Satellite phased array hopping beam uplink resource allocation method based on genetic algorithm

Abstract

The invention relates to a satellite phased array hopping beam uplink resource allocation method based on a genetic algorithm, and belongs to the technical field of satellite communication. The method comprises the following steps: defining an objective function of a satellite phased array hopping beam uplink resource allocation problem, performing grid division on a satellite phased array hopping beam uplink resource pool, and performing coding; setting genetic individuals, wherein each individual comprises a cell division code and a carrier group division code; performing population initialization; setting a fitness function of grid division of each resource pool; selecting individuals to be reserved through random sampling to complete coding selection; performing crossover and mutation operations; and iteration is performed until the process is finished. According to the invention, the terminal and cell management of the phased array uplink beam based on the genetic algorithm is realized, and the cache queue length of each terminal in the satellite phased array hopping beam uplink can be effectively reduced, so that the communication delay is reduced, and the throughput of the network is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a satellite phased array beam-hopping uplink resource allocation method based on a genetic algorithm, which is suitable for an on-board resource allocation scenario in a satellite phased array beam-hopping uplink. Background Art

[0002] At present, the new generation of domestic broadband communication satellites and satellite Internet node satellites generally adopt on-board processing systems and are equipped with phased array antennas, which are quite different from traditional communication satellites based on transparent forwarding in terms of orbital characteristics, working modes, and payload configuration. This poses many challenges to the allocation of satellite phased array beam-hopping uplink resources, including:

[0003] (1) Flexibility of phased array antennas. Phased array antennas increase the diversity of the system in the spatial dimension and can provide time-sharing coverage of multiple dispersed areas on the surface within a certain period, thereby covering more areas with fewer hopping beams. At the same time, by adjusting the residence time of the beam in different areas, communication capabilities that match the business can be provided. Therefore, which areas the phased array antenna illuminates within a period and the residence time in each area need to be considered as one-dimensional resources and dynamically scheduled.

[0004] (2) Diversity of station types. In order to reconcile the differences in transmission capacity caused by different antenna apertures and output powers of ground user station types, the uplink channel generally adopts a multi-carrier group design. Different carrier groups can be configured with different symbol rates to adapt to different station types. On the other hand, the adaptability of the carrier group to the station type is inversely proportional to the amount of information it can carry. In general, the lower the symbol rate, the larger the adaptability, and the less information can be carried. Therefore, when determining the combination of carrier groups, a trade-off needs to be made to ensure access to all user station types and maximize the use of frequency resources.

[0005] (3) Link complexity. Phased array antennas mostly operate in high frequency bands such as Ka, Q, and V, which have large rain attenuation and are easily affected by weather changes. In response to this situation, it is necessary to compensate for link differences through power control, adaptive modulation coding, and other means to ensure communication quality. However, there is currently a lack of satellite phased array beam-hopping uplink resource allocation methods that model the above problems at home and abroad. Summary of the invention

[0006] To solve the above problems, the present invention proposes a satellite phased array beam hopping uplink resource allocation method based on genetic algorithm. The present invention realizes the terminal and cell management of phased array uplink beam based on genetic algorithm, which can effectively reduce the cache queue length of each terminal in the satellite phased array beam hopping uplink, thereby reducing communication delay and improving network throughput.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A satellite phased array beam hopping uplink resource allocation method based on genetic algorithm comprises the following steps:

[0009] Step 1: Define the objective function of the satellite phased array beam-hopping uplink resource allocation problem and divide the satellite phased array beam-hopping uplink resource pool into grids;

[0010] Step 2: Encode the grid division of the satellite phased array beam hopping uplink resource pool, set the genetic individuals, each of which contains the cell division code X and the carrier group division code G;

[0011] Step 3: Initialize the population. The cell division code X is obtained by reordering the random individuals. The carrier group division code G is obtained by taking the natural value of each base with equal probability to obtain the random individuals.

[0012] Step 4: According to the cell division code X and the carrier group division code G, the resource allocation problem is decomposed into multiple cell time slot allocation sub-problems, and the sum of the allocated resource values ​​of all sub-problems is calculated as the fitness function of each resource pool grid division;

[0013] Step 5: The fitness of each resource pool grid is normalized by the roulette selection method, and the interval is divided into multiple segments according to the normalized fitness. The individuals to be retained are selected by random sampling to complete the coding selection;

[0014] Step 6: According to the cell division code X and the carrier group division code G, a crossover point is randomly selected to perform probabilistic code exchange, and then only the cell division code X is adjusted for co-location exchange until the coding requirements are met, completing the crossover operator step;

[0015] Step 7: Randomly select positions with different values ​​for the cell division code X and exchange them; randomly select a position for the carrier group division code G, and update the carrier group number at the position to another value, completing the mutation operator step;

[0016] Step 8: Determine whether the number of iterations is met. If so, the process ends; otherwise, repeat steps 4 to 8 until the process ends.

