CN115038095A - Multi-beam coverage planning method for low-earth orbit satellite - Google Patents
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- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/24—Cell structures
- H04W16/28—Cell structures using beam steering
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/24—Cell structures
- H04W16/32—Hierarchical cell structures
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- 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]
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention discloses a multi-beam coverage planning method for a low-orbit satellite, which comprises the following steps of: s1, dividing a beam layer and a beam cell in a beam coverage area of a satellite; s2, determining a target function and a constraint condition of an optimization problem of beam coverage; and S3, obtaining a required beam coverage planning scheme by using a particle swarm optimization algorithm through iterative solution according to the target function and the constraint condition. According to the invention, the beam layer and the beam cell are divided in the beam coverage area of the satellite, then the optimization problem is established according to the coverage requirement, and the solution is carried out based on the particle swarm optimization algorithm, so that the optimal beam coverage planning scheme meeting the constant flux coverage requirement can be rapidly obtained, and the multi-beam coverage of the low-orbit satellite can be realized.
Description
Technical Field
The present invention relates to beam coverage planning, and more particularly, to a multi-beam coverage planning method for a low earth orbit satellite.
Background
Compared with the ground communication mode, the satellite communication has the advantages of wide coverage area and no influence from the terrain, and is mainly applied to the aspects of broadcast television, emergency disaster relief, remote area network access and the like. Low Earth Orbit (LEO) satellites have the advantages of Low path loss and Low delay due to their Low height. After the twenty-first century, a global-coverage communication system consisting of a plurality of low-orbit satellites has been the main direction of development.
The low earth orbit satellite adopts multi-beam subareas to cover the ground, namely, the whole coverage area is divided into a plurality of cells, and a plurality of independent spot beams are used for covering each cell. On the basis, the frequency resources of the whole system are divided into a plurality of frequency bands, and adjacent beams avoid mutual interference by adopting frequency division or code division. Compared with the satellite communication system covered by single beam, the satellite communication system with multi-beam and frequency reuse capability can obtain higher system capacity and flexibility. Of course, the smaller number of cells can effectively improve the utilization rate of frequency spectrum resources and reduce the interference between beams.
Because the path loss of the low-orbit satellite is different from that of different ground positions, the satellite multi-beam antenna generally adopts a constant flux coverage design, namely the gain at the far end is high, and the gain at the near end is low, so that the transmitting power and the receiving power of the satellite and different ground positions are consistent, and the signal interference is reduced. Under the conditions of meeting the equal flux coverage and the like, the efficient and reasonable planning of the multi-beam coverage scheme is very important.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a multi-beam coverage planning method for a low-orbit satellite.
The purpose of the invention is realized by the following technical scheme: a method of multi-beam coverage planning for a low earth orbit satellite, comprising the steps of:
s1, dividing a beam layer and a beam cell in a beam coverage area of a satellite;
the beam coverage area of the satellite is circular, and the division mode is as follows:
dividing a beam coverage area into a plurality of beam layers, wherein the beam layers are sequentially arranged from the circle center to the outside, then dividing each beam layer into a plurality of beam cells, and enabling the beam cells in the same beam layer to be the same in shape and size;
in one beam layer closest to the circle center, each beam cell is a sector, and in other beam layers, each beam cell is a sector ring.
S2, determining a target function and a constraint condition of an optimization problem of beam coverage;
suppose the beam coverage area is divided into M beam layers, the M beam layer has N m The beam outer diameter angle of the m-th layer beam is theta in each beam cell m Known at an angle θ m The gain required by the corresponding equal flux coverage condition is G r (θ m ) Connecting a line with the satellite from any point on the outer diameter of the m-th layer of wave beams, wherein the included angle between the line and the normal is the angle of the outer diameter of the wave beams of the m-th layer of wave beams; the normal line is a line passing through the center of the beam coverage area and vertical to the upward direction of the beam coverage area;
the estimated average gain of the mth layer beam cell is denoted as G m ，G m Is shown as
Wherein g (theta) m ) The array antenna directional diagram is a normalized array element directional diagram and is used for estimating gain reduction caused by the increase of the array antenna directional diagram along with the increase of a scanning angle, and the coefficient k is a preset correction parameter and is used for performing error correction on a calculation result;
the objective function of the beam coverage optimization problem is expressed as:
by optimizing the outer edge angle theta of the layers m Number N of sum beams m And obtaining the minimum value of f so as to find the optimal beam planning scheme.
