Wireless resource allocation method of GEO satellite communication system based on downlink time delay

Abstract

The invention discloses a wireless resource allocation method based on downlink time delay for a GEO satellite communication system, and the method comprises the steps: calculating a satellite antenna gain and a channel coefficient based on an OFDMA model of downlink transmission of a multi-beam GEO satellite, and constructing a shortest downloading time problem model based on a user data demand by taking the total data downloading time of all users as a target function; solving by a resource allocation algorithm based on a convex difference relaxation SCA algorithm aiming at the shortest downloading time problem model based on the user data demand; in the solving process of the resource allocation algorithm based on the convex difference relaxation SCA algorithm, the initial value of the variable is set based on the initial value allocation algorithm based on the demand weight. According to the method, the initial subcarriers are allocated by using the algorithm based on the demand weight, and the carrier and power are optimized by proposing the low-complexity SCA algorithm based on convex difference relaxation, so that the minimum requirement of downlink data downloading of the system can be better met under the low complexity.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a wireless resource allocation method based on downlink delay in a GEO satellite communication system. Background Art

[0002] With the continuous development of communication technology, the number of users and base stations in satellite communication systems is increasing, and the demand for data is also increasing. Users in satellite communication systems can be divided into real-time users and non-real-time users. Real-time users usually require highly reliable and low-latency communication services, such as voice calls, video calls, etc. The system needs to ensure the downlink rate indicators of real-time users. The business needs of non-real-time users are often file downloads, large data transmission, etc., and the real-time requirements are low. The data required by some users needs to be obtained through long-term downloads. At this time, the satellite wireless resource allocation method will affect the time it takes for users to download data from the satellite, and latency has become an important indicator for measuring the performance of the communication system. Due to different business scenarios such as user channel conditions, antenna conditions, and required data volume, various wireless resources on the satellite need to be flexibly allocated to meet the diverse needs of different users.

[0003] Traditional problems with downlink rate as the objective function often use resources such as power and frequency as optimization variables, but do not consider the impact of the total amount of data required by users on resource allocation. Problems with data download delay as the objective function also do not consider the impact of the amount of data required by users on optimization. In the scenario where satellite communication is combined with edge computing and the Internet of Things, the delay minimization problem can better improve user satisfaction, reduce network congestion, and improve system efficiency. In the prior art, there is a problem of computing offloading based on queuing theory in the scenario of satellite edge computing and a game framework for computing offloading is designed, with the calculation of corresponding time and energy consumption as the goal, but it does not consider the resource allocation at the beam level. In other prior arts, the constructed models and objective functions are different, such as: (1) Constructing a GEO-LEO double-layer satellite communication structure to minimize the download delay. The communication needs of the device can be collaboratively calculated by the LEO satellite, or forwarded by the GEO satellite to the ground gateway, making full use of the characteristics of different satellites for communication, and using deep reinforcement learning and convex optimization methods to solve the two sub-problems. (2) Aiming at the delay-oriented space-ground communication network architecture, the minimization of the task computation offloading time is taken as the objective function, the online scheduling problem is expressed as an energy-constrained Markov decision process, and the reinforcement learning algorithm is used to solve it. (3) In the satellite beam hopping scenario, the three objectives of delay minimization, throughput maximization, and fairness between beams are optimized, and their weighted addition is summarized into a single objective function for solution. (4) The resource allocation problem in the multi-satellite and multi-gateway scenario is considered, and the task offloading decision variables, computing and communication resources are optimized at the same time, and finally the delay and energy consumption constitute a single variable for solution. (5) Assuming that the data stream can be separated during system transmission, a joint routing selection and time slot allocation algorithm is proposed to minimize the total power consumption of the system. (6) Considering the needs of each user in the multi-beam satellite, user scheduling and resource allocation between beams are performed, with data download delay as the optimization goal, but the resource allocation in the frequency domain is not considered, and the power is required to be selected only between two discrete values.

[0004] The following problem has not been solved in existing research: When there are multiple users who need to download data in a satellite communication scenario, the data download time will greatly affect the system operation efficiency. However, due to the different data volume requirements and channel conditions of each user, a more intelligent resource allocation algorithm is needed to flexibly schedule satellite resources. Summary of the invention

[0005] Technical purpose: In view of the defects in the prior art, the present invention discloses a wireless resource allocation method based on downlink delay in a GEO satellite communication system. In the GEO satellite downlink OFDMA communication system, based on the joint allocation of downlink power and carrier, an algorithm based on demand weight is used to allocate initial subcarriers, and a low-complexity SCA algorithm based on convex relaxation is proposed to optimize the carrier and power. Compared with baseline algorithms such as fixed access and branch-and-bound, the method described in this patent can better meet the system downlink data download minimization requirements with low complexity.

[0006] Technical solution: In order to achieve the above technical objectives, the present invention adopts the following technical solution.

[0007] A method for allocating wireless resources based on downlink delay in a GEO satellite communication system comprises the following steps:

[0008] S1. Based on the OFDMA model of multi-beam GEO satellite downlink transmission, the satellite antenna gain and channel coefficient are calculated, the resource allocation method and data download time estimation are initialized, and the total data download time of all users is used as the objective function to build a model for the shortest download time problem based on user data requirements;

[0009] S2. For the problem model of shortest download time based on user data demand, a resource allocation algorithm based on convex difference relaxation SCA algorithm is used to solve it;

[0010] S3. In the process of solving the resource allocation algorithm based on the convex difference relaxation SCA algorithm, the initial value allocation algorithm based on the demand weight is used to set the initial value of the variable.

[0011] Beneficial effects: The present invention establishes a multi-beam GEO satellite downlink OFDMA communication system, and allocates resources based on the data download requirements of different users, so that the data download time is minimized, while optimizing the carrier allocation variables and power allocation variables. In order to deal with hybrid optimization problems, the large M method and CCCP algorithm are used to transform the hybrid optimization problem into a continuous problem for solution, and relaxation variables are introduced to construct a convex difference relaxation-based SCA algorithm with low computational complexity, and an algorithm based on demand weights is used to allocate initial subcarriers. The simulation results show that compared with the baseline algorithm, the algorithm of this patent can better meet the system downlink data download minimization requirements at a low complexity and improve system efficiency. The carrier initial allocation method proposed in this patent can also find a better initial value and improve the optimization convergence speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a flow chart of the method of the present invention;

[0013] Figure 2 A schematic diagram of the OFDMA model topology structure of multi-beam GEO satellite downlink transmission of the present invention;

[0014] Figure 3 It is the beam distribution diagram of the satellite during the simulation;

[0015] Figure 4 It is the convergence diagram of the objective function and the LEO satellite data download time during the simulation process;

[0016] Figure 5 This is a graph showing the change in data download time when the rated power and the number of ground users change during the simulation;

[0017] Figure 6 This is a graph showing the change in data download time when the mean value of data volume demand and rated power change during the simulation process;

[0018] Figure 7 This is a graph showing the time variation of data downloading for different numbers of subcarriers in different algorithm scenarios during the simulation process;

[0019] Figure 8 This is a graph showing the changes in data download time for different numbers of users under different algorithm scenarios during the simulation process. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] As attached Figure 1 As shown, a wireless resource allocation method based on downlink delay in a GEO satellite communication system of this embodiment includes the following steps:

[0022] S1. Based on the OFDMA model of multi-beam GEO satellite downlink transmission, the satellite antenna gain and channel coefficient are calculated, the resource allocation method and data download time estimation are initialized, and the total data download time of all users is used as the objective function to build a model for the shortest download time problem based on user data requirements;

[0023] The coverage area of ​​the N beams of the GEO satellite includes M users in total. The distance from the projection center of the nth beam on the ground to the mth user on the ground is d nm , 1≤n≤N, 1≤m≤M;

[0024] The coverage of the satellite's N beams is dispersed on the ground, and they share the satellite's full-band resources. One beam will provide services to multiple users within its coverage area, and use OFDMA to allocate resources, dividing the frequency band with a total bandwidth of B into K subcarriers, with each subcarrier bandwidth B0 = B / K. Each user in the beam uses one subcarrier, and there is no interference between users in the beam. Assume that the data download demand of the mth user is D m , and D m >0, unit is bit. The model topology structure is as follows Figure 2 As shown in the figure, GEO satellite is GEO satellite, Beam is beam, and user is user. f1, f2, etc. represent different subcarrier frequencies. The same beam has frequency division multiplexing resources, and there is frequency aliasing between different beams.

[0025] Assume that the coverage area of ​​the N beams of the GEO satellite includes M users in total. The users may be distributed on land or in the ocean and have different downloading needs. Each user is equipped with an isotropic omnidirectional transmitting antenna pointing in the direction of the satellite. Each user accesses the beam closest to it. Assume that the distance from the projection center of the nth beam on the ground to the mth user on the ground is d nm , 1≤n≤N; then the beam selected for the user access is d nm The smallest nth beam. Assume that the set of users served by the nth beam of the GEO satellite is but

[0026]

[0027] in, express The number of elements in .

[0028] Suppose the beam gain from the nth beam of the GEO satellite to the mth user on the ground is G(θ nm ), θ nm is the off-axis angle between the mth user and the nth beam, that is, the angle between the antenna main axis direction of the nth beam of the GEO satellite and the line connecting the mth user and the satellite, G(θ nm ) is calculated as:

[0029] G(θ nm )=G max (J1(s nm ) / 2s nm +36J3(s nm ) / (s nm ) 3 ) 2 (2)

[0030] Among them, s nm=2.07123·sinθ nm / sinθ 3dB ; G max is the maximum antenna gain, θ 3dB is the 3 dB power angle of the GEO satellite, J1(·) and J3(·) are the first-order and third-order Bessel functions of the first kind, respectively.

[0031] Due to the use of OFDMA technology, each subcarrier is orthogonal to each other. The channel from the nth beam on the kth subcarrier to the mth user on the ground is There are the following expressions

[0032]

[0033] Among them, G R is the user's receiving gain, l mk is the path loss from the kth subcarrier of the satellite to the mth user, and the calculation formula is: mk =(4πd m / λ k ) 2 , d m is the distance from the GEO satellite to the mth user, λ k is the wavelength of the kth subcarrier.

[0034] Initialization resource allocation method and data download time estimation:

[0035] The GEO satellite uses OFDMA to allocate bandwidth and frequency domain resources in the beam, so in each scheduling time slot, each user in a beam can access multiple subcarriers, but a subcarrier can only be used by one user in the beam. When the mth user connects to the kth subcarrier on the nth beam of the GEO satellite, set the indicator variable otherwise Define the following notation

[0036]

[0037] Among them, 1≤k≤K;

[0038] Due to the characteristics of OFDMA, one beam can only connect to one user, and the following constraints can be obtained:

[0039]

[0040] In addition, only when the mth user accesses the nth beam can the subcarrier be used. To be non-zero, there must be the following constraints:

[0041]

[0042] On each subcarrier, the beam can allocate corresponding power resources. The power obtained by the mth user from the kth subcarrier of the nth beam of the satellite is recorded as Define the following notation:

[0043]

[0044] Among them, p (k) is the kth subcarrier power, P is the power matrix of all subcarriers, with a size of NxKM;

[0045] To ensure the upper limit of system resources, power The following constraints should also be met:

[0046]

[0047] Among them, P 0 is the satellite beam transmit rated power, P T is the total transmit rated power of the satellite.

[0048] Only when the kth subcarrier of the nth beam is allocated power, the subcarrier can be selected for access, which can be represented by the symbol function sgn(·), and The relationship can be expressed as:

[0049]

[0050] Define the following notation:

[0051]

[0052] Among them, c (k) is the access indication of the mth user accessing the kth subcarrier of the nth beam, and C is the access indication matrix of all users;

[0053] The signal-to-interference-noise ratio received by the mth user from the kth subcarrier of its nth beam can be expressed as The signal-to-interference-noise ratio of the mth user served by the GEO satellite beam to the kth subcarrier of the nth beam is It can be expressed as formula (9).

[0054]

[0055] Where n′ is the beam index from 1 to N (not equal to n), m′ refers to the m′th user, and m′≠m, U n′ is the set of all users corresponding to beam n′; σ is the noise energy;

[0056] Then the instantaneous achievable downlink rate r obtained by the mth user from the nth beam of the GEO satellite is nm for:

[0057]

[0058] in, is the instantaneous downlink rate of the mth user from the nth beam and kth subcarrier of the GEO satellite;

[0059] Assume that for the mth user, it is located within the coverage of the nth beam, that is Then for the determined m, only For the rest n′≠n, According to formula (9), we can know that: According to formula (10), we know that: n′m = 0, only r nm ≠0, then the instantaneous achievable downlink rate r obtained by the mth user from the GEO satellite m It can be expressed as:

[0060]

[0061] The data download time t of the mth user m It can be divided into two parts: one is the time it takes for the electromagnetic wave signal to be sent from the satellite to the user, and the other is the time it takes to connect to the corresponding beam, through which the instantaneous downlink rate r can be achieved. m The time to download the required data.

[0062]

[0063] Among them, v light It is the speed of light;

[0064] At this time, the total data download time of all users is t, and the calculation formula is:

[0065]

[0066] Construct a model for minimizing download time based on user data requirements:

[0067] In order to minimize the total data download time t of all users on the ground, let t be the objective function, and optimize the optimization variables P and C so that t reaches the minimum value. Since the objective function in this optimization problem has taken into account the fairness factor between different users, the user's minimum signal to noise ratio restriction is no longer required. The model for the problem of minimizing the download time based on user data requirements can be expressed as:

[0068]

[0069] The first constraint C1 means that the transmission power of the GEO satellite must be greater than or equal to zero. The second constraint C2 means that the total transmission power of each beam of the satellite cannot exceed the rated power of the beam. The third constraint C3 means that the total power of all satellite beams does not exceed the maximum rated total transmission power of the GEO satellite. The fourth constraint C4 means that each subcarrier in a beam can only connect to a maximum of one user. The fifth constraint C5 means that the sign function of the power on the subcarrier of each beam is the subcarrier allocation variable. The sixth constraint C6 means that if the user is within the coverage of a beam, the subcarrier allocation variable can be 0 or 1. The seventh constraint C7 means that if the user is not within the coverage of a beam, the subcarrier allocation variable can only be 0.

[0070] S2. For the problem model of shortest download time based on user data demand, a resource allocation algorithm based on convex difference relaxation SCA algorithm is used to solve it;

[0071] The optimization problem corresponding to formula (14) is a non-convex problem and contains an integer optimization variable C, so this is a mixed integer programming problem, in which the objective function is a non-convex function and the constraint C5 contains integer variables, which is also non-convex. The objective function can be sorted out as follows:

[0072]

[0073] The latter term is the total delay of electromagnetic waves in the air when each user receives information. When the user position is determined, this term is a constant and can be ignored in the objective function. Therefore, the optimization problem corresponding to formula (14) can be expressed as:

[0074]

[0075] Analyzing the above problem, we can find that the objective function is non-convex because of the presence of signal-to-interference-noise ratio in the denominator. C5 in the constraint condition is an integer non-convex constraint. In order to solve this problem, it is necessary to transform the non-convex constraints and non-convex problems into convex constraints and convex problems, and then use the convex optimization method to deal with it.

[0076] The resource allocation algorithm based on the convex difference relaxation SCA algorithm includes a subcarrier and power allocation algorithm based on the convex difference relaxation SCA algorithm. The algorithm includes the following contents:

[0077] S2.1, introduce upper bound slack variables to transform the non-convex objective function into a convex function;

[0078] In order to solve the non-convex problem of the objective function in the optimization problem corresponding to formula (16), the upper bound slack variable φ can be introduced m ,φm It can be expressed as Φ = (φ1, ..., φ m ) 1×M , Φ is the upper bound slack variable matrix. When the optimization problem converges, φ m The physical meaning of is the data download time of the mth user. The upper bound relaxation condition can be expressed as:

[0079]

[0080] At this time, the optimization problem corresponding to formula (16) can be transformed into:

[0081]

[0082] Among them, the eighth constraint condition C8 is added. At this time, the objective function in the optimization problem corresponding to formula (18) is a convex function, which meets the conditions of convex optimization.

[0083] S2.2, on the basis that the objective function is a convex function, converting the non-convex constraint condition into a convex function includes the following steps:

[0084] S2.2.1. Transformation of constraint C5:

[0085] Use the Big M method and CCCP algorithm to process the non-convex constraints in constraint condition C5. The CCCP algorithm requires iterative solution. Assume Calculated for the tth iteration The value of is converted using the Big M method as follows:

[0086]

[0087] Among them, ε is a very small positive number. At this time, constraint conditions C5.1 and constraint conditions C5.2 are both linear convex functions, and constraint condition C5.3 is transformed into:

[0088]

[0089] Among them, constraints C5.3.2 and C5.3.3 are both convex functions, and constraint C5.3.1 is a concave function. By transposing them and using the CCCP algorithm, we can get:

[0090]

[0091] At this time, constraint C5.3.4 is a convex constraint, which meets the requirements of convex optimization problem.

[0092] The constraint condition C5 is transformed into the following formula:

[0093]

[0094] S2.2.2. Conversion of constraint C8

[0095] From the constraint C8 in the optimization problem corresponding to formula (18), we can see that the numerator and denominator of the fraction on the left side of the inequality sign are both positive numbers, so φ on the right side of the inequality sign is m is also a positive number. Now we can get

[0096]

[0097] Introducing the lower bound constraint variable of the rate function Make Define symbols:

[0098]

[0099] in, is the lower bound constraint variable, α is the lower bound constraint variable subset;

[0100] At this time, constraint C8 can be converted into constraint C8.1 and constraint C8.2, and the calculation formula is as follows:

[0101]

[0102]

[0103] The constraint C8.1 is a convex constraint. The constraint C8.2 is not a convex function due to the existence of the SINR term, so it is common denominator. 2 The magnitudes of the channel coefficients are very small, and normalization processing yields:

[0104]

[0105] The problem corresponding to constraint C8.2 is a convex difference (DC) problem, which can be solved using the CCCP algorithm. However, the result of taking the derivative of the logarithmic function is very complicated, so the SCA algorithm based on convex difference relaxation is proposed to solve it. In order to avoid taking the derivative of the logarithmic function, two logarithmic function relaxation variables are introduced. Constraint C8.2 is transformed into the following formula:

[0106]

[0107] in, is the lower bound constraint, is an upper bound constraint, and also defines the symbol:

[0108]

[0109] in, is the lower bound constraint, is the upper bound constraint, η is the lower bound constraint subset, and ξ is the upper bound constraint subset;

[0110] By transforming the constraints C8.3 and C8.4, we can obtain:

[0111]

[0112] At this time, the constraint C8.1 in formula (23), the constraint C8.2.1 in formula (26), and the constraint C8.3 in formula (29) are all convex constraints. The constraint C8.4 in formula (30) is a concave constraint and needs to be processed by the SCA algorithm. Since the problem is organized into an exponential function e x The related expressions of , the calculation will be simpler, and the exponential function can be obtained by processing:

[0113]

[0114] Among them, x (t) is the previous value of variable x in iteration i, and substituting it into constraint condition C8.4 in formula (30) yields

[0115]

[0116] At this time, C8.4.1 in formula (32) is a linear constraint, which is also a convex constraint. At this time, C8 in formula (18) is organized as a convex constraint.

[0117] Constraint C8 is transformed into the following formula:

[0118]

[0119] In S2,2, on the basis that the objective function is a convex function, the non-convex constraints are converted into convex functions, and the calculation formula is:

[0120]

[0121] At this time, the optimization problem corresponding to formula (33) is a convex problem, and most of the constraints are linear constraints. The convex problem can be solved directly using the CVX toolbox in MATLAB. (t) is the objective function value calculated in each iteration. Thus, the subcarrier allocation indication of the corresponding user is obtained. The solution results include P, C, Φ, α, η, ξ, that is, all subcarrier power matrices, all user access indication matrices, upper bound slack variable matrix, lower bound constraint variable subset, lower bound constraint subset, and upper bound constraint subset.

[0122] S3. In the process of solving the resource allocation algorithm based on the convex difference relaxation SCA algorithm, the initial value allocation algorithm based on the demand weight is used to set the initial value of the variable;

[0123] In this application, it is necessary to iterate the initial value setting of the C and ξ variables. Since the method of this application optimizes the data download time of all users, and the objective function is the data demand of each user divided by the information transmission rate, it is possible to first assume that the power is distributed according to the average, and obtain the initial value of the orthogonal subcarrier allocation variable, and then divide the power of each beam according to the initial value of the orthogonal subcarrier allocation, and then obtain the initial value of the relaxation variable ξ.

[0124] The initial value setting of the variable includes the following steps:

[0125] S3.1. Allocate initial subcarrier values ​​based on demand weights;

[0126] Assume that in the optimization problem corresponding to formula (33), the objective function is the total time for all users to download data, expressed as:

[0127]

[0128] Among them, W m is the download time of the mth user, W m The calculation formula is:

[0129]

[0130] Since the download time of each user needs to be as short as possible, each user must access more than one orthogonal subcarrier to obtain resources. When studying the users within the nth beam, the W of the users within the beam is m The expression can be expressed as:

[0131]

[0132] From the above formula, we can see that if the user's data download demand D m The larger the numerator, the larger the molecule should be, so that W m Similarly, if the channel condition of the user in the beam is worse, the SINR will be lower, and more subcarriers need to be allocated to prevent W m Too large.

[0133] When initially allocating orthogonal subcarriers, it is assumed that the power on each beam is the rated power, and the power on each subband of each beam is P0 / K. Since the difference in channel coefficients on different subcarriers comes from fading, and within the frequency band, the frequency has little effect on fading. Therefore, when setting the iterative processing, the channel coefficient on the first subcarrier can be calculated for all subcarriers, that is, assuming that the rate value on the first subcarrier is the same as that on other carriers, for comparison between users. At this time, W m The expression is

[0134]

[0135] in, is the channel capacity of the nth beam to the mth user on the ground on the first subcarrier;

[0136] Therefore, the W of each user in the beam can be calculated according to formula (37): m value, then follow W m The subcarriers are divided according to the size of the value, W m The larger the bit is, the more subcarriers are allocated.

[0137] Assume that the nth beam is connected users, respectively At this time, calculate each user's W m The values ​​are Subcarriers are divided according to their size. Assume that each user is assigned K m subcarriers, the calculation formula is:

[0138]

[0139] in, is the symbol rounded down. After each user in the beam determines the number of subcarriers according to formula (38), due to the use of rounding down calculation, there will be several subcarriers that are not allocated. At this time, the excess subcarriers are allocated according to the number of user W. m The values ​​are assigned to W one by one in order of size m The user with the largest value. Assume that:

[0140]

[0141] At this time, the calculation formula for the number of redundant subcarriers K0 is:

[0142]

[0143] Then there are K0 users who are allocated one more subcarrier:

[0144]

[0145] After the number of subcarriers allocated to each user is calculated, the subcarriers are allocated to the first user starting from the first subcarrier k=1 until all subcarriers are allocated.

[0146] The algorithm for allocating orthogonal subcarriers described above is shown in Table 1.

[0147] Table 1 Subcarrier allocation algorithm based on demand weight

[0148]

[0149]

[0150] S3.2, initially set the slack variables;

[0151] After allocating the subcarriers, the P variable can be allocated. Assuming that the satellite rated power is evenly distributed to each beam, the beam is then evenly distributed to each subcarrier according to the usage of the subcarrier. According to the subcarrier initialization algorithm, each subcarrier is used initially.

[0152]

[0153] in, for The initial value of for The initial value of

[0154] After getting the initial value of the P variable, we need to get the initial value ξ (0) The initial value ξ can be directly set according to the constraints in formula (28) (0) The calculation formula is as follows:

[0155]

[0156] By summarizing the above algorithm steps, we can obtain the solution algorithm for the optimization problem corresponding to formula (33) as shown in Table 2.

[0157] Table 2 Carrier and power allocation algorithm based on convex relaxation SCA algorithm

[0158]

[0159] Simulation verification:

[0160] In this application, the complexity of the solution is first analyzed. The optimization problem corresponding to formula (33) has 5NMK+M decision variables and 8NMK+NK+MK+N+2M+1 constraints. Assuming that the optimization problem requires E iterations to achieve convergence, the algorithm complexity of the problem is as follows, which is a polynomial function and can be solved by a computer in polynomial time.

[0161] ○(E(5NMK+M) 3 (8NMK+NK+MK+N+2M+1)) (44)

[0162] Assume that the user's location is randomly generated within the coverage of the GEO satellite beam, and the user's data volume demand is also randomly generated. The impact of factors such as altitude and climate on user coordinates, distance from the satellite, and data volume demand are not considered. Assume that the user's antenna always points to the corresponding satellite beam. Each user has a data download volume demand D m , assuming that the user's data download demand is D m Satisfies normal distribution The unit is Mbit. Antenna parameters and other user parameters are given in Table 3 below.

[0163] Table 3 GEO satellite antenna parameters and user parameters

[0164]

[0165] The beam positions of GEO satellite antennas are distributed as follows: Figure 3 As shown, the GEO satellite is located directly above the middle beam, that is, the satellite sub-satellite point coincides with the beam center of the middle beam, and GEO Beam Center represents the GEO satellite beam center.

[0166] The optimization problem corresponding to formula (33) needs to be solved by iterative optimization. When solving the optimization problem, the optimization result of the previous step is continuously used as a constant to substitute into the optimization problem. Multiple iterations are required to achieve the final convergence. Here, the number of users M is changed to allow users to be randomly and evenly distributed within the satellite coverage area. The convergence characteristics of the algorithm with different numbers of users are obtained as follows: Figure 4 shown. Figure 4 The solid line in the middle is the objective function value ∑φ in formula (33) obtained by each iterative optimization. m , the dotted line is the LEO satellite downlink data download time calculated after each iteration. Figure 4 It can be seen that during the iteration process, the objective function value ∑φ in formula (33) m The upper bound constraint on the user data download time can be well set, indicating that the introduction of the slack variable φ does not change the convergence of the optimization problem itself. When the number of users increases, the iterative convergence speed will slow down, and the data download time will also increase. Since two slack variables are introduced to relax the logarithmic function and then the CCCP algorithm is used, the algorithm converges faster. Figure 4 It is shown that the wireless resource allocation method in the present application can achieve convergence quickly within a limited time.

[0167] The experimental simulation is conducted on the relationship between the satellite downlink data download time under different rated powers of the GEO satellite beam. When the rated power of the GEO satellite beam is changed, the rated power of the GEO satellite is also changed to 7 times the rated power of the beam. Under different numbers of users and different rated powers of the beam, 100 Monte Carlo simulation experiments are conducted under each set of parameters, and the average value is calculated. The experimental results are shown in the figure. Figure 5 As shown. Figure 5 It can be seen that as the number of users increases, the data download time becomes longer and longer. This is because the overall system demand is increasing, while the total amount of satellite resources remains unchanged, so the data download time will increase. When the beam rated power increases, the data download time continues to decrease. 0 When the rated power is increased from 10W to 30W, the data download time is reduced most significantly. This is because the rated power of 10W is too low and the satellite downlink rate is low, which cannot meet user needs. When the rated power is increased from 30W to 100W, the speed of reducing the data download time becomes slower and slower. When the rated power is increased from 100W to 300W, the power is increased by 200W, but the system data download time is reduced very little. This is because as the rated power increases, the interference is also increasing, which in turn affects the signal-to-interference-to-noise ratio value, thereby limiting the rapid increase in the satellite downlink rate. In order to better save resources, a trade-off can be made between data download time and system energy consumption to achieve better system performance with lower power consumption.

[0168] The user's data download demand directly affects the final data download time. Assuming that the number of users remains unchanged, the average user data demand is 6000, 8000, 10000, 12000, and 14000 Mbit respectively. At the same time, the rated power of the beam is changed, and the satellite rated power is changed to 7 times the rated power of the beam. Under different demand averages and different beam rated powers, 100 Monte Carlo simulation experiments are conducted under each set of parameters, and the average value is calculated. The experimental results are shown in the figure. Figure 6 As shown. Figure 6 It can be seen that when the average value of the download data volume demand continues to increase, the data download time is also increasing. This is because the downlink rate remains unchanged, but the demand increases. When the rated power of the satellite increases, the downlink rate of the GEO satellite will increase and the data download time will be shortened. However, the continuous increase in power will not shorten the download time indefinitely, because when the power increases, the interference will also increase, resulting in a slower and slower growth in the downlink rate.

[0169] For the cases where the number of orthogonal subcarriers in the OFDMA system is different, the GEO satellite downlink data download time under the three algorithm scenarios is compared and analyzed, and the satellite beam rated power is taken as 100W.

[0170] Table 4 Comparison of algorithms in this patent

[0171]

[0172] The optimization of the three algorithm scenarios is shown in Table 4. Proposed refers to the algorithm proposed in this patent, O-Power refers to the algorithm that only optimizes power, the fixed orthogonal subcarrier allocation is the calculation result of the subcarrier allocation initial value algorithm, and O-Carrier refers to the algorithm that only optimizes orthogonal subcarriers, and the fixed power is the power value of the rated power divided by the subcarrier initial value. Under different numbers of orthogonal subcarriers, 100 Monte Carlo simulation experiments were performed under each set of parameters, and the average value was calculated. The experimental results are shown in Figure 4. Figure 7 shown.

[0173] Depend on Figure 7 It can be seen that compared with the algorithm that only optimizes power and only optimizes orthogonal subcarriers, the user data download time is significantly lower in the algorithm scenario proposed in this patent. With the increase of the number of orthogonal subcarriers K, the data download time becomes shorter and shorter. This is because after the number of carriers increases, the power resources allocated on each subcarrier are also smaller. When calculating the signal-to-interference-to-noise ratio, the interference term will be reduced more, so that the overall rate increases, thereby reducing the data download time. In the algorithm scenario proposed in this patent, as the number of subcarriers continues to increase, the speed of data download time decreases gradually. This is because the power on the subcarrier will also decrease, resulting in a slower and slower increase in the signal-to-interference-to-noise ratio, and the rate increase rate will also slow down. Therefore, when the total bandwidth is constant, an excessive number of orthogonal subcarriers will not bring about a rapid increase in rate. In actual scenarios, it is still necessary to compromise between algorithm complexity and system performance.

[0174] When the number of users is different, the GEO satellite downlink data download time is compared under different algorithm scenarios. Under different numbers of users, 100 Monte Carlo simulation experiments are performed for each set of parameters, and the average value is calculated. Among them, Fixed is the result of the calculation of the initial value algorithm with fixed power and carrier allocation, and Branch and bound is an algorithm that uses the branch and bound method to allocate carrier power.

[0175] When using the branch and bound method to solve the optimization problem, the orthogonal subcarrier allocation variable C is used as a 0-1 variable and can be solved directly without the need for continuous approximate solution through the CCCP algorithm. The power variable optimization is solved using the SCA algorithm based on convex relaxation. At this time, there are 4NMK+M decision variables in the optimization problem. Assuming that the branch and bound method is used to divide the optimization problem into several sub-problems, it takes E iterations to achieve convergence. The complexity of the branch and bound algorithm is exponential, and in the worst case, all possible solutions need to be enumerated. At this time, the algorithm complexity of the branch and bound method is

[0176] ○(E12 4NMK+M ) (45)

[0177] According to formula (44), the complexity of the carrier power allocation algorithm based on the convex difference relaxation SCA algorithm proposed in this patent is ○(E(5NMK+M) 3 (8NMK+NK+MK+N+2M+1)) (46)

[0178] Compared with the complexity of the algorithm proposed in this patent, the branch and bound method requires the optimization problem to be divided into multiple sub-problems for solution, so the complexity is very high. The experimental results under relevant parameters and algorithm scenarios are as follows Figure 8 shown.

[0179] Depend on Figure 8 It can be seen that as the number of users continues to increase, the data download time will also continue to increase. This is because as the number of users increases, the number of subcarriers allocated to each user will also decrease, which will reduce the user's downlink rate and increase the overall data download time. Compared with the fixed allocation algorithm, the algorithm proposed in this patent can reduce the user's data download time. When the orthogonal subcarriers are fixed at the initial value, the data download time is greater than the proposed algorithm, indicating that the optimization of orthogonal subcarriers can improve system performance. When the power is evenly distributed and only the orthogonal subcarriers are optimized, the data download time is much greater than the proposed algorithm, indicating that the optimization of power can greatly help users download data quickly.

[0180] The algorithm proposed in this patent is very close to the branch and bound method, but the computational complexity of the algorithm in this patent is much lower than that of the branch and bound method, which shows that the resource allocation of the SCA algorithm based on convex relaxation proposed in this patent can complete the resource allocation task with low complexity. Figure 8 The O-Carrier algorithm is only slightly better than the Fixed power and carrier allocation as the initial value algorithm, which shows that the initial value allocation method based on demand weight proposed in this patent is better, and the initial value allocation result is close to the result of carrier optimization, which can improve the optimization convergence speed.

[0181] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for allocating wireless resources based on downlink delay in a GEO satellite communication system, characterized in that: The following steps are involved: S1. Based on the OFDMA model of multi-beam GEO satellite downlink transmission, the satellite antenna gain and channel coefficient are calculated, the resource allocation method and data download time estimation are initialized, and the total data download time of all users is used as the objective function to build a model for the shortest download time problem based on user data requirements; S2. For the problem model of shortest download time based on user data demand, a resource allocation algorithm based on convex difference relaxation SCA algorithm is used to solve it; S3. In the process of solving the resource allocation algorithm based on the convex difference relaxation SCA algorithm, the initial value allocation algorithm based on the demand weight is used to set the initial value of the variable.

2. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 1, characterized in that: The model for minimizing download time based on user data requirements includes: in, is the power obtained by the mth user from the kth subcarrier of the nth beam of the satellite, 1≤k≤K, 1≤n≤NN, 1≤m≤M, the NN beams of the GEO satellite cover a total of M users, and the frequency band with a total bandwidth of B is divided into K subcarriers; P 0 is the satellite beam transmit rated power, P T is the total transmit rated power of the satellite, sgn(·) is the sign function, The access indication for the mth user to access the kth subcarrier of the nth beam, v light is the speed of light, D m is the data download requirement of the mth user, d m is the distance from the GEO satellite to the mth user, r m is the instantaneous achievable downlink rate obtained by the mth user from the GEO satellite, is the set of users served by the nth beam of the GEO satellite. P and C are the power matrix of all subcarriers and the access indicator matrix of all users, respectively.

3. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 1, characterized in that: The resource allocation algorithm based on the convex difference relaxation SCA algorithm includes a subcarrier and power allocation algorithm based on the convex difference relaxation SCA algorithm. Includes the following: S2.1, introduce upper bound slack variables to transform the non-convex objective function into a convex function; S2.

2. On the basis that the objective function is a convex function, the Big M method and CCCP algorithm are used to convert the non-convex constraints into convex functions.

4. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 3, characterized in that: On the basis that the objective function is a convex function, the Big M method and CCCP algorithm are used to convert the non-convex constraints into convex functions. The calculation formula is: Among them, φ m is the upper bound slack variable, is the power obtained by the mth user from the kth subcarrier of the nth beam of the satellite, 1≤k≤K, 1≤n≤N, 1≤m≤M. The coverage of the N beams of the GEO satellite includes M users in total, and the frequency band with a total bandwidth of B is divided into K subcarriers; P 0 is the satellite beam transmit rated power, P T is the total transmit rated power of the satellite, sgn(·) is the sign function, The access indication for the mth user to access the kth subcarrier of the nth beam, Calculated for the tth iteration The value of D m is the data download requirement of the mth user, The set of users served by the nth beam of the GEO satellite, is the lower bound constraint, is the upper bound constraint, are lower bound constraint variables, P, C, Φ, α, η, ξ are the power matrix of all subcarriers, the access indicator matrix of all users, the upper bound slack variable matrix, the lower bound constraint variable subset, the lower bound constraint subset, and the upper bound constraint subset, respectively.

5. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 1, characterized in that: The initial value setting of the variable includes the following steps: S3.

1. Allocate initial subcarrier values ​​based on demand weights to obtain C variables, i.e., initial values ​​of access indicator matrices of all users; S3.

2. Initially set the slack variables to obtain the P variables, that is, the initial values ​​of all subcarrier power matrices.

6. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 5, characterized in that: The subcarrier initial value allocation formula includes: Among them, W m is the download time of the mth user, σ is the noise energy, is the channel capacity of the n′th beam to the mth ground user on the first subcarrier, is the channel capacity of the nth beam on the first subcarrier to the mth user on the ground, P 0 is the satellite beam transmit rated power, D m is the data download demand of the mth user, 1≤n≤N, the coverage area of ​​the N beams of the GEO satellite includes M users in total, the frequency band with a total bandwidth of B is divided into K subcarriers, and B0 is the bandwidth of each subcarrier; The set of users served by the nth beam of the GEO satellite; Each user is assigned K m subcarriers, the calculation formula is: Among them, W m′ for, is the symbol for rounding down; There are K0 users and one more subcarrier is allocated: After the number of subcarriers allocated to each user is calculated, the subcarriers are allocated to the first user starting from the first subcarrier k=1 until all subcarriers are allocated.

7. The method for allocating wireless resources based on downlink delay in a GEO satellite communication system according to claim 5, characterized in that: The initial value calculation formula of P variable includes: The calculation formula for the initial value ξ(0) of ξ includes: in, is the upper bound constraint, is the power obtained by the mth user from the kth subcarrier of the nth beam of the satellite, 1≤k≤K, 1≤n≤N, 1≤m≤M. The coverage of the N beams of the GEO satellite includes M users in total, and the frequency band with a total bandwidth of B is divided into K subcarriers; P 0 is the transmit rated power of the satellite beam, is the channel from n beams to the mth user on the ground on the kth subcarrier.

Images