CN114757084A - Dynamic beam hopping method of broadband satellite communication system - Google Patents

Dynamic beam hopping method of broadband satellite communication system Download PDF

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CN114757084A
CN114757084A CN202210365485.9A CN202210365485A CN114757084A CN 114757084 A CN114757084 A CN 114757084A CN 202210365485 A CN202210365485 A CN 202210365485A CN 114757084 A CN114757084 A CN 114757084A
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姚如贵
李童
韩闯
左晓亚
王立波
樊晔
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Abstract

The invention provides a dynamic beam hopping method of a broadband satellite communication system, which adopts an ANW-PSO algorithm to complete the dynamic beam distribution process in the broadband satellite communication system. A nonlinear inertial weight selection strategy, an adaptive random exploration strategy and a roulette strategy for generating an effective solution are added. The roulette strategy is added in the algorithm, the nonlinear weight is used for adjusting the particle speed, and the particle self-adaptive random learning mechanism is used, so that when the high-dimensional problem of beam hopping pattern design is processed, the local optimal solution can be rapidly skipped, a better solution effect is obtained, and better convergence is kept.

Description

Dynamic beam hopping method of broadband satellite communication system
Technical Field
The invention relates to the technical field of wireless communication, and provides an Adaptive Non-linear Weighted (ANW) -PSO (Adaptive beam hopping) method based on a Particle Swarm Optimization (PSO) algorithm, which is suitable for solving the problem of dynamic beam hopping in a broadband satellite communication system.
Background
With the development of broadband satellite communication systems, it is becoming increasingly important to achieve broader coverage with fewer transmitters due to the high cost of transmitters for multi-beam satellites. The broadband satellite communication system adopting the beam hopping technology can meet the service requirement of a user by utilizing the limited beam to the maximum extent under the condition of limited capacity, provides the service covered as required, improves the frequency spectrum utilization efficiency and reduces the waste of resources.
As the number of users in a terrestrial cell with different priority and traffic demands continues to grow, the resource requirements of the terrestrial cell will eventually exceed the capacity limitations of the communication satellite. By adopting the beam hopping technology, the broadband satellite communication system fully utilizes the flexibility of beam hopping, and measures the result of the beam hopping by adopting the Quality of Service (QoS) of the ground cell, thereby meeting the communication requirement of the ground cell as much as possible.
Document 1 "Hu X, Zhang Y, Liao X, et al, dynamic Beam steering Method Based on Multi-Objective Deep recovery Learning for Next Generation Satellite broadcast Systems [ J ]. IEEE Transactions on Broadcasting,2020, PP (99): 1-17" proposes that when considering diversity services, it is crucial to flexibly adjust Satellite resources to meet different conditions, and how to match system capacity requirements with efficient use of beams by solving the high dimensional problem of Beam Hopping is a completely new challenge.
Document 2, "a Genetic Algorithm (GA) is used for optimization of beam-hopping Satellite resource allocation, but the calculation timeliness is low due to the non-linearity and spatial dimensionality of the objective function, and is not suitable for a scenario in which ground traffic dynamically changes faster.
Document 3 "Alberti X, Ce Brian J M, Bianco AD, et al. system capacity optimization in time and frequency for multi-beam multi-media Systems [ C ]// Advanced Satellite multi-media Systems Conference & the Signal Processing for Space Communications workshop. ieee, 2010" proposes a closed solution that can solve the problem of beam hopping using convex optimization, thereby improving the computation speed while solving this high-dimensional problem. But this ideal case without considering co-channel interference is not suitable for practical engineering.
