WO2022105621A1 - 软件定义卫星网络系统中基于演化博弈的多用户切换方法 - Google Patents

软件定义卫星网络系统中基于演化博弈的多用户切换方法 Download PDF

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WO2022105621A1
WO2022105621A1 PCT/CN2021/128921 CN2021128921W WO2022105621A1 WO 2022105621 A1 WO2022105621 A1 WO 2022105621A1 CN 2021128921 W CN2021128921 W CN 2021128921W WO 2022105621 A1 WO2022105621 A1 WO 2022105621A1
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satellite
users
user
time
handover
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PCT/CN2021/128921
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French (fr)
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李云
吴广富
刘梦梦
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重庆邮电大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H04W36/322—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data by location data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/08—Reselecting an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/32—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data
    • H04W36/324—Reselection being triggered by specific parameters by location or mobility data, e.g. speed data by mobility data, e.g. speed data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
    • H04W84/04—Large scale networks; Deep hierarchical networks
    • H04W84/06—Airborne or Satellite Networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/08—Access restriction or access information delivery, e.g. discovery data delivery
    • H04W48/12—Access restriction or access information delivery, e.g. discovery data delivery using downlink control channel

Definitions

  • the present application relates to the field of mobile communication technologies, and in particular to a multi-user switching method based on evolutionary game in a software-defined satellite network system.
  • Satellite communication forms a satellite constellation through the joint networking of multiple satellites, which has the characteristics of wide coverage and no geographical and airspace restrictions, attracting people's attention.
  • the integration of satellite communication network and terrestrial mobile communication network will become an important development direction of mobile communication.
  • low-orbit satellite systems have received extensive attention due to their low latency, small path loss, and wide coverage.
  • the coverage time of a single low-orbit satellite to the ground terminal is limited, and the user needs to switch frequently during communication.
  • Common satellite switching includes inter-satellite switching and beam switching.
  • the satellite switching strategy in the traditional software-defined network is generally based on three criteria: the elevation angle of the candidate satellite, the remaining coverage time, and the number of idle channels.
  • the remaining coverage time of the satellite is related to the number of handovers and the call drop rate of the user. The longer the remaining coverage time of the satellite, the longer the service time the satellite can provide to the user, which can reduce the number of user handovers and the call drop rate.
  • the number of idle channels of the satellite represents the load of the satellite. The more idle channels of the satellite, the lighter the load of the satellite.
  • the number of idle channels of the satellite to reflect the load status of the satellite has disadvantages: in the actual communication process, all users in the satellite coverage area at the same time will receive the number of idle channels of the satellite message, and all users in the area will send out Selecting the satellite with the lightest load for access based on the principle of the greatest self-interest may lead to the problem of overloading the satellite. Therefore, the number of idle channels of the satellite cannot reflect the load condition of the satellite well, and other indicators need to be used to judge the load condition of the satellite.
  • the traditional satellite handover strategy is selected based on signal strength, so users may fail to handover; and the traditional satellite handover strategy does not describe the dynamic resource competition process between users.
  • the embodiment of the present application provides a multi-user switching method based on evolutionary game in a software-defined satellite network system.
  • the multi-user switching scenario under multi-satellite coverage is considered first. Due to the motion characteristics of low-orbit satellites, when the received signal strength of a user in the coverage area of a certain satellite is less than the set threshold, the user will send a handover signal. Apply and choose to switch to other visible satellites to ensure that the communication process is continuously interrupted. Then, when selecting switching satellites, three factors, the bandwidth that can be allocated to the user by the candidate satellites, the remaining coverage time of the satellites and the satellite elevation angle are comprehensively considered, and the evolutionary game method is used to select the switching satellites.
  • the balanced state not only effectively ensures the balance of satellite load, but also ensures the fairness of users, so that the service quality of all users can be optimal, and the failure rate of user handover is effectively reduced.
  • a first aspect of the embodiments of the present application provides a multi-user switching method based on an evolutionary game in a software-defined satellite network system.
  • the multi-user switching method may include the following steps:
  • All users respectively calculate the elevation angle information and remaining coverage time of the satellite according to the information obtained;
  • each user in all users calculates their own income according to satellite capacity, elevation angle and remaining coverage time and the defined income function;
  • the controller calculates the average income of users in the area, and broadcasts it to all users;
  • step S4 determine whether the income of all users is higher than the average income, if not, then go to step S5, otherwise, end the multi-user switching;
  • step S5 the user selects other switching satellites, and makes the profit after the handover higher than the profit before the handover; return to step S2.
  • the benefit function defined by the user is the difference between the utility function after the user's compensation and the cost function of the user.
  • the utility function after the user compensation is expressed as:
  • the cost function of the user is expressed as:
  • the average revenue of all users in the area is expressed as:
  • ⁇ i (t) represents the revenue of users connected to satellite i at time t
  • xi (t) represents the proportion of users who select the satellite i at time t.
  • step S5 the user obtains higher returns by dynamically adjusting the strategy selection process, and adopts the replicator dynamics to model the process of strategy dynamic adjustment, and the replicator dynamics is expressed as follows:
  • Replicator state representing the proportion of users who select satellite i at time t
  • ⁇ i (t) represents the revenue of users connected to satellite i at time t
  • ⁇ is the replication dynamic formula parameter, which is used to control the rate of strategy adjustment of the player
  • the replicator dynamically satisfies constraints.
  • the present application is a multi-user switching method based on evolutionary game in a software-defined satellite network system.
