergency communication network generally refers to a special communication network constructed for ensuring emergency treatment and necessary communication by comprehensively utilizing various communication resources when a major natural disaster or sudden emergency occurs. With the continuous development of technology, the emergency communication network has been developed from a ground fixed or mobile emergency communication network, a star-ground double-layer emergency communication network to an air-ground integrated emergency communication network. Compared with the traditional emergency communication network, the space-sky-ground integrated emergency communication network has the advantages of comprehensive information service guarantee capability, extensible flexible networking capability, efficient and reliable disaster relief capability, more efficient resource utilization capability and the like. The space-time integrated emergency communication network architecture has the characteristics of various node types, heterogeneous network interconnection, complex space-time behaviors, dynamic topology change, various service types, huge demand difference and the like, so that the problems of low data transmission efficiency, difficult guarantee of service quality and the like are particularly outstanding. One of the key technologies to solve this difficulty is task scheduling technology. Currently, satellite task scheduling based on task resource matching, unmanned aerial vehicle task scheduling based on position deployment and path planning, computation offload scheduling based on MEC, and dynamic task scheduling based on multi-objective decision-making and machine learning have been widely studied. However, the trend of multi-task fusion, quick response and collaborative scheduling in the space-sky-earth emergency communication network is increasingly prominent, and the traditional task scheduling scheme faces the problems of task resource mismatch caused by node isomerism, variable task priority caused by high real-time disaster data, system throughput reduction caused by limited computing capacity of an edge server and the like. Therefore, how to perform multi-layer, multi-resource and multi-task cooperative scheduling under an air-to-ground integrated emergency communication network architecture, and design a multi-layer cooperative task scheduling scheme, so that the maximization of network utility and resource utilization rate becomes a key problem to be solved urgently. In order to solve the problems, the invention provides an air-space cooperative scheduling method for guaranteeing task priority and system throughput. Aiming at the problem of multiple task priorities, a time-varying task priority is defined to quantify task dynamic benefits according to disaster situation data timeliness and user priorities, aiming at the problem of mismatching task resources, task priority service is realized by cooperatively designing unmanned plane tracks and user-associated scheduling, transmission rate is maximized by dynamically distributing backhaul bandwidth, and aiming at the problem of reduced system throughput caused by limited computing capacity and overlarge arrival data volume of an edge server, a particle swarm algorithm based on limited secondary neighborhood search is provided to optimize uplink connection between satellites and high-altitude platforms, and a partial offloading strategy based on priority ordering is provided to optimize offloading decision variables. And finally, constructing a task scheduling model based on the problems, and obtaining an optimal space-to-ground cooperative scheduling scheme by using a block coordinate descent algorithm to alternately and iteratively solve. Disclosure of Invention The present invention is directed to solving the above problems of the prior art. The space-space cooperative scheduling method for guaranteeing the task priority and the system throughput is provided. The technical scheme of the invention is as follows: an air-space cooperative scheduling method for guaranteeing task priority and system throughput comprises the following steps: S1, constructing a task model in an air-space-earth emergency communication network, describing task demands, and defining a time-varying task priority to quantify task dynamic benefits; S2, modeling a task scheduling process in space-to-ground emergency communication by cooperatively considering unmanned plane track design, user association, high-altitude platform bandwidth allocation, uplink connection selection and calculation unloading decision; S3, calculating the transmission rate and the system