The constraint conditions comprise:
(1) the number of the wave beams of each layer is limited to be integral multiple of 4 so as to realize the symmetry of the wave beam planning scheme, and further, the phase distribution of the wave beams in one quadrant only needs to be calculated during wave beam forming;
(2) because the gain requirement of the inner layer is low, the beam width is large, the number of required beam cells is small, the gain requirement of the outer layer is high, the beam width is small, the number of required beam cells is large, and the number of beams of the inner layer is always smaller than that of beams of the outer layer in any two adjacent beam layers.
And S3, obtaining a required beam coverage planning scheme by using a particle swarm optimization algorithm through iterative solution according to the target function and the constraint condition.
S301, firstly, the beam number is in the feasible region of 1-N MAX And the beam outer diameter angle feasible region 0-theta o Randomly generating a feasible solution x of Z particles i Velocity v corresponding to said feasible solution i Wherein the feasible solution of the ith particle is
Wherein
Indicates the number of beams included in the jth beam layer in the ith particleThe number of beams in each beam layer is limited to be integral multiple of 4 by constraint conditions;
represents the beam outer diameter angle of the jth beam layer in the ith particle, j being 1, 2.
Then the outer diameter angle of the outermost layer is adjusted
Is set to theta o ；
The beam number feasible region and the beam outer diameter angle feasible region refer to: presetting a wave beam number range and a wave beam outer diameter angle range;
s302, initializing the iteration number n to be 1;
s303. according to the formula
Calculating an adaptive value f corresponding to a feasible solution of each particle in Z particles to obtain an individual optimal position p of each particle i And the group optimum position P of Z particles as a whole g ；
Wherein, the smaller the f, the better the position;
when the individual optimal position of any particle is obtained, the adaptive value obtained by the current iteration of the particle is compared with the adaptive value obtained by the previous iteration, and the position of the particle with the minimum adaptive value is used as the individual optimal position p of the particle i (ii) a During the first iteration, the position corresponding to the adaptive value of the adaptive value is directly used as the optimal position of the individual without comparison; wherein, the particle position is the feasible solution of the particle;
when the group optimal position is obtained, the adaptive values of the Z particle individual optimal positions are compared, and the individual optimal position with the minimum adaptive value is the group optimal position P of the Z examples g ；
S304, judging whether a termination condition is reached: if the termination condition is reached, the optimal position of the population [ N ] is obtained 1 ,N 2 ,...,N M ,θ 1 ,θ 1 ...θ M ]As the most importantOutputting the optimal solution to obtain the number of the M layers of wave beams and the outer diameter of the wave beams, and obtaining the optimal wave beam planning scheme; if the termination condition is not reached, go to step S305; the iteration termination condition is that the size of the adaptive value is 0 or the iteration number n reaches the set maximum iteration number;
s305, updating feasible solutions of the Z particles and the corresponding speeds of the feasible solutions:
wherein the ith particle is updated using the following equation
v i ′＝v i +c 1 r 1 (p i -x i +c 2 r 2 (P g -x i )
x i ′＝x i +v i
Wherein, c 1 And c 2 For real numbers greater than zero, called acceleration factor, the effect of which is to control the maximum step size, r, of the particle approaching its own optimum position and the optimum position in the population 1 And r 2 Is a random number greater than 0 and less than 1, so that the particle motion is random; x is the number of i Represents the feasible solution before the ith particle update, x i ' represents the feasible solution after the ith particle update; v. of i Represents the velocity, v, corresponding to the feasible solution before the ith particle update i ' speed corresponding to feasible solution after updating ith particle; 1,2, ·, Z;
s306, updating the iteration times: n is n +1, namely n after updating is equal to n before updating plus 1, and the feasible solution x after updating is added i ' as a novel x i I ═ 1,2, ·, Z; the speed v corresponding to the updated feasible solution i ' As a novel v i I 1, 2.., Z, and then returns to step S303.
The invention has the beneficial effects that: according to the invention, the beam layer and the beam cell are divided in the beam coverage area of the satellite, then the optimization problem is established according to the coverage requirement, and the solution is carried out based on the particle swarm optimization algorithm, so that the optimal beam coverage planning scheme meeting the constant flux coverage requirement can be rapidly obtained, and the multi-beam coverage of the low-orbit satellite can be realized.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic view of a cell division of a coverage of a high orbit satellite or terrestrial signal base station;
FIG. 3 is a schematic diagram of the beam cell division principle of the present invention;
FIG. 4 is a schematic diagram illustrating gain requirements for different coverage angles in an embodiment;
fig. 5 is a schematic diagram of a beam coverage scheme in an embodiment.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a multi-beam coverage planning method for low earth orbit satellites includes the following steps:
s1, dividing a beam layer and a beam cell in a beam coverage area of a satellite;
as shown in fig. 2, in the coverage problem of the high orbit satellite or the ground signal base station, the hexagonal cellular structure is more adopted because the number of hexagons used is the least in the pattern (such as triangle, square, hexagon, etc.) which can cover a certain area without overlapping or blind areas.