[0017] Furthermore, in step 1, the objective function is:

[0018]

[0019] in, Indicates the number of cells, Indicates the number of carrier groups, Indicates The member terminal set under a cell, Indicates The member terminal set of a carrier group, Indicates terminal The value of the resources allocated, Indicates the parameter that makes the following expression take the maximum value ;

[0020] The specific grid division method of the satellite phased array beam hopping uplink resource pool is as follows:

[0021] Use The member terminals of the carrier group United The member terminal set of a cell , divide the satellite phased array beam-hopping uplink resource pool into grids, and corresponding resources to be allocated.

[0022] Furthermore, in step 2, the cell division code X is encoded using binary code, which includes "0" bases, and use The "1" bases divide X into The number of "0" bases in each gene segment represents the number of resident time slots in the corresponding cell, where: is the number of time slots;

[0023] The carrier group division code G contains N bases in total, where N is the number of terminals. Each base represents the carrier group number to which a user terminal belongs, and the value range is , bases with the same carrier group number represent the member terminal set of the corresponding carrier group .

[0024] Furthermore, in step 3, the population size is , randomly generated during initialization individual;

[0025] For the cell division code X, "0" bases and The "1" bases are reordered to obtain random individuals;

[0026] For the carrier group division code G, a natural value is taken in the range of 1 to M with equal probability for each base to obtain a random individual.

[0027] Further, in step 4, The district and The grid marked by the carrier group and the member terminals in the grid are expressed as , according to the cell division code X, carrier group division code G and resource pool grid division , decomposing the resource allocation problem into For each cell time slot allocation sub-problem, the resource satisfaction rate is used to calculate the time slot allocation rate of each terminal. The value of the resources allocated , and calculate the sum of the allocated resource values ​​of all cell time slot allocation sub-problems as the fitness function of each resource pool grid partition .

[0028] Furthermore, the specific method of step 5 is:

[0029] The roulette wheel selection method is used to normalize the fitness of each resource pool grid partition:

[0030] ,

[0031] Get the normalized fitness , the sum of the normalized fitness of all individuals in the population is 1;

[0032] According to the normalized fitness ,Will The interval is divided into part;

[0033] The coding selection is completed by selecting the individuals to be retained through random sampling.

[0034] Furthermore, the specific method of step 6 is:

[0035] The intersection point is randomly determined for the cell division code X and the carrier group division code G at the same time, and the data segments in the two groups of codes are randomly exchanged with the intersection point as the boundary;

[0036] For the cell division code X, the extra "1" base in the cell division code of individual A and the "0" base in the cell division code of individual B are swapped at the same position.

[0037] Furthermore, the specific method of step 7 is:

[0038] Code the cell division X, randomly select a "1" base and a "0" base, and swap the two bases;

[0039] The carrier group division code G is randomly selected, and the carrier group number of the base is updated to other values ​​in combination with the constraint of user terminal capability differences.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The present invention aims at the characteristics of flexibility of phased array antenna, diversity of station types and complexity of links in satellite beam-hopping uplink scenarios, realizes terminal and cell management of phased array uplink beam based on genetic algorithm, and provides fitness function design, coding selection, mutation and crossover operator design in specific scenarios.

[0042] 2. In practical applications, the present invention can effectively reduce the cache queue length of each terminal in the satellite phased array beam-hopping uplink, thereby reducing communication delay and improving network throughput, and is particularly suitable for scenarios with large terminal business volume. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] FIG1 is a flow chart of a satellite phased array beam-hopping uplink resource allocation method based on a genetic algorithm in an embodiment of the present invention.

[0044] FIG2 is a schematic diagram of genetic coding and crossover of a satellite phased array beam-hopping uplink resource allocation method based on a genetic algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm, as shown in FIG1 , comprises the following steps:

[0047] Step 1: Define the objective function of the satellite phased array beam-hopping uplink resource allocation problem and complete the grid division of the satellite phased array beam-hopping uplink resource pool;

[0048] Step 2: Encode the grid division of the satellite phased array beam hopping uplink resource pool, set the genetic individual, and the individual contains two groups of codes, X and G, which represent the cell division code and the carrier group division code respectively;

[0049] Step 3: Initialize the population, where the cell division code X is obtained by reordering random individuals, and the carrier group division code G is obtained by taking natural values ​​of each base with equal probability;

[0050] Step 4: According to the cell division code X and the carrier group division code G, the resource allocation problem is decomposed into multiple cell time slot allocation sub-problems, and the sum of the allocated resource values ​​of all sub-problems is calculated as the fitness function of each resource pool grid division;

[0051] Step 5: normalize the fitness of each resource pool grid partition by a roulette wheel selection method, divide the interval into multiple segments according to the normalized fitness, perform random sampling to select the retained individuals, and complete the coding selection;

[0052] Step 6: According to the two sets of codes X and G, the crossover points are randomly selected to perform probabilistic code exchange. Then, only the cell division code X is adjusted by the same position exchange until the coding requirements are met, and the crossover operator step is completed;

[0053] Step 7: Randomly select positions with different values ​​for the cell division code X and exchange them; randomly select a position for the carrier group division code G, and update the carrier group number at the position to another value, completing the mutation operator step;

[0054] Step 8: Determine whether the number of iterations is met. If so, the process ends; otherwise, repeat steps 4 to 8 until the process ends.

