CN112996019A - Terahertz frequency band distributed constellation access control method based on multi-objective optimization - Google Patents

Terahertz frequency band distributed constellation access control method based on multi-objective optimization Download PDF

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CN112996019A
CN112996019A CN202110227684.9A CN202110227684A CN112996019A CN 112996019 A CN112996019 A CN 112996019A CN 202110227684 A CN202110227684 A CN 202110227684A CN 112996019 A CN112996019 A CN 112996019A
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path
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user
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CN112996019B (en
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何元智
刘宏宇
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Institute of Network Engineering Institute of Systems Engineering Academy of Military Sciences
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • 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
    • 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

Abstract

The invention discloses a terahertz frequency band distributed constellation access control method based on multi-objective optimization, which comprises the following steps: constructing a terahertz frequency band distributed constellation network model, and establishing an access control multi-objective optimization model; acquiring new access task request information sent by a user satellite, and acquiring the resource state of each node of the distributed constellation; determining the priority of all new access task requests and sequencing; reading task request information with the highest priority; solving an optimal transmission path by utilizing a multi-objective ant colony optimization algorithm; judging whether an optimal transmission path exists or not; allocating the on-satellite resources; judging whether the set L is an empty set; and completing the access control requested by the user satellite. The distributed constellation access control method provided by the invention comprehensively considers the transmission path of the user access request in the internal network of the constellation, can more reasonably and effectively realize the access control of the user, and improves the resource utilization rate.

Description

Terahertz frequency band distributed constellation access control method based on multi-objective optimization
Technical Field
The invention relates to the technical field of satellite communication networks, in particular to a terahertz frequency band distributed constellation access control method based on multi-objective optimization.
Background
In recent years, with the continuous development and progress of information technology, the information transmission demand of the space-based backbone network is increasing, and higher requirements are made on the transmission processing capacity of the space-based backbone nodes, so that the concept of distributed star groups is proposed. The distributed constellation is a network structure which integrates and equates a plurality of satellites into one large satellite on a synchronous orbit by utilizing common rail control and networking cooperative technology to realize service capability enhancement, and is one of important ways for constructing a space-based backbone network. In the distributed constellation, high-speed interconnection transmission of mass data is realized among all satellite nodes through an inter-satellite terahertz link, so that the processing of user service data is closely related to the state of the distributed constellation network. The traditional user access control method is mainly oriented to a single-node large satellite platform, the resource states of all nodes and links of an internal network of a constellation do not need to be considered, if the traditional access control method is directly applied to a space distributed constellation, part of inter-satellite links are congested due to over concentrated services, and the other part of inter-satellite links are in an idle state due to less user services. The star node resources cannot be reasonably and effectively distributed, and timeliness of spatial information transmission is severely restricted, so that a user access control method suitable for a distributed star is designed based on the characteristics of a spatial distributed star network, and the reasonability of node resource distribution and the resource utilization rate are improved, which is very necessary.
In the study of capacity analysis and access control strategy of non-geostationary orbit satellite communication system (university of electronic technology, university of Master academic thesis, 4 months 2018), the capacity of a satellite communication system and the access control strategy under a large-scale satellite constellation communication system are intensively analyzed; chinese patent CN106454872A discloses a method for controlling channel access in a satellite formation network based on directional antennas, which solves the problem of deafness and hidden terminal caused by introducing directional antennas, improves the success rate of establishing data link by nodes in a wireless ad hoc network, effectively improves the throughput of data and reduces the data access delay, and can be better applied to various wireless ad hoc networks, but the above-mentioned techniques do not consider user access control under distributed constellation.
Disclosure of Invention
Aiming at the problem of user access control under a distributed constellation, the invention discloses a terahertz frequency band distributed constellation access control method based on multi-objective optimization, which comprises the following steps:
s1, constructing a terahertz frequency band distributed constellation network model, and establishing an access control multi-objective optimization model;
s2, acquiring new access task request information sent by a user satellite, and acquiring resource states of nodes of the distributed constellation;
s3, determining the priority of all new access task requests, sequencing all new access task requests according to the sequence of the priorities from top to bottom, and storing all new access task request information into a set L;
s4, reading task request information with the highest priority in the set L;
s5, inputting the task request information read in the step S4 into a multi-objective ant colony optimization algorithm, and solving an optimal transmission path of the task in the distributed star network by using the multi-objective ant colony optimization algorithm;
s6, judging whether an optimal transmission path exists according to the solving result of the step S5, if the optimal transmission path does not exist, removing the current task request information from the set L, and executing S7; if the optimal transmission path exists, performing S7;
s7, according to the optimal transmission path obtained in the step S5, the on-satellite resources are distributed, and the current task request information is removed from the set L;
s8, judging whether the set L is an empty set, if so, executing S9; otherwise, jumping to S4;
and S9, completing the access control requested by the user satellite.
The invention discloses a terahertz frequency band distributed constellation access control method for a multi-objective optimization algorithm, which comprises the following steps of:
s1, constructing a terahertz frequency band distributed constellation network model, and establishing an access control multi-objective optimization model; terahertz frequency band distributed typeThe constellation network adopts a multi-beam technology and a frequency division multiple access system and is provided with a global beam and K spot beams, wherein the global beam is a control channel, and the spot beams are data transmission channels; the terahertz frequency band distributed constellation network model is characterized in that the distributed constellation network is composed of mu distributed satellite nodes, and the set of the satellite nodes is represented as V0＝{v1,v2,...,vk,...,vμIn which v iskK is 1,2, …, μ, and represents the group V0The k-th satellite node in (c),
Figure BDA0002957148670000021
to be at satellite node vkAnd satellite node vlA terahertz communication link therebetween, wherein vk∈V0,vl∈V0,k＝1,2,…,μ,l＝1,2,…,μ；Θ＝{Θ1,Θ2,...,Θk,...,ΘχIs the set of user satellites, ΘkAnd showing the kth user satellite, wherein x is the total number of the user satellites, and the user satellites send user access requests to the terahertz frequency band distributed constellation network. The total number of user access requests sent from the user satellite to the terahertz frequency band distributed constellation network is M,
Figure BDA0002957148670000031
to describe the satellite node v associated with the access request of the r-th userkAnd satellite node vlThe binary variable factor of the terahertz communication link between, r is 1,2, …, M, k is 1,2, …, μ, l is 1,2, …, μ, if the r-th user access request passes through the terahertz link
Figure BDA0002957148670000032
Then
Figure BDA0002957148670000033
Is 1, if the access request of the r-th user does not pass through the terahertz link
Figure BDA0002957148670000034
Then
Figure BDA0002957148670000035
The value of (d) is 0.
Establishing an access control multi-objective optimization model, wherein three objectives to be optimized in the model are respectively represented as O1，O2And O3，O1The total time delay of all access requests in the terahertz frequency band distributed constellation network is minimum, and the expression is,
Figure BDA0002957148670000036
O2the method refers to that the throughput of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure BDA0002957148670000037
O3the method refers to that the network benefit of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure BDA0002957148670000038
the multi-objective optimization function is represented as:
Figure BDA0002957148670000039
wherein, from the satellite node vkTo satellite node vlIs expressed as
Figure BDA0002957148670000041
Figure BDA0002957148670000042
To slave satellite node vkTo satellite node vlThe bandwidth of the terahertz communication link of (a),
Figure BDA0002957148670000043
to slave satellite node vkTo satellite node vlTerahertz communication link power of, N0Is the white gaussian noise of the terahertz communication link,
Figure BDA0002957148670000044
the benefit weighting factor of the terahertz link bandwidth of the access request for the r-th user,
Figure BDA0002957148670000045
and the constraint conditions of the multi-objective optimization function are as follows:
Figure BDA0002957148670000046
wherein the content of the first and second substances,
Figure BDA0002957148670000047
the node resource requirement of the access request of the r-th user, p represents the state of the access request, p-1 represents the existing access request, p-2 represents the new access request, ClAnd CkAre respectively satellite nodes vlAnd vkThe node resources of (a) are set,
Figure BDA0002957148670000048
for the bandwidth requirement of the access request of the r-th user,
Figure BDA0002957148670000049
power requirement for access request of the r-th user, C1、C2、C3、C4Constraint conditions of node resources, bandwidth resources, power resources and link resources, w1、w2、w3Respectively 3 weight values of the objective function, satisfying wi≥0,i＝1,2,3，w1+w2+w31 is ═ 1; the resulting integrated objective function is:
G＝w1O1+w2O2+w3O3，
the access control method comprises three strategies, specifically: a minimum delay call access control strategy, a maximum throughput call access control strategy and a maximum network benefit call access control strategy;
when the minimum delay call access control strategy is used for access control, an alternative path set is firstly calculated for the access request of each user; then, according to the hop count of each path, sorting the paths in the selectable path set in a descending order of the hop count; finally, selecting the path with the least hop number to realize the call access control of the user;
when the maximum throughput call access control strategy is used for access control, firstly, a part of paths are selected as a standby path set of a user access request; then, calculating the path transmission rate of each user access request in the standby path set; finally, the paths in the standby path set are arranged in a descending order according to the path transmission rate, and then the path with the maximum path transmission rate is selected to realize the call access control of the user;
when the call access control strategy with the maximum network benefit is used for access control, available paths are divided according to the characteristics of the available paths and are used as a standby path set of an access request; then, respectively calculating the total network benefits of the bandwidth and the power of each path in the standby path set; and finally, according to the descending order of the total network benefits of the bandwidth and the power of the paths, sequencing the paths in the standby path set, and selecting the path with the maximum total network benefit to realize the call access control of the user.