When the low orbit satellite beam is covered, because the coverage area is circular and the target gain is rapidly increased from the circle center to the outside, the honeycomb cell is not suitable, and the circular symmetrical sector ring is more reasonable in arrangement;
specifically, as shown in fig. 3, the division is as follows:
dividing a beam coverage area into a plurality of beam layers, wherein the beam layers are sequentially arranged from the circle center to the outside, then dividing each beam layer into a plurality of beam cells, and enabling the beam cells in the same beam layer to be the same in shape and size;
in one beam layer closest to the circle center, each beam cell is a sector, and in other beam layers, each beam cell is a sector ring.
S2, determining a target function and a constraint condition of a beam coverage optimization problem;
in a specific planning, it is necessary to estimate the beam gain corresponding to a certain beam width more accurately, and under the constraint relationship between the beam width and the number of beams, the whole area is covered by the beam cell, and the gain requirements of different distance positions are met. The beam optimization problem determination process described in step S2 includes:
suppose the beam coverage area is divided into M beam layers, the M beam layer has N m The beam outer diameter angle of the m-th layer beam is theta in each beam cell m Known at an angle θ m The gain required by the corresponding equal flux coverage condition is G r (θ m ) Connecting a line with the satellite from any point on the outer diameter of the m-th layer of wave beams, wherein the included angle between the line and the normal is the angle of the outer diameter of the wave beams of the m-th layer of wave beams; the normal line is a line passing through the center of the beam coverage area and vertical to the upward direction of the beam coverage area;
the estimated average gain of the mth layer beam cell is denoted as G m ，G m Is shown as
Wherein g (theta) m ) The normalized array element directional diagram is used for estimating gain reduction of the array antenna directional diagram caused by the increase of the scanning angle, and the coefficient k is a preset correction parameter and used for correcting errors of a calculation result;
the objective function of the beam coverage optimization problem is expressed as:
by optimizing the outer edge angle theta of the layers m Number N of sum beams m And obtaining the minimum value of f, thereby finding the optimal beam planning scheme.
The constraint conditions described in step S2 include:
(1) the number of the wave beams of each layer is limited to be integral multiple of 4 so as to realize the symmetry of the wave beam planning scheme, and further, the phase distribution of the wave beams in one quadrant is only required to be calculated during wave beam forming;
(2) because the gain requirement of the inner layer is low, the beam width is large, the number of required beam cells is small, the gain requirement of the outer layer is high, the beam width is small, the number of required beam cells is large, and the number of beams of the inner layer is always smaller than that of beams of the outer layer in any two adjacent beam layers.
And S3, obtaining a required beam coverage planning scheme by using a particle swarm optimization algorithm through iterative solution according to the target function and the constraint condition.
The step S3 includes the following sub-steps:
s301, firstly, the beam number is in a feasible region from 1 to N MAX And the beam outer diameter angle feasible region 0-theta o Randomly generating a feasible solution x of Z particles i Velocity v corresponding to said feasible solution i Wherein the feasible solution of the ith particle is
Wherein
Representing the number of beams contained in the jth beam layer in the ith particle, and limiting the number of beams in each beam layer to be integral multiple of 4 by constraint conditions;
denotes an outer beam diameter angle of a jth beam layer in the ith particle, j being 1, 2.. multidot.m;
then the outer diameter angle of the outermost layer is adjusted
Is set to theta o ；
The beam number feasible region and the beam outer diameter angle feasible region refer to: presetting a wave beam number range and a wave beam outer diameter angle range; wherein the number of beams is between 1 and a certain limit value N MAX The beam outer diameter angle is more than 0 and less than or equal to the maximum coverage angle theta o This is thatThe feasible region is determined by practical problems, and is preset according to practical conditions, and the solution generated in the feasible region range is a feasible solution;
s302, initializing the iteration number n to be 1;
s303. according to the formula
Calculating an adaptive value f corresponding to a feasible solution of each particle in Z particles to obtain an individual optimal position p of each particle i And the group optimum position P of Z particles as a whole g ；
Wherein, the smaller the f, the better the position;
