CN114928400B - Low-orbit satellite dynamic resource allocation method based on beam hopping - Google Patents

Low-orbit satellite dynamic resource allocation method based on beam hopping Download PDF

Info

Publication number
CN114928400B
CN114928400B CN202210532194.4A CN202210532194A CN114928400B CN 114928400 B CN114928400 B CN 114928400B CN 202210532194 A CN202210532194 A CN 202210532194A CN 114928400 B CN114928400 B CN 114928400B
Authority
CN
China
Prior art keywords
time
cell
resource allocation
low
orbit satellite
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210532194.4A
Other languages
Chinese (zh)
Other versions
CN114928400A (en
Inventor
曾鸣
张校宁
李维彪
王新尧
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN202210532194.4A priority Critical patent/CN114928400B/en
Publication of CN114928400A publication Critical patent/CN114928400A/en
Application granted granted Critical
Publication of CN114928400B publication Critical patent/CN114928400B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • H04B7/18519—Operations control, administration or maintenance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Astronomy & Astrophysics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Radio Relay Systems (AREA)

Abstract

The invention provides a low-orbit satellite dynamic resource allocation method based on beam hopping, which comprises the steps of firstly dividing time slices according to the height of a low-orbit satellite and the radius of a cell, and regarding the time slices T 1 The method comprises the steps of performing initial time resource allocation based on a fair objective function, obtaining a power resource matrix by adopting maximum system capacity as an optimization target, classifying cells according to the time of each cell leaving a low-orbit satellite coverage area for subsequent time fragmentation, determining weight coefficient ratios of various cells, performing dynamic time resource allocation based on a weighting objective function by combining the weight coefficient ratios, obtaining the power resource allocation matrix by adopting the maximum system capacity as the optimization target under the conditions of total power limitation and single-hop beam power limitation, and accordingly achieving joint optimization of low-orbit satellite beam-hopping system time and power resource allocation to obtain higher resource utilization rate and system throughput.