In summary, under the background that the number of satellite transmitters is limited, when heuristic methods such as the traditional PSO algorithm and the GA algorithm are used to process the high-dimensional problem of beam hopping, a long time is required or even the optimal beam hopping result cannot be converged, and the practical problems of invalid drag-solution cumulative computation speed and computation resource consumption exist. The traditional convex optimization method cannot solve the actual engineering problem in the presence of co-channel interference.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a dynamic beam hopping method of a broadband satellite communication system. The invention adopts ANW-PSO algorithm to complete the dynamic beam distribution process in the broadband satellite communication system. Unlike traditional heuristic algorithms, the ANW-PSO algorithm incorporates a nonlinear inertial weight selection strategy, an adaptive random exploration strategy, and a roulette strategy to generate an effective solution. The invention provides a dynamic beam hopping method of a broadband communication satellite. The method adopts an ANW-PSO algorithm on the wave beam hopping.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: determining input parameters of an ANW-PSO algorithm: the number of particles POP in the solution space, the dimension L of each particle, the value range 1, 2, the number of iteration times I, the maximum inertia weight w, the effective dimension limit epsilon of the particle, and the value range L of each particle are determined according to the number of the particles POP, the iteration times I and the maximum inertia weight wmaxMinimum inertial weight wminMaximum speed limit vmaxMinimum speed limit vmin；
And 2, step: initialization: the iteration number i is 1, and the global optimal QoS is fgbestGlobal optimum QoS contrast value of 0
Figure BDA0003585707110000021
Optimal QoS contrast value of particle pop
Figure BDA0003585707110000022
Optimal position pbest of particle pop pop0, in iteration 1, particle pop velocity vpop,1＝(vpop,1,1,...,vpop,1,l,…,vpop,1,L) Random assignment, where velocity v is in dimension lpop,1,lThe assignment method comprises the following steps:
vpop,1,l＝vmax+rand()*(vmax-vmin)
in iteration 1, according to vmaxAnd vminFor the velocity v in the dimension l of the particle poppop,1,lAnd (4) limiting, wherein the limiting method comprises the following steps:
Figure BDA0003585707110000023
and step 3: entering a loop, and directly entering the step 4 when the iteration number I is less than or equal to I, or directly entering the step 16;
and 4, step 4: judging whether i is 1, if i is greater than 1, entering the step 5, otherwise, turning to the step 7;
and 5: in the ith iteration, the non-linear weight w is carried outiSelecting, wherein the selection method comprises the following steps:
Figure BDA0003585707110000031
step 6: in the ith iteration, the velocity v of the particle pop is carried out pop,iThe updating method comprises the following steps:
Figure BDA0003585707110000032
wherein c is1And c2Is a learning constant, with reference to the parameter settings in the classical PSO algorithm, c1And c2Are all set to 1.8. pbrand()Is to solvePosition of a random particle in space, xpop,i-1Is the position of the particle pop in the last iteration, pbestpopIs the optimal position of the particle pop and gbest is the globally optimal particle position. Velocity vpop,iAfter update, according to vmaxAnd vminFor the velocity v in the dimension l of the particlepop,i,lAnd (4) limiting, wherein the limiting method comprises the following steps:
Figure BDA0003585707110000033
and 7: using the current globally optimal QoSfgbestUpdating the optimal satisfaction control value
Figure BDA0003585707110000034
The updating method comprises the following steps:
Figure BDA0003585707110000035
and 8: in the ith iteration, the position x of the particle pop is determinedpop,iResetting is carried out, and the resetting method comprises the following steps:
xpop,i＝0
and step 9: in the ith iteration, the velocity v according to the particle poppop,iCalculating the probability p of roulettepop,i＝(ppop,i,1,...,ppop,i,l,…,ppop,i,L)
The probability calculation method of the dimension l comprises the following steps:
Figure BDA0003585707110000036
step 10: in the ith iteration, the particle pop position x is updatedpop,iArranging the probabilities of the dimensions in reverse order by l _ indexiAnd the dimension value l _ index of the front epsiloniThe (1: epsilon) is set to be 1, and the updating method comprises the following steps:
{xpop,i,l_1＝1|l_1∈l_indexi(1:ε),l_indexi＝arg{sort(ppop,i,′descend′)}}
step 11: in the ith iteration, the QoS value f of the particle pop is calculatedpop,iThe calculation method comprises the following steps:
Figure BDA0003585707110000041
where N represents the number of terrestrial cells served by the broadband communications satellite system, each dimension of the particle represents each cell of the terrestrial,
Figure BDA0003585707110000042
The QoS values representing the ground cells n are all numbers between 0 and 1, and the larger the QoS value is, the higher the priority of the cell is, so that the QoS value is determined according to the actual situation of the ground cell;
step 12: in the ith iteration, if
Figure BDA0003585707110000043
Updating the optimal position pbest of the particle poppopAnd an optimal QoS contrast value
Figure BDA0003585707110000044
The updating method comprises the following steps:
pbestpop＝xpp,i
Figure BDA0003585707110000045
step 13: by using
Figure BDA0003585707110000046
Update of the maximum value of fgbestAnd updating the global optimal position gbest, wherein the setting method comprises the following steps:
Figure BDA0003585707110000047
Figure BDA0003585707110000048
gbest＝xpop,i
step 15: increasing iteration times, adding 1 to i, and returning to the step 3;
step 16: outputting a final wave beam hopping result: and gbest.
In the input parameters of the ANW-PSO algorithm in the step 1, the POP setting range of the number of the particles is as follows: 100-500, the dimension L of the particles is equal to the number N of the ground cells, L is equal to N, and the iteration number I is within a range: 200 to 1000, maximum inertial weight wmax0.8, minimum inertial weight wminMaximum speed limit v of 0.4max6, minimum speed limit vmin-6, the particle effective dimension constraint ∈ is equal to the number K of multibeam satellite emitters, ∈ ═ K;
the ANW-PSO algorithm has the beneficial effects that the optimization performance of the ANW-PSO algorithm is obviously higher than that of the traditional heuristic algorithm, and because a roulette strategy is added in the algorithm, the nonlinear weight is used for adjusting the particle speed, and a particle self-adaptive random learning mechanism is adopted, when the high-dimensional problem of beam hopping pattern design is processed, a local optimal solution can be rapidly skipped, a better solving effect is obtained, and meanwhile, better convergence is kept.
Drawings
Fig. 1 is a schematic diagram of a beam hopping model in a broadband satellite communication system.
Fig. 2 is a schematic diagram illustrating a dynamic beam hopping process in a broadband satellite communication system.
FIG. 3 is a diagram illustrating performance comparison between ANW-PSO and other heuristic algorithms.
FIG. 4 is a schematic diagram of the convergence comparison between ANW-PSO and other heuristic algorithms.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the drawings.
The invention provides a dynamic beam hopping method of a broadband communication satellite, which takes a ground cell N-49 in a satellite scene as an example, and provides a method for performing dynamic beam hopping by adopting an ANW-PSO algorithm. A beam hopping model in a broadband satellite communication system is shown in fig. 1, a dynamic beam hopping process is shown in fig. 2, and the specific implementation is as follows:
step 1: determining input parameters of an ANW-PSO algorithm: the number of particles in the solution space POP is 400, the dimension L of each particle is 49, the particle effective dimension limit epsilon is 4, the number of iterations I is 500, and the maximum inertia weight wmax0.8, minimum inertial weight wminMaximum speed limit v of 0.4max6, minimum speed limit vmin＝-6。
Step 2: initialization: the iteration number i is 1, and the QoSf is globally optimalgbestGlobal optimum QoS contrast value of 0
Figure BDA0003585707110000051
Optimal QoS contrast value for particle pop