when the individual optimal position of any particle is obtained, the adaptive value obtained by the current iteration of the particle is compared with the adaptive value obtained by the previous iteration, and the position of the particle with the minimum adaptive value is used as the individual optimal position p of the particle i (ii) a During the first iteration, the position corresponding to the adaptive value of the adaptive value is directly used as the optimal position of the individual without comparison; wherein, the particle position is the feasible solution of the particle;
when the group optimal position is obtained, the adaptive values of the Z particle individual optimal positions are compared, and the individual optimal position with the minimum adaptive value is the group optimal position P of the Z examples g ；
S304, judging whether a termination condition is reached: if the termination condition is reached, the optimal position of the population [ N ] is obtained 1 ,N 2 ,...,N M ,θ 1 ,θ 1 ...θ M ]Outputting as an optimal solution to obtain the number of the M layers of wave beams and the outer diameter of the wave beams, and obtaining an optimal wave beam planning scheme; if the termination condition is not reached, go to step S305; the iteration termination condition is that the size of the adaptive value is 0 or the iteration number n reaches the set maximum iteration number;
s305, updating feasible solutions of the Z particles and the corresponding speeds of the feasible solutions:
wherein the ith particle is updated using the following equation
v i ′＝v i +c 1 r 1 (p i -x i +c 2 r 2 (P g -x i )
x i ′＝x i +v i
Wherein, c 1 And c 2 For real numbers greater than zero, called acceleration factor, the effect of which is to control the maximum step size, r, of the particle approaching its own optimum position and the optimum position in the population 1 And r 2 Is a random number greater than 0 and less than 1, so that the particle motion is random; x is a radical of a fluorine atom i Represents the feasible solution, x, before the ith particle update i ' represents the feasible solution after the ith particle update; v. of i Represents the velocity, v, corresponding to the feasible solution before the ith particle update i ' speed corresponding to feasible solution after updating ith particle; 1,2, Z;
s306, updating the iteration times: n is n +1, namely n after updating is equal to n before updating plus 1, and the feasible solution x after updating is added i ' as a novel x i I ═ 1,2, ·, Z; the speed v corresponding to the updated feasible solution i ' As a novel v i 1, 2., Z, and then returns to step S303.
In the embodiment of the present application, taking the satellite multibeam antenna covering the 0-55 degree region as an example, the gain requirement to be achieved by the coverage edge 55 degree is set to 18.6dB, and the gain requirements of all coverage angles are obtained according to the path loss difference between the coverage angle and the coverage edge angle, as shown in fig. 4.
Considering the total number of layers M as 2, the number of the first and second layers is 8 and 28 according to the multi-beam coverage planning algorithm, and the outer diameter angles are 39 and 55 degrees respectively. Considering the case that the total number of layers M is 3, the number of the first, second and third layers of beams is 4, 8 and 20 according to the multi-beam coverage planning algorithm, and the outer diameter angles are 32.4, 46 and 55 degrees respectively.
It is sufficient to complete the beam coverage using 2-layer or 3-layer beams, regardless of the 4-layer beam case. Selecting between the 2-layer and 3-layer beam coverage schemes, the 3-layer beam coverage scheme is selected from the perspective of completing area coverage with as few beams as possible, as shown in fig. 5.
The foregoing is a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments, and is capable of other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A method of multi-beam coverage planning for a low earth orbit satellite, characterized by: the method comprises the following steps:
s1, dividing a beam layer and a beam cell in a beam coverage area of a satellite;
s2, determining a target function and a constraint condition of an optimization problem of beam coverage;
and S3, obtaining a required beam coverage planning scheme by using a particle swarm optimization algorithm through iterative solution according to the target function and the constraint condition.
2. A multi-beam coverage planning method for low earth orbit satellites according to claim 1, characterized in that: in step S1, the beam coverage area of the satellite is circular, and the division method is as follows:
dividing a beam coverage area into a plurality of beam layers, wherein the beam layers are sequentially arranged from the circle center to the outside, then dividing each beam layer into a plurality of beam cells, and enabling the beam cells in the same beam layer to be the same in shape and size;
in one beam layer closest to the circle center, each beam cell is a sector, and in other beam layers, each beam cell is a sector ring.
