Genetic algorithm-based time-frequency resource allocation method

Abstract

The invention discloses a time-frequency resource allocation method based on a genetic algorithm. The method comprises the following steps: establishing a user scheduling and resource allocation model; performing preliminary resource block allocation based on the worst interference situation; calculating the rate of each resource block based on the actual signal-to-noise ratio, calculating the total rate of the resource blocks occupied by each ground user for each ground user, and releasing the redundant resource blocks occupied by the ground users according to actual demands; and redistributing the released redundant resource blocks by adopting a genetic algorithm. According to the method, the actual multi-scale resource structure of the satellite-ground fusion network is fully considered, the resource allocation strategy is initialized based on the worst interference situation, and finally the resource allocation effect is optimized by using the GA algorithm, so that the resource utilization rate is maximized and the same-frequency interference is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite communication, and particularly to a time-frequency resource allocation method based on a genetic algorithm. Background Art

[0002] Terrestrial Networks (TN) have been able to provide effective communication services in densely populated areas. However, it is challenging for TN to cover remote areas with sparse populations, and Satellite Networks (SN) can be used as a supplement. Combining SN with TN to form an integrated satellite and terrestrial network is regarded as one of the most important research directions for 6G to achieve ubiquitous wireless coverage.

[0003] In a satellite-terrestrial integrated network, a Ground User (GU) can choose SN or TN for service. This gives rise to a problem of scheduling user access to different networks and allocating multiple resources accordingly in an effective manner. The choice between SN and TN may provide completely different transmission modes for users and have different scales in terms of time-frequency frames. This will lead to complex interference patterns in the time-frequency domain, so resource allocation in a satellite-terrestrial integrated network is more complex than in a homogeneous network. How to achieve efficient user scheduling and resource allocation is the key to the implementation of a satellite-terrestrial integrated network. However, most existing studies have ignored the differences in the frame structures of SN and TN, which may reduce performance in practice. Summary of the Invention

[0004] Aiming at the spatio-temporal scale heterogeneity problem of a satellite-terrestrial integrated network, the present invention provides a time-frequency resource allocation method based on a genetic algorithm, which fully considers the actual multi-scale resource structure of the satellite-terrestrial integrated network. First, a resource allocation strategy is initialized based on the worst interference scenario, and finally, the GA algorithm is used to optimize the resource allocation effect, so as to maximize the resource utilization rate and reduce co-channel interference.

[0005] To achieve the above technical objectives, the technical solution adopted by the present invention is as follows:

[0006] The present invention discloses a time-frequency resource allocation method based on a genetic algorithm, and the method includes the following steps:

[0007] S1, during each beam hopping time slot, ground users are assigned to the satellite network or the terrestrial network according to their positions to generate a ground user allocation method; the first ground users associated with the terrestrial network are further paired with transmission blocks for transmission to obtain a pairing matrix; constraint conditions are introduced to establish a user scheduling and resource allocation model;

[0008] S2, perform preliminary resource block allocation based on the worst interference scenario; specifically, calculate the number of resource blocks required by each terrestrial user, and allocate resource blocks to the terrestrial users within each cell based on the values of the number of resource blocks required by the terrestrial users to obtain a resource allocation matrix; wherein, each resource block only provides the minimum rate;

[0009] S3, calculate the rate of each resource block based on the actual signal-to-noise ratio. For each terrestrial user, calculate the total rate of the resource blocks it occupies, and release the redundant resource blocks occupied by the terrestrial users according to the actual requirements; reallocate the released redundant resource blocks using the genetic algorithm, and repeat the iteration until the iteration termination condition is reached.

[0010] In step S1, the following steps are further included:

[0011] S11, during each beam hopping time slot, terrestrial users are allocated to the satellite network or the terrestrial network according to their positions; the terrestrial user allocation method is represented by X = {x i , i = 1,..., I}, for the J i terrestrial users covered by the i-th cell, x i = {x i，j | j = 1, 2,..., J i}, where

[0012]

[0013] where x i，j = 1, 0 terrestrial users are respectively called the first terrestrial user and the second terrestrial user;

[0014] Given the terrestrial user allocation method X, the first terrestrial user associated with the terrestrial network is further paired with a transport block for transmission, and the pairing matrix is represented as S = {S i , i = 1,..., I}, where the elements in the matrix S i of size M i × J i , s i，m，j ∈ {0, 1} indicates whether the first terrestrial user j is matched with the transport block m in cell i, m = 1,..., M i , j = 1,..., J i ;