Description

Low-orbit satellite dynamic resource allocation method based on beam hopping
Technical Field
The invention relates to a low-orbit satellite dynamic resource allocation method based on beam hopping.
Background
Compared with a ground communication base station, a low-orbit satellite has a larger coverage area, but the ground service demand distribution is usually uneven due to the increase of the coverage area, if a traditional uniform resource allocation scheme is continuously adopted, the resource utilization rate and the system performance are reduced, and the beam hopping technology provides a possibility for solving the technology. The core idea of the beam hopping technology is to preset the total coverage area of a satellite into a plurality of cells, only serve a single cell or a plurality of cells in the coverage area at the same time, and improve the resource utilization rate by allocating more resources to the cells with large service demand. However, in the prior art, most studies on beam hopping are concentrated on high-orbit satellites, reliable studies on application of the beam hopping technology to low-orbit satellites are lacking, and due to the fact that communication resources and communication requirements are changed drastically in the low-orbit satellite environment, an existing beam hopping resource allocation algorithm is high in complexity and large in calculation amount and cannot be directly used for the low-orbit satellites.
Disclosure of Invention
The invention provides a dynamic resource allocation method for a low earth orbit satellite based on beam hopping, which jointly optimizes beam hopping time and power resource allocation of the low earth orbit satellite, thereby obtaining higher resource utilization rate and system throughput.
The invention is realized by the following technical scheme:
a low orbit satellite dynamic resource allocation method based on beam hopping comprises the following steps:
step S1, dividing S time slices { T } 1 ，T 2 ，...，T N ，...，T S For time slicing T 1 Step S2 is entered for initial resource allocation, for subsequent time slices { T } 2 ,...,T N ,...,T S Step S4 is entered to perform dynamic resource allocation, wherein the coverage area of the low-orbit satellite is divided into a plurality of cells, and the time slicing length is related to the height of the low-orbit satellite and the radius of the cells;
step S2, based on fair objective function
Figure BDA0003633415720000021
Performing initial time resource allocation, performing logarithm operation on a fair objective function and obtaining an optimized time resource allocation matrix by using a Lagrange equation on the premise of using average power allocation
Figure BDA0003633415720000022
According to
Figure BDA0003633415720000023
The value of the medium element is in relation with the threshold value of the service cell to obtain a time resource distribution matrix v 1 (t, i) wherein,
Figure BDA0003633415720000024
representing time slices T 1 The traffic demand of the ith cell in the next tth time slot,
Figure BDA0003633415720000025
indicating channel capacity, serving cell threshold is related to co-channel interference between beams in the same time slot under time slicing; v. of 1 The elements in (t, i) include 1 and 0,v 1 (T, i) =1 denotes time slicing T 1 The ith cell is served in the next t time slot, v 1 (t, i) =0 and vice versa;
step S3, according to v 1 (t, i) obtaining a set of served cells Φ, and according to v 1 (t, i) and the set phi, and obtaining a power resource allocation matrix by adopting the maximum system capacity as an optimization target
Figure BDA0003633415720000026
Step S4, slicing time T N Dividing all the cells into m types according to the time of each cell leaving the low-orbit satellite coverage area, and determining the weight coefficient ratio { omega [ omega ]) of each type of cell 1 ：ω 2 ：...：ω m }，ω 1 And ω m Corresponding to the first and last departing cell, omega, respectively 1 ，ω 2 ，...，ω m Sequentially increasing;
step S5, based on the weighted objective function
Figure BDA0003633415720000027
Dynamic time resource allocation is carried out, on the premise of using average power allocation, the weighted objective function is subjected to logarithm operation, and the Lagrange equation is utilized to obtain an optimized time resource allocation matrix
Figure BDA0003633415720000028
According to
Figure BDA0003633415720000029
The value of the medium element is in the size relation with the threshold value of the service cell to obtain a time resource distribution matrix v N (t, i) wherein,
Figure BDA00036334157200000210
representing time slices T N The traffic demand of the ith cell in the next tth time slot,
Figure BDA00036334157200000211
which is indicative of the capacity of the channel,
Figure BDA00036334157200000212
corresponding to time slicing T N Weight coefficient, v, of ith cell in the next t time slot N The elements in (t, i) include 1 and 0,v N (T, i) =1 denotes a time slice T N The ith cell is served in the next t time slot, v N (t, i) =0 and vice versa;
step S6, according to the time resource distribution matrix v N (t, i) obtaining a set of served cells Φ, and according to v N (t, i) and phi set, adopting maximum system capacity as optimization target, under the condition of total power limitation and single-hop wave beam power limitation obtaining power resource distribution matrix
Figure BDA0003633415720000031
Further, in step S1, each of the cell areas S cell ＝S tot /M，S tot The area of a coverage area of the low-orbit satellite, M is the number of cells, the arrangement direction of each cell is the same as the movement direction of the low-orbit satellite so as to ensure that the cells positioned at the edge of the coverage area can leave the coverage area at the same time, and the time slice length