3. A multi-beam coverage planning method for low earth orbit satellites according to claim 1, characterized in that: the beam optimization problem determination process described in step S2 includes:
suppose the beam coverage area is divided into M beam layers, the M beam layer has N m One beam cellThe beam outer diameter angle of the m-th layer beam is theta m At an angle theta m The gain required by the corresponding equal flux coverage condition is G r (θ m ) Connecting a line with the satellite from any point on the outer diameter of the m-th layer of wave beams, wherein the included angle between the line and the normal is the angle of the outer diameter of the wave beams of the m-th layer of wave beams; the normal line is a line passing through the center of the beam coverage area and vertical to the upward direction of the beam coverage area;
the estimated average gain of the mth layer beam cell is denoted as G m ，G m Is shown as
Wherein g (theta) m ) The normalized array element directional diagram is used for estimating gain reduction of the array antenna directional diagram caused by the increase of the scanning angle, and the coefficient k is a preset correction parameter;
the objective function of the beam coverage optimization problem is expressed as:
by optimizing the outer edge angle theta of the layers m Number N of sum beams m And obtaining the minimum value of f, thereby finding the optimal beam planning scheme.
4. A multi-beam coverage planning method for low earth orbit satellites according to claim 1, characterized in that: the constraint conditions described in step S2 include:
(1) the number of wave beams of each layer is limited to be integral multiple of 4;
(2) because the gain requirement of the inner layer is low, the beam width is large, the number of required beam cells is small, the gain requirement of the outer layer is high, the beam width is small, the number of required beam cells is large, and the number of beams of the inner layer is always smaller than the number of beams of the outer layer in any two adjacent beam layers.
5. A multi-beam coverage planning method for low earth orbit satellites according to claim 1, characterized in that: the step S3 includes the following sub-steps:
s301, firstly, the beam number is in a feasible region from 1 to N MAX And the beam outer diameter angle feasible region 0-theta o In randomly generating a feasible solution x of Z particles i Velocity v corresponding to said feasible solution i Wherein the feasible solution of the ith particle is
Wherein
Representing the number of beams contained in the jth beam layer in the ith particle, and limiting the number of beams in each beam layer to be integral multiple of 4 by constraint conditions;
represents the beam outer diameter angle of the jth beam layer in the ith particle, j being 1, 2.
Then the outer diameter angle of the outermost layer is adjusted
Is set to theta o ；
The beam number feasible region and the beam outer diameter angle feasible region refer to: presetting a wave beam number range and a wave beam outer diameter angle range;
s302, initializing the iteration number n to be 1;
s303. according to the formula
Calculating an adaptive value f corresponding to a feasible solution of each particle in Z particles, and acquiring an individual optimal position p of each particle i And the group optimum position P of Z particles as a whole g ；
Wherein, the smaller the f, the better the position;
when the individual optimal position of any particle is obtained, the adaptive value obtained by the current iteration of the particle is compared with the adaptive value obtained by the previous iteration, and the position of the particle with the minimum adaptive value is used as the individual optimal position p of the particle i (ii) a During the first iteration, the position corresponding to the adaptive value of the adaptive value is directly used as the optimal position of the individual without comparison; wherein, the particle position is the feasible solution of the particle;
when the group optimal position is obtained, the adaptive values of the Z particle individual optimal positions are compared, and the individual optimal position with the minimum adaptive value is the group optimal position P of the Z examples g ；
S304, judging whether a termination condition is reached: if the termination condition is reached, the optimal position of the population [ N ] is obtained 1 ,N 2 ,...,N M ,θ 1 ,θ 1 ...θ M ]Outputting as an optimal solution to obtain the number of the M layers of wave beams and the outer diameter of the wave beams, and obtaining an optimal wave beam planning scheme; if the termination condition is not reached, go to step S305; the iteration termination condition is that the size of the adaptive value is 0 or the iteration number n reaches the set maximum iteration number;
s305, updating feasible solutions of the Z particles and the corresponding speeds of the feasible solutions:
wherein the ith particle is updated using the following equation
v i ′＝v i +c 1 r 1 (p i -x i +c 2 r 2 (P g -x i )
x i ′＝x i +v i
Wherein, c 1 And c 2 For real numbers greater than zero, called acceleration factor, the effect of which is to control the maximum step size, r, of the particle approaching its own optimum position and the optimum position in the population 1 And r 2 Is a random number greater than 0 and less than 1, so that the particle motion is random; x is a radical of a fluorine atom i Represents the feasible solution, x, before the ith particle update i ' represents the feasible solution after the ith particle update; v. of i Represents the velocity, v, corresponding to the feasible solution before the ith particle update i ' first toThe speed corresponding to the feasible solution after the i particles are updated; 1,2, ·, Z;
s306, updating the iteration times: n is n +1, namely n after updating is equal to n before updating plus 1, and the feasible solution x after updating is added i ' As a novel x i 1,2, ·, Z; the speed v corresponding to the updated feasible solution i ' As a novel v i 1, 2., Z, and then returns to step S303.
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