[0055] Here is a more specific example:

[0056] The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm is still shown in Figure 1. The specific process is as follows:

[0057] Step S1: Define the objective function of the satellite phased array beam hopping uplink resource allocation problem:

[0058]

[0059] in, Indicates the number of cells, Indicates the number of carrier groups, Indicates The member terminal set under a cell, Indicates The member terminal set of a carrier group, Indicates terminal The value of the resources allocated.

[0060] The specific method of resource pool grid division is: divide the phased array beam hopping resource pool into grid, using the The member terminals of the carrier group United The member terminal set of a cell , the user terminals under the phased array beam are divided into grids, and corresponding resources to be allocated.

[0061] During the iteration process, The district and The grid marked by the carrier group and the member terminals in the grid are used To express.

[0062] Step S2: Encode the grid division of the satellite phased array beam hopping uplink resource pool. As shown in Figure 2, the inherited individuals are set to include two groups of codes, X and G, which represent the cell division code and the carrier group division code respectively. Among them, the cell division code X adopts binary coding. If the number of time slots is , then it contains "0" bases, and use "1" base is divided into The number of "0" bases in each gene segment represents the number of resident time slots in the corresponding cell.

[0063] The carrier group division code G contains N bases in total, where N is the number of terminals. Each base represents the carrier group number to which a user terminal belongs, and the value range is ; Bases with the same carrier group number represent the member terminal set of the corresponding carrier group .

[0064] Step S3: Initialize the population of the cell division code and the carrier group division code. Set the population size to , randomly generated during initialization The cell division code obtains random individuals by reordering, and the carrier group division code obtains random individuals by taking natural values ​​for each base with equal probability.

[0065] Specifically, for the cell division code X, "0" bases and The "1" bases are reordered to obtain random individuals. For the carrier group division code G, each base is given a natural value with equal probability in the range of 1 to M to obtain a random individual. It should be noted that in actual applications, due to the difference in the transmission capabilities of user terminals, the available carrier group range may be smaller than , which can be adjusted according to actual needs.

[0066] Step S4: Calculate the fitness function. , the resource allocation problem is decomposed into For each resource pool grid division, the phased array beam hopping resource pool is divided into grids, each grid calculates each terminal through resource satisfaction rate Value of allocated resources , and calculate the sum of the allocated resource values ​​of all cell time slot allocation sub-problems, which is the fitness function value of each resource pool grid partition.

[0067] Step S5: Using the roulette wheel selection (RWS) method, first normalize the fitness of each resource pool grid partition:

[0068] ,

[0069] Get the normalized fitness , the sum of the normalized fitness of all individuals in the population is 1;

[0070] Then, according to the normalized fitness Will The interval is divided into part;

[0071] Finally, the individuals to be retained are selected through random sampling to complete the coding selection.

[0072] Step S6: Design and operate a crossover operator according to the cell division code X and the carrier group division code G. Still referring to FIG2 , for the two groups of codes X and G, a crossover point is randomly determined at the same time, and the data segments in the two groups of codes are interchanged with this point at a certain probability, and the probability of the crossover operator is set according to an empirical value.

[0073] For the cell division code X, the same position swap adjustment is adopted, and the specific method is: the extra "1" base in the code of individual A is swapped with the "0" base in the code of individual B at the same position. The reason for this is that according to the actual resource allocation cell division rule, the number of "1" bases contained in the cell division code X is fixed, but after the operation of randomly selecting intersection points for probabilistic code swapping at the same time, the number of "1" bases in the newly generated code no longer meets the coding requirements.

[0074] Step S7: Design and operate mutation operators for the cell division code X and the carrier group division code G. Specifically, for the cell division code X, randomly select a "1" base and a "0" base, and swap the two bases; for the carrier group division code G, randomly select a base position, and update the carrier group number of the base to another value in combination with the constraint of the user terminal capability difference. By performing the mutation steps on the cell division code X and the carrier group division code G, the algorithm evolution process can be prevented from falling into a local optimum. The probability of the mutation operator is set according to the empirical value.

[0075] Step S8: Determine whether the number of iterations is met. If so, the process ends; otherwise, repeat the above steps 4 to 8 until the process ends.

