A sky wave cooperative transmission method for air-ground communication

Abstract

The application provides a sky-wave cooperative transmission method for air-ground communication, comprising the following steps: step one, establishing a sky-wave cooperative transmission model for air-ground communication; step two, establishing an optimization problem for realizing user rate maximization; step three, modifying the optimization problem and establishing a sub-problem based on a two-layer structure; step four, using an adaptive Nelder-Mead algorithm to solve a fixed station power distribution problem; step five, establishing a fixed station and working frequency matching problem under an optimal power distribution condition; step six, preprocessing resource blocks according to channel quality; for each user, determining a signal-to-noise ratio of each resource block after power distribution, and obtaining top M resource blocks and ranking; and step seven, a fixed station and working frequency matching method based on an alternating optimization. The application can improve system capacity, optimize resource utilization, and adapt to complex environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a sky wave coordinated transmission method for space-air communication. BACKGROUND

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

[0003] Over the decades, researchers have continuously improved and researched sky wave communication systems. The progress of vacuum tube technology in the 1920s and 1930s led to the development of amplitude modulation radios, enabling voice communication over longer distances. During World War II, sky wave communication became an important tool for military communication and intelligence gathering. After the war, the development of transistor technology made it possible to create smaller and more portable radios, expanding the application of sky wave communication beyond the military and commercial fields.

[0004] Today, sky wave communication is widely used for beyond-line-of-sight communication by governments and non-governmental organizations in remote areas, such as ships at sea, aircraft networks beyond line-of-sight, disaster areas, and remote areas lacking other communication means. However, the dynamic nature of the ionosphere poses a significant challenge to sky wave communication, resulting in channel changes over time and unreliable transmission. The instability of the ionosphere is mainly 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 significant changes in the height, density, and electron concentration of the ionosphere over time, which in turn affects the signal reflection and propagation characteristics of sky wave communication. In addition, it is challenging to design an efficient and robust communication system in the traditional device-to-device transmission scenario due to the multipath propagation of sky wave channels. Multipath propagation refers to the phenomenon where a signal encounters multiple paths during transmission, causing changes in the phase and amplitude of the signal, resulting in signal distortion and fading. This phenomenon is particularly pronounced in sky wave communication, as the irregular structure of the ionosphere makes the signal reflection and refraction paths complex and variable, increasing the uncertainty of signal propagation.

[0005] To improve the performance of sky-wave communication systems for air-to-ground communication, researchers have conducted extensive work in various aspects of data transmission, link establishment, and network synthesis. However, it is crucial to explore the cooperative transmission model of sky-wave communication networks. This model aims to improve communication efficiency and rate, but there is a lack of comprehensive and systematic research in existing work. Specifically, existing research has gaps and challenges in the following aspects: first, existing network synthesis methods fail to fully exploit the potential advantages of sky-wave communication networks, such as the application potential of multi-station cooperation technology in sky-wave communication. Second, there is a lack of effective network management and resource allocation strategies to optimize the performance and reliability of sky-wave communication networks. SUMMARY

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

[0007] The present application provides a sky-wave cooperative transmission method for air-to-ground communication, which comprises the following steps:

[0008] Step one, establish a sky-wave cooperative transmission model for air-to-ground communication;

[0009] Step two, establish an optimization problem to maximize user rate;

[0010] Step three, modify the optimization problem and establish a two-layer structure-based sub-problem;

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

[0012] Step five, establish a fixed station and working frequency matching problem under the optimal power allocation condition;

[0013] Step six, preprocess the resource blocks according to the channel quality; 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;

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

[0015] Further, step one, establishing a sky-wave cooperative transmission model for air-to-ground communication, comprises:

[0016] Let the positions of N fixed stations be α1, α2,...α N , and the positions of M aircraft users be l1, l2,...l M , for the nth fixed station, when the working frequency is f n , the channel gain position distribution G(α n , ξ, f nwhere ξ is the receiver location, and let the transmit power of the fixed station n to user m be P n，m The channel gain from the fixed station to user m is denoted as:

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

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

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

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

[0021]

[0022] where and are temporary variables, S n，m (f) represents the energy of the signal, is the interference of the co-frequency signal from other fixed stations, is the interference of the co-frequency signal from the fixed station to other users, and N0 is the noise;

[0023] The corresponding achievable rate is:

[0024] R n，m = log2(l + p n，m (f m )) (4).

