Sky wave cooperative transmission method for air-ground communication

Abstract

The invention provides a sky wave cooperative transmission method for air-ground communication. The sky wave cooperative transmission method comprises the following steps: step 1, establishing a sky wave cooperative transmission model for air-ground communication; step 2, establishing a target optimization problem for realizing user rate maximization; 3, modifying the optimization problem, and establishing a sub-problem based on a two-layer structure; step 4, solving a fixed station power distribution problem by using an adaptive Nelder-Mead algorithm; 5, establishing a fixed station and working frequency matching problem under the condition of optimal power distribution; step 6, pre-processing the resource block according to the channel quality; for each user, determining a corresponding signal-to-noise ratio of each resource block after power allocation, and obtaining resource blocks ranked in the first M and a sequence; and step 7, a fixed station and working frequency matching method based on alternate optimization. The system capacity can be improved, resource utilization is optimized, and the method adapts to a complex environment.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a skywave cooperative transmission method for air-to-ground communication. Background Art

[0002] With the development of aviation technology, the demand for long-distance communication of aircraft and global signal coverage is increasing. This demand has prompted researchers to explore reliable and high data rate communication solutions for aviation systems. Skywave communication technology operates in the frequency range of 3-30MHz and uses the reflection of signals in the ionosphere to achieve long-distance transmission as an alternative solution to satellite communications to avoid high costs, vulnerabilities and sovereignty issues. Marconi, a pioneer of skywave communication, successfully transmitted the first transatlantic message in 1901, which revolutionized the way of long-distance communication and paved the way for the development of skywave communication technology.

[0003] For decades, researchers continued to refine and study skywave communication systems. Advances in vacuum tube technology in the 1920s and 1930s led to the development of amplitude modulation radio, which enabled voice communications over longer distances. During World War II, skywave communications became an important tool for military communications and intelligence gathering. After the war, advances in transistor technology made it possible to create smaller, more portable radios, expanding the applications of skywave communications beyond military and commercial fields.

[0004] Today, skywave communications are widely used by governments and non-governmental organizations for over-the-horizon communications in remote areas, such as ships at sea, aircraft networks beyond the horizon, disaster areas, and remote areas lacking other means of communication. However, the dynamic nature of the ionosphere poses a major challenge to skywave communications, resulting in time-varying channels and unreliable transmissions. Ionospheric instabilities are primarily affected by solar activity, changes in the Earth's magnetic field, and chemical and physical processes in the atmosphere. These factors work together to cause the height, density, and electron concentration of the ionosphere to vary significantly over time, which in turn affects the signal reflection and propagation characteristics of skywave communications. In addition, it is challenging to design efficient and robust communication systems in traditional device-to-device transmission scenarios due to the multipath propagation of skywave channels. Multipath propagation refers to the fact that a signal encounters multiple paths during propagation, resulting in changes in the phase and amplitude of the signal, which can cause signal distortion and fading. This phenomenon is particularly evident in skywave communications, because the irregular structure of the ionosphere makes the signal reflection and refraction paths complex and variable, increasing the uncertainty of signal propagation.

[0005] In order to improve the performance of skywave communication systems for air-to-ground communications, researchers have carried out extensive work in various aspects of data transmission, link establishment, and network integration. However, it is crucial to explore the cooperative transmission model of skywave communication networks. This model aims to improve communication efficiency and total rate, but there is a lack of comprehensive and systematic research in existing work. Specifically, there are gaps and challenges in existing research in the following aspects: First, existing network integration methods fail to fully utilize the potential advantages of skywave communication networks, such as the application potential of multi-station collaborative technology in skywave communications. Second, there is a lack of effective network management and resource allocation strategies to optimize the performance and reliability of skywave communication networks. Summary of the invention

[0006] The present application provides a skywave cooperative transmission method for air-to-ground communication, which can be used to solve the technical problem that the prior art lacks effective network management and resource allocation strategies.

[0007] The present application provides a skywave coordinated transmission method for air-to-ground communication, the method comprising:

[0008] Step 1: Establish a skywave cooperative transmission model for air-to-ground communication;

[0009] Step 2: Establish an optimization problem to achieve the user rate maximization goal;

[0010] Step 3: Modify the optimization problem and establish sub-problems based on a two-layer structure;

[0011] Step 4, using the adaptive Nelder-Mead algorithm to solve the fixed station power allocation problem;

[0012] Step 5: Establish the matching problem between fixed stations and working frequencies under the optimal power allocation condition;

[0013] Step 6: pre-process the resource blocks according to the channel quality; for each user, determine the signal-to-noise ratio of each resource block after power allocation, and obtain the top M resource blocks and their ranking;

[0014] Step seven, fixed station and working frequency matching method based on alternating optimization.

[0015] Furthermore, in step one, a skywave cooperative transmission model for air-to-ground communication is established, including:

[0016] Assume the positions of N fixed stations are α1, α2, ...α N , the positions of M aircraft users are l1, l2, ...l M , for the nth fixed station, when the working frequency is f n When the channel gain position distribution G(α n ,ξ,f n), where ξ is the receiver position, and the transmission power of fixed station n to user m is P n，m , the channel gain from the fixed station to user m is expressed as:

[0017] g n，m (f) = G(α n , l m ,f), (1)

[0018] in is the operating frequency; then, the power of the signal received by user m from fixed station n at frequency point f is:

[0019] S n，m (f) = P n，m g n，m (f). (2)

[0020] Assume that the noise power is N0; the signal-to-noise ratio of the signal received by user m from fixed station n at frequency f is:

[0021]

[0022] in and is a temporary variable, S n,m (f) represents the energy of the signal, The interference from the same frequency signals of other fixed stations is is the interference of the same frequency signal sent by the fixed station to other users, and N0 is the noise;

[0023] The corresponding achievable rate is:

[0024] R n，m =log2(1+ρ n，m (f m )). (4).

[0025] Furthermore, in step 2, an optimization problem for achieving the user rate maximization objective is established, including:

[0026] The maximization target optimization problem is to achieve the user rate maximization goal by optimizing the user-fixed station matching and the fixed station transmission power under the constraints of the maximum transmission power of a single base station and the number of fixed stations matched for each user. The initial optimization problem and the corresponding constraints are expressed as:

[0027]

[0028]

[0029] Where P ：，： is the transmit power matrix, P n，mNon-zero means user m is matched with fixed station n and is allocated power P n，m ,Constraint C1 means that each user can only match one working frequency of a fixed station, constraint C2 means that the transmission power of each fixed station must be less than the maximum value, and constraint C3 means that the transmission power allocated to each user must be a non-negative number.

[0030] Furthermore, in step three, the optimization problem is modified and sub-problems based on a two-layer structure are established, including:

[0031] The initial problem P1 is decomposed into two sub-problems: transmit power optimization and frequency-fixed station selection; a two-layer structure is adopted, with transmit power optimization at the bottom layer and frequency-fixed station selection at the upper layer; the bottom layer is used to optimize the transmit power allocation under the given frequency-fixed station selection scheme, and the upper layer optimizes the frequency-fixed station selection scheme through the system and rate returned by the bottom layer;

[0032] For a given fixed station and operating frequency selection scheme, the fixed station transmission power allocation problem is expressed as:

[0033]

[0034]

[0035] Considering that each user is matched with a fixed station and uses one working frequency at the same time, the fixed station transmission power allocation problem P2 is simplified to:

[0036]

[0037]

[0038] Obviously, P2.1 is non-convex, and the power allocation variables of each fixed station to its users are coupled with each other, and there is a probability that some signals are interferences of other signals;

[0039] To convert P2 into unconstrained form, define:

[0040]

[0041] Therefore, P2.1 is rewritten as:

[0042]

[0043] Furthermore, in step 4, the adaptive Nelder-Mead algorithm is used to solve the fixed station power allocation problem, including:

[0044] In the Nelder-Mead algorithm, the input is the fixed station and the frequency allocation plan Output is the power distribution scheme Here’s how:

[0045] Step 4-1, Initialization:

[0046] Step 4-2, set loop n = 1: N, and set another loop inside. Power allocation for each user in

[0047] Step 4-3, Setting To optimize the goal;

[0048] Step 4-4, Setting For optimizing variables, the current value is the initial value.

[0049] Furthermore, in step five, a matching problem between fixed stations and operating frequencies under optimal power allocation is established, including:

[0050] Based on the transmission power allocation scheme under the given fixed station selection and working frequency setting conditions, the achievable rate of user m who selects fixed station n is defined as Then the optimization problem of the two discrete variables of fixed station and working frequency allocation can be expressed as:

[0051]

[0052]

[0053] Among them, constraint C1 means that only one fixed station and one working frequency can be matched for one user, and constraint C2 means that all users are assigned corresponding fixed stations. Substituting constraint C2 into the optimization objective, P3 is simplified as follows:

[0054]

[0055]

[0056] Further, step six: pre-processing the resource blocks according to the channel quality; for each user, determining the signal-to-noise ratio corresponding to each resource block after power allocation, and obtaining the top M resource blocks and their ranking, including:

[0057] Step 6-1, initialization, define the tensor to store the results

[0058] Step 6-2, set the cycle m = 1:M, calculate the signal-to-noise ratio of each resource block after power allocation according to formula (3), and obtain the current optimal (n t , f t ), use Top-k sorting to get the top M resource blocks and their sorting, and save the results to

[0059] After preprocessing, when the number of users is less than the total number of resource blocks, the search range for allocating resource blocks to each user is limited to the top M resource blocks.

[0060] Furthermore, step seven, a method for matching fixed stations and operating frequencies based on alternating optimization, includes:

[0061] First, the first user is assigned a fixed station and frequency corresponding to the maximum channel gain; for each subsequent user, the fixed station and operating frequency are selected taking into account the performance of the fixed station after power allocation;

[0062] When a round is completed, a new round starts from the first user until the fixed stations and frequency selections of all users in a round are no longer updated, or the maximum number of rounds t is reached. max End of time;

[0063] The process is as follows:

[0064] Step 7-1, first perform the initialization process; at this stage, the resource block is preprocessed according to the channel quality, and the set is obtained. Applies to every n belonging to the set and every f belongs to the set Set variable ∈=0;

[0065] Step 7-2, perform a loop, the loop range is from t = 1 to t max ;

[0066] Step 7-2-a, at the beginning of each iteration of the loop, reset the variable ∈ to 0;

[0067] Step 7-2-b, for each sub-cycle where m is from 1 to M, calculate the corresponding signal-to-noise ratio of each resource block after power allocation is completed; identify the current optimal pairing (n t , f t );

[0068] Step 7-2-c, if the current iteration t is greater than 1, and the best pairing (n t , f t ) and the pairing of the previous iteration (n t-1 , f t-1 ) are not the same, then the set needs to be updated; the update operation is to add the current m to the set Right now And from the collection Remove m from At the same time, the variable ∈ is set to 1;

[0069] Step 7-2-d, after the sub-loop ends, if the variable ∈=0, it means that there is no change in this complete loop, and the entire loop is terminated at this time;

[0070] Step 7-3, finally the result set as output.

[0071] This application has the following effects:

[0072] 1. Improve the overall rate of the system: By introducing multi-fixed station coordinated transmission and optimized resource allocation strategy, this method significantly improves the overall rate of the system. Figure 5 As shown in (a), compared with the distance-based and signal-to-noise ratio-based greedy algorithms, this method can achieve a higher total rate under different numbers of users.

[0073] 2. Improve the average user rate: This method not only improves the total system rate, but also effectively improves the average user rate. Figure 5 (b) shows that when the number of users increases, the average rate of this method decreases significantly slower than that of other comparison methods.

[0074] 3. Optimize resource utilization: Figure 6 As shown, this method can more effectively utilize available resource blocks. Compared with the greedy algorithm based on signal-to-noise ratio, step 3 avoids the crowding phenomenon of multiple users on the same resource block, thereby improving resource utilization efficiency.

[0075] 4. Strong adaptability: This method takes into account the characteristics of shortwave communication, such as Figure 1-3 The time, frequency and space variations shown in the figure. By dynamically adjusting the selection of fixed stations and frequencies, this method can adapt to complex shortwave propagation environments.

[0076] 5. Improved power distribution: e.g. Figure 4 As shown, the improved water filling algorithm used in step 1 is superior to the Nelder-Mead method and the average power allocation method in terms of power allocation, and can better utilize limited transmission power resources.

[0077] 6. In summary, the method proposed in the present invention shows obvious technical effects in improving system capacity, optimizing resource utilization, adapting to complex environments, etc., and provides an effective solution for improving the performance of the skywave communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 A signal strength distribution diagram emitted by a fixed station after radial basis function interpolation provided in an embodiment of the present application;

[0079] Figure 2 A time-frequency diagram of a fixed link in one day provided in an embodiment of the present application;

[0080] Figure 3 A graph showing changes in the signal-to-noise ratio at different times of the day for the 12 MHz and 5 MHz frequency points provided in an embodiment of the present application;

[0081] Figure 4 A graph showing the change in rate with fixed station transmit power under different fixed station-frequency matching methods provided in an embodiment of the present application;

[0082] Figure 5 A graph showing changes in the sum rate and average sum rate with the number of users under different fixed station-frequency matching methods provided in an embodiment of the present application;

[0083] Figure 6 A graph showing the change in the number of resource blocks used versus the number of users provided in an embodiment of the present application. DETAILED DESCRIPTION

[0084] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0085] The following first introduces the embodiments of the present application in conjunction with the accompanying drawings.

[0086] Step 1: Establish a skywave collaborative transmission model for air-to-ground communication.

[0087] Assume the positions of N fixed stations are α1, α2, ...α N , the positions of M aircraft users are l1, l2, ...l M , for the nth fixed station, when the working frequency is f n When the channel gain position distribution G(α n ,ξ,f n ), where ξ is the receiver position, and the transmission power of fixed station n to user m is P n，m , the channel gain from the fixed station to user m is expressed as:

[0088] g n，m (f) = G(α n , l m ,f), (1)

[0089] in is the operating frequency; then, the power of the signal received by user m from fixed station n at frequency point f is:

[0090] S n，m (f) = P n，m g n，m (f). (2)

[0091] Assume that the noise power is N0; the signal-to-noise ratio of the signal received by user m from fixed station n at frequency f is:

[0092]

[0093] in and is a temporary variable, S n，m (f) represents the energy of the signal, The interference from the same frequency signals of other fixed stations is is the interference of the same frequency signal sent by the fixed station to other users, and N0 is the noise;

[0094] The corresponding achievable rate is:

[0095] R n，m =log2(1+ρ n，m (f m )). (4).

[0096] Step 2: Establish an optimization problem to achieve the goal of maximizing user rate;

[0097] The maximization target optimization problem is to achieve the user rate maximization goal by optimizing the user-fixed station matching and the fixed station transmission power under the constraints of the maximum transmission power of a single base station and the number of fixed stations matched for each user. The initial optimization problem and the corresponding constraints are expressed as:

[0098]

[0099]

[0100] Where P ：，： is the transmit power matrix, P n，m Non-zero means user m is matched with fixed station n and is allocated power P n，m ,Constraint C1 means that each user can only match one working frequency of a fixed station, constraint C2 means that the transmission power of each fixed station must be less than the maximum value, and constraint C3 means that the transmission power allocated to each user must be non-negative. Obviously, problem P1 is a mixed integer optimization problem and cannot be solved directly.

[0101] Step 3: Modify the optimization problem and establish sub-problems based on a two-layer structure.

[0102] In the initial problem P1, the fixed station transmission power is a continuous variable, and the frequency selection and the fixed station selection are discrete variables; therefore, the initial problem P1 is decomposed into two sub-problems: transmission power optimization and frequency-fixed station selection; a two-layer structure is adopted, with the transmission power optimization at the bottom layer and the frequency-fixed station selection at the upper layer; the bottom layer is used to optimize the transmission power allocation under the given frequency-fixed station selection scheme, and the upper layer optimizes the frequency-fixed station selection scheme through the system and rate returned by the bottom layer.

[0103] For a given fixed station and operating frequency selection scheme, the fixed station transmission power allocation problem is expressed as:

[0104]

[0105]

[0106] Considering that each user is matched with a fixed station and uses one working frequency at the same time, the fixed station transmission power allocation problem P2 is simplified to:

[0107]

[0108]

[0109] Obviously, P2.1 is non-convex, and the power allocation variables of each fixed station to its users are coupled with each other, and some signals have the probability of interfering with other signals; this makes the traditional water injection power allocation method invalid. Therefore, this problem needs to be transformed before it can be solved.

[0110] According to the structure of the optimization objective expression in P2, the adaptive Nelder-Mead algorithm is used to solve this problem. The adaptive Nelder-Mead algorithm is suitable for solving unconstrained complex nonlinear optimization problems. In order to transform P2 into an unconstrained form, define:

[0111]

[0112] Therefore, P2.1 is rewritten as:

[0113]

[0114] This form can be solved using the adaptive Nelder-Mead algorithm. The specific process is as follows:

[0115] Step 4: Use the adaptive Nelder-Mead algorithm to solve the fixed station power allocation problem.

[0116] In the Nelder-Mead algorithm, the input is the fixed station and the frequency allocation plan Output is the power distribution scheme Here’s how:

[0117] Step 4-1, Initialization:

[0118] Step 4-2, set loop n = 1: N, and set another loop inside. Power allocation for each user in

[0119] Step 4-3, Setting To optimize the goal;

[0120] Step 4-4, Setting For the optimization variable, the current value is the initial value;

[0121] Complexity analysis: The complexity of the calculation rate is O(M 2 ), the main step of the adaptive Nelder-Mead algorithm is sorting, the complexity is o(MlogM), so the total complexity is O(M 3 logM).

[0122] Step 5: Establish the matching problem between fixed stations and working frequencies under the optimal power allocation condition.

[0123] Based on the transmission power allocation scheme under the given fixed station selection and working frequency setting conditions, the achievable rate of user m who selects fixed station n is defined as Then the optimization problem of the two discrete variables of fixed station and working frequency allocation can be expressed as:

[0124]

[0125]

[0126] Among them, constraint C1 means that only one fixed station and one working frequency can be matched for one user, and constraint C2 means that all users are assigned corresponding fixed stations. Substituting constraint C2 into the optimization objective, P3 is simplified as follows:

[0127]

[0128]

[0129] P3.1 is an integer optimization problem. The complexity of solving P3.1 using the exhaustive search method is O(N M F M ), this exponential complexity is very high and requires an algorithm with lower complexity.

[0130] Step 6: Preprocess the resource blocks according to the channel quality; perform resource block preprocessing to reduce the complexity of working frequency and fixed station matching; for each user, determine the corresponding signal-to-noise ratio of each resource block after power allocation, and obtain the top M resource blocks and their ranking.

[0131] Step 6-1, initialization, define the tensor to store the results

[0132] Step 6-2, set the cycle m = 1:M, calculate the signal-to-noise ratio of each resource block after power allocation according to formula (3), and obtain the current optimal (n t , f t ), use Top-k sorting to get the top M resource blocks and their sorting, and save the results to

[0133] Complexity analysis: Step 2-2 has M loops, and the complexity of the sub-steps of step 2-2 is O(NF) and O(NM) respectively. Therefore, the complexity of preprocessing is O(NM 2 +NFM). Therefore, step 2 can improve the system and speed while reducing complexity.

[0134] After preprocessing, when the number of users is less than the total number of resource blocks, the search range for resource blocks allocated to each user is limited to the top M resource blocks; the complexity of using the exhaustive search method to solve this problem is reduced to O(M M ), but the complexity is still very high.

[0135] Step 7: Fixed station and working frequency matching method based on alternating optimization.

[0136] Inspired by the idea of ​​dynamic programming and alternating optimization, this application proposes a fixed station-frequency selection method based on alternating optimization. First, the fixed station and frequency corresponding to the maximum channel gain are assigned to the first user; for each subsequent user, the fixed station and working frequency are selected while considering the performance after the fixed station power allocation; when one round is completed, a new round starts from the first user until the fixed station and frequency selection of all users in a round are no longer updated, or the maximum number of rounds t is reached. max End of time;

[0137] Step 7-1, first perform the initialization process; at this stage, the resource block is preprocessed according to the channel quality, and the set is obtained. Applies to every n belonging to the set and every f belongs to the set Set variable ∈=0;

[0138] Step 7-2, perform a loop, the loop range is from t = 1 to t max ;

[0139] Step 7-2-a, at the beginning of each iteration of the loop, reset the variable ∈ to 0;

[0140] Step 7-2-b, for each sub-cycle where m is from 1 to M, calculate the corresponding signal-to-noise ratio of each resource block after power allocation is completed; through such calculation, identify the current optimal pairing (n t , f t );

[0141] Step 7-2-c, if the current iteration t is greater than 1, and the best pairing (n t , f t ) and the pairing of the previous iteration (n t-1 , f t-1 ) are not the same, then the set needs to be updated; the update operation is to add the current m to the set Right now And from the collection Remove m from At the same time, the variable ∈ is set to 1;

[0142] Step 7-2-d, after the sub-loop ends, if the variable ∈=0, it means that there is no change in this complete loop, and the entire loop is terminated at this time;

[0143] Step 7-3, finally the result set As output. For M user scenarios, the loop of step 7-2 and the calculation of resource block performance are both repeated M times, so the algorithm complexity is O(M 2 ). At the same time, considering that the complexity of each power allocation is O(M·Mlog(M)), the overall complexity is o(M 4 log(M)).

[0144] In order to further illustrate the advantages of the present application, other comparative methods are now provided for comparison with the present application.

[0145] An easy-to-think-of method for matching fixed stations is to match each user with the nearest fixed station, and then select the operating frequency point with the largest channel ratio of the fixed station. The specific method is as follows:

[0146] Step 8: Fixed station and working frequency matching method based on distance:

[0147] Step 8-1, initialization: set ∈ = 0 and k = 0;

[0148] Step 8-2, for each user i from 1 to N, perform the following operations:

[0149] Step 8-2-a, calculate the distance from each fixed station to the user and get the nearest fixed station n ＊ ;

[0150] Step 8-2-b, for fixed station n ＊ For each frequency point, calculate the signal-to-noise ratio and select the frequency point f corresponding to the maximum channel ratio ＊ ;

[0151] Step 8-2-c, Update the collection

[0152] Step 8-3, output set

[0153] The method provided in step 8 is more in line with the intuitive impression in most communication scenarios, because the signal strength decreases with the increase of propagation distance. However, the propagation distance of the signal reaching the receiver through ionosphere reflection is greater than the straight-line distance, and the blind spot effect at close range and the multipath superposition effect at long distance will affect the signal strength noise. Therefore, it is necessary to further consider the characteristics of shortwave signals and design a more appropriate fixed station-working frequency allocation method.

[0154] Another way is as follows:

[0155] Step 9: Fixed station and working frequency matching method based on signal-to-noise ratio:

[0156] A naive and greedy resource allocation method is to start from user 1 and each user selects the resource block with the maximum path gain.

[0157] Step 9-1, Initialization: Setting and ∈ = 0;

[0158] Step 9-2, for each m from 1 to M, do the following:

[0159] Step 9-2-a, calculate the signal-to-noise ratio of each resource block after power allocation is completed according to Algorithm 1, and obtain the current optimal (n opt , f opt );

[0160] Step 9-2-b, Update the collection

[0161] Step 9-3, output set

[0162] Step 9 is very intuitive and works well when there are few users and they do not occupy the same frequency resources. However, for scenarios with more users, this solution will appear blind and cannot effectively reduce interference between users and improve resource utilization efficiency. On the one hand, it causes too many users to occupy the same high-quality resources, resulting in increased interference and lower overall sum rates; on the other hand, it causes many suboptimal resources to be vacant, reducing the utilization efficiency of spectrum and fixed station resources.

[0163] The present application is further described below based on specific embodiments.

[0164] The parameter settings are shown in Table 1. In order to more accurately reflect the average performance, the Monte Carlo simulation method is used to perform 300 experiments on each set of parameter settings in the multi-user scenario in an area with latitude and longitude [70°, 140°] and [0°, 70°], and then take the average. For users and fixed stations at a given location, the azimuth angle and the closest distance to the surface are calculated using the geometric method.

[0165] Table 1 Simulation parameter settings

[0166]

[0167] In order to obtain the actual shortwave signal coverage information, the ITSHFPropagation software in the coverage analysis program (VOACAP) was used for regional coverage analysis. The simulation time was set to November 27, 2022, when the number of sunspots was 89. All fixed stations and users were equipped with omnidirectional antennas, and the noise on each Hertz band corresponding to the value of the 3MHz frequency point was set to -145dBW / Hz. Based on the set parameters, the signal area coverage results were collected and saved every hour within a day.

[0168] The coverage data collected in each region contains 99 × 99 sampling points. In order to obtain the link signal-to-noise ratio from the fixed station to the receiver at any location, the radial basis function interpolation method is used to obtain a continuous two-dimensional function.

[0169] Figure 1 The regional distribution of the signal transmitted by the fixed station after RBF interpolation is shown. Figure 1 (a) and Figure 1 (b) It can be seen that when the operating frequency is 17MHz, the high signal-to-noise ratio area is roughly circular, and the signal-to-noise ratio is lower in areas close to and far from the transmitter. This is because areas closer to the fixed station require higher elevation angles, but at slightly higher frequencies, the signal can easily penetrate the ionosphere. Transmitting the signal farther away from the fixed station will cause the signal propagation distance to increase and attenuate more severely, which may result in a weaker received signal strength. Therefore, there may be an intermediate distance within the signal coverage range where the signal is optimal. On the contrary, if Figure 1 As shown in (b), when the operating frequency is 7MHz, there is no ring-shaped high SNR area in the signal strength coverage diagram. This is because when the operating frequency is lower, the near-vertical incidence skywave mode can cover areas closer to the fixed station and eliminate the blind area effect.

[0170] Figure 2 The time-frequency diagram of a fixed link in one day is shown. It can be seen that in the afternoon (both the transmitter and the receiver are in the UTC+8 time zone), the available bandwidth is wider and concentrated in the higher frequency band, and the optimal operating frequency is higher. At night and in the early morning, the available bandwidth is narrower and concentrated in the lower frequency band, and the optimal operating frequency is lower. This shows that in shortwave communication, the communication quality of each working frequency band varies greatly over time, and when using shortwave communication, it should be adjusted to the appropriate frequency in time.

[0171] Figure 3 The change of signal-to-noise ratio of 12MHz and 2.5MHz frequency points at different times is shown. It can be seen that the communication quality of 12MHz frequency point is better in the afternoon, but worse at night and early morning, while 2.5MHz frequency point has better communication quality at night. At the same time, it can be noticed that the signal-to-noise ratio of a specific frequency point changes greatly over time. If a single frequency is maintained during communication, good communication quality cannot be guaranteed.

[0172] First, the total rate performance of the proposed transmit power allocation method of the improved water filling algorithm is compared with other baseline methods. Specifically, a scenario consisting of 10 users is considered, and the maximum transmit power of each FS ranges from 1W to 100W.

[0173] Figure 5 The performance of the proposed water-filling power allocation method, the Nelder-Mead method, and the average power allocation method are compared. It can be seen that as the number of users increases, the rate gradually increases, and the performance of the proposed power allocation method is better than other benchmark methods. This is because the proposed power allocation method can allocate more power to users with better channel conditions and higher gain space, while the average allocation and Nelder-Mead methods lack consideration of the actual link conditions of each user.

[0174] Secondly, the total rate performance of the proposed fixed station-frequency matching method based on alternating optimization is compared with other benchmark methods. Specifically, the scenario where the maximum transmit power of each FS is 100W and the number of users ranges from 1 to 25 is considered.

[0175] Figure 5 (a) and Figure 5(b) depicts the total rate and average total rate as a function of the number of users, respectively. It can be seen that as the number of users increases, the total rate also increases because more resource blocks are utilized. On the other hand, the performance of the proposed method decreases more slowly with the increase in the number of users than the other baseline methods. At the same time, the performance of the proposed fixed station-frequency matching method is better than that of the SNR greedy algorithm-based method, and the saturation region corresponding to the proposed method is larger than the number of users corresponding to the saturated performance. This is because the proposed method can alleviate the crowding phenomenon of multiple users on the same resource block, such as Figure 6 At the same time, in a multi-user scenario, with t max =∞ has a better performance than the proposed method with t max = 1 is better because the former can perform more rounds of optimization, thus achieving a higher system and rate. In addition, when there are few users, the performance of the fixed station-frequency selection method based on distance is worse than that based on signal-to-noise ratio. This is because within the coverage of the shortwave signal, the closer area does not necessarily have a higher signal-to-noise ratio.

[0176] The above-described embodiments of the present application do not constitute a limitation on the protection scope of the present application.

Claims

1. A skywave cooperative transmission method for air-to-ground communication, characterized in that: The method comprises: Step 1: Establish a skywave cooperative transmission model for air-to-ground communication; Step 2: Establish an optimization problem to achieve the user rate maximization goal; Step 3: Modify the optimization problem and establish sub-problems based on a two-layer structure; Step 4, using the adaptive Nelder-Mead algorithm to solve the fixed station power allocation problem; Step 5: Establish the matching problem between fixed stations and working frequencies under the optimal power allocation condition; Step 6: pre-process the resource blocks according to the channel quality; for each user, determine the signal-to-noise ratio of each resource block after power allocation, and obtain the top M resource blocks and their ranking; Step seven, fixed station and working frequency matching method based on alternating optimization.

2. The method according to claim 1, characterized in that Step 1: Establish a skywave cooperative transmission model for air-to-ground communication, including: Assume the positions of N fixed stations are α1, α2, ...α N , the positions of M aircraft users are l1, l2, ...l M , for the nth fixed station, when the working frequency is f n When the channel gain position distribution G(α n ,ξ,f n ), where ξ is the receiver position, and the transmission power of fixed station n to user m is P n,m , the channel gain from the fixed station to user m is expressed as: g n，m (f)=G(α n ,l m ,f), (1) in is the operating frequency; then, the power of the signal received by user m from fixed station n at frequency point f is: S n，m (f)=P n，m g n，m (f). (2) Assume that the noise power is N0; the signal-to-noise ratio of the signal received by user m from fixed station n at frequency f is: in and is a temporary variable, S n，m (f) represents the energy of the signal, The interference from the same frequency signals of other fixed stations is is the interference of the same frequency signal sent by the fixed station to other users, and N0 is the noise; The corresponding achievable rate is: R n，m =log2(1+ρ n，m (f m )). (4).

3. The method according to claim 1, characterized in that Step 2: Establish an optimization problem to maximize the user rate, including: The maximization target optimization problem is to achieve the user rate maximization goal by optimizing the user-fixed station matching and the fixed station transmission power under the constraints of the maximum transmission power of a single base station and the number of fixed stations matched for each user. The initial optimization problem and the corresponding constraints are expressed as: Where P ：，： is the transmit power matrix, P n，m Non-zero means user m is matched with fixed station n and is allocated power P n，m ,Constraint C1 means that each user can only match one working frequency of a fixed station, constraint C2 means that the transmission power of each fixed station must be less than the maximum value, and constraint C3 means that the transmission power allocated to each user must be a non-negative number.

4. The method according to claim 1, characterized in that: Step 3: Modify the optimization problem and establish sub-problems based on a two-layer structure, including: The initial problem P1 is decomposed into two sub-problems: transmit power optimization and frequency-fixed station selection; a two-layer structure is adopted, with transmit power optimization at the bottom layer and frequency-fixed station selection at the upper layer; the bottom layer is used to optimize the transmit power allocation under the given frequency-fixed station selection scheme, and the upper layer optimizes the frequency-fixed station selection scheme through the system and rate returned by the bottom layer; For a given fixed station and operating frequency selection scheme, the fixed station transmission power allocation problem is expressed as: Considering that each user is matched with a fixed station and uses one working frequency at the same time, the fixed station transmission power allocation problem P2 is simplified to: Obviously, P2.1 is non-convex, and the power allocation variables of each fixed station to its users are coupled with each other, and there is a probability that some signals are interferences of other signals; To convert P2 into unconstrained form, define: Therefore, P2.1 is rewritten as:

5. The method according to claim 1, characterized in that Step 4: Use the adaptive Nelder-Mead algorithm to solve the fixed station power allocation problem, including: In the Nelder-Mead algorithm, the input is the fixed station and the frequency allocation plan Output is the power distribution scheme Here’s how: Step 4-1, Initialization: Step 4-2, set loop n = 1: N, and set another loop inside. Power allocation for each user in Step 4-3, Setting To optimize the goal; Step 4-4, Setting For optimizing variables, the current value is the initial value.

6. The method according to claim 1, characterized in that Step 5: Establish the matching problem between fixed stations and working frequencies under optimal power allocation, including: Based on the transmission power allocation scheme under the given fixed station selection and working frequency setting conditions, the achievable rate of user m who selects fixed station n is defined as Then the optimization problem of the two discrete variables of fixed station and working frequency allocation can be expressed as: Among them, constraint C1 means that only one fixed station and one working frequency can be matched for one user, and constraint C2 means that all users are assigned corresponding fixed stations. Substituting constraint C2 into the optimization objective, P3 is simplified as follows:

7. The method according to claim 2, characterized in that Step 6: Preprocess the resource blocks according to the channel quality; for each user, determine the signal-to-noise ratio of each resource block after power allocation, and obtain the top M resource blocks and their ranking, including: Step 6-1, initialization, define the tensor to store the results Step 6-2, set the cycle m = 1:M, calculate the signal-to-noise ratio of each resource block after power allocation according to formula (3), and obtain the current optimal (n t , f t ), use Top-k sorting to get the top M resource blocks and their sorting, and save the results to After preprocessing, when the number of users is less than the total number of resource blocks, the search range for allocating resource blocks to each user is limited to the top M resource blocks.

8. The method according to claim 7, characterized in that Step 7, a method for matching fixed stations and operating frequencies based on alternating optimization, including: First, the first user is assigned a fixed station and frequency corresponding to the maximum channel gain; for each subsequent user, the fixed station and operating frequency are selected taking into account the performance of the fixed station after power allocation; When a round is completed, a new round starts from the first user until the fixed stations and frequency selections of all users in a round are no longer updated, or the maximum number of rounds t is reached. max End of time; The process is as follows: Step 7-1, first perform the initialization process; at this stage, the resource block is preprocessed according to the channel quality, and the set is obtained. Applies to every n belonging to the set and every f belongs to the set Set variable ∈=0; Step 7-2, perform a loop, the loop range is from t = 1 to t max ; Step 7-2-a, at the beginning of each iteration of the loop, reset the variable ∈ to 0; Step 7-2-b, for each sub-cycle where m is from 1 to M, calculate the corresponding signal-to-noise ratio of each resource block after power allocation is completed; identify the current optimal pairing (n t , f t ); Step 7-2-c, if the current iteration t is greater than 1, and the best pairing (n t , f t ) and the pairing of the previous iteration (n t-1 , f t-1 ) are not the same, then the set needs to be updated; the update operation is to add the current m to the set Right now And from the collection Remove m from At the same time, set the variable ∈ to 1; Step 7-2-d, after the sub-loop ends, if the variable ∈=0, it means that there is no change in this complete loop, and the entire loop is terminated at this time; Step 7-3, finally the result set as output.

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