[0076] The present invention aims at the characteristics of flexibility of phased array antennas, diversity of station types and complexity of links in satellite beam-hopping uplink scenarios, realizes terminal and cell management of phased array uplink beams based on genetic algorithms, and provides fitness function design, coding selection, mutation and crossover operator design in specific scenarios. In practical applications, the cache queue length of each terminal in the satellite phased array beam-hopping uplink is effectively reduced, thereby reducing communication delay and improving network throughput. The invention is particularly suitable for scenarios with large terminal business volume.

[0077] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. A satellite phased array beam-hopping uplink resource allocation method based on genetic algorithm, characterized in that: The following steps are involved: Step 1: Define the objective function of the satellite phased array beam-hopping uplink resource allocation problem and grid the satellite phased array beam-hopping uplink resource pool; Step 2: Encode the grid division of the satellite phased array beam hopping uplink resource pool, set the genetic individuals, each of which contains the cell division code X and the carrier group division code G; Step 3: Initialize the population. The cell division code X is obtained by reordering the random individuals. The carrier group division code G is obtained by taking the natural value of each base with equal probability. Step 4: According to the cell division code X and the carrier group division code G, the resource allocation problem is decomposed into multiple cell time slot allocation sub-problems, and the sum of the allocated resource values ​​of all sub-problems is calculated as the fitness function of each resource pool grid division; Step 5: The fitness of each resource pool grid is normalized by the roulette selection method, and the interval is divided into multiple segments according to the normalized fitness. The individuals to be retained are selected by random sampling to complete the coding selection; Step 6: According to the cell division code X and the carrier group division code G, a crossover point is randomly selected to perform probabilistic code exchange, and then only the cell division code X is adjusted for co-location exchange until the coding requirements are met, completing the crossover operator step; Step 7: Randomly select positions with different values ​​for the cell division code X and exchange them; randomly select a position for the carrier group division code G, and update the carrier group number at the position to another value, completing the mutation operator step; Step 8: Determine whether the number of iterations is met. If so, the process ends; otherwise, repeat steps 4 to 8 until the process ends.

2. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 1, characterized in that: In step 1, the objective function is: in, Indicates the number of cells, Indicates the number of carrier groups. Indicates The member terminal set under a cell, Indicates The member terminal set of a carrier group, Indicates terminal The value of the resources allocated, Indicates the parameter that makes the following expression take the maximum value ; The specific grid division method of the satellite phased array beam hopping uplink resource pool is as follows: Use The member terminals of the carrier group United The member terminal set of a cell , divide the satellite phased array beam-hopping uplink resource pool into grids, and corresponding resources to be allocated.

3. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 2, characterized in that: In step 2, the cell division code X is encoded in binary format, which includes "0" bases, and use "1" bases divide X into The number of "0" bases in each gene segment represents the number of resident time slots in the corresponding cell, where: is the number of time slots; The carrier group division code G contains N bases in total, where N is the number of terminals. Each base represents the carrier group number to which a user terminal belongs, and the value range is , bases with the same carrier group number represent the member terminal set of the corresponding carrier group .

4. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 3, characterized in that: In step 3, the population size is , randomly generated during initialization individual; For the cell division code X, "0" bases and The "1" bases are reordered to obtain random individuals; For the carrier group division code G, a natural value is taken in the range of 1 to M with equal probability for each base to obtain a random individual.

5. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 4, characterized in that: In step 4, The district and The grid marked by the carrier group and the member terminals in the grid are expressed as , according to the cell division code X, carrier group division code G and resource pool grid division , decomposing the resource allocation problem into For each cell time slot allocation sub-problem, the resource satisfaction rate is used to calculate the time slot allocation rate of each terminal. The value of the resources allocated , and calculate the sum of the allocated resource values ​​of all cell time slot allocation sub-problems as the fitness function of each resource pool grid partition .

6. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 5, characterized in that: The specific method of step 5 is: The roulette wheel selection method is used to normalize the fitness of each resource pool grid partition: ， Get the normalized fitness , the sum of the normalized fitness of all individuals in the population is 1; According to the normalized fitness ,Will The interval is divided into part; The coding selection is completed by selecting the individuals to be retained through random sampling.

7. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 6, characterized in that: The specific method of step 6 is: The intersection point is randomly determined for the cell division code X and the carrier group division code G at the same time, and the data segments in the two groups of codes are randomly exchanged with the intersection point as the boundary; For the cell division code X, the extra "1" base in the cell division code of individual A and the "0" base in the cell division code of individual B are swapped at the same position.

8. The satellite phased array beam hopping uplink resource allocation method based on genetic algorithm according to claim 7, characterized in that: The specific method of step 7 is: Code the cell division X, randomly select a "1" base and a "0" base, and swap the two bases; The carrier group division code G is randomly selected, and the carrier group number of the base is updated to other values ​​in combination with the constraint of user terminal capability differences.

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