[0025] Further, step two, the optimization problem of achieving the maximum user rate objective is established, including:

[0026] The optimization problem of the maximum objective is to maximize the user rate by optimizing the user-fixed station matching and the fixed station transmit power under the constraints of the maximum transmit power of a single base station and the matching number of fixed stations for each user; the initial optimization problem and the corresponding constraints are represented as:

[0027]

[0028] where P ：，： is the transmit power matrix, P n，m non-zero indicates that user m is matched with fixed station n and is allocated power P n，mThe constraint C1 means that each user can match only one operating frequency of one fixed station, the constraint C2 means that the transmit power of each fixed station must be less than the maximum value, and the constraint C3 means that the transmit power allocated to each user must be non-negative.

[0029] Further, step three, the optimization problem is modified and a two-layer structure based sub-problem is established, including:

[0030] The initial problem P1 is decomposed into two sub-problems: transmit power optimization and frequency-fixed station selection; a two-layer structure is adopted, the transmit power optimization is located at the bottom layer, and the frequency-fixed station selection is located at the upper layer; the bottom layer is used to optimize the transmit power allocation under the condition of a given frequency-fixed station selection scheme, and the upper layer returns the system and rate optimized frequency-fixed station selection scheme through the bottom layer;

[0031] For a given fixed station and operating frequency selection scheme, the fixed station transmit power allocation problem is represented as:

[0032]

[0033] Considering that each user matches one fixed station and uses one operating frequency, the fixed station transmit power allocation problem P2 is simplified as:

[0034]

[0035] 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 exist as interference of other signals;

[0036] In order to convert P2 into an unconstrained form, the following is defined:

[0037]

[0038] Therefore, P2.1 is rewritten as:

[0039]

[0040] Further, step four, the adaptive Nelder-Mead algorithm is used to solve the fixed station power allocation problem, including:

[0041] In the Nelder-Mead algorithm, the input is a fixed station and frequency allocation scheme The output is a power allocation scheme The method is as follows:

[0042] Step 4-1, initialization:

[0043] Step 4-2, set the loop n=1:N, and set the inner loop again, for power allocation for each user user in

[0044] Step 4-3, set for optimization target.

[0045] Step 4-4, set for optimization variable, the current value is the initial value.

[0046] Further, step five, establish the power allocation optimal case of fixed station and working frequency matching problem, including:

[0047] Based on the given fixed station selection and working frequency setting conditions, the transmit power allocation scheme is defined, and the achievable rate of user m selecting fixed station n is obtained Then the optimization problem of two discrete variables of fixed station and working frequency allocation is represented as:

[0048]

[0049] Among them, constraint C1 represents that only one user can be matched with one fixed station and one working frequency point, and constraint C2 represents that all users are allocated corresponding fixed stations; By substituting constraint C2 into the optimization target, P3 is simplified as:

[0050]

[0051] Further, step six: according to the channel quality, the resource blocks are preprocessed; 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 ranking, including:

[0052] Step 6-1, initialization, define the tensor to store the results

[0053] Step 6-2, set loop m = 1: M, calculate the corresponding 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 obtain the top M resource blocks and ranking, and save the results to

[0054] After preprocessing, when the number of users is less than the total number of resource blocks, the search range of resource blocks allocated to each user is limited to the top M resource blocks.

[0055] Further, step seven, based on the alternating optimization of fixed station and working frequency matching method, including:

[0056] First, the first user is arranged with the fixed station and frequency point corresponding to the maximum channel gain; for each user, the fixed station and working frequency point are selected in consideration of the performance after the power allocation of the fixed station;

[0057] When a round is completed, a new round starts from the first user until the fixed station and frequency selection of all users in a round is no longer updated, or the maximum number of rounds t is reached max ;

[0058] The process is as follows:

[0059] Step 7-1, first, the initialization process; in this stage, according to the indication of the channel quality to the resource block preprocessing, the set is obtained for each n belonging to the set and each f belonging to the set ;

[0060] Step 7-2, a loop is performed, the range of the loop is from t = 1 to t max ;

[0061] Step 7-2-a, at the beginning of each iteration of the loop, the variable ∈ is reset to 0;

[0062] Step 7-2-b, for each m from 1 to M sub-loop, calculate the signal-to-noise ratio corresponding to each resource block after the power allocation is completed; identify the current optimal pair (n t , f t );

[0063] Step 7-2-c, if the current iteration t is greater than 1, and the optimal pair (n t , f t ) obtained at present is not the same as the pair (n t-1 , f t-1 ) of the previous iteration, then the set needs to be updated; the update operation is to add the current m to the set , i.e. and remove m from the set , i.e. , and set the variable ∈ to 1;

[0064] Step 7-2-d, after the sub-loop is completed, if the variable ∈ = 0, it means that no change has occurred in this complete loop, at this time the entire loop is terminated;

[0065] Step 7-3, finally, the result set is output.

[0066] The present application has the following effects:

[0067] 1. Improving system sum rate: By introducing multi-fixed station cooperative transmission and optimized resource allocation strategy, the proposed method significantly improves the system sum rate. As shown in (a) of Figure 5 , the proposed method achieves higher sum rate than the distance-based and SNR-based greedy algorithms under different user numbers.

[0068] 2. Improving average user rate: The proposed method not only improves the system sum rate but also effectively improves the average user rate. Figure 5 As shown in (b), the average rate of the proposed method decreases significantly slower than other comparative methods as the number of users increases.

[0069] 3. Optimizing resource utilization: As shown in (c), the proposed method can more effectively utilize available resource blocks. Compared with the SNR-based greedy algorithm, step 3 avoids the congestion of multiple users on the same resource block, thereby improving resource utilization efficiency. Figure 6

[0070] 4. Strong adaptability: The proposed method considers the characteristics of shortwave communication, such as Figures 1-3 time, frequency, and spatial variation. By dynamically adjusting the selection of fixed stations and frequencies, the proposed method can adapt to complex shortwave propagation environments.

[0071] 5. Improved power allocation: As shown in (d), 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 transmit power resources. Figure 4

[0072] 6. In summary, the proposed method shows obvious technical effects in improving system capacity, optimizing resource utilization, and adapting to complex environments, providing an effective solution for performance improvement of skywave communication systems. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 A signal strength distribution diagram of one fixed station after radial basis function interpolation provided for the embodiments of the present application;

[0074] Figure 2 A time-frequency diagram of a certain fixed link in a day provided for the embodiments of the present application;

[0075] Figure 3 SNR variation diagrams of 12MHz and 5MHz frequency points at different times of the day provided for the embodiments of the present application;

[0076] Figure 4 Rate variation diagrams with fixed station transmit power under different fixed station-frequency matching methods provided for the embodiments of the present application;

[0077] ​​Figure 5 The graph shows the variation of the sum rate and average sum rate with the number of users under different fixed station-frequency matching methods provided in the embodiments of this application.

[0078] Figure 6 This is a graph showing the change in the number of resource blocks used as a function of the number of users, provided in an embodiment of this application. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0080] The embodiments of this application will now be described in conjunction with the accompanying drawings.

[0081] Step 1: Establish a skywave cooperative transmission model for air-to-ground communication.