S2, acquiring new access task request information sent by a user satellite, and acquiring resource states of nodes of the distributed constellation;
s3, determining the priority of all new access task requests, sequencing all new access task requests according to the sequence of the priorities from top to bottom, and storing all new access task request information into a set L;
s4, reading task request information with the highest priority in the set L;
s5, inputting the task request information read in the step S4 into a multi-objective ant colony optimization algorithm, and solving an optimal transmission path of the task in the distributed star network by using the multi-objective ant colony optimization algorithm; which specifically comprises the steps of preparing a mixture of a plurality of organic compounds,
s51, improving the initial search efficiency of the ant colony algorithm; leading in a guide factor, the more iteration times, the faster the guide factor can guide ants to find the correct path node in the initial routing, and the guide factor lambda of the path node iiComprises the following steps:
Figure BDA0002957148670000051
wherein m is the total number of ants, uiNumber of ants on node i, o destination node, dioDistance of path node i to destination node o, NmaxThe total number of iterations is N is the number of iterations performed; after introducing the guiding factor, the transfer probability P of the ant k from the node i to the node j in the t-th cycleij k(t) is a group of,
Figure BDA0002957148670000052
wherein λi(t) is the bootstrap function of node i for t cycles, λj(t) is the steering function of node j over t cycles, τij(t) pheromone concentration on the path between node i and node j at t cycles, τis(t) pheromone concentration on the path between node i and node s at t cycles, allowedkRepresents the set of next available nodes of ant k, j, s belongs to allowedkRespectively representing node j and node s.
S52, improving the pheromone concentration updating rule; introducing an elite strategy, after each iteration, improving the pheromone concentration of all found optimal paths so as to enable the found optimal paths to obtain more resources in the next cycle, and after one iteration is completed, updating the pheromone on a path e (i, j) between a node i and a node j:
τij(t+1)＝ρ×τij(t)+Δτij+Δτij *，
τij(t+1)、τij(t) indicates the pheromone concentration on the path e (i, j) at cycles t +1 and t, respectively, where ρ is the pheromone volatility factor, let L*For the length of the optimum path, Δ τij *Denotes the pheromone concentration, χ, of the Elite ants remaining on the path e (i, j) in the current cycle1For the number of elite ants, Δ τ is given if path e (i, j) is part of the optimal pathij *＝χ1C/L*Otherwise, is Δ τij *Initial pheromone concentration with constant C e (i, j) of 0 for Δ τijThe specific steps of the calculation include: after all ants complete one-time traversal, sorting the ants in the order from short to long according to the path length, selecting the first m/3 ants in the order as elite ants, adjusting and weighting the ranking of each ant according to the concentration value of the pheromone left on the path e (i, j) by each ant, wherein the weighting coefficient is LΔ-Lh/LΔ-L*，LΔFor this iteration average path length, LhThe length of the path traveled by the elite ants with the sequence number h is delta tauijThe calculation formula of (2) is as follows:
Figure BDA0002957148670000061
where Δ τ isij hIndicates the pheromone concentration of the elite ant with the serial number h left on e (i, j) in the current cycle, if the elite ant with the serial number h passes through e (i, j), delta tauij hComprises the following steps:
Figure BDA0002957148670000062
if the elite ant with the serial number h does not pass through e (i, j), then delta tauij hIs 0.
S53, improving pheromone volatilization factors; the value of the pheromone volatilization factor rho obeys normal distribution, and the calculation formula of the probability density function rho (g) of the pheromone volatilization factor is as follows:
Figure BDA0002957148670000063
Figure BDA0002957148670000064
Figure BDA0002957148670000065
g is the serial number of the current optimal ant, mu is the search expectation value of the excellent solution, sigma is the variance value of the concentration difference between the optimal ant g and the poor ant pheromone after one iteration, and N ismaxFor the total number of iterations, τgThe pheromone concentration of the current optimal ant.