Figure BDA0003585707110000052
Optimal position pbest of particle pop pop0, initial velocity v of particle pop at randompop,1＝(vpop,1,1,vpop,1,2,…,vpop,1,L) The initial speed assignment method of the dimension l comprises the following steps:
vpop,1,l＝vmax+rand()*(vmax-vmin)
further, v is limited according to the particle velocitymaxAnd vminThe speed of each dimension of the particles is limited, and the limiting method comprises the following steps:
Figure BDA0003585707110000053
wherein v ispop,i,lIs the velocity of the particle pop in dimension l in the ith iteration.
And 3, step 3: and (4) entering a loop, executing the following operation steps when the iteration number I is less than or equal to I, and otherwise, directly entering the step 16.
And 4, step 4: and judging whether i is equal to 1, if i is greater than 1, entering the step 5, and otherwise, turning to the step 7.
And 5: in thatThe non-linear weight w performed in the ith iterationiSelecting, wherein the selection method comprises the following steps:
Figure BDA0003585707110000054
step 6: in the ith iteration, the velocity v of the particle pop is carried outpop,iThe updating method comprises the following steps:
Figure BDA0003585707110000061
wherein c is1And c2Is a learning constant, with reference to the parameter settings in the classical PSO algorithm, c1And c2Are all set to 1.8. pbrand()Is the position of a random particle in solution space, xpop,i-1Is the position of the particle pop in the last iteration, pbestpopIs the optimal position of the particle pop and gbest is the globally optimal particle position. As in step 2, speed limitation is performed after the speed update.
And 7: using the current globally optimal QoSf gbestUpdating the optimal satisfaction control value
Figure BDA0003585707110000062
The updating method comprises the following steps:
Figure BDA0003585707110000063
and 8: in the ith iteration, the position x of the particle pop is determinedpop,iResetting is carried out, and the resetting method comprises the following steps:
xpop,i＝0
and step 9: in the ith iteration, the velocity v according to the particle poppop,iCalculating the probability p of roulettepop,i＝(ppop,i,1,ppop,i,2,…,ppop,i,L)，
The probability calculation method of the dimension l comprises the following steps:
Figure BDA0003585707110000064
step 10: in the ith iteration, the particle pop position x is updatedpop,iThe probabilities of all dimensions are arranged in a reverse order, the dimension value of the front epsilon is set to be 1, and the updating method comprises the following steps:
{xpop,i,l_1＝1|l_1∈l_indexi(1:ε),l_indexi＝arg{sort(ppop,i,′descend′)}}
step 11: in the ith iteration, the QoS value f of the particle pop is calculatedpop,iThe calculation method comprises the following steps:
Figure BDA0003585707110000065
where N represents the number of terrestrial cells served by the broadband communications satellite system and each dimension of the particle represents each cell on the ground.
Figure BDA0003585707110000066
Representing the QoS of the ground cell n, and the QoS values of 49 ground cells are all random numbers between 0 and 1.
Step 12: in the ith iteration, if
Figure BDA0003585707110000067
Updating the optimal position pbest of the particle poppopAnd an optimal QoS contrast value
Figure BDA0003585707110000068
The updating method comprises the following steps:
pbestpop＝xpp,i
Figure BDA0003585707110000071
step 13: by using
Figure BDA0003585707110000072
Update of the maximum value of fgbestAnd updating the global optimal position gbest, wherein the setting method comprises the following steps:
Figure BDA0003585707110000073
Figure BDA0003585707110000074
step 15: and (3) increasing the iteration times: and i is i +1, and the step 3 is returned.
Step 16: outputting a final wave beam hopping result: and gbest.
Fig. 3 is a comparison result of dynamic beam hopping performance based on the ANW-PSO algorithm and other heuristic algorithms, and fig. 4 is a comparison result of particle convergence of the ANW-PSO algorithm and other heuristic algorithms. It can be seen that the optimization performance of the ANW-PSO algorithm is obviously higher than that of the traditional heuristic algorithm, and meanwhile, better convergence is kept when the high-dimensional problem is processed.