[0082] Let the locations of the N fixed stations be α1, α2, ... α N The locations of M aircraft users are l1, l2, ... l M For the nth fixed station, when the operating frequency is f n At that time, the channel gain location distribution G(α) formed on the ground surface n , ξ, f n ), where ξ is the receiver position, and let the transmit power from fixed station n to user m be P. n，m The channel gain from the fixed station to user m is expressed as:

[0083] g n,m (f)=G(α n , l m f) (1)

[0084] in Let f be the operating frequency; then, the power of the signal received by user m from fixed station n at frequency point f is:

[0085] S n，m (f)=P n，m g n，m (f) (2)

[0086] Let the noise power be N0; the signal-to-noise ratio of the signal received by user m from fixed station n at frequency point f is:

[0087]

[0088] in and S is a temporary variable. n，m (f) represents the energy of the signal. Interference from co-channel signals from other fixed stations, The interference of the same frequency signal sent by the fixed station to other users is N0, which is the noise;

[0089] The corresponding achievable rate is:

[0090] R n，m = log2(1+ρ n,m (f m )) (4).

[0091] Step two: Establish the optimization problem to maximize the user rate;

[0092] The optimization problem to maximize the user rate is to optimize 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 matching of a fixed station for each user. The initial optimization problem and the corresponding constraints are represented as:

[0093]

[0094] where P ：，： is the transmission power matrix, P n，m is the non-zero value indicating that user m is matched with fixed station n and is allocated power P n，m , constraint C1 indicates that each user can only match one working frequency of one fixed station, constraint C2 indicates that the transmission power of each fixed station must be less than the maximum value, and constraint C3 indicates 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.

[0095] Step three: Modify the optimization problem and establish a two-layer structure-based subproblem.

[0096] In the initial problem P1, the fixed station transmission power is a continuous variable, and the frequency selection and fixed station selection are discrete variables. Therefore, the initial problem P1 is decomposed into two subproblems: transmission power optimization and frequency-fixed station selection. A two-layer structure is adopted, with transmission power optimization at the bottom layer and 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 by returning the system and rate from the bottom layer.

[0097] For a given fixed station and working frequency selection scheme, the fixed station transmission power allocation problem is represented as:

[0098]

[0099] Considering that each user is matched with one fixed station and uses one working frequency, the fixed station transmission power allocation problem P2 is simplified as:

[0100]

[0101] It is obvious that P2.1 is non-convex, and the power allocation variables of each base station to its users are coupled with each other, some signals exist probability is the interference of other signals; this makes the traditional water-filling power allocation method invalid. Therefore, the problem needs to be transformed before it can be solved.

[0102] 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 complex non-linear optimization problems without constraints. In order to transform P2 into an unconstrained form, define:

[0103]

[0104] Therefore, P2.1 is rewritten as:

[0105]

[0106] This form can be solved using the adaptive Nelder-Mead algorithm. The specific process is as follows:

[0107] Step four: use the adaptive Nelder-Mead algorithm to solve the base station power allocation problem.

[0108] In the Nelder-Mead algorithm, the input is the base station and frequency allocation scheme The output is the power allocation scheme The method is as follows:

[0109] Step 4-1, initialization:

[0110] Step 4-2, set the loop n = 1: N, and set another loop inside, for each user user in

[0111] Step 4-3, set as the optimization objective;

[0112] Step 4-4, set as the optimization variable, the current value is the initial value;

[0113] Complexity analysis: the complexity of the calculation rate is O(M 2 ), the main step of the adaptive Nelder-Mead algorithm is sorting, and the complexity is O(MlogM), therefore, the total complexity is O(M 3 log M).

[0114] Step five: establish the base station and working frequency matching problem under the optimal power allocation condition.

[0115] Based on the given fixed station selection and working frequency setting conditions, the achievable rate of user m selected by fixed station n is defined as Then the optimization problem of the two discrete variables, fixed station and working frequency, is represented as:

[0116]

[0117] In which, constraint C1 means that only one user can be matched with one fixed station and one working frequency, and constraint C2 means that all users are assigned with corresponding fixed stations; substituting constraint C2 into the optimization objective, P3 is simplified as:

[0118]

[0119] P3.1 is an integer optimization problem, let The complexity of solving P3.1 by using the exhaustive search method is O(N M F M ), which is very high, and a lower complexity algorithm is needed.