  • the invention not only balances the network load of the satellite greatly, reduces the handover failure rate of the user, but also ensures the fairness of the user.
  • the main innovation of this method is that it proposes a handover process based on the software-defined satellite network architecture, models the multi-user handover problem as an evolutionary game problem, uses a set of differential equations to describe the selection and adjustment process of user handover strategies, and regards evolutionary equilibrium as a game
  • the optimal strategy ratio is obtained to ensure that all users can get the best service after switching, thus ensuring the fairness of users; in addition, since the satellite is in constant motion, in order to improve the accuracy of the results,
  • the invention improves the utility function and adds a compensation value to reduce the error of the user's income; meanwhile, the user's overhead is designed as a function of the transmission delay and the scheduling delay, which is more in line with the actual satellite communication situation.
  • Fig. 1 is the multi-user switching method flow chart based on evolutionary game in the software-defined satellite network system of the present application
  • Fig. 2 is the multi-user switching scene under Samsung's coverage in the software-defined satellite network of the present application
  • Fig. 3 is the handover schematic diagram in the software-defined satellite network adopted by this application.
  • Fig. 4 is the handover flow chart in the software-defined satellite network adopted by this application.
  • Fig. 5 is the replication dynamic phase diagram used in the present application.
  • FIG. 6 is a graph of changes in the number of users in an embodiment of the present application.
  • Fig. 7 is a satellite utility diagram in an embodiment of the present application.
  • FIG. 8 is a graph of changes in the number of users under different initial conditions in an embodiment of the present application.
  • FIG. 9 is an evolution equilibrium diagram under different weights in an embodiment of the present application.
  • the present application provides a schematic flowchart of a multi-user switching method based on evolutionary game in a software-defined satellite network system. As shown in FIG. 1 , the multi-user switching method includes:
  • All users respectively calculate the elevation angle information and remaining coverage time of the satellite according to the information obtained;
  • the remaining coverage time of the satellite is related to the number of handovers and call drop rates of users.
  • the longer the remaining coverage time of the satellite the longer the service time that the satellite can provide to the user, which can reduce the number of handovers and the call drop rate of the user.
  • all visible satellites send pilot signals to the user terminal.
  • the terminal on the ground extracts useful information from the pilot signal, which includes the longitude and latitude coordinates of the satellite sub-satellite point ( ⁇ s , ⁇ s ), the satellite height h.
  • the latitude and longitude coordinates ( ⁇ u , ⁇ u ) of the user terminal can be obtained through the user's GPS. Based on this information, the functional relationship between the satellite elevation angle and time can be calculated,
  • w E is the angular velocity of the earth's rotation
  • ⁇ E ⁇ 7.292115 ⁇ 10 -5 (rad/s)
  • w S is the angular velocity of the satellite in the geocentric inertial coordinate system
  • a is the satellite orbit inclination
  • Re is the earth’s radius
  • R is the satellite’s orbit radius
  • R R e +h
  • h is the orbit height
  • ⁇ c is The minimum elevation angle of the satellite.
  • the user's elevation angle ⁇ i at the time of handover can be calculated. Then bring it into the functional relationship between the satellite elevation angle and time, the running time t of the satellite i can be calculated, and then the remaining coverage time T i of the satellite can be obtained.
  • the user calculates their own income according to the defined income function
  • this method uses the value observed at the initial moment of switching for calculation. Since the satellite is in constant motion, in order to improve the accuracy of the result, this method considers the utility function to add a compensation value to reduce the adjustment strategy selection. Error in the process; this compensation value can take the utility value of other satellites in the same orbital plane as a reference, calculate the standard deviation ⁇ of the utility value of other satellites in the same orbital plane with satellite i at time t, and determine if it is greater than the average The number of utility values, if the number of satellites greater than the average utility value exceeds the number of satellites less than the average utility value, the as the compensation value, otherwise the As a compensation value, K here is a control factor, which controls the precision of the compensation value.
  • the present application may also adopt similarity matching operation for all satellites in orbit, and the factors considered here may include satellite telemetry parameters, etc., including thermodynamic parameters, power system parameters, kinetic parameters and The working status and working pressure of the satellite load, etc.; after the signal and information processing of these telemetry parameters, the corresponding features of each satellite are extracted according to the method of machine learning, and the satellites with the closest similarity distance are selected according to the feature classification. , the utility value of several satellites with the closest similarity distance is weighted and averaged and fed back to the original satellite, and used as the compensation value of the original satellite.
  • satellite telemetry parameters etc.
  • the factors considered here may include satellite telemetry parameters, etc., including thermodynamic parameters, power system parameters, kinetic parameters and The working status and working pressure of the satellite load, etc.; after the signal and information processing of these telemetry parameters, the corresponding features of each satellite are extracted according to the method of machine learning, and the satellites with the closest similarity distance are selected according to the feature classification. , the utility
  • the present application defines a cost function for a user to select a satellite as a function of the number of users who select the satellite. When the number of users who choose this satellite increases, the cost of each user will also increase, and the user's income will decrease, so the user will consider choosing other satellites to improve their own income.
  • the cost of user selection of satellite i is defined as:
  • the scheduling delay of that is, the time it takes to calculate the average revenue and formulate switching strategies, is much smaller than t i,trans .
  • the average revenue for all users in the region is:
  • each user will know the income ⁇ i (t) and the current average income of the entire group. That is, the average income of all users in the area; when the user's income is less than the average income, the user will learn from the high-yield users, adjust his strategy selection at time t+1 to obtain higher income, and the user keeps repeating this dynamic The strategy adjustment process until the equilibrium state is reached.
  • the process of policy dynamic adjustment is modeled with replicator dynamics, which determines the rate at which the policy changes.
  • the replicator dynamics can be represented as follows:
  • Replicator Dynamic Satisfaction constraints Represents the replica status of the proportion of users who choose satellite i at time t; ⁇ is the replica dynamic formula parameter, which is used to control the rate of strategy adjustment of the player. Replicator Dynamic Satisfaction constraints.
  • the controller calculates the average income of users in the area, and broadcasts it to the users;
  • the controller calculates the benefits of each user according to the utility function and the cost function, and averages these benefits, finally determines the average revenue of users in the area controlled by the controller, and returns it to the user by broadcasting.
  • step S4 determine whether the income of all users is higher than the average income, if not, then go to step S5, otherwise, end the multi-user switching;
  • step S5 the user selects other switching satellites, and makes the profit after the selection at this moment higher than the previous profit; return to step S2.
  • FIG. 2 is a multi-user handover scenario under the coverage of Samsung in the software-defined satellite network of the present application.
  • N switching users UE j ,j ⁇ 1,2,...,N ⁇ competing for satellite resources form a population
  • each user is a game player, and at the same time there are S satellites can provide services to users.
  • the strategy is each player's choice of switching satellites, denoted as LEO i ,i ⁇ 1,2 ⁇ .
  • Use p ij to represent the probability of UE j selecting LEO i , then p ij ⁇ 0,1 ⁇ , and satisfy This means that each player can only choose one satellite to switch.
  • the group proportion represents the proportion of the number of players who choose a certain strategy to the total number of groups, n i is the number of players who choose strategy i,
  • the data plane consists of low-orbit satellites that only perform the forwarding function
  • the control plane consists of a control server and a location server located at the ground station
  • the handover is completed by the controller shown in Figure 3.
  • the key idea of software-defined satellite networks is to let the control plane generate and send flow tables to low-orbit satellites through the Satellite Network Interface (SNOF) channel, simplifying the satellite's data plane.
  • the controller is logically connected with the location server, and the location server stores the international satellite identities of all mobile terminals and the joint addresses assigned by the gateway low-orbit satellites. The controller sends instructions to the user via the high-orbit satellite.
  • SNOF Satellite Network Interface
  • all visible satellites send pilot signals to the terminal.
  • the user terminal receives the pilot signal of the low-orbit satellite, it requests a logical address from the low-orbit satellite to keep the link in an active state. If the terminal is covered by multiple satellites, it selects the data link with the best communication quality as the primary data link, and other data links as secondary data links (downlink data packets can be sent through the primary data link and the secondary data link, Uplink packets can only be sent over the primary data link).
  • PST 2 when PST 2 wants to communicate with PST 1 , PST 2 first sends a location query request to the location server, and the location server sends the address of PST 1 to the terminal PST 2 : LEO 2 _PST 1 , and then PST 2 sends its The gateway satellite LEO 1 sends data, which is forwarded by LEO 1 to LEO 2 . So far, the call procedures of PST 1 and PST 2 are established. At this time, PST 2 regards the data link connected with LEO 2 as the primary data link, and the links between other satellites that can cover the user are regarded as secondary data links.
  • LEO 2 Due to the moving characteristics of low-orbit satellites, LEO 2 gradually moves away from PST 2 , and users will switch to ensure that the communication process is not interrupted.
  • the user determines LEO 3 as the handover satellite through the proposed satellite handover selection algorithm. The specific process is shown in Figure 4:
  • 3PST 1 sends data and confirmation identification through LEO 3 ;
  • 4LEO 3 receives the data packet from PST 1 and detects that the terminal has changed its main data link, and LEO 3 sends a path change report to the location server;
  • the location server notifies the controller of the route change report
  • the controller updates the information related to PST 1 and sends the information to each satellite;
  • the present application selects switching satellites based on evolutionary game, which effectively balances the satellite network load, reduces the failure rate of user switching, and at the same time ensures the communication quality of users, so that the service quality of all users can be optimal.
  • FIG. 6 is a graph of changes in the number of users. Describe the evolution of the number of access users for two satellites. At the beginning, the number of users who choose satellite 1 is large, and the income of users is reduced. Therefore, some users will change their strategy choices. After many strategy adjustments, they will eventually reach an evolutionary equilibrium state. In addition, it can be observed from the figure that the strategy with a high initial proportion does not necessarily have a high proportion when the equilibrium is reached, which reflects the fairness of the proposed method.
  • Figure 7 is a graph of changes in satellite utility. As shown in Figure 7, the income of the satellite is related to the number of users accessing the satellite. After many games, the income of the two satellites converges, that is, no user changes his strategy choice in order to obtain higher income, all users' The benefits are the same, and the system reaches an evolutionary stable state.
  • FIG. 8 is a graph showing changes in the number of users accessing the satellite 1 . As shown in Figure 8, the number of users who select satellite 1 in the initial stage are all different, but after the evolutionary game process, the number of users who finally select satellite 1 is approximately the same, which shows the stability of the algorithm.
  • FIG. 9 is a schematic diagram comparing the number of users who select LEO1 for switching under different weights. As shown in Figure 9, the selection algorithm based only on single attribute converges faster, and the convergence speed of multi-attribute decision-making is relatively slow, and the convergence points are different under different weights.
  • the terms “installation”, “arrangement”, “connection”, “fixation”, “rotation” and other terms should be understood in a broad sense, for example, it may be a fixed connection or a It can be a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between the two elements, Unless otherwise clearly defined, those of ordinary skill in the art can understand the specific meanings of the above terms in this application according to specific situations.

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Abstract

本申请涉及移动通信技术领域，具体涉及一种软件定义卫星网络系统中基于演化博弈的多用户切换方法。在该多用户切换方法中，根据获知的信息分别计算出卫星的仰角和剩余覆盖时间，并根据卫星的容量、仰角和剩余覆盖时间三个基本因素计算出收益。控制器计算出该区域内用户的平均收益，并广播给用户；并在判断出所有用户的收益均高于平均收益时结束多用户切换，否则用户选择其他收益更高的切换卫星。本申请将多用户切换问题建模成演化博弈问题，将演化均衡作为博弈的均衡解，不断重复这个动态的策略调整过程直到达到均衡状态，求出最优的策略比例，保证所有用户切换后均能得到最好的服务，从而保证了用户的公平性。

Description

软件定义卫星网络系统中基于演化博弈的多用户切换方法
本申请要求于2020年11月17日提交中国国家知识产权局、申请号为202011286960.0、发明名称为“软件定义卫星网络系统中基于演化博弈的多用户切换方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及移动通信技术领域，具体涉及软件定义卫星网络系统中基于演化博弈的多用户切换方法。
背景技术
由于覆盖受限，地面蜂窝网络不能提供偏远山区、海洋、高原等地区的移动业务。卫星通信通过多颗卫星进行联合组网形成卫星星座，具有覆盖范围广、不受地域和空域限制的特点引起人们的关注。卫星通信网络与地面移动通信网络的融合将成为移动通信的重要发展方向。其中，低轨卫星系统因具有低时延、路径损耗小以及覆盖面积广等特点而受到人们的广泛关注。然而，由于卫星相对地面的高速移动以及用户终端的移动性，单颗低轨卫星对地面终端的覆盖时间有限，用户在进行通信时需要进行频繁地切换。常见的卫星切换包括星间切换和波束切换。
大多数对卫星切换的研究均基于传统的卫星网络架构。随着软件定义网络在地面通信网络的广泛应用，将软件定义网络技术应用于低轨卫星互联网可实现数据层与控制层解耦，使得网络架构更加灵活方便。
然而，传统的软件定义网络中的卫星切换策略一般是基于候选卫星的仰角、剩余覆盖时间和空闲信道数三个准则。卫星仰角与信号损耗之间存在非线性关系，卫星仰角越小，信道状况越差，通信质量越差。卫星的剩余覆盖时间与用户的切换次数和通话掉话率有关，卫星的剩余覆盖时间越长，表示卫星可提供给用户的服务时间越长，可降低用户的切换次数和通话掉话率。卫星的空闲信道数来表示卫星的负载情况，卫星空闲信道数越多，表明卫星的负载较轻。但利用卫星的空闲信道数来反映卫星的负载状态存在弊端：在实际的通信过程中，同一时刻卫星覆盖区域内的所有用户都会接收到卫星的空闲信道数消息，该区域内所有用户均会出于自身利益最大的原则选择负载最轻的卫星进行接入，可能导致卫星负载过重问题。因此，卫星空闲信道数不能很好地反映卫星的负载状况，需要通过其他指 标来判断卫星的负载情况。此外，传统的卫星切换策略基于信号强度进行选择，因此用户会出现切换失败的问题；并且传统的卫星切换策略也并未将用户之间动态的资源竞争过程刻画出来。
发明内容
本申请实施例提供一种软件定义卫星网络系统中基于演化博弈的多用户切换方法。在该方法中，首先考虑多星覆盖下的多用户切换场景，由于低轨卫星的运动特性，当处于某颗卫星覆盖区域下的用户的接收信号强度小于设定的阈值时，用户会发出切换申请，选择切换到其他可视卫星中去保证通信过程不断中断。然后，在选择切换卫星时综合考虑候选卫星集中卫星可分配给用户的带宽、卫星的剩余覆盖时间和卫星仰角三个因素，采用演化博弈方法进行切换卫星的选择，该方法通过不断的调整策略达到均衡化状态，既有效地保证了卫星负载均衡，又保证了用户的公平性，使得所有用户的服务质量均能最佳，有效地降低了用户的切换失败率。
本申请实施例第一方面提供一种软件定义卫星网络系统中基于演化博弈的多用户切换方法。该多用户切换方法可以包括以下步骤：
S1、所有用户根据获知的信息分别计算出卫星的仰角信息和剩余覆盖时间；
S2、所有用户中的每一个用户根据卫星容量、仰角和剩余覆盖时间和定义的收益函数计算出各自的收益；
S3、控制器计算出该区域内用户的平均收益，并广播给所有用户；
S4、判断所有用户的收益是否均高于平均收益，若不高于，则转至步骤S5，否则结束多用户切换；
S5、用户选择其他切换卫星，并使得切换后的收益高于切换前的收益；返回步骤S2。
在一些可能的实施方式中，用户所定义的收益函数为用户补偿后的效用函数与用户的开销函数之差。
在另一些可能的实施方式中，所述用户补偿后的效用函数表示为：
Figure PCTCN2021128921-appb-000001
其中，U i(t)表示在t时刻选择卫星i的效用；α表示候选卫星负载因素的权重，β表示候选卫星的仰角因素的权重，γ表示候选卫星的剩余覆盖时间的权重，满足约束条件 α+β+γ＝1；V i是卫星i的容量；n i(t)是t时刻选择卫星i进行切换的用户数；θ i min是卫星i可视的最小仰角；θ i是申请切换时用户观测到的卫星i的仰角值；T i max是卫星i的最长覆盖时间；T i是申请切换时用户计算的卫星i的剩余服务时间；Δu表示补偿函数。
在另一些可能的实施方式中，所述用户的开销函数表示为：
C i(t)＝α′×n i(t)×t i,trans+β′×t pro
其中，C i(t)表示用户在t时刻选择卫星i的开销；α′表示所述候选卫星传输时延的权重，β′表示所述控制器的调度时延的权重，满足约束条件α′+β′＝1；t i,trans是t时刻所述卫星i的传播时延，即切换信令从所述卫星i传输到用户所经历的时间，
Figure PCTCN2021128921-appb-000002
d ij是所述卫星i到用户j的距离，c是电磁波的传播速度；n i(t)是t时刻选择所述卫星i进行切换的用户数；t pro表示所述控制器的调度时延，所述控制器的调度时延包括计算平均收益和制定切换策略过程所花费的时间。
在另一些可能的实施方式中，区域内的所有用户的平均收益表示为：
Figure PCTCN2021128921-appb-000003
其中，
Figure PCTCN2021128921-appb-000004
表示t时刻所述区域内所有用户的平均收益，π i(t)表示t时刻连接到卫星i的用户的收益；x i(t)表示t时刻选择所述卫星i的用户比例。
在另一些可能的实施方式中，在步骤S5中，用户通过动态调整策略选择过程来获得更高的收益，采用复制者动态对策略动态调整的过程进行建模，所述复制者动态表示如下：
Figure PCTCN2021128921-appb-000005
其中，
Figure PCTCN2021128921-appb-000006
表示t时刻选择卫星i的用户比例的复制者状态；
Figure PCTCN2021128921-appb-000007
表示t时刻区域内所有用户的平均收益，π i(t)表示t时刻连接到卫星i的用户的收益；σ是复制动态公式参数，用来控制博弈方策略调整的速率；复制者动态满足
Figure PCTCN2021128921-appb-000008
的约束。
本申请是一种软件定义卫星网络系统中基于演化博弈的多用户切换方法。本发明不仅极大地均衡了卫星的网络负载，降低了用户的切换失败率，而且保证了用户的公平性。本方法的主要创新在于，提出基于软件定义卫星网络架构的切换流程，将多用户切换问题建模成演化博弈问题，用一组微分方程来描述用户切换策略的选择调整过程，将演化均衡作为博弈的均衡解，求出最优的策略比例，保证所有用户切换后均能得到最好的服务，从而 保证了用户的公平性；另外，由于卫星处于不断的运动中，为了提高结果的精确性，本发明对效用函数进行改进并增加了一个补偿值，用来降低用户收益的误差；同时将用户的开销设计为传输时延与调度时延的函数，这与实际的卫星通信情况更符合。
附图说明
图1是本申请的软件定义卫星网络系统中基于演化博弈的多用户切换方法流程图；
图2是本申请的软件定义卫星网络中三星覆盖下的多用户切换场景；
图3是本申请所采用的软件定义卫星网络中的切换示意图；
图4是本申请所采用的软件定义卫星网络中的切换流程图；
图5是本申请所使用的复制动态相位图；
图6是本申请一个实施例中的用户数量变化图；
图7是本申请一个实施例中的卫星效用图；
图8是本申请一个实施例中在不同初始条件下的用户数量变化图；
图9是本申请一个实施例中的在不同权重下的演化均衡图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请提供了一种软件定义卫星网络系统中基于演化博弈的多用户切换方法的流程示意图，如图1所示，该多用户切换方法包括：
S1、所有用户根据获知的信息分别计算出卫星的仰角信息和剩余覆盖时间；
在一个实施例中，考虑到卫星仰角与信号损耗之间存在非线性关系，卫星仰角越小，信道状况越差，通信质量越差；卫星的剩余覆盖时间与用户的切换次数和通话掉话率有关，卫星的剩余覆盖时间越长，表示卫星可提供给用户的服务时间越长，可降低用户的切换次数和通话掉话率。
切换时刻所有可视卫星给用户终端发送导频信号，地面上的终端接收导频信息后，从导频信号中提取有用信息，该信息中包括卫星星下点的经纬度坐标(λ s,η s)，卫星高度h。 用户终端的经纬度坐标(λ u,η u)可通过用户的GPS获得。根据这些信息可计算出卫星仰角与时间之间的函数关系，
Figure PCTCN2021128921-appb-000009
其中：
Figure PCTCN2021128921-appb-000010
同时可以求出卫星的最长服务时间
Figure PCTCN2021128921-appb-000011
其中，
Figure PCTCN2021128921-appb-000012
w E是地球自转的角速度，ω E≈7.292115×10 -5(rad/s)，w S是卫星在地心惯性坐标系中的角速度
Figure PCTCN2021128921-appb-000013
μ是开普勒常量μ＝398600(km 3/s 2)，a是卫星轨道倾角，R e是地球半径，R是卫星轨道半径，R＝R e+h，h是轨道高度，θ c是卫星的最小仰角。
同时根据用户终端坐标和卫星星下点的经纬度坐标可计算出切换时刻观测到的卫星i和用户终端间的地心角κ i＝arcos[cosλ u cos(η u-η s)]，然后根据用户的地心角与仰角之间的关系
Figure PCTCN2021128921-appb-000014
可计算出切换时刻用户的仰角θ i。再将其带入卫星仰角与时间的函数关系中，可计算出卫星i的运行时间t进而求出卫星的剩余覆盖时间T i。
S2、用户按照卫星的容量、仰角信息以及剩余覆盖时间三个基本因素，根据定义的收益函数计算出各自的收益；
在本实施例中，申请人发现在实际的通信过程中，同一时刻卫星覆盖区域内的所有用户都会接收到卫星的空闲信道数消息，该区域内所用用户均会出于自身利益最大的原则选择负载最轻的卫星进行接入，可能导致卫星负载过重问题；所以传统技术中利用卫星的空闲信道数来反映卫星的负载状态存在弊端：所以本申请考虑在卫星容量一定的情况下，卫 星接入用户数越多，每个用户分到的带宽越小。因此，通过计算不同时刻每个用户分到的带宽可以实时有效地反映卫星的负载情况；而由于卫星仰角和剩余服务时间在很短的时间内变化微小，因此本申请忽略了这两者随时间的变化，均采用初始时刻观测到的值。因此定义用户选择卫星的效用函数为：
Figure PCTCN2021128921-appb-000015
其中，U i(t)表示在t时刻选择卫星i的效用；α表示候选卫星负载因素的权重，β表示候选卫星的仰角因素的权重，γ表示候选卫星的剩余覆盖时间的权重，满足约束条件α+β+γ＝1；V i是卫星i的容量；n i(t)是t时刻选择卫星i进行切换的用户数；θ i min是卫星i可视的最小仰角；θ i是申请切换时用户观测到的卫星i的仰角值；T i max是卫星i的最长覆盖时间；T i是申请切换时用户计算的卫星i的剩余服务时间；Δu表示补偿函数。为了简化运算过程，本方法均采用切换初始时刻观测到的值进行计算，由于卫星处于不断的运动中，为了提高结果的精确性，本方法考虑效用函数增加一个补偿值，用于降低调整策略选择过程中的误差；这个补偿值可以以同一轨道平面的其他卫星的效用值作为参考，计算出与卫星i处于同一轨道平面的其他卫星在t时刻的效用值的标准差σ，并确定出大于平均效用值的个数，若大于平均效用值的卫星个数超过小于平均效用值的卫星个数，则直接将
Figure PCTCN2021128921-appb-000016
作为补偿值，否则将
Figure PCTCN2021128921-appb-000017
作为补偿值，这里的K是控制因子，控制补偿值的精度。
在一些可能实现的方式中，本申请还可以采用对在轨的所有卫星采用相似度匹配运算，这里所考虑的因素可以包括卫星的遥测参数等，包括热力学参数、电源系统参数、动力学参数以及卫星载荷的工作状态和工作压低等等；将这些遥测参数进行信号与信息处理后，按照机器学习的方式提取出每个卫星所对应的特征，按照特征分类选择出相似度距离最接近的若干卫星，将相似度距离最接近的若干卫星的效用值加权平均后反馈给原卫星，并作为原卫星的补偿值。
在用户选择切换卫星的过程中，不同用户间存在竞争现象，每个用户均会出于自身利益最大化选择切换卫星，因此会出现多个用户同时选择同一颗卫星，容易造成该卫星负载过重的现象，进而导致每个用户实际的通信质量下降。为了平衡卫星的负载，本申请将用户选择卫星的代价函数定义为选择该卫星的用户数的函数。当选择该卫星的用户数增加时， 每个用户的开销也会增加，用户收益会降低，因此用户会考虑选择其他卫星来提高自己的收益。
根据以上分析，定义用户选择卫星i的开销为：
C i(t)＝α′×n i(t)×t i,trans+β′×t pro
其中，C i(t)表示用户在t时刻的开销；α′表示候选卫星传输时延的权重，β′表示控制器的调度时延的权重，满足约束条件α′+β′＝1；t i,trans是t时刻卫星i的传播时延，即切换信令从卫星i传输到用户所经历的时间，
Figure PCTCN2021128921-appb-000018
Figure PCTCN2021128921-appb-000019
c＝2.99792×10 8m/s，d ij是卫星i到用户j d的距离，c是电磁波的传播速度；n i(t)是t时刻选择卫星i进行切换的用户数；t pro表示控制器的调度时延，即计算平均收益、制定切换策略等花费的时间，比t i,trans小很多。
该区域内所有用户的平均收益为：
Figure PCTCN2021128921-appb-000020
每个用户在t时刻会获知自己此次选择卫星获得的收益π i(t)和当前整个群体的平均收益
Figure PCTCN2021128921-appb-000021
即区域内所有用户的平均收益；当用户的收益小于平均收益时，用户会在向高收益用户进行学习，在时刻t+1调整自己的策略选择来获得更高的收益，用户不断重复这个动态的策略调整过程直到达到均衡状态。用复制者动态对策略动态调整的过程进行建模，它决定策略改变的速率。复制者动态可表示如下：
Figure PCTCN2021128921-appb-000022
其中，
Figure PCTCN2021128921-appb-000023
表示t时刻选择卫星i的用户比例的复制者状态；σ是复制动态公式参数，用来控制博弈方策略调整的速率。复制者动态满足
Figure PCTCN2021128921-appb-000024
的约束。
S3、控制器计算出该区域内用户的平均收益，并广播给用户；
控制器根据效用函数和代价函数计算出每个用户的收益，并将这些收益求平均，最终确定出该控制器所控制的区域内用户的平均收益，并按照广播的方式返回给用户。
S4、判断所有用户的收益是否均高于平均收益，若不高于，则转至步骤S5，否则结束多用户切换；
S5、用户选择其他切换卫星，并使得该时刻选择后的收益高于之前的收益；返回步骤S2。
本实施例将按照在时刻t+1调整自己的策略选择来获得更高的收益，用户不断重复这个动态的策略调整过程直到达到均衡状态。
图2本申请的软件定义卫星网络中三星覆盖下的多用户切换场景。如图2所示，在该场景中，竞争卫星资源的N个切换用户UE j,j∈{1,2,...,N}构成一个种群，每个用户均为博弈方，同一时刻有S颗卫星可为用户提供服务。策略是每个博弈方可选择的切换卫星，表示为LEO i,i∈{1,2}。用p ij表示UE j选择LEO i的概率，则p ij∈{0,1}，且满足
Figure PCTCN2021128921-appb-000025
这意味着每个博弈方只能选择一个卫星进行切换。群体比例表示选择某个策略的博弈方数量占整个群体数量的比例，
Figure PCTCN2021128921-appb-000026
n i是选择策略i的博弈方数量，
Figure PCTCN2021128921-appb-000027
一个群体中所有策略的群体比例构成了种群状态，X＝{x 1,x 2}。每个用户作为博弈方会根据自己选择某个策略带来的收益来调整其策略选择。
在软件定义卫星网络架构中，数据平面由仅执行转发功能的低轨卫星组成，控制平面由位于地面站的控制服务器和位置服务器组成，切换由图3所示的控制器完成。软件定义卫星网络的关键思想是让控制平面通过卫星网络接口(SNOF)信道产生和发送流表给低轨卫星，简化卫星的数据平面。控制器在逻辑上与位置服务器相连，位置服务器存储所有移动终端的国际卫星标识和网关低轨卫星分配的联合地址。控制器经过高轨卫星给用户发送指令。
切换时刻所有可视卫星给终端发送导频信号，一旦用户终端接收到低轨卫星的导频信号，它向低轨卫星请求一个逻辑地址，保持链路处于活跃状态。如果终端被多个卫星覆盖，它选择通信质量最好的数据链路作为主数据链路，其他数据链路作为次数据链路(下行数据包可通过主数据链路和次数据链路发送，上行数据包只能通过主数据链路发送)。如图3所示，当PST 2想和PST 1进行通信，PST 2首先向位置服务器发送位置查询请求，位置服务器向终端PST 2发送PST 1的地址：LEO 2_PST 1，然后PST 2向它的网关卫星LEO 1发送数据，由LEO 1转发给LEO 2。至此，PST 1和PST 2的呼叫过程被建立。此时，PST 2将与LEO 2连接的数据链路视为主数据链路，其他可覆盖该用户的卫星之间的链路视为次数据链路。
由于低轨卫星的移动特性，LEO 2逐渐远离PST 2，用户为了保证通信过程不被中断，会进行切换。用户通过提出的卫星切换选择算法确定将LEO 3作为切换卫星。具体流程如图4所示：
①当终端决定切换到LEO 3时，即将LEO 3设置为主数据链路；
②因为位置服务器还未被告知终端的切换，由PST 2发送给PST 1的数据包仍通过LEO 1_LEO 2_PST 1进行传输；
③PST 1通过LEO 3发送数据和确认标识；
④LEO 3收到来自PST 1的数据包，检测到终端改变了它的主数据链路，LEO 3将路径改变报告发给位置服务器；
⑤位置服务器将路径改变报告通知控制器，
⑥控制器更新与PST 1相关的信息，并将该信息发送给每个卫星；
⑦所有信息更新完成后切换完成。所有的下行数据通过LEO 3发送给PST 1。
本申请基于演化博弈进行切换卫星的选择，有效地均衡了卫星网络负载，降低了用户切换失败率，同时保证了用户的通信质量，使得所有用户的服务质量均能最佳。
在图3示出的切换场景下，假设有两颗卫星符合切换条件，该区域内有1000个用户同时申请切换，为了简化运算，采用该区域内中心用户的观测值进行计算，根据以上分析可计算出两颗卫星的基本信息。加权指数α,β,γ分别设置为0.5，0.2，0.3；α′、β′分别设置为0.7,0.3；复制动态公式参数σ为1，调度时延与传播时延相差很大，将其取为固定值。其他仿真参数由表1给出：
表1仿真参数
Figure PCTCN2021128921-appb-000028
图5是策略1(选择LEO1)的复制动态相位图。从图5可以看出，描述出了博弈方动态的策略调整过程，
Figure PCTCN2021128921-appb-000029
表示选择策略1的博弈方的数量增加；
Figure PCTCN2021128921-appb-000030
表示选择策略1的博弈方的数量减少。考虑到0<x 1<1，则演化均衡点为x 1＝0.524。即有52.4％的切换用户选择LEO1，47.6％的切换用户选择LEO2，所有用户效用均能达到最优。
图6是用户数量变化图。描述两个卫星的接入用户数量的演变过程。初始时选择卫星1的用户数量较多，用户获得收益降低，因此部分用户会改变自己的策略选择，经过多次的策略调整，最终达到演化均衡状态。此外，从图中可观测到，初始占比较高的策略，在达到均衡时所占比例不一定很高，反映了所提方法的公平性。
图7为卫星效用的变化图。如图7所示，卫星的收益与接入卫星的用户数有关，经过 多次博弈后，两颗卫星的收益均收敛，即没有用户为了获得更高的收益改变自己的策略选择，所有用户的收益均相同，系统达到了演化稳定状态。
图8为接入卫星1的用户数量的变化图。如图8所示，初始阶段选择卫星1的用户数量均不相同，但经过演化博弈过程，最终选择卫星1的用户数都近似相同，说明了算法的稳定性。
图9为比较了不同权重下选择LEO1进行切换的用户数的示意图。如图9所示，仅基于单属性的选择算法收敛更快，多属性决策的收敛速度相对慢一些，且在不同权重下的收敛点不同。
在本申请的描述中，需要理解的是，术语“同轴”、“底部”、“一端”、“顶部”、“中部”、“另一端”、“上”、“一侧”、“顶部”、“内”、“外”、“前部”、“中央”、“两端”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本申请和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本申请的限制。
在本申请中，除非另有明确的规定和限定，术语“安装”、“设置”、“连接”、“固定”、“旋转”等术语应做广义理解，例如，可以是固定连接，也可以是可拆卸连接，或成一体；可以是机械连接，也可以是电连接；可以是直接相连，也可以通过中间媒介间接相连，可以是两个元件内部的连通或两个元件的相互作用关系，除非另有明确的限定，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语在本申请中的具体含义。
尽管已经示出和描述了本申请的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本申请的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本申请的范围由所附权利要求及其等同物限定。

Claims (6)

  1. 一种软件定义卫星网络系统中基于演化博弈的多用户切换方法，其特征在于，所述多用户切换方法包括：
    S1、所有用户根据获知的信息分别计算出卫星的仰角和剩余覆盖时间；
    S2、所述所有用户中的每个用户按照所述卫星的容量、所述仰角以及所述剩余覆盖时间和定义的收益函数计算出各自的收益；
    S3、控制器计算出区域内用户的平均收益，并广播给所述所有用户；
    S4、判断所述所有用户的收益是否均高于平均收益，若不高于，则转至步骤S5，否则结束多用户切换；
    S5、用户选择其他切换卫星，并使得切换后的收益高于切换前的收益；返回步骤S2。
  2. 根据权利要求1所述的多用户切换方法，其特征在于，所述定义的收益函数为用户补偿后的效用函数与用户的开销函数之差。
  3. 根据权利要求2所述的多用户切换方法，其特征在于，所述用户补偿后的效用函数表示为：
    Figure PCTCN2021128921-appb-100001
    其中，U i(t)表示在t时刻选择卫星i的效用；α表示候选卫星负载因素的权重，β表示所述候选卫星的仰角因素的权重，γ表示所述候选卫星的剩余覆盖时间的权重，满足约束条件α+β+γ＝1；V i是卫星i的容量；n i(t)是t时刻选择所述卫星i进行切换的用户数；
    Figure PCTCN2021128921-appb-100002
    是所述卫星i可视的最小仰角；θ i是申请切换时用户观测到的所述卫星i的仰角值；T i max是所述卫星i的最长覆盖时间；T i是申请切换时用户计算的所述卫星i的剩余服务时间；Δu表示补偿函数。
  4. 根据权利要求2所述的多用户切换方法，其特征在于，所述用户的开销函数表示为：
    C i(t)＝α′×n i(t)×t i,trans+β′×t pro
    其中，C i(t)表示用户在t时刻选择卫星i的开销；α′表示所述候选卫星传输时延的权重，β′表示所述控制器的调度时延的权重，满足约束条件α′+β′＝1；t i,trans是t时刻所述卫星i的传播时延，即切换信令从所述卫星i传输到用户所经历的时间，
    Figure PCTCN2021128921-appb-100003
    d ij是 所述卫星i到用户j的距离，c是电磁波的传播速度；n i(t)是t时刻选择所述卫星i进行切换的用户数；t pro表示所述控制器的调度时延，所述控制器的调度时延包括计算平均收益和制定切换策略过程所花费的时间。
  5. 根据权利要求1所述的多用户切换方法，其特征在于，所述区域内的所有用户的平均收益表示为：
    Figure PCTCN2021128921-appb-100004
    其中，
    Figure PCTCN2021128921-appb-100005
    表示t时刻所述区域内所有用户的平均收益，π i(t)表示t时刻连接到卫星i的用户的收益；x i(t)表示t时刻选择所述卫星i的用户比例。
  6. 根据权利要求1所述的多用户切换方法，其特征在于，在步骤S5中，用户通过动态调整策略选择过程来获得更高的收益，采用复制者动态对策略动态调整的过程进行建模，所述复制者动态表示如下：
    Figure PCTCN2021128921-appb-100006
    其中，
    Figure PCTCN2021128921-appb-100007
    表示t时刻选择卫星i的用户比例的复制者状态；
    Figure PCTCN2021128921-appb-100008
    表示t时刻区域内所有用户的平均收益，π i(t)表示t时刻连接到卫星i的用户的收益；σ是复制动态公式参数，用来控制博弈方策略调整的速率；复制者动态满足
    Figure PCTCN2021128921-appb-100009
    的约束。
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