Claims (2)

1. A dynamic beam hopping method of a broadband satellite communication system is characterized by comprising the following steps:
step 1: determining input parameters of an ANW-PSO algorithm: the number of particles POP in the solution space, the dimension L of each particle, the value range 1, 2, the L, the effective dimension limit epsilon of the particles, the iteration number I and the maximum inertia weight wmaxMinimum inertial weight wminMaximum speed limit vmaxMinimum speed limit vmin；
Step 2: initialization: the iteration number i is 1, and the global optimal QoS is fgbestGlobal optimum QoS contrast value of 0
Figure FDA0003585707100000011
Optimal QoS contrast value of particle pop
Figure FDA0003585707100000012
Of particle popOptimal position pbestpop0, in iteration 1, particle pop velocity vpop,1＝(vpop,1,1,...,vpop,1,l,…,vpop,1,L) Random assignment, where velocity v is in dimension lpop,1,lThe assignment method comprises the following steps:
vpop,1,l＝vmax+rand()*(vmax-vmin)
in iteration 1, according to vmaxAnd vminFor the velocity v in the dimension l of the particle poppop,1,lAnd (4) limiting, wherein the limiting method comprises the following steps:
Figure FDA0003585707100000013
And 3, step 3: entering a loop, and directly entering the step 4 when the iteration number I is less than or equal to I, or directly entering the step 16;
and 4, step 4: judging whether i is 1, if i is greater than 1, entering the step 5, otherwise, turning to the step 7;
and 5: in the ith iteration, the non-linear weight w is carried outiSelecting, wherein the selection method comprises the following steps:
Figure FDA0003585707100000014
step 6: in the ith iteration, the velocity v of the particle pop is carried outpop,iThe updating method comprises the following steps:
Figure FDA0003585707100000015
wherein c is1And c2Is a learning constant, with reference to the parameter settings in the classical PSO algorithm, c1And c2Are all set as 1.8 pbrand()Is the position of a random particle in solution space, xpop,i-1Is the position of the particle pop in the last iteration, pbestpopIs the optimal position of the particle pop, and gbest is the globally optimal particleA location; velocity vpop,iAfter update, according to vmaxAnd vminFor the velocity v in the dimension l of the particlepop,i,lAnd (4) limiting, wherein the limiting method comprises the following steps:
Figure FDA0003585707100000021
and 7: using the current globally optimal QoSfgbestUpdating the optimal satisfaction control value
Figure FDA0003585707100000022
The updating method comprises the following steps:
Figure FDA0003585707100000023
and 8: in the ith iteration, the position x of the particle pop is determinedpop,iResetting is carried out, and the resetting method comprises the following steps:
xpop,i＝0
and step 9: in the ith iteration, the velocity v according to the particle poppop,iCalculating the probability p of roulettepop,i＝(ppop,i,1,...,ppop,i,l,…,ppop,i,L)
The probability calculation method of the dimension l comprises the following steps:
Figure FDA0003585707100000024
step 10: in the ith iteration, the particle pop position x is updated pop,iArranging the probabilities of the dimensions in reverse order by l _ indexiAnd the dimension value l _ index of the front epsiloniThe (1: epsilon) is set to be 1, and the updating method comprises the following steps:
{xpop,i,l_1＝1|l_1∈l_indexi(1:ε),l_indexi＝arg{sort(ppop,i,′descend′)}}
step 11: in the ith iteration, the QoS value of the particle pop is calculatedfpop,iThe calculation method comprises the following steps:
Figure FDA0003585707100000025
where N represents the number of terrestrial cells served by the broadband communications satellite system, each dimension of the particle represents each terrestrial cell,
Figure FDA0003585707100000026
the QoS values representing the ground cells n are all numbers between 0 and 1, and the higher the QoS value is, the higher the priority of the cell is, so that the QoS value is determined according to the actual situation of the ground cell;
step 12: in the ith iteration, if
Figure FDA0003585707100000027
Updating the optimal position pbest of the particle poppopAnd an optimal QoS contrast value
Figure FDA0003585707100000028
The updating method comprises the following steps:
pbestpop＝xpp,i
Figure FDA0003585707100000029
step 13: by using
Figure FDA00035857071000000210
Update of the maximum value of fgbestAnd updating the global optimal position gbest, wherein the setting method comprises the following steps:
Figure FDA0003585707100000031
Figure FDA0003585707100000032
gbest＝xpop,i
step 15: increasing iteration times, adding 1 to i, and returning to the step 3;
step 16: outputting a final wave beam hopping result: and gbest.
2. The dynamic beam hopping method for a broadband satellite communication system according to claim 1, wherein:
in the input parameters of the ANW-PSO algorithm in the step 1, the POP setting range of the number of the particles is as follows: 100-500, the dimension L of the particles is equal to the number N of the ground cells, L is equal to N, and the iteration number I is within a range: 200 to 1000, maximum inertial weight w max0.8, minimum inertial weight wmin0.4, maximum speed limit vmax6, minimum speed limit vmin-6, the particle effective dimension constraint, epsilon, is equal to the number of multi-beam satellite emitters, K.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018072351A1 (en) * 2016-10-20 2018-04-26 北京工业大学 Method for optimizing support vector machine on basis of particle swarm optimization algorithm
CN113222096A (en) * 2021-04-30 2021-08-06 桂林理工大学 Improved particle swarm algorithm for cloud computing task scheduling
CN114285456A (en) * 2021-12-21 2022-04-05 西安电子科技大学 Low-earth-orbit satellite communication system-oriented beam hopping communication method and satellite load equipment

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018072351A1 (en) * 2016-10-20 2018-04-26 北京工业大学 Method for optimizing support vector machine on basis of particle swarm optimization algorithm
CN113222096A (en) * 2021-04-30 2021-08-06 桂林理工大学 Improved particle swarm algorithm for cloud computing task scheduling
CN114285456A (en) * 2021-12-21 2022-04-05 西安电子科技大学 Low-earth-orbit satellite communication system-oriented beam hopping communication method and satellite load equipment

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
康岚兰;董文永;田降森;: "一种自适应柯西变异的反向学习粒子群优化算法", 计算机科学, no. 10, 15 October 2015 (2015-10-15) *

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