Figure BDA0003633415720000032
h is the low orbit satellite orbit height, R is the cell radius, R is the earth radius, and GM is the gravitational constant.
Further, the step S2 specifically includes the following steps:
step S21, time slicing T 1 Then, the weight coefficients of all cells are the same, so that the time slice T is obtained 1 The fairness objective function in the lower time slot t is:
Figure BDA0003633415720000033
Figure BDA0003633415720000034
wherein, B tot Which represents the total bandwidth of the system and,
Figure BDA0003633415720000035
representing time slices T 1 Channel gain, N, of the lower ith cell 0 Representing the noise power, Λ representing the neighbor set of the served cell, P 1 (T, i) denotes slicing T in time 1 Power allocation of beam hopping serving ith cell in the next t-th time slot, N max Represents the maximum number of beams that can be generated at the same time;
Figure BDA0003633415720000036
indicating the use of an average power allocation, P tot The total power of the low-orbit satellite transmission is represented;
s22, carrying out logarithmic operation on the fair objective function and obtaining the optimal time resource distribution matrix by using a Lagrange equation
Figure BDA0003633415720000037
Step S23, considering time slicing T 1 The co-channel interference between beams in the same time slot will optimize the time resource allocation matrix
Figure BDA0003633415720000038
The values in the table are arranged from large to small, and the Nth value is selected max The value is used as the serving cell threshold η under the time slicing 1 Then according to the formula
Figure BDA0003633415720000039
Obtaining a time resource allocation matrix v 1 (t,i)。
Further, the step S3 specifically includes: according to v 1 (t, i) obtaining a set of served cells Φ, and according to v 1 (t, i) and a set phi, the maximum system capacity is taken as an optimization target, and the optimization problem is expressed as:
Figure BDA0003633415720000041
Figure BDA0003633415720000042
Figure BDA0003633415720000043
P 1 (t,i)≤P sb ，
Figure BDA0003633415720000044
thereby obtaining a power resource allocation matrix
Figure BDA0003633415720000045
Wherein, P sb Representing the maximum single hop beam power.
Further, the step S5 specifically includes the following steps:
step S51, slice T in time N The weighted objective function in the following t-th slot is:
Figure BDA0003633415720000046
s.t.
Figure BDA0003633415720000047
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003633415720000048
representing time slices T N Channel gain, P, of the lower ith cell N (T, i) denotes slicing T in time N Power allocation of a hop beam serving the ith cell in the next t-th slot,
Figure BDA0003633415720000049
indicating the use of an average power allocation;
step S52, carrying out logarithm operation on the weighted objective function and obtaining an optimized time resource distribution matrix by utilizing a Lagrange equation
Figure BDA00036334157200000410
Step (ii) ofS53, considering time slicing T N The co-channel interference between beams in the same time slot will optimize the time resource allocation matrix
Figure BDA00036334157200000411
The values in the table are arranged from large to small, and the Nth value is selected max The value is used as the serving cell threshold η under the time slicing N Then according to the formula
Figure BDA00036334157200000412
Obtaining a time resource allocation matrix v N (t,i)。
Further, step S6 specifically includes: according to v N (t, i) obtaining a set of served cells Φ, and according to v N (t, i) and a set phi, the maximum system capacity is taken as an optimization target, and the optimization problem is expressed as:
Figure BDA0003633415720000051
Figure BDA0003633415720000052
Figure BDA0003633415720000053
P N (t,i)≤P sb ，
Figure BDA0003633415720000054
thereby obtaining a power resource allocation matrix
Figure BDA0003633415720000055
Further, the step S22 specifically includes:
carrying out logarithm operation on a fair objective function to obtain
Figure BDA0003633415720000056
Establishing a Lagrange equation to obtain the time resource allocation without considering the influence of the common channel interference on the time resource allocation
Figure BDA0003633415720000057
To V 1 (t, i) obtaining a partial derivative
Figure BDA0003633415720000058
Making the partial derivative equal to 0 to obtain the optimized time resource distribution matrix
Figure BDA0003633415720000059
Order to
Figure BDA00036334157200000510
Computing lagrange operators
Figure BDA00036334157200000511
Introducing lagrange operators into
Figure BDA0003633415720000061
Obtaining an optimized time resource allocation matrix
Figure BDA0003633415720000062
Further, step S52 specifically includes:
carrying out logarithm operation on a fair objective function to obtain
Figure BDA0003633415720000063
Establishing a Lagrange equation to obtain the time resource allocation without considering the influence of the common channel interference on the time resource allocation
Figure BDA0003633415720000064
To V N (t, i) obtaining a partial derivative
Figure BDA0003633415720000065
Making the partial derivative equal to 0 to obtain the optimized time resource distribution matrix
Figure BDA0003633415720000066
Order to
Figure BDA0003633415720000067
Computing lagrange operators
Figure BDA0003633415720000068
By introducing the Lagrangian lambda into
Figure BDA0003633415720000069
Obtaining an optimized time resource allocation matrix
Figure BDA00036334157200000610
The invention has the following beneficial effects:
1. the invention firstly divides time slices according to the height of the low-earth satellite and the radius of a cell, performs initial resource allocation on the first time slice, performs dynamic resource allocation on the subsequent time slices, and performs time slice T 1 Performing initial time resource allocation based on a fair objective function, obtaining a power resource matrix by taking the maximum system capacity as an optimization target, classifying cells according to the time of each cell leaving a low-orbit satellite coverage area for subsequent time slicing, determining the weight coefficient ratio of each cell, performing dynamic time resource allocation based on a weighting objective function by combining the weight coefficient ratio, obtaining the power resource allocation matrix by taking the maximum system capacity as the optimization target under the conditions of limited total power and limited single-hop beam power, and realizing the joint optimization of the low-orbit satellite hop-beam system time and power resource allocation to obtain higher resource utilizationThe rate and the system throughput, thereby realizing the application of the beam hopping technology to low-orbit satellites.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings.
FIG. 1 is a schematic diagram illustrating coverage area changes of a low earth orbit satellite moving according to the present invention.
FIG. 2 is a diagram illustrating the relationship between low earth orbit satellite movement and cell arrangement according to the present invention.
FIG. 3 is a simulation diagram of the present invention under different weight coefficient ratios.
Detailed Description
The method for dynamically allocating the low-orbit satellite resources based on the beam hopping comprises the following steps:
step S1, dividing S time slices { T } 1 ，T 2 ，...，T N ，...，T S For time slicing T 1 Step S2 is entered for initial resource allocation, for subsequent time slices { T } 2 ,...,T N ,...,T S Step S4 is carried out to carry out dynamic resource allocation, wherein the coverage area of the low-orbit satellite is divided into a plurality of cells, and the time slicing length is related to the height of the low-orbit satellite and the radius of the cells;
specifically, in this embodiment, the study object is a single low-orbit satellite, and the coverage area of the low-orbit satellite is S tot Dividing the coverage area into M cells, the area of each cell is S cell ＝S tot and/M. The low-earth orbit satellite is provided with a steerable multi-hop beam antenna for downlink service transmission, and the maximum N can be generated at the same time max A single-hop wave beam, the coverage area of each single-hop wave beam antenna is S cell . The same satellite uses the full frequency band of the system by different beams at the same time, i.e. the bandwidth of each single beam is equal to the total bandwidth B of the system tot . Total power P of satellite transmission tot Is non-uniformly used by each sub-hop beam in the time slot t.
As shown in fig. 1, an arrow indicates a movement direction of the low earth orbit satellite, a large circle indicates a coverage area of the low earth orbit satellite, a small circle indicates a cell, and an arrangement manner of the cells is determined according to the movement direction of the low earth orbit satellite. Within each time slice, the coverage area, the traffic distribution and the traffic demand are considered to be unchanged. After a time slice, the coverage area of the low earth satellite will sweep through a certain number of cells and a new cell enters the coverage area.
All served users are considered to be in the center of the cell, and due to the high orbit height of the low orbit satellite, the channel gains of different cells are considered only in the case of free space loss
Figure BDA0003633415720000086
Approximately equal, the channel capacities of different cells are approximately equal under the average power allocation scheme, and the channel capacities under each time slice are also equal.
Multiple time slices need to be considered in a dynamic scene, and the length of the time slices
Figure BDA0003633415720000081
Number of time slots within a single time slice
Figure BDA0003633415720000082
Wherein h is the low orbit satellite orbit height, R is the cell radius, R is the earth radius, GM is the gravity constant, T slot Indicating the slot length.
Step S2, based on fair objective function
Figure BDA0003633415720000083
Performing initial time resource allocation, performing logarithm operation on a fair target function and utilizing a Lagrange equation under the premise of using average power allocation to obtain a time slice T 1 Optimized time resource allocation matrix of
Figure BDA0003633415720000084
According to
Figure BDA0003633415720000085
The relation between the value of the middle element and the threshold value of the service cell is obtained in time slicing T 1 Time resource allocation matrix v of 1 (t,i)；
The method specifically comprises the following steps:
step S21, time slicing T 1 In the following, the system is in the initial resource allocation state, there is no difference in service priority of all cells in the coverage area of the low-earth orbit satellite, i.e. all cell weight coefficients are consistent, and it is specified in this embodiment that
Figure BDA0003633415720000091
I.e. the weight coefficient of each cell is set to 1, so that a time slice T is obtained 1 The fairness objective function in the lower time slot t is:
Figure BDA0003633415720000092
s.t.
Figure BDA0003633415720000093
Figure BDA0003633415720000094
wherein the content of the first and second substances,
Figure BDA0003633415720000095
representing time slices T 1 The traffic demand of the ith cell in the next tth time slot,
Figure BDA0003633415720000096
representing the channel capacity, B tot Which represents the total bandwidth of the system,
Figure BDA0003633415720000097