[0015] Assume that within the beam hopping time slot, each first terrestrial user is paired with the same transport block, and the number of first terrestrial users served by one transport block is limited by , which is mathematically described as:

[0016]

[0017] Describe the resource allocation as a set of matrices where represents the resource block allocation of terrestrial users in a hopping beam time slot, and the hopping beam time slot contains n bh time slots; the matrix a i，j has a size equal to L×n bh , where the (l, t) element indicates whether the t-th resource block of the l-th channel is allocated to the j-th terrestrial user in the i-th cell, l = 1,..., L, t = 1,..., n bh ; the following constraint conditions are introduced:

[0018] 1) Dedicated channel constraint:

[0019]

[0020] 2) Continuity constraint of satellite network channel occupancy:

[0021]

[0022] 3) Continuity constraint of satellite network time slot occupancy:

[0023]

[0024] 4) Resource block uniqueness constraint of satellite network:

[0025]

[0026] 5) Resource block reuse constraint of transmission block:

[0027]

[0028] The user scheduling and resource allocation model is established as:

[0029]

[0030] subject to(4),(5),(6),(8),(9)

[0031] Furthermore, in step S2, the process of calculating the number of resource blocks required for each terrestrial user includes the following steps:

[0032] Let the terrestrial user only reach the minimum rate, and the number of resource blocks required, n i，j is expressed as where corresponds to the worst signal-to-noise ratio and is calculated by setting all to 1; for satellite users, the number of channels required for each second terrestrial user is given by , and the actual number of resource blocks required is expressed as where due to the continuity of the satellite network frame structure in the time domain; for the terrestrial network, the total number of RBs allocated to terrestrial users paired with the same transport block is denoted as n i，m = ∑ j s i，m，j n i，j , which satisfies the resource block reuse constraint of the transport block and does not exceed n max ; if n i，m > n max , then the excess part is deleted Arrange the n of the paired GUs in descending order i，j , and sequentially delete the redundant RBs until n i，j and The update of is given by the following formula:

[0033]

[0034] Furthermore, in step S2, the process of allocating resource blocks to terrestrial users in each cell based on the value of the number of resource blocks required by terrestrial users to obtain a resource allocation matrix includes the following steps:

[0035] First, allocate the idle RBs; if there are no remaining idle RBs, then reallocate the occupied RBs to GUs according to the CFI, where the RBs occupied by fewer GUs will be reallocated with higher priority; in each cell, let be used to track the number of GUs occupying the same RB:

[0036]

[0037] where, Ω max = maxΩ indicates that all RBs have been occupied, and Ω + = ∑ l ∑ t (Ω max - Ω(l, t)) refers to the number of available RBs in the case of Ω(l, t) < Ω max ; in the cell irradiated by the spot beam, first allocate the RBs of SGUs, and then allocate the RB of TGU;

[0038] a) RB allocation for SGUs:

[0039] For each cell The RBs are allocated to SGUs in ascending order of their n i，j , and the allocation continues until the resources exceed T bh ; update the RBs allocation matrix a of the SGU occupying the RB i，j , and accordingly update Ω to Ω(l, t) = …(l.t) + 1;

[0040] b) RB allocation for TGUs:

[0041] In each cell, the RBs are allocated to the TGUs in ascending order of their n i，j ;

[0042] If Ω max = 0 or Ω max = 1, and Ω + ≥ n i，j , the idle RBs are sequentially allocated to the TGUs;

[0043] If Ω max = 2, and Ω + ≥ n i，j , the RBs occupied by the SGUs are preferentially allocated; specifically, when there are RBs occupied only by the SGUs, and n sgu > 0, the RBs are allocated to the TGUs in descending order of , where j * represents the SGU; when updating n sgu , if η sgu ≥ n i，j , then n sgu is updated by n sgu - n i，j , otherwise, the RBs required by the TGU are updated to n sgu = n i，j - n sgu , and n sgu is set to 0; when n sgu = 0, the TGUs are allocated the RBs occupied by each other, and these RBs are allocated in descending order of ;

[0044] If Ω max > 2, and Ω + ≥ n i，j , the RBs are allocated in descending order of m * , where m * represents the previously occupied TGUs paired with the TBSs;

[0045] If Ω + < n i，j , the RBs with Ω(l, t) < Ω max are allocated, and the RBs required by the TGU are n i，j = n i，j - Ω + ; Ω max is set to Ω max + 1, and then the remaining n i，j RBs are allocated.