S6, judging whether an optimal transmission path exists according to the solving result of the step S5, if the optimal transmission path does not exist, removing the current task request information from the set L, and executing S7; if the optimal transmission path exists, performing S7;
s7, according to the optimal transmission path obtained in the step S5, the on-satellite resources are distributed, and the current task request information is removed from the set L;
s8, judging whether the set L is an empty set, if so, executing S9; otherwise, jumping to S4;
and S9, completing the access control requested by the user satellite.
The invention has the following advantages:
1. compared with the prior art, the distributed constellation access control method provided by the invention comprehensively considers the transmission path of the user access request in the internal network of the constellation, can more reasonably and effectively realize the access control of the user, and improves the resource utilization rate.
2. According to the method, the guidance factors are introduced in the initial path searching process of the multi-target ant colony optimization algorithm, so that the pheromone concentration difference of each path is improved, and the initial searching efficiency of the algorithm is improved; by improving the pheromone concentration updating rule and introducing the elite strategy, the pheromone concentration of the found optimal path is increased after each iteration, and therefore the algorithm searching efficiency is further improved.
Drawings
FIG. 1 is a flow chart of a terahertz frequency band distributed constellation access control method based on multi-objective optimization;
fig. 2 is a schematic diagram of a distributed constellation network based on a terahertz frequency band.
Detailed Description
To better describe the present disclosure, an embodiment is presented herein.
The embodiment discloses a terahertz frequency band distributed constellation access control method based on a multi-objective optimization algorithm. FIG. 1 is a flow chart of the method, comprising the steps of:
s1, constructing a terahertz frequency band distributed constellation network model, and establishing an access control multi-objective optimization model; fig. 2 is a schematic diagram of a distributed constellation network based on a terahertz frequency band, which adopts a multi-beam technology and a frequency division multiple access system and has a global beam and K spot beams, wherein the global beam is a control channel and the spot beams are data transmission channels; the terahertz frequency band distributed constellation network model is characterized in that the distributed constellation network is composed of mu distributed satellite nodes, and the set of the satellite nodes is represented as V0＝{v1,v2,...,vk,...,vμIn which v iskK is 1,2, …, μ, and represents the group V0The k-th satellite node in (c),
Figure BDA0002957148670000081
to be at satellite node vkAnd satellite node vlA terahertz communication link therebetween, wherein vk∈V0,vl∈V0,k＝1,2,…,μ,l＝1,2,…,μ；Θ＝{Θ1,Θ2,...,Θk,...,ΘχIs the set of user satellites, ΘkAnd showing the kth user satellite, wherein x is the total number of the user satellites, and the user satellites send user access requests to the terahertz frequency band distributed constellation network. The total number of user access requests sent from the user satellite to the terahertz frequency band distributed constellation network is M,
Figure BDA0002957148670000082
to describe the satellite node v associated with the access request of the r-th userkAnd satellite node vlThe binary variable factor of the terahertz communication link between, r is 1,2, …, M, k is 1,2, …, μ, l is 1,2, …, μ, if the r-th user access request passes through the terahertz link
Figure BDA0002957148670000083
Then
Figure BDA0002957148670000084
Is 1, if the access request of the r-th user does not pass through the terahertz link
Figure BDA0002957148670000085
Then
Figure BDA0002957148670000086
The value of (d) is 0.
In order to fully utilize limited network resources in a terahertz frequency band distributed constellation network, an access control multi-objective optimization model is established, and three objectives to be optimized in the model are respectively represented as O1，O2And O3，O1The total time delay of all access requests in the terahertz frequency band distributed constellation network is minimum, and the expression is,
Figure BDA0002957148670000087
O2the method refers to that the throughput of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure BDA0002957148670000088
O3the method refers to that the network benefit of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure BDA0002957148670000089
the multi-objective optimization function is represented as:
Figure BDA0002957148670000091
wherein, from the satellite node vkTo satellite node vlIs expressed as
Figure BDA0002957148670000092
Figure BDA0002957148670000093
To slave satellite node vkTo satellite node vlThe bandwidth of the terahertz communication link of (a),
Figure BDA0002957148670000094
to slave satellite node vkTo satellite node vlTerahertz communication link power of, N0Is the white gaussian noise of the terahertz communication link,
Figure BDA0002957148670000095
the benefit weighting factor of the terahertz link bandwidth of the access request for the r-th user,
Figure BDA0002957148670000096
considering the conditions of resource allocation and the like in the terahertz frequency band distributed constellation network, for the benefit weight factor of the terahertz link power of the r-th user access request, the constraint conditions of the multi-objective optimization function are as follows:
Figure BDA0002957148670000097
wherein the content of the first and second substances,
Figure BDA0002957148670000098
the node resource requirement of the access request of the r-th user, p represents the state of the access request, p-1 represents the existing access request, p-2 represents the new access request, ClAnd CkAre respectively satellite nodes vlAnd vkThe node resources of (a) are set,
Figure BDA0002957148670000099
for the bandwidth requirement of the access request of the r-th user,
Figure BDA00029571486700000910
power requirement for access request of the r-th user, C1、C2、C3、C4Constraint conditions of node resources, bandwidth resources, power resources and link resources, w1、w2、w3Respectively 3 weight values of the objective function, satisfying wi≥0,i＝1,2,3，w1+w2+w31 is ═ 1; the resulting integrated objective function is:
G＝w1O1+w2O2+w3O3，
the access control method comprises three strategies, specifically: a minimum delay call access control strategy, a maximum throughput call access control strategy and a maximum network benefit call access control strategy;
when the minimum delay call access control strategy is used for access control, an alternative path set is firstly calculated for the access request of each user; then, according to the hop count of each path, sorting the paths in the selectable path set in a descending order of the hop count; finally, selecting the path with the least hop number to realize the call access control of the user;
when the maximum throughput call access control strategy is used for access control, firstly, a part of paths are selected as a standby path set of a user access request; then, calculating the path transmission rate of each user access request in the standby path set; finally, the paths in the standby path set are arranged in a descending order according to the path transmission rate, and then the path with the maximum path transmission rate is selected to realize the call access control of the user;
when the call access control strategy with the maximum network benefit is used for access control, available paths are divided according to the characteristics of the available paths and are used as a standby path set of an access request; then, respectively calculating the total network benefits of the bandwidth and the power of each path in the standby path set; and finally, according to the descending order of the total network benefits of the bandwidth and the power of the paths, sequencing the paths in the standby path set, and selecting the path with the maximum total network benefit to realize the call access control of the user.
S2, acquiring new access task request information sent by a user satellite, and acquiring resource states of nodes of the distributed constellation;
s3, determining the priority of all new access task requests, sequencing all new access task requests according to the sequence of the priorities from top to bottom, and storing all new access task request information into a set L;
s4, reading task request information with the highest priority in the set L;
s5, inputting the task request information read in the step S4 into a multi-objective ant colony optimization algorithm, and solving an optimal transmission path of the task in the distributed star network by using the multi-objective ant colony optimization algorithm; which specifically comprises the steps of preparing a mixture of a plurality of organic compounds,
s51, improving the initial search efficiency of the ant colony algorithm; in the ant colony algorithm, when the path is initially searched, the probability of searching the next path node is influenced by the pheromone concentration and the heuristic information, however, in the initial stage of path searching, the difference between the pheromone concentration and the heuristic information on each path is not large, so that the initial stage of path searching is low in efficiency, and the next path node can be correctly found after a long time; thus introducing a leading factor, an overlapThe more times, the faster the guide factor can guide ants to find the correct path node in the initial routing, and the guide factor lambda of the path node iiComprises the following steps:
Figure BDA0002957148670000101
wherein m is the total number of ants, uiNumber of ants on node i, o destination node, dioDistance of path node i to destination node o, NmaxThe total number of iterations is N is the number of iterations performed; after introducing the guiding factor, the transfer probability P of the ant k from the node i to the node j in the t-th cycleij k(t) is a group of,
Figure BDA0002957148670000111
wherein λi(t) is the bootstrap function of node i for t cycles, λj(t) is the steering function of node j over t cycles, τij(t) pheromone concentration on the path between node i and node j at t cycles, τis(t) pheromone concentration on the path between node i and node s at t cycles, allowedkRepresents the set of next available nodes of ant k, j, s belongs to allowedkRespectively representing node j and node s.
S52, improving the pheromone concentration updating rule; after each iteration of the ant colony algorithm, all ants update the pheromone concentration indiscriminately in the routing process, the pheromone concentration on some paths is far lower than that on other paths, and no optimal path is expected, and the indiscriminate global pheromone concentration updating rule can reduce the searching efficiency of the algorithm, so that the pheromone concentration updating rule is improved, an elite strategy is introduced, after each iteration, the pheromone concentration of all found optimal paths is improved, so that the found optimal paths obtain more resources in the next cycle, the searching efficiency is effectively improved, and after one iteration is completed, the pheromone on a path e (i, j) between a node i and a node j is updated:
τij(t+1)＝ρ×τij(t)+Δτij+Δτij *，
τij(t+1)、τij(t) indicates the pheromone concentration on the path e (i, j) at cycles t +1 and t, respectively, where ρ is the pheromone volatility factor, let L*For the length of the optimum path, Δ τij *Indicates the concentration of pheromone left on the path e (i, j) by Elite ants in the current cycle, X1For the number of elite ants, Δ τ is given if path e (i, j) is part of the optimal pathij *＝X1C/L*Otherwise, is Δ τij *Initial pheromone concentration with constant C e (i, j) of 0 for Δ τijThe specific steps of the calculation include: after all ants complete one-time traversal, sorting the ants in the order from short to long according to the path length, selecting the first m/3 ants in the order as elite ants, adjusting and weighting the ranking of each ant according to the concentration value of the pheromone left on the path e (i, j) by each ant, wherein the weighting coefficient is LΔ-Lh/LΔ-L*，LΔFor this iteration average path length, LhThe length of the path traveled by the elite ants with the sequence number h is delta tauijThe calculation formula of (2) is as follows:
Figure BDA0002957148670000112
where Δ τ isij hIndicates the pheromone concentration of the elite ant with the serial number h left on e (i, j) in the current cycle, if the elite ant with the serial number h passes through e (i, j), delta tauij hComprises the following steps:
Figure BDA0002957148670000121
if the elite ant with the serial number h does not pass through e (i, j), then delta tauij hIs 0.
S53, improving pheromone volatilization factors; the size of the pheromone volatilization factor rho can influence the convergence capacity of the algorithm and the concentration of pheromones on a path which has been walked by ants, the value of the rho in the ant colony algorithm is very important, if the value of the rho is too large, the pheromones on the path are easy to dissipate, and although the convergence capacity of the algorithm can be improved, the final optimal result can be influenced; if the value of rho is too small, the volatilization speed of the pheromone concentration on the paths is too low, so that the paths cannot be normally distinguished, and the searching efficiency is reduced; the rho value is required to be changed along with the road searching process, and the rho value is required to be smaller at the initial stage of road searching because ants mainly search the road according to the concentration of pheromones in the early stage; in summary, the value of ρ should be gradually increased at the initial stage of routing, so as to improve the search efficiency, and the value of ρ should be gradually decreased at the later stage of routing, because the number of available paths in the later stage is decreased, and the influence of pheromone concentration on the full-weight ghost effect is decreased, so that ants mainly route the routing according to the pheromone guiding capability, the value of the pheromone volatility factor ρ obeys normal distribution, and the calculation formula of the probability density function ρ (g) of the pheromone volatility factor is:
Figure BDA0002957148670000122
Figure BDA0002957148670000123
Figure BDA0002957148670000124
g is the serial number of the current optimal ant, mu is the search expectation value of the excellent solution, sigma is the variance value of the concentration difference between the optimal ant g and the poor ant pheromone after one iteration, and N ismaxFor the total number of iterations, τgThe pheromone concentration of the current optimal ant.
S6, judging whether an optimal transmission path exists according to the solving result of the step S5, if the optimal transmission path does not exist, removing the current task request information from the set L, and executing S7; if the optimal transmission path exists, performing S7;
s7, according to the optimal transmission path obtained in the step S5, the on-satellite resources are distributed, and the current task request information is removed from the set L;
s8, judging whether the set L is an empty set, if so, executing S9; otherwise, jumping to S4;
and S9, completing the access control requested by the user satellite.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (3)

1. A terahertz frequency band distributed constellation access control method based on multi-objective optimization is characterized by comprising the following steps:
s1, constructing a terahertz frequency band distributed constellation network model, and establishing an access control multi-objective optimization model;
s2, acquiring new access task request information sent by a user satellite, and acquiring resource states of nodes of the distributed constellation;
s3, determining the priority of all new access task requests, sequencing all new access task requests according to the sequence of the priorities from top to bottom, and storing all new access task request information into a set L;
s4, reading task request information with the highest priority in the set L;
s5, inputting the task request information read in the step S4 into a multi-objective ant colony optimization algorithm, and solving an optimal transmission path of the task in the distributed star network by using the multi-objective ant colony optimization algorithm;
s6, judging whether an optimal transmission path exists according to the solving result of the step S5, if the optimal transmission path does not exist, removing the current task request information from the set L, and executing S7; if the optimal transmission path exists, performing S7;
s7, according to the optimal transmission path obtained in the step S5, the on-satellite resources are distributed, and the current task request information is removed from the set L;
s8, judging whether the set L is an empty set, if so, executing S9; otherwise, jumping to S4;
and S9, completing the access control requested by the user satellite.
2. The terahertz frequency band distributed constellation access control method based on multi-objective optimization as claimed in claim 1, wherein in step S1, a terahertz frequency band distributed constellation network model is constructed, and an access control multi-objective optimization model is established; the terahertz frequency band distributed constellation network system adopts a multi-beam technology and a frequency division multiple access system and is provided with a global beam and K spot beams, wherein the global beam is a control channel, and the spot beams are data transmission channels; the terahertz frequency band distributed constellation network model is characterized in that the distributed constellation network is composed of mu distributed satellite nodes, and the set of the satellite nodes is represented as
Figure FDA0002957148660000011
Wherein v iskK is 1,2, …, μ, and represents the group V0The k-th satellite node in (c),
Figure FDA0002957148660000012
to be at satellite node vkAnd satellite node vlA terahertz communication link therebetween, wherein vk∈V0,vl∈V0,k＝1,2,…,μ,l＝1,2,…,μ；Θ＝{Θ1,Θ2,...,Θk,...,ΘχIs the set of user satellites, ΘkRepresenting the kth user satellite, wherein x is the total number of the user satellites, and the user satellites send user access requests to the terahertz frequency band distributed constellation network; the total number of user access requests sent from the user satellite to the terahertz frequency band distributed constellation network is M,
Figure FDA0002957148660000021
to describe the satellite node v associated with the access request of the r-th userkAnd satellite node vlThe binary variable factor of the terahertz communication link between, r is 1,2, …, M, k is 1,2, …, μ, l is 1,2, …, μ, if the r-th user access request passes through the terahertz link
Figure FDA0002957148660000022
Then
Figure FDA0002957148660000023
Is 1, if the access request of the r-th user does not pass through the terahertz link
Figure FDA0002957148660000024
Then
Figure FDA0002957148660000025
Is 0;
establishing an access control multi-objective optimization model, wherein three objectives to be optimized in the model are respectively represented as O1，O2And O3，O1The total time delay of all access requests in the terahertz frequency band distributed constellation network is minimum, and the expression is,
Figure FDA0002957148660000026
O2the method refers to that the throughput of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure FDA0002957148660000027
O3the method refers to that the network benefit of all access requests in the terahertz frequency band distributed constellation network is the maximum, and the expression is,
Figure FDA0002957148660000028
the multi-objective optimization function is represented as:
Figure FDA0002957148660000031
wherein, from the satellite node vkTo satellite node vlIs expressed as
Figure FDA0002957148660000032
Figure FDA0002957148660000033
To slave satellite node vkTo satellite node vlThe bandwidth of the terahertz communication link of (a),
Figure FDA0002957148660000034
to slave satellite node vkTo satellite node vlTerahertz communication link power of, N0Is the white gaussian noise of the terahertz communication link,
Figure FDA0002957148660000035
the benefit weighting factor of the terahertz link bandwidth of the access request for the r-th user,
Figure FDA0002957148660000036
and the constraint conditions of the multi-objective optimization function are as follows:
Figure FDA0002957148660000037
wherein the content of the first and second substances,
Figure FDA0002957148660000038
the node resource requirement of the access request of the r-th user, p represents the state of the access request, p-1 represents the existing access request, p-2 represents the new access request, ClAnd CkAre respectively satellite nodes vlAnd vkThe node resources of (a) are set,
Figure FDA0002957148660000039
bandwidth requirement for the access request of the r-th user, Pr pPower requirement for access request of the r-th user, C1、C2、C3、C4Constraint conditions of node resources, bandwidth resources, power resources and link resources, w1、w2、w3Respectively 3 weight values of the objective function, satisfying wi≥0,i＝1,2,3，w1+w2+w31 is ═ 1; the resulting integrated objective function is:
G＝w1O1+w2O2+w3O3，
the access control method comprises three strategies, specifically: a minimum delay call access control strategy, a maximum throughput call access control strategy and a maximum network benefit call access control strategy;
when the minimum delay call access control strategy is used for access control, an alternative path set is firstly calculated for the access request of each user; then, according to the hop count of each path, sorting the paths in the selectable path set in a descending order of the hop count; finally, selecting the path with the least hop number to realize the call access control of the user;
when the maximum throughput call access control strategy is used for access control, firstly, a part of paths are selected as a standby path set of a user access request; then, calculating the path transmission rate of each user access request in the standby path set; finally, the paths in the standby path set are arranged in a descending order according to the path transmission rate, and then the path with the maximum path transmission rate is selected to realize the call access control of the user;
when the call access control strategy with the maximum network benefit is used for access control, available paths are divided according to the characteristics of the available paths and are used as a standby path set of an access request; then, respectively calculating the total network benefits of the bandwidth and the power of each path in the standby path set; and finally, according to the descending order of the total network benefits of the bandwidth and the power of the paths, sequencing the paths in the standby path set, and selecting the path with the maximum total network benefit to realize the call access control of the user.
3. The terahertz frequency band distributed constellation access control method based on multi-objective optimization as claimed in claim 1, wherein the step S5 specifically comprises,
s51, improving the initial search efficiency of the ant colony algorithm; leading in a guide factor, the more iteration times, the faster the guide factor can guide ants to find the correct path node in the initial routing, and the guide factor lambda of the path node iiComprises the following steps:
Figure FDA0002957148660000041
wherein m is the total number of ants, uiNumber of ants on node i, o destination node, dioDistance of path node i to destination node o, NmaxThe total number of iterations is N is the number of iterations performed; after introducing the guiding factor, the transfer probability P of the ant k from the node i to the node j in the t-th cycleij k(t) is a group of,
Figure FDA0002957148660000042
wherein λi(t) is the bootstrap function of node i for t cycles, λj(t) is the steering function of node j over t cycles, τij(t) pheromone concentration on the path between node i and node j at t cycles, τis(t) pheromone concentration on the path between node i and node s at t cycles, allowedkRepresents the set of next available nodes of ant k, j, s belongs to allowedkRespectively represent node j and node s;
s52, improving the pheromone concentration updating rule; introducing an elite strategy, after each iteration, improving the pheromone concentration of all found optimal paths so as to enable the found optimal paths to obtain more resources in the next cycle, and after one iteration is completed, updating the pheromone on a path e (i, j) between a node i and a node j:
τij(t+1)＝ρ×τij(t)+Δτij+Δτij *，
τij(t+1)、τij(t) indicates the pheromone concentration on the path e (i, j) at cycles t +1 and t, respectively, where ρ is the pheromone volatility factor, let L*For the length of the optimum path, Δ τij *Denotes the pheromone concentration, χ, of the Elite ants remaining on the path e (i, j) in the current cycle1For the number of elite ants, Δ τ is given if path e (i, j) is part of the optimal pathij *＝χ1C/L*Otherwise, is Δ τij *Initial pheromone concentration with constant C e (i, j) of 0 for Δ τijThe specific steps of the calculation include: after all ants complete one-time traversal, sorting the ants in the order from short to long according to the path length, selecting the first m/3 ants in the order as elite ants, adjusting and weighting the ranking of each ant according to the concentration value of the pheromone left on the path e (i, j) by each ant, wherein the weighting coefficient is LΔ-Lh/LΔ-L*，LΔFor this iteration average path length, LhThe length of the path traveled by the elite ants with the sequence number h is delta tauijThe calculation formula of (2) is as follows:
Figure FDA0002957148660000051
where Δ τ isij hIndicates the pheromone concentration of the elite ant with the serial number h left on e (i, j) in the current cycle, if the elite ant with the serial number h passes through e (i, j), delta tauij hComprises the following steps:
Figure FDA0002957148660000052
if the elite ant with the serial number h does not pass through e (i, j), then delta tauij hIs 0;
s53, improving pheromone volatilization factors; the value of the pheromone volatilization factor rho obeys normal distribution, and the calculation formula of the probability density function rho (g) of the pheromone volatilization factor is as follows:
Figure FDA0002957148660000053
Figure FDA0002957148660000054
Figure FDA0002957148660000055
g is the serial number of the current optimal ant, mu is the search expectation value of the excellent solution, sigma is the variance value of the concentration difference between the optimal ant g and the poor ant pheromone after one iteration, and N ismaxFor the total number of iterations, τgThe pheromone concentration of the current optimal ant.
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