[0120] Step six: Preprocessing resource blocks according to channel quality; in order to reduce the complexity of working frequency and fixed station matching, resource blocks are preprocessed; for each user, the signal-to-noise ratio of each resource block after power allocation is determined, and the top M resource blocks and the ranking are obtained.

[0121] Step 6-1, initialization, define the tensor for storing the results

[0122] Step 6-2, set the loop 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 obtain the top M resource blocks and the ranking, and save the results to

[0123] 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 two can improve the system and rate while reducing the complexity.

[0124] After preprocessing, when the number of users is less than the total number of resource blocks, the search range of allocating resource blocks to each user is limited to the top M resource blocks; the complexity of solving this problem by using the exhaustive search method is reduced to O(M M ), but the complexity is still very high.

[0125] Step seven: Fixed station and working frequency matching method based on alternate optimization.

[0126] Inspired by the dynamic programming idea and alternate optimization, the present application proposes a fixed station-frequency selection method based on alternate optimization. First, the first user is arranged with the fixed station and frequency point corresponding to the maximum channel gain; for each user afterwards, the fixed station and working frequency are selected in consideration of the performance after the power allocation of the fixed station; when a round is completed, a new round starts from the first user until the fixed station and frequency selection of all users in a round no longer updates, or the maximum number of rounds t is reached max ;

[0127] Step 7-1, first initialization process; in this stage, according to the indication of the pre-processing of the resource block according to the channel quality, the set is obtained for each n belonging to the set and each f belonging to the set ;

[0128] Step 7-2, a loop is performed, the range of the loop is from t = 1 to t max ;

[0129] Step 7-2-a, at the beginning of each iteration of the loop, the variable ∈ is reset to 0;

[0130] Step 7-2-b, for each m from 1 to M sub-loop, calculate the signal-to-noise ratio of each resource block after completing the power allocation; through such calculation, the current optimal pair (n t , f t ) is identified;

[0131] Step 7-2-c, if the current iteration t is greater than 1, and the optimal pair (n t , f t ) obtained at present is not the same as the pair (n t-1 , f t-1 ) of the previous iteration, then the set needs to be updated; the update operation is to add the current m to the set , i.e. and remove m from the set , i.e. , and set the variable ∈ to 1;

[0132] Step 7-2-d, after the sub-loop ends, if the variable ∈ = 0, it means that no change has occurred in this complete loop, at this time the entire loop is terminated;

[0133] Step 7-3, finally the result set As output. For M user scenarios, the loop and calculation of resource block performance in step 7-2 are both looped 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·M log(M)), the overall complexity is O(M 4 log(M)).

[0134] In order to further illustrate the advantages of the present application, other comparative methods are provided for comparison with the present application.

[0135] One easily thought of fixed station matching method is to match the nearest fixed station to each user, and then select the working frequency point with the largest channel ratio of the fixed station. The specific method is as follows:

[0136] Step eight: fixed station and working frequency matching method based on distance:

[0137] Step 8-1, initialization: set ∈ = 0 and k = 0;

[0138] Step 8-2, for each user i from 1 to N, perform the following operations:

[0139] Step 8-2-a, calculate the distance from each fixed station to the user to get the nearest fixed station n ＊ ;

[0140] Step 8-2-b, for each frequency point of the fixed station n ＊ , calculate the signal-to-noise ratio, and select the frequency point f ＊ with the largest channel ratio;

[0141] Step 8-2-c, update the set

[0142] Step 8-3, output the set

[0143] Step eight provides a method that is more in line with the intuitive impression in most communication scenarios, because the strength of the signal decreases with the increase of the propagation distance. However, the propagation distance of the signal reflected by the ionosphere to the receiver is greater than the straight-line distance, and the near-distance blind area effect and the far-distance multipath superposition effect will affect the signal strength noise. Therefore, further consideration of the characteristics of short-wave signals is needed to design a more suitable fixed station-working frequency allocation method.

[0144] Another way is as follows:

[0145] Step nine: fixed station and working frequency matching method based on signal-to-noise ratio:

[0146] A naive and greedy resource allocation method is to start from user 1, and each user selects the resource block with the largest path gain.

[0147] Step 9-1, initialization: set and ∈ = 0;

[0148] Step 9-2, for each m from 1 to M, perform the following operations:

[0149] Step 9-2-a, calculate the signal-to-noise ratio corresponding to each resource block after completing the power allocation according to the algorithm one, and get the current optimal (n opt ,f opt );

[0150] Step 9-2-b, update the set

[0151] Step 9-3, output the set

[0152] The step nine method is intuitive and works well when the number of users is small and there is no occupation of the same time-frequency resource. However, for a more user scenario, this scheme will be blind and cannot effectively reduce the interference between users and improve the resource utilization efficiency. On the one hand, it leads to excessive users occupying the same high-quality resources, resulting in increased interference and lower overall rate. On the other hand, it leads to many suboptimal resource vacancies, reducing the utilization efficiency of the spectrum and fixed station resources.

[0153] The present application will be further described below according to specific embodiments.

[0154] The parameter settings are shown in Table 1. In order to more accurately reflect the average performance, the Monte Carlo simulation method is used, and 300 experiments are performed for each group of parameter settings in a multi-user scenario, and then the average is taken. For a given location of the user and the fixed station, the azimuth angle and the closest distance to the ground are calculated by geometric method.

[0155] Table 1 Simulation parameter settings

[0156]

[0157] In order to obtain the actual short-wave signal coverage information, the ITS HF Propagation software in the coverage analysis program (VOACAP) is used for regional coverage analysis. The simulation time is set to November 27, 2022, and the sunspot number on this day is 89. All fixed stations and users are equipped with omnidirectional antennas, and the noise on each hertz frequency band is set to -145 dBW / Hz corresponding to the value of 3 MHz frequency point. Based on the set parameters, the signal regional coverage results of each hour are collected within a day of time range and saved.

[0158] The collected coverage data for each region contains 99x99 sample points. To obtain the link SNR from the fixed station to any location receiver, a radial basis function interpolation method is used to obtain a continuous two-dimensional function.

[0159] Figure 1 The regional distribution of the signal transmitted by the fixed station after RBF interpolation is shown. From Figure 1 (a) and Figure 1 (b) of FIG. 7, it can be seen that when the operating frequency is 17 MHz, the high SNR region is roughly annular, and the regions close to and far from the transmitter have low SNR. This is because the areas closer to the fixed station require a higher elevation angle, but at a slightly higher frequency, the signal can easily penetrate the ionosphere. Propagating signals from a greater distance from the fixed station will result in an increase in signal propagation distance and more severe attenuation, which can result in a weaker received signal strength. Therefore, there may be an intermediate distance in the signal coverage range where the signal is optimal. In contrast, as shown in Figure 1 (b), when the operating frequency is 7 MHz, there is no annular high SNR region in the signal strength coverage map. This is because when the operating frequency is lower, the near-vertical incident skywave mode can cover areas closer to the fixed station and eliminate the blind zone effect.

[0160] Figure 2 The time-frequency diagram of a certain fixed link over a day is shown. It can be seen that in the afternoon (both the transmitter and receiver are in the UTC+8 time zone), the available bandwidth is wider and concentrated in a higher frequency band, and the optimal operating frequency is higher. In the night and early morning, the available bandwidth is narrower and concentrated in a lower frequency band, and the optimal operating frequency is lower. This shows that in shortwave communication, the communication quality of each operating frequency band changes greatly over time, and shortwave communication should be adjusted to the appropriate frequency in a timely manner.

[0161] Figure 3 The SNR variation of 12 MHz and 2.5 MHz frequency points at different times is shown. It can be seen that the communication quality of the 12 MHz frequency point is better in the afternoon, but worse at night and in the early morning, while the 2.5 MHz frequency point has better communication quality at night. At the same time, it can be noted that the SNR of a specific frequency point varies greatly over time. If a single frequency is maintained during communication, it cannot guarantee good communication quality.

[0162] First, the sum rate performance of the proposed improved water-filling algorithm for transmit power allocation method and other benchmark methods