[0046] Furthermore, in step S3, the process of reallocating the released redundant resource blocks by using the genetic algorithm includes the following steps:

[0047] For each individual, the gene corresponds to the occupancy of each a i，j and each individual contains ∑ i J i genes; In each iteration, two individuals are selected: one is obtained from the result of the previous iteration and one is randomly generated; The random set is generated as a binary set satisfying different TN and SN frame structures and occupancy constraints; These two individuals are selected as the parent generation to obtain offspring

[0048] Set the number of successfully served GUs as the fitness function to evaluate the performance of each individual for the optimization problem; The fitness of the parent generation uses or the larger number, denoted as η, and the fitness of the offspring is denoted as η * ;

[0049] Use Q i，j and Q′ i，j to evaluate the genes of the parents, and the operations are selected as follows:

[0050] 1) If Q i，j = 0 and Q′ i，j = 1, then use crossover, and each element in the gene is replaced with the corresponding element cr with probability e

[0051] 2) If Q i，j = 0 and Q′ i，j = 1, then apply mutation, and the elements in the corresponding gene of the offspring are set to with probability e mu ;

[0052] 3) Otherwise, the gene a i，j is selected for the offspring;

[0053] Using the above process, the offspring is generated as

[0054] Compare the fitness functions η and η * , and select the individual with the larger value for the next iteration; Among them, the fitness function of the offspring η * is optimized and calculated after the squeezing step.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] The time-frequency resource allocation method based on genetic algorithm of the present invention studies and obtains an integrated resource allocation strategy in the satellite-ground integrated network based on the actual multi-scale resource structure of the satellite-ground integrated network. In this method, the satellite network and the ground network adopt a shared frequency band scheme, which can provide a higher successful service ratio for users in the overall area compared with the existing non-common frequency scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the system model structure of the satellite-ground integrated network; among them, (a) represents the coverage model, and (b) represents the transmission model;

[0058] Figure 2 It is a schematic diagram of different structures of the time and frequency frames of the satellite-ground integrated network (left figure) and the resulting interference pattern (right figure);

[0059] Figure 3 It is the performance diagram of P sat for different algorithms;

[0060] Figure 4 It is the performance diagram of different algorithms and P tn for different algorithms. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0062] The present invention discloses an integrated resource allocation method for a satellite-ground integrated network based on genetic algorithm. Considering the spatio-temporal scale heterogeneity problem between the satellite network and the ground network in the satellite-ground integrated network, we first perform a preliminary RB allocation based on the worst interference scenario, calculate the number of RBs required for each GU, preferentially allocate resources to SN users, and adjust the resource allocation on the TBS according to the interference situation. Then, the GA algorithm is used to further optimize the resource allocation, and the redundant resource usage is eliminated through the "compression" step to improve the actual rate, so as to meet the needs of more GUs. Finally, through these two-step optimization methods, it is ensured that each GU can obtain the required bandwidth as much as possible under the condition of limited resources.

[0063] Specifically, it includes the following steps:

[0064] S1. Establish a user scheduling and resource allocation model;

[0065] S2. Perform a preliminary RB allocation based on the worst interference scenario;

[0066] S3. Further optimize the resource allocation based on the GA algorithm;

[0067] S4. Through these two-step optimization methods, ensure that each GU can obtain the required bandwidth as much as possible under the condition of limited resources;

[0068] Points to be protected by the present invention:

[0069] During each beam hopping time slot, the GUs are assigned to the SN or TN according to their positions. The GUs in the satellite serving cell during the beam hopping period can be served by the SN, and the GUs within the coverage of the TBS can also be served by the TN. The specific assignment of GUs is represented by X = {x i , i = 1,..., I}. Specifically, for the J i GUs covered by the i-th cell, x i = {x i，j | j = 1, 2,..., J i}, where

[0070]

[0071] Among them, the GUs with x i，j = 1 and 0 are called SGU and TGU respectively.

[0072] Given X, the TGUs associated with the TN should be further paired with specific TBSS for transmission. The pairing matrix is represented as S = {S i , i = 1,..., I}, where the elements in the matrix S i of size M i × J i , s i，m，j ∈ {0, 1}, m = 1,..., M i , j = 1,..., J i , indicate whether the TGUj is matched with the TBSm in the cell i. For simplicity, we assume that within the beam hopping time slot, each TGU is paired with the same TBS, and the number of TGUs that a TBS can serve is limited by . These constraints are described mathematically as

[0073]

[0074] Based on the above network association and TGU-TBS matching, the resource allocation is described as a set of matrices where represents the RB allocation of the GUs in a beam hopping time slot, and this time slot contains n bh time slots. The size of the matrix a i，j is equal to L × n bh , where the (l, t) element indicates whether the t-th RB of the L-th channel is allocated to the j-th GU in the i-th cell, l = 1,..., L, t = 1,..., n bh . To achieve the following constraints are added.

[0075] 1) Dedicated channel constraint to ensure that TGUs do not occupy the SN dedicated channel, and vice versa for SGUs.

[0076]

[0077]

[0078] 2) Continuity constraint on SN channel occupancy to ensure that the allocated SN channels are continuous.

[0079]

[0080] 3) Continuity constraint on SN time slot occupancy, corresponding to the fact that SN time slots always occupy a group of continuous time slots.

[0081]

[0082] 4) RB uniqueness constraint for SNs to ensure that different SGUs in the same unit do not occupy the same RB.

[0083]

[0084] 5) RB reuse constraint for TBSs, corresponding to the fact that different TGUs paired with the same TBS do not occupy the same RB (while allowing different TBSs to reuse the same RB).

[0085]

[0086] The time and frequency resource allocation problem can be formulated as

[0087]

[0088] subject to (4), (5), (6), (8), (9)

[0089] This problem is a two-dimensional bin-packing problem with overlaps, and genetic algorithms have been recognized as an effective method to solve this problem. However, the different frame structures of SNs and TNs make the application of genetic algorithms challenging, especially in terms of population generation. Coupled with a large number of GUs and potentially complex CFI, directly adopting GA may face extremely high complexity. We propose a two-step optimization method to solve this problem. The proposed scheme first assumes the worst-case CFI to obtain a preliminary allocation, which reduces the complexity of subsequent optimization. Thereafter, an algorithm based on genetic algorithms is proposed to optimize the allocation, maximizing the elimination of redundant resource usage due to pessimistic CFI assumptions.

[0090] 1) Preliminary allocation assuming the worst CFI:

[0091] Initial resource allocation for the worst CFI: We first calculate the number of RBs required for each GU, assuming that the GUs may be interfered by all other transmitters and thus can only achieve the lowest rate. In this case, to meet their requirements, the number of RBs required, n i，j is denoted as where corresponds to the worst SINR and is calculated by setting all to 1. For SN, the number of channels required for each SGU is given by and the actual number of RBs required is denoted as where is due to the continuity of the SN frame structure in the time domain, as shown in (7). For TN, the total number of RBs allocated to the GUs paired with the same TBS is denoted as n i，m = ∑ j s i，m，j n i，j should satisfy (9) and not exceed n max . If n i，m > n max , then the excess part must be deleted (This corresponds to the situation that some GUs with high demand will not be satisfied under the assumption of the worst CFI). Specifically, for each n i，m , the n i，j of the paired GUs are arranged in descending order, and the redundant RBs are deleted sequentially until n i，j and are updated as follows

[0092]

[0093] Then, based on the value of n i，j , the RBs are allocated to the GUs within each cell. First, the idle RBs are allocated. If there are no remaining idle RBs, the occupied (i.e., already allocated) RBs can be reallocated to the GUs according to the CFI, where the RBs occupied by fewer GUs will be reallocated with higher priority because it causes less mutual interference. During the whole process, within each cell, let be used to track the number of GUs occupying the same RB, i.e.:

[0094]

[0095] where, Ω max = maxΩ means that all RBs are occupied, and Ω + = ∑ l ∑ t (Ω max-Ω(l, t)) means that Ω(l, t) < Ω max The number of available RBs in the case of max . Due to the continuity constraint of the SN frame, the RB allocation of SGUs is not very flexible, especially when sharing RBs with TGUs, because the continuous RB usage makes the CFI evaluation and mitigation complex. Therefore, in the cells with spot beam illumination, the RBs of SGUs are allocated first (which is beneficial to continuity), and then the RBs of TGUs are allocated. The overall algorithm is shown in Algorithm 1.

[0096]

[0097]

[0098] Some details about Algorithm 1 are further discussed as follows:

[0099] a) RB allocation of SGUs:

[0100] For each cell The RBs are allocated to SGUs in ascending order of their n i，j and the allocation continues until the resources exceed T bh . Update the RB allocation matrix a i，j of the SGUs that occupy the RBs, and accordingly update Ω to Ω(l.t) = Ω(l.t) + 1.

[0101] b) RB allocation of TGUs:

[0102] This corresponds to steps 16 to 25 of Algorithm 1. In each cell, the RBs are allocated to the TGU in ascending order of their n i，j . The detailed rules are based on Ω and are described as follows.

[0103] Example 1: Ω max = 0 or Ω max = 1, Ω + ≥ n i，j

[0104] In this case, there are enough idle RBs. To avoid interference, these idle RBs are sequentially allocated to the TGU.

[0105] Example 2: Ω max = 2, Ω + ≥ n i，j

[0106] In this case, the TGU sharing with another GU has enough RBs. To mitigate interference, we preferentially allocate the RBs occupied by the SGU because the satellite interference is relatively constant due to the longer transmission distance. Specifically, when there are RBs occupied only by SGUs (i.e., n sgu > 0), we follow Assign RBs to TGU in descending order to reduce the interference of paired TBS to SGU, where j * represents SGU. When updating n sgu , the relationship between n sgu and n i，j is considered. If n sgu ≥n i，j , then through n sgu =n sgu -n i，j for update. Otherwise, the RBs required by TGU will be updated to n sgu =n i，j -n sgu , and n sgu is set to 0. When n sgu =0, TGU is assigned to the RBs occupied by each other, and these RBs are assigned in descending order of to reduce the interference of paired TBSm * from the pre-occupied TGU.

[0107] Example 3: Ω max >2, Ω + ≥n i，j

[0108] In this case, TGU has enough RBs to share with other Ω max GUs. To reduce ground interference, the RBs are assigned in descending order of m * , where m * represents the paired TBSs of the previously occupied TGUs.

[0109] Example 4: Ω + <n i，j

[0110] In this case, the required RBs exceed Ω + , which means there are not enough resource blocks for the shared GUs of Ω max . Based on the principle that the fewer GUs per RB, the better, some RBs with Ω(l, t) < Ω max are assigned first. Then the RBs required by TGU are n i，j =n i，j -Ω + , which is less than n max . Therefore, we set Ω max =Ω max +1, and allocate the remaining n i，j RBs according to Example 2 or Example 3.

[0111] 2) Genetic algorithm-assisted time-frequency resource allocation:

[0112] The initial allocation is overly pessimistic because it assumes the worst CFI. Thus, when calculating n i，j each RB can only provide the minimum rate, and the demands of some GUs cannot be met. In the actual interference environment, RBs can provide higher rates, so the number of RBs required for each GU can be reduced. Based on this consideration, we now design a genetic algorithm with a "squeezing" step to further optimize RB occupancy, so as to successfully meet the demands of more GUs. The details of the optimized RB allocation set are described in Algorithm 2.

[0113]

[0114]

[0115] In Algorithm 2, Steps 1 to 10 constitute the initial "clean-up" phase of the potentially wasted RBs, where the actual rate achieved by each GU is calculated according to the current allocation. For each GU, the total rate of the RBs it occupies is calculated until the demand is met, and then the remaining RBs occupied by the GU are released (i.e., reset ). The rate of each RB is calculated based on the actual SINR, which depends on the current occupancy rate of the GUs on the RBs. That is, after the release step described above, the subsequent GUs will calculate their total rates according to the updated RB occupancy status. Note that as long as the demands of the GUs are met in the initial allocation, the updated RB allocation set after the clean-up will still satisfy the GUs.

[0116] Given GA is used to reallocate those "released" RBs and improve the performance through iteration. The key elements of the genetic algorithm are explained in detail below.

[0117] Population: The population corresponds to a set of potential solutions to the optimization problem, and each individual in each generation represents a feasible solution. For each individual, the gene corresponds to the occupancy rate of each a i，j and each individual contains ∑ i J i genes. To reduce complexity, two individuals are selected in each iteration: one is obtained from the result of the previous iteration, and one is randomly generated. The random set is generated as a binary set that satisfies different TN and SN frame structures and the occupancy constraints from (4) to (9). These two individuals are selected as "parent parents" to obtain offspring

[0118] Fitness function: The fitness function is to evaluate the performance of each individual for the optimization problem. It is the number of successfully served GUs. The fitness of the parents is calculated using or The larger number is denoted as η, and the fitness of the offspring is denoted as η * 。

[0119] Operators: The main operators for generating offspring are represented as the crossover operator, mutation operator, and selection operator, which are selected based on the corresponding genes of the parents. We use Q i，j and Q′ i，j to evaluate the genes of the parents, and the operations are selected as follows:

[0120] 1) If Q i，j = 0 and Q′ i，j = 1, then crossover is used, and each element in the gene is replaced with the corresponding element cr with probability e

[0121] 2) If Q i，j = 0 and Q′ i，j = 1, then mutation is applied, and the elements in the corresponding gene of the offspring are set to with probability e mu 。

[0122] 3) Otherwise, the gene a i，j is selected for the offspring.

[0123] Using the above process, the offspring can be generated as

[0124] Tournament selection: Compare the fitness functions η and η * , and select the individual with the larger value for the next iteration. It should be noted that the fitness function of the offspring η * is optimized and calculated after the squeezing step.

[0125] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0126] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions run by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0127] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are run on the computer or other programmable device to generate a computer-implemented process, so that the instructions running on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or one or more of the blocks.

[0129] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A time-frequency resource allocation method based on genetic algorithm, characterized in that: The method comprises the following steps: S1, during each beam hopping time slot, the ground user is assigned to the satellite network or the ground network according to its location, and the ground user allocation method is generated; the first ground user associated with the ground network is further paired with the transmission block for transmission, and a pairing matrix is ​​obtained; constraint conditions are introduced to establish a user scheduling and resource allocation model; S2, performing preliminary resource block allocation based on the worst interference scenario; specifically, calculating the number of resource blocks required by each terrestrial user, and allocating resource blocks to terrestrial users in each cell based on the value of the number of resource blocks required by the terrestrial user, to obtain a resource allocation matrix; wherein each resource block only provides a minimum rate; S3, calculates the rate of each resource block based on the actual signal-to-noise ratio. For each terrestrial user, calculates the total rate of the resource blocks occupied by it, and releases the redundant resource blocks occupied by the terrestrial user according to actual needs; uses a genetic algorithm to reallocate the released redundant resource blocks, and repeats the iteration until the iteration termination condition is reached.

2. The method for allocating time-frequency resources based on genetic algorithm according to claim 1, characterized in that: In step S1, the following steps are further included: S11, during each beam hopping time slot, the terrestrial user is assigned to the satellite network or the terrestrial network according to its location; X = {x i，i =1, ..., I} to represent the terrestrial user allocation mode. For J covered by the i-th cell i terrestrial users, x i ={x i，j |j=1,2,...,J i },in where x i，j =1, 0 are called the first terrestrial user and the second terrestrial user respectively; Given a terrestrial user allocation pattern X, the first terrestrial user associated with the terrestrial network is further paired with a transmission block for transmission, and the pairing matrix is ​​represented as S = {S i , i=1,...,I}, where the size is M i ×J i The matrix S i The elements in s i，m，j ∈{0, 1} indicates whether the first terrestrial user j matches the transmission block m in cell i, m=1,...,M i , j = 1, ..., J i ; Assume that within a beam-hopping slot, each first terrestrial user is paired with the same transmission block, and the number of first terrestrial users served by a transmission block is limited by The limit is mathematically described as: Describe resource allocation as a set of matrices in represents the resource block allocation of terrestrial users in a beam-hopping time slot, which contains n bh time slots; matrix a i，j The size is equal to L×n bh , where (l, t) elements Indicates whether the tth resource block of the Lth channel is allocated to the jth terrestrial user in the ith cell, l = 1, ..., L, t = 1, ..., n bh ; Introduce the following constraints: 1) Dedicated channel constraints: 2) Continuity constraints on satellite network channel occupancy: 3) Continuity constraints on satellite network time slot occupancy: In the formula, Indicates the number of time slots; 4) Resource block uniqueness constraints of satellite networks: 5) Resource block reuse constraints for transport blocks: The user scheduling and resource allocation model is established as: In the formula, Q i,j Indicates the number of ground users that meet the requirements.