representing time slices T 1 Channel gain, N, of the lower ith cell 0 Denotes the noise power, phi denotes the set of served cells, a denotes the set of neighbouring cells of the served cells, P 1 (T, i) denotes slicing T in time 1 Power allocation of a hop beam serving the ith cell in the next t-th slot,N max representing the maximum number of hopping wave beams which can be generated at the same time;
Figure BDA0003633415720000098
indicating the use of an average power allocation, P tot Representing the total power of low-orbit satellite transmission;
s22, carrying out logarithmic operation on the fair objective function and obtaining the optimal time resource distribution matrix by using a Lagrange equation
Figure BDA0003633415720000099
The method specifically comprises the following steps:
performing logarithm operation on the fair objective function to obtain
Figure BDA00036334157200000910
s.t.
Figure BDA00036334157200000911
Figure BDA00036334157200000912
Establishing a Lagrange equation to obtain the time resource allocation without considering the influence of Common Channel Interference (CCI)
Figure BDA00036334157200000913
To V 1 (t, i) obtaining a partial derivative
Figure BDA00036334157200000914
Making the equation obtained by the partial derivation equal to 0 to obtain the optimized time resource distribution matrix
Figure BDA0003633415720000101
Computing lagrange operators
Figure BDA0003633415720000102
Order to
Figure BDA0003633415720000103
Simplifying the Lagrangian operator to
Figure BDA0003633415720000104
Introducing lagrange operators
Figure BDA0003633415720000105
Obtaining an optimized time resource allocation matrix
Figure BDA0003633415720000106
Step S23, considering time slicing T 1 The common channel interference between beams in the same time slot will satisfy the maximum number of beam hopping and the adjacent cells can not be served at the same time
Figure BDA0003633415720000107
The cell combination with larger median value is selected and assigned to the time resource distribution matrix v 1 (t, i), more specifically, the time resource allocation matrix will be optimized
Figure BDA0003633415720000108
The values in the table are arranged from large to small, and the Nth value is selected max The value is used as the serving cell threshold η under the time slicing 1 Then according to the formula
Figure BDA0003633415720000109
s.t.0≤|V 1 (t,i)|≤N max ，
Figure BDA00036334157200001010
Obtaining a time resource allocation matrix v 1 (t,i)，，v 1 The elements in (t, i) include 1 and 0,v 1 (T, i) =1 denotes time slicing T 1 I cell served in the next t time slot, v 1 (t, i) =0 is the opposite.
Step S3, according to v 1 (tI) obtaining a set of served cells phi, and according to v 1 (t, i) and a set phi, the maximum system capacity is taken as an optimization target, and the optimization problem is expressed as:
Figure BDA00036334157200001011
Figure BDA0003633415720000111
Figure BDA0003633415720000112
P 1 (t,i)≤P sb ，
Figure BDA0003633415720000113
thereby obtaining a power resource allocation matrix
Figure BDA0003633415720000114
Wherein, P sb Representing the maximum single hop beam power.
Step S4, slicing { T ] for subsequent time 2 ,...,T N ,...,T S }, which is a dynamic scenario in which a low earth orbit satellite will sweep the ground at a fixed speed, corresponding to a time slice T 1 Compared with the static scenario of the cell, the most important difference is that the time that a certain cell can be in the coverage area of the low-orbit satellite is limited, and the time that the low-orbit satellite can provide service for the cell in different relative positions is different because the coverage area of the low-orbit satellite is equivalent to a circle in shape. Therefore, the total time length covered by the low-earth orbit satellite is mainly considered for setting the weight coefficients of different cells in a dynamic scene.
In the present embodiment, as shown in fig. 2, taking an M =19 low-earth-orbit satellite beam hopping system as an example, arrows indicate low-earth-orbit satellite movementIn the direction, the great circle represents the coverage area of a low earth orbit satellite. The cells are classified into 5 categories, cell 11, cell 12, cell 13, cell 14, and cell 15, according to the time of leaving the coverage area, according to the direction of movement of the low earth orbit satellite. It is assumed that the leftmost cell 11 will be in the next time slice T N+1 Leaving the coverage area of the low earth orbit satellite, the cell 13 located in the middle will be time sliced T N+3 From the coverage area of the low earth orbit satellite, the cell located furthest to the right will be time sliced T N+5 Away from the coverage area of the low earth orbit satellite. For a cell 11 that will leave the coverage of a low earth orbit satellite, as much resources as possible should be allocated to meet its traffic demand, i.e. higher weight coefficients. Therefore, in the weight coefficient updating part of the dynamic single-satellite beam hopping problem, the weight coefficient is determined by the time length of the cell leaving the coverage area of the low-orbit satellite and the time slice number. Thus time slicing T N Then, according to the time when each cell leaves the low-orbit satellite coverage area, determining the weight coefficient ratio { omega ] of each cell 1 ,ω 2 ,...,ω 5 }，ω 1 And ω 5 Corresponding to the first and last departing cell, omega, respectively 1 ,ω 2 ,…,ω 5 Sequentially increasing; for each time slice, the weight coefficient ratio determination process is required;
all served users are considered to be in the center of the cell, and due to the high orbit height of the low orbit satellite, the channel gains of different cells are considered only in the case of free space loss
Figure BDA0003633415720000121
Approximately equal, the channel capacities of different cells are approximately equal under the average power allocation scheme.
Step S5, based on the weighted objective function
Figure BDA0003633415720000122
Dynamic time resource allocation is carried out, on the premise that average power allocation is used, logarithm operation is carried out on the weighted target function, and the lagrangian equation is utilized to obtain the time slice T N Optimized time resources ofDistribution matrix
Figure BDA0003633415720000123
According to
Figure BDA0003633415720000124
The value of the medium element and the size of the serving cell threshold are obtained in time slicing T N Time resource allocation matrix v of N (t,i)；
The method specifically comprises the following steps: step S51, slice T in time N The weighted objective function in the following t-th slot is:
Figure BDA0003633415720000125
Figure BDA0003633415720000126
wherein the content of the first and second substances,
Figure BDA0003633415720000127
representing time slices T N The traffic demand of the ith cell in the next tth time slot,
Figure BDA0003633415720000128
which is indicative of the capacity of the channel,
Figure BDA0003633415720000129
corresponding to time slicing T N The weighting factor of the ith cell in the next t-th time slot,
Figure BDA00036334157200001210
representing time slices T N Channel gain, P, of the lower ith cell N (T, i) denotes slicing T in time N Power allocation of a hop beam serving the ith cell in the next t-th slot,
Figure BDA00036334157200001211
indicating the use of an average power allocation;
step S52, carrying out logarithmic operation on the weighted objective function to obtain optimizationThe problems are as follows:
Figure BDA00036334157200001212
V N (t,i)≤N max ，
Figure BDA00036334157200001213
establishing a Lagrange equation to obtain the time resource allocation without considering the influence of the common channel interference on the time resource allocation
Figure BDA00036334157200001214
To V N (t, i) obtaining a partial derivative
Figure BDA0003633415720000131
Making the partial derivative equal to 0 to obtain the optimized time resource distribution matrix
Figure BDA0003633415720000132
Computing lagrange operators
Figure BDA0003633415720000133
Order to
Figure BDA0003633415720000134
Simplifying the Lagrangian operator to
Figure BDA0003633415720000135
By introducing the Lagrangian lambda into
Figure BDA0003633415720000136
Obtaining an optimized time resource allocation matrix
Figure BDA0003633415720000137
Step S53, considering time slicing T N The co-channel interference between the beams in the same time slot satisfies the maximumUnder the premise that the number of the hopping wave beams and the adjacent cells can not be served at the same time, the method will be used
Figure BDA0003633415720000138
The cell combination with larger median value is selected and assigned to the time resource distribution matrix v N (t, i), in particular, the time resource allocation matrix will be optimized
Figure BDA0003633415720000139
The values in the table are arranged from large to small, and the Nth value is selected max The value is used as the serving cell threshold η under the time slicing N Then according to the formula
Figure BDA00036334157200001310
s.t.0≤|V N (t,i)|≤N max ，
Figure BDA00036334157200001311
Obtaining a time resource allocation matrix v N (t, i) wherein v N The elements in (t, i) include 1 and 0,v N (T, i) =1 denotes a time slice T N I cell served in the next t time slot, v N (t, i) =0 and vice versa;
step S6, according to the time resource distribution matrix v N (t, i) obtaining a set of served cells Φ, and according to v N (t, i) and a set phi, the maximum system capacity is used as an optimization target, and the optimization problem is expressed as follows:
Figure BDA00036334157200001312
Figure BDA0003633415720000141
Figure BDA0003633415720000142
P N (t,i)≤P sb ，
Figure BDA0003633415720000143
therefore, the power resource distribution matrix is obtained under the conditions that the total power is limited and the power of the single-hop wave beam is limited
Figure BDA0003633415720000144
The performance of the dynamic resource allocation method of this embodiment is measured by using the system throughput, which is defined as the ratio of the system throughput to the theoretical maximum throughput. In the embodiment, the orbit height h =600km of the low orbit satellite, the radius r =5km of the sub-beam, the total number of covered cells M =19, and the total number of maximum beam jumps N max =4, carrier frequency f =20GHz, single star (i.e. single low earth orbit satellite) system bandwidth B tot =400MHz, total single satellite transmission power P tot =100W, maximum single beam power P sb =30W, slot length T slot =10ms, total initial traffic demand D =2Gbps, traffic demand increment D seg ＝0.5/0.7/0.9Gbps。
FIG. 1 is a schematic diagram of simulation under different weight coefficient ratios in this embodiment (in the figure, "no weight" means { ω } ω 1 :ω 2 :ω 3 :ω 4 :ω 5 1, and "weight coefficient [5 4 3 21]"denotes { omega } 1 :ω 2 :ω 3 :ω 4 :ω 5 ) = {5]"means ω 1 :ω 2 :ω 3 :ω 4 :ω 5 And = { 5. When the ground service demand is low, the three weighting coefficients are configured with similar system throughput, because the satellite system in each time slice can completely meet the service demand under the condition of low service demand. As the ground traffic demand increases, the system performance of the three weight coefficient configurations varies.
The above description is only a preferred embodiment of the present invention, and therefore should not be taken as limiting the scope of the invention, and the equivalent variations and modifications made in the claims and the description of the present invention should be included in the scope of the present invention.

Claims (2)

1. A low orbit satellite dynamic resource allocation method based on beam hopping is characterized in that: the method comprises the following steps:
step S1, dividing S time slices { T } 1 ，T 2 ，...，T N ，...，T S For time slicing T 1 Step S2 is entered for initial resource allocation, for subsequent time slices { T } 2 ,...,T N ,...,T S Step S4 is carried out to carry out dynamic resource allocation, wherein the coverage area of the low-orbit satellite is divided into a plurality of cells, and the time slicing length is related to the height of the low-orbit satellite and the radius of the cells;
step S2, based on fair objective function
Figure FDA0004051380580000011
Performing initial time resource allocation, performing logarithm operation on a fair objective function and obtaining an optimized time resource allocation matrix by using a Lagrange equation on the premise of using average power allocation
Figure FDA0004051380580000012
According to
Figure FDA0004051380580000013
The relation between the value of the middle element and the threshold value of the service cell obtains the time slice T 1 Time resource allocation matrix v of 1 (t, i) wherein,
Figure FDA0004051380580000014
representing time slices T 1 The traffic demand of the ith cell in the next tth time slot,
Figure FDA0004051380580000015
indicating channel capacity, serving cell threshold is related to co-channel interference between beams in the same time slot under time slicing; v. of 1 The elements in (t, i) include 1 and 0,v 1 (T, i) =1 denotes a time slice T 1 I cell served in the next t time slot, v 1 (t, i) =0 and vice versa;
step S3, according to v 1 (t, i) obtaining a set of served cells Φ, and according to v 1 (t, i) and the set phi, and obtaining a power resource allocation matrix by adopting the maximum system capacity as an optimization target
Figure FDA00040513805800000110
Step S4, slicing time T N Dividing all the cells into m types according to the time of each cell leaving the low-orbit satellite coverage area, and determining the weight coefficient ratio { omega [ omega ]) of each type of cell 1 ：ω 2 ：...：ω m }，ω 1 And ω m Corresponding to the first and last departing cell, omega, respectively 1 ，ω 2 ，...，ω m Sequentially increasing;
step S5, based on the weighted objective function
Figure FDA0004051380580000016
Dynamic time resource allocation is carried out, on the premise of using average power allocation, logarithm operation is carried out on the weighting objective function, and the Lagrange equation is utilized to obtain an optimized time resource allocation matrix
Figure FDA0004051380580000017
According to
Figure FDA0004051380580000018
The relation between the value of the middle element and the threshold value of the service cell obtains the time slice T N Time resource allocation matrix v of N (t, i) wherein,
Figure FDA0004051380580000019
representing time slices T N The traffic demand of the ith cell in the next tth time slot,
Figure FDA0004051380580000021
which is indicative of the capacity of the channel,
Figure FDA0004051380580000022
corresponding to time slicing T N Weight coefficient, v, of ith cell in the next t time slot N The elements in (t, i) include 1 and 0,v N (T, i) =1 denotes a time slice T N I cell served in the next t time slot, v N (t, i) =0 and vice versa;
step S6, according to the time resource distribution matrix v N (t, i) obtaining a set of served cells Φ, and according to v N (t, i) and phi set, adopting maximum system capacity as optimization target, under the condition of total power limitation and single-hop wave beam power limitation obtaining power resource distribution matrix
Figure FDA0004051380580000023
Wherein M is the number of cells, T 1 Representing the first time slice, T N Representing the nth time slice.
2. The method of claim 1, wherein the method for dynamically allocating resources to the low earth orbit satellite based on beam hopping is characterized in that: in the step S1, the area S of each cell cell ＝S tot /M，S tot The area of a coverage area of the low-orbit satellite, M is the number of cells, the arrangement direction of each cell is the same as the movement direction of the low-orbit satellite so as to ensure that the cells positioned at the edge of the coverage area can leave the coverage area at the same time, and the time slice length
Figure FDA0004051380580000024
h is the low orbit satellite orbit height, R is the cell radius, R is the earth radius, and GM is the gravitational constant.
CN202210532194.4A 2022-05-09 2022-05-09 Low-orbit satellite dynamic resource allocation method based on beam hopping Active CN114928400B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210532194.4A CN114928400B (en) 2022-05-09 2022-05-09 Low-orbit satellite dynamic resource allocation method based on beam hopping

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210532194.4A CN114928400B (en) 2022-05-09 2022-05-09 Low-orbit satellite dynamic resource allocation method based on beam hopping

Publications (2)

Publication Number Publication Date
CN114928400A CN114928400A (en) 2022-08-19
CN114928400B true CN114928400B (en) 2023-03-03

Family

ID=82808148

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210532194.4A Active CN114928400B (en) 2022-05-09 2022-05-09 Low-orbit satellite dynamic resource allocation method based on beam hopping

Country Status (1)

Country Link
CN (1) CN114928400B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110996394A (en) * 2019-12-12 2020-04-10 南京邮电大学 Satellite communication system resource scheduling method combining beam hopping and precoding
CN113572517A (en) * 2021-07-30 2021-10-29 哈尔滨工业大学 Beam hopping resource allocation method, system, storage medium and equipment based on deep reinforcement learning

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020110094A1 (en) * 2001-02-13 2002-08-15 Reddy Naveen S. Spot beam hopping packet scheduler system
US10511379B2 (en) * 2010-05-02 2019-12-17 Viasat, Inc. Flexible beamforming for satellite communications
CN109450498B (en) * 2018-11-05 2021-05-04 南京邮电大学 Beam hopping method of high-throughput satellite communication system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110996394A (en) * 2019-12-12 2020-04-10 南京邮电大学 Satellite communication system resource scheduling method combining beam hopping and precoding
CN113572517A (en) * 2021-07-30 2021-10-29 哈尔滨工业大学 Beam hopping resource allocation method, system, storage medium and equipment based on deep reinforcement learning

Also Published As

Publication number Publication date
CN114928400A (en) 2022-08-19

Similar Documents

Publication Publication Date Title
CN110518956B (en) Time slot allocation algorithm-based hopping pattern optimization method and device, and storage medium
Lei et al. Joint power and carrier allocation for the multibeam satellite downlink with individual SINR constraints
Grace et al. Improving the system capacity of broadband services using multiple high-altitude platforms
US8244266B2 (en) Scheduling method in a cellular system using wired relay station
US9876539B2 (en) Method and apparatus for scalable load balancing across wireless heterogeneous MIMO networks
US8134945B2 (en) Apparatus and method for increasing cell capacity through optional signal combining between relay stations in a cellular system using wired relay stations
JPH08242481A (en) Method and apparatus for allocating up-link channel for cellular mobile communication system employing multibeam antenna with beam selection
US10644788B2 (en) Method of allocating frequency resources for a satellite telecommunication system
CN111865398B (en) Satellite-ground transmission method under large-scale LEO satellite deployment
CN111416648A (en) Multi-beam adaptive management method and device for low-earth-orbit satellite system
CN112994782B (en) Satellite mobile communication method combining space-time-frequency triple multiplexing and bandwidth self-adaption
Zhang et al. Resource allocation in beam hopping communication system
US20080299981A1 (en) Method of coordinated wireless downlink transmission
Kisseleff et al. A new optimization tool for mega-constellation design and its application to trunking systems [International Communications Satellite Systems Conference]
Zhang et al. System-level evaluation of beam hopping in NR-based LEO satellite communication system
CN114337976A (en) Transmission method combining AP selection and pilot frequency allocation
Gaytan et al. Dynamic scheduling for high throughput satellites employing priority code scheme
Gu et al. Cooperative spectrum sharing in a co-existing LEO-GEO satellite system
Cocco et al. Radio resource management strategies for DVB-S2 systems operated with flexible satellite payloads
JP2002176385A (en) Radio resource management method
Wang et al. Channeling partitioning policies for multi-class traffic in LEO-MSS
CN114928400B (en) Low-orbit satellite dynamic resource allocation method based on beam hopping
Shibata et al. Two-step dynamic cell optimization algorithm for HAPS mobile communications
CN114978282B (en) Satellite power distribution method and satellite power multiple-input multiple-output distribution system
CN107124726B (en) Multi-beam GEO system access control method based on maximized throughput

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant