CN114845310A - Artificial bee colony algorithm-based LEO satellite channel allocation method - Google Patents

Artificial bee colony algorithm-based LEO satellite channel allocation method Download PDF

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CN114845310A
CN114845310A CN202210398216.2A CN202210398216A CN114845310A CN 114845310 A CN114845310 A CN 114845310A CN 202210398216 A CN202210398216 A CN 202210398216A CN 114845310 A CN114845310 A CN 114845310A
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leo satellite
bee colony
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周陬
宁耀龙
周晓燕
袁仲慧
郑飞
邱飞鹏
王朝
黎文明
黄鹏钦
高海金
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Abstract

The invention relates to the technical field of low-orbit satellite wireless resource management, in particular to an LEO satellite channel allocation method based on an artificial bee colony algorithm, which initializes the parameters of an LEO satellite system, generates a channel allocation matrix according to the combination of an available channel matrix in a model and a feasible solution in the artificial bee colony algorithm, and establishes a two-step allocation scheme combining fixed channel pre-allocation and dynamic channel scheduling aiming at the distribution difference of communication traffic among beams on the premise of considering co-channel interference; further, the artificial bee colony algorithm is applied to LEO satellite channel allocation, each honey source position vector is represented as a possible channel allocation strategy, complexity of an allocation scheme is reduced, finally, the LEO satellite communication system can adapt to difference of service quantity among beams, and channel utilization rate and system throughput are effectively improved.

Description

Artificial bee colony algorithm-based LEO satellite channel allocation method
Technical Field
The invention relates to the technical field of low-earth-orbit satellite wireless resource management, in particular to an LEO satellite channel allocation method based on an artificial bee colony algorithm.
Background
A Low Earth Orbit (LEO) satellite has the advantages of wide coverage, short transmission delay, small path loss and the like, can meet the requirements of seamless coverage and global service, and is an important component of a global integrated communication network. With the increasing demand of communication services, the requirements of users on the system capacity and the resource utilization efficiency of satellite communication are continuously increased. Compared with the traditional single-beam satellite, the LEO satellite system covers a target area by using a plurality of spot beams with high gain, and improves the system capacity and the resource utilization rate of the satellite system by combining a frequency reuse technology. However, satellite channel resources are limited, and with the increasing number of communication users and communication service types, the demand of users for channel resources is increasing, and the traditional satellite communication systems in the C frequency band and the Ku frequency band are limited by bandwidth resources and are difficult to meet the transmission of high-capacity data transmission and multimedia services, so that on-satellite resources need to be reasonably and efficiently allocated.
Meanwhile, due to the non-uniform distribution of the user terminals on the geographic space, the difference of the traffic distribution among the beams is large. The beam with large traffic volume is lack of resources such as channel power, so that the system performance is reduced, and the beam with small traffic volume causes waste of resources. LEO satellites use full-band multiplexing to improve channel utilization, but this technique can cause severe inter-beam interference. Meanwhile, the inter-beam interference becomes more serious due to the requirement of multiple coverage of the satellite. Meanwhile, the LEO satellite moves continuously relative to the ground, the coverage area, users and services of the LEO satellite also change continuously, fixed channel allocation is difficult to adapt to the dynamic property of communication service requirements in an actual scene, and waste of channel resources is easily caused.
Disclosure of Invention
The invention aims to provide an LEO satellite channel allocation method based on an artificial bee colony algorithm, which combines the distribution difference characteristic of communication traffic among beams on the premise of considering the interference among the beams, improves the utilization rate of a satellite channel and the throughput of a system and completes channel resource allocation at the same time.
In order to achieve the aim, the invention provides an LEO satellite channel distribution method based on an artificial bee colony algorithm, which comprises the following steps:
initializing LEO satellite system parameters, and pre-allocating fixed channel resources for each beam cell;
dividing two scenes that the difference of the service volume between beams is large and the service volume between beams is similar, and establishing an LEO satellite channel allocation optimization model;
and (4) realizing LEO satellite channel allocation by utilizing an artificial bee colony algorithm to obtain an optimal channel allocation matrix.
In the process of initializing LEO satellite system parameters and pre-allocating fixed channel resources for each beam cell, the system pre-allocates the fixed channel resources for each beam cell before a user requests access, and dynamically schedules the channel resources when the user requests access, such as the pre-allocated channel resources cannot meet the current traffic.
In the process of dividing two scenes of large service quantity difference between beams and similar service quantity between beams and establishing an LEO satellite channel allocation optimization model, in the scene of large service quantity difference between beams, partial channels are pre-allocated to meet the requirements of users, and in the scene of similar service quantity between beams, the channel with the largest beam interval is preferentially scheduled.
In the process of realizing LEO satellite channel allocation by using the artificial bee colony algorithm and obtaining the optimal channel allocation matrix, firstly, initializing parameters of the artificial bee colony algorithm, calculating an adaptive value of a target function, and finally obtaining the optimal channel allocation matrix.
The artificial bee colony algorithm comprises the following steps:
step 1: randomly generating a honey source group and marking;
step 2: employing bees to find the positions of honey sources and calculating the adaptive values of the honey sources;
and step 3: all the hiring bees fly back to the information exchange area to share the honey source information, and the selection probability of each honey source is calculated by the following bees;
and 4, step 4: determining a local optimal honey source according to the selection probability of the honey source, and converting a corresponding hiring bee into a reconnaissance bee random search;
and 5: whether the termination condition is met or not is judged,
if yes, all the optimal honey sources found by the current bee colony are found, and adaptive values of the optimal honey sources are calculated;
otherwise, returning to the step 2.
The invention provides an LEO satellite channel allocation method based on an artificial bee colony algorithm, which is characterized in that parameters of an LEO satellite system are initialized, a channel allocation matrix is generated according to the combination of an available channel matrix in a model and a feasible solution in the artificial bee colony algorithm, and a two-step allocation scheme combining fixed channel pre-allocation and dynamic channel scheduling is formulated by the system aiming at the distribution difference of communication traffic among beams on the premise of considering co-channel interference; in a scene with large traffic difference between beams, the system pre-allocates partial channels to meet the user requirements. In a scene that the service volume between wave beams is similar, the system preferentially schedules a channel with the largest wave beam interval so as to reduce the co-channel interference; further, the artificial bee colony algorithm is applied to LEO satellite channel allocation, each honey source position vector is represented as a possible channel allocation strategy, complexity of an allocation scheme is reduced, finally, an LEO satellite communication system can adapt to difference of traffic among beams, and channel utilization rate and system throughput are effectively improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an LEO satellite channel allocation method based on an artificial bee colony algorithm according to the present invention.
Fig. 2 is a schematic illustration of LEO satellite downlink inter-beam interference in accordance with an embodiment of the present invention.
Fig. 3 is a schematic flow chart of an artificial bee colony algorithm in an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Referring to fig. 1, the present invention provides a LEO satellite channel allocation method based on artificial bee colony algorithm, which includes the following steps:
s1: initializing LEO satellite system parameters, and pre-allocating fixed channel resources for each beam cell;
s2: dividing two scenes that the difference of the service volume between beams is large and the service volume between beams is similar, and establishing an LEO satellite channel allocation optimization model;
s3: and (4) realizing LEO satellite channel allocation by utilizing an artificial bee colony algorithm to obtain an optimal channel allocation matrix.
In the process of initializing LEO satellite system parameters and pre-allocating fixed channel resources for each beam cell, the system pre-allocates the fixed channel resources for each beam cell before a user requests access, and dynamically schedules the channel resources when the user requests access, such as the pre-allocated channel resources cannot meet the current traffic.
The following is further explained from the execution steps:
in S1, initializing relevant parameters of the LEO satellite system, specifically:
set of beams S ═ S for one LEO satellite i 1, 2., N }, and the available channel set B ═ B in each beam m 1, 2.,. M }. User is in beam s i May be expressed as U ═ U i,k |i＝1,2,...,N,k＝1,2,...,K}。
The system allocates channels by a co-channel multiplexing technique. The channel allocation matrix may be defined as:
Figure BDA0003598380950000041
in matrix V V i,m E {0,1} represents the beam s i Intermediate channel b m V. distribution status of i,m 1 denotes at beam s i Channel b of m Used by the user, otherwise unused.
In a LEO satellite communication system, users between different beams may communicate using the same channel resources, but this may create co-channel interference. If the co-channel interference is too large, the normal communication of the user is seriously affected, and the system performance is reduced. The inter-beam interference for the downlink of a LEO satellite communication system is illustrated in fig. 2.
The magnitude of the inter-beam interference is often related to the multi-beam antenna gain and antenna radiation pattern, transmission loss, etc., and a channel gain matrix G may be defined between each beam antenna to the user,
Figure BDA0003598380950000042
wherein g is i,m Denoted as beam s i For user u i,k The link gain of (1).
Let user u i,k The obtained transmission power and bandwidth are respectively p i,m And W i,k Then user u i,k The signal-to-interference ratio of (c) can be expressed as:
Figure BDA0003598380950000043
thus, user u i,k In channel b m The throughput above can be expressed as:
Figure BDA0003598380950000044
according to the above equation, the throughput of all users can be expressed as:
Figure BDA0003598380950000051
in S2, different optimization objectives are established according to different scenarios, and the specific method is as follows:
in a scene with large traffic difference between beams, the optimization aim is to improve the channel utilization rate. The user blocking function may be defined herein as follows:
Figure BDA0003598380950000052
wherein r is th Representing the lowest signal-to-interference ratio for each user.
Figure BDA0003598380950000053
Representing user u i,k Can be normalAnd accessing the channel, otherwise, indicating user blocking. The number of user blocks in the system is:
Figure BDA0003598380950000054
thus, the optimization objective in this scenario can be expressed as:
Figure BDA0003598380950000055
in the scenario of close inter-beam traffic, the optimization goal is to maximize system throughput. As can be seen from the inter-beam interference analysis, the shorter the distance between two beams, the greater the co-channel interference. Therefore, co-channel interference can be reduced by controlling the distance between co-channel beams, thereby improving system throughput.
Thus, the optimization objective in this scenario can be expressed as:
Figure BDA0003598380950000056
the constraints for both scenarios are the same, and can be expressed as:
Figure BDA0003598380950000057
the first constraint in the above equation is expressed as the signal-to-interference ratio of each user should be greater than r th . The second constraint and the third constraint indicate that a channel can only be allocated to one user in each beam.
In S3, the LEO satellite channel allocation is implemented by using artificial bee colony algorithm, which includes the following steps:
1) the algorithm randomly generates an initialization group, and the position of each honey source is defined by a D-dimensional vector F x ＝[f x1 ,f x2 ,…f xD ]Represents, where x ∈ {1,2, … NP }, D ∈ {1,2, … D }, f ∈ xd ∈(f x min ,f x max ). NP denotes the number of honey sources, f x max And f x min Representing the upper and lower limits of the search space, respectively.
The artificial bee colony algorithm is suitable for solving an optimization problem of a continuous space, and the channel allocation model is binary, so that the positions of honey sources need to be discretized, and any real number can be mapped between (0,1) through a sigmoid function, as shown in the following formula.
Figure BDA0003598380950000061
The honey source is converted into 0 or 1 with a certain probability according to the following formula.
Figure BDA0003598380950000062
In the formula, e xd Representing the position of the discretized honey source, rand being [0,1 ]]A random number in between.
2) And in the bee hiring stage, the current optimal individuals are introduced to perform neighborhood search so as to accelerate the convergence speed of the algorithm. Employing bees to find a new honey source location H according to the following formula x ＝[h x1 ,h x2 ,…,h xD ]And calculates an adaptation value thereof.
h xd ＝f best,d +φ xd (f best,d -f yd )
In the formula, x, y belongs to {1,2, … NP }, y ≠ x, and D belongs to {1,2, … D }. f. of best,d Representing the best individual of the current population, f y Represents a randomly selected neighborhood honey source, phi xd Is [ -1,1 [ ]]The random numbers uniformly distributed among them determine the jitter amplitude.
3) All hired bees fly back to the information exchange area to share the honey source information. Calculating honey source F by following bees x The selection probability of (2) is:
Figure BDA0003598380950000063
in the formula, fit x Is a honey source F x The adaptive value of (a). The method of roulette is adopted by the following bees to select the employed bees and update the honey source information, and meanwhile, the optimal honey source is reserved.
According to the immunological principle, the collection of solutions is called antibodies. In the course of evolution, low concentrations of antibodies were promoted, while high concentrations of antibodies were suppressed, thereby ensuring antibody diversity. In this section, an antibody concentration regulation mechanism is introduced into the algorithm to overcome the disadvantage that the population diversity of the artificial bee colony algorithm is rapidly reduced along with the evolution.
Antibody concentration can be determined by affinity between antibodies, antibody (honey source) F x And F y The affinity of (a) is defined as:
Figure BDA0003598380950000064
wherein ED is antibody F x And F y The Euclid distance between.
Any one of the antibodies F x The concentration of (A) is as follows:
Figure BDA0003598380950000065
Figure BDA0003598380950000071
wherein, TH is a preset threshold value.
In order to achieve a low probability of antibody selection at a high concentration and a high probability of antibody selection at a low concentration, the selection probability formula determined by the antibody concentration may be defined as:
Figure BDA0003598380950000072
wherein, λ is constant and defined as adjusting factor, λ is more than 0 and less than or equal to 1.
In following bee selectionIn the process of selecting the honey source, an antibody concentration regulation mechanism is introduced to improve the global search capability of the algorithm. Selecting honey source F by following bees x The probability of (d) can be expressed as:
P x ＝βPt x +(1-β)Pb x
wherein beta is a constant, and beta is more than 0 and less than or equal to 1.
4) In search processes, e.g. honey source F x The adaptive value of (1) is not improved after L times of searching, which indicates that the honey source is trapped in local optimum and needs to discard the honey source F x . The hiring bee corresponding to the honey source will become a scout bee and a new honey source is randomly generated according to the following formula.
f xd ＝f x min +rand(0,1)(f x max -f x min )
Judging whether the termination condition is met, if not, returning to the step 2); if so, recording all the optimal honey sources found by the current bee colony, and calculating the adaptive values of the optimal honey sources.
Specifically, the flow steps of the artificial bee colony algorithm of the invention are shown in fig. 3.
Further, an LEO channel allocation flow based on the artificial bee colony algorithm is shown in the following table:
Figure BDA0003598380950000073
while the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. An LEO satellite channel distribution method based on artificial bee colony algorithm is characterized by comprising the following steps:
initializing LEO satellite system parameters, and pre-allocating fixed channel resources for each beam cell;
dividing two scenes that the difference of the service volume between beams is large and the service volume between beams is similar, and establishing an LEO satellite channel allocation optimization model;
and (4) realizing LEO satellite channel allocation by utilizing an artificial bee colony algorithm to obtain an optimal channel allocation matrix.
2. The artificial bee colony algorithm based LEO satellite channel allocation method of claim 1, wherein,
in the process of initializing LEO satellite system parameters and pre-allocating fixed channel resources for each beam cell, the system pre-allocates the fixed channel resources for each beam cell before a user requests access, and dynamically schedules the channel resources when the user requests access, such as the pre-allocated channel resources cannot meet the current traffic.
3. The artificial bee colony algorithm based LEO satellite channel allocation method of claim 1, wherein,
in the process of dividing two scenes of large service difference between beams and similar service difference between beams and establishing an LEO satellite channel allocation optimization model, in the scene of large service difference between beams, partial channels are pre-allocated to meet the requirements of users, and in the scene of similar service difference between beams, the channel with the largest beam interval is preferentially scheduled.
4. The artificial bee colony algorithm based LEO satellite channel allocation method of claim 1, wherein,
in the process of realizing LEO satellite channel allocation by using the artificial bee colony algorithm and obtaining the optimal channel allocation matrix, firstly, initializing parameters of the artificial bee colony algorithm, calculating an adaptive value of a target function, and finally obtaining the optimal channel allocation matrix.
5. The artificial bee colony algorithm based LEO satellite channel allocation method of claim 4, wherein,
the artificial bee colony algorithm comprises the following steps:
step 1: randomly generating a honey source group and marking;
step 2: employing bees to find the positions of honey sources and calculating the adaptive values of the honey sources;
and step 3: all the hiring bees fly back to the information exchange area to share the honey source information, and the selection probability of each honey source is calculated by the following bees;
and 4, step 4: determining a local optimal honey source according to the selection probability of the honey source, and converting a corresponding hiring bee into a reconnaissance bee random search;
and 5: whether the termination condition is met or not is judged,
if yes, all the optimal honey sources found by the current bee colony are found, and adaptive values of the optimal honey sources are calculated;
otherwise, returning to the step 2.
CN202210398216.2A 2022-04-15 2022-04-15 Artificial bee colony algorithm-based LEO satellite channel allocation method Pending CN114845310A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115459916A (en) * 2022-11-09 2022-12-09 江苏翔晟信息技术股份有限公司 Electronic signature management system based on quantum encryption technology

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115459916A (en) * 2022-11-09 2022-12-09 江苏翔晟信息技术股份有限公司 Electronic signature management system based on quantum encryption technology

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