3. The method for allocating time-frequency resources based on genetic algorithm according to claim 1, characterized in that: In step S2, the process of calculating the number of resource blocks required by each terrestrial user includes the following steps: To ensure that the ground user can only reach the minimum rate, the number of resource blocks required is n i，j Expressed as in Corresponding to the worst signal-to-noise ratio, by and Set to 1 for calculation; for satellite users, the number of channels required for each second terrestrial user is given by Given, the actual number of resource blocks required is expressed as in It is determined by the continuity constraint of the satellite network frame structure in the time domain; for the terrestrial network, the total number of RBs allocated to terrestrial users paired with the same transmission block is expressed as n i，m =∑ j s i，m，j n i，j , satisfying the resource block reuse constraints of the transport block and not exceeding n max ; if n i，m >n max , then delete the excess part The deletion method is: sort the n of paired GU in descending order i，j , and delete the redundant RBs sequentially until n i，j and The update of is given by:

4. The method for allocating time-frequency resources based on genetic algorithm according to claim 1, characterized in that: In step S2, resource blocks are allocated to ground users in each cell based on the value of the number of resource blocks required by the ground users. The process of obtaining the resource allocation matrix includes the following steps: First, allocate idle RBs; if there are no idle RBs left, reallocate occupied RBs to GUs according to CFI, where RBs occupied by fewer GUs will be reallocated with higher priority; in each unit, let To track the number of GUs occupying the same RB: Among them, Ω max =maxΩ means all RBs are occupied, Ω + =Σ l Σ t (Ω max -Ω(l, t)) means Ω(l, t)<Ω max The number of available RBs in the case; in the spot beam irradiated unit, the RBs of SGUs are allocated first, and then the RBs of TGU; a) RB allocation of SGUs: For each cell RBs according to their n i，j SGUs in ascending order of T and continue to allocate until the resource exceeds T bh ; Update the RBs allocation matrix a of the SGU occupying the RB i，j , and update Ω to Ω(lt)=Ω(lt)+1 accordingly; b) RB allocation of TGUs: In each unit, RB is divided into i，j The ascending order of is assigned to TGU; If Ω max =0 or Ω max =1,Ω+≥n i，j , allocate idle RBs to TGU sequentially; If Ω max =2,Ω + ≥n i，j , prioritize the allocation of RBs occupied by SGUs; specifically, when there are RBs occupied only by SGUs, n sgu >0, according to RBs are allocated to TGU in descending order, where j* represents SGU; when updating n sgu If n sgu ≥n i，j , then through n sgu =n sgu -n i，j Otherwise, the RB required by TGU will be updated to n sgu =n i，j -n sgu , n sgu Set to 0; when n sgu = 0, TGUs are allocated to RBs occupied by each other. 's descending order distribution; If Ω max >2,Ω + ≥n i，j , RB press The descending assignment of m*, where m* represents the paired TBSs of previously occupied TGUs; If Ω + <n i，j , assign Ω(l, t)<Ω max RBs, the RB required for TGU is n i，j =n i，j -Ω + ; Set Ω max =Ω max +1, redistribute the remaining n i，j RB.

5. The method for allocating time-frequency resources based on genetic algorithm according to claim 1, characterized in that: In step S3, the process of reallocating the released redundant resource blocks using a genetic algorithm includes the following steps: For each individual, the gene corresponds to each a i，j The occupancy rate of each individual contains ∑ i J i Gene; two individuals are selected in each iteration: one obtained from the result of the previous iteration and one randomly generated; the random set is generated as a binary set Satisfy different TN and SN frame structures and occupancy constraints; these two individuals are selected as parents to obtain offspring The number of GUs successfully served is set as the fitness function to evaluate the performance of each individual on the optimization problem; the fitness of the parent generation is calculated using or The larger number is recorded as η, and the fitness of the offspring is recorded as η*; Using Q i，j and Q′ i，j To evaluate the genes of the parents, the operation options are as follows: 1) If Q i，j =0 and Q′ i，j = 1, then crossover is used, and the gene Each element in has probability e cr Replace with the corresponding element 2) If Q i，j =0 and Q′ i，j = 1, then apply mutation and set the offspring The element in the corresponding gene is set to Its probability is e mu ; 3) Otherwise, gene a i，j selected for future generations; Using the above process, the offspring is generated as Compare the fitness functions η and η*, and select the individual with the larger value for the next iteration; the fitness function of the offspring η* is optimized and calculated after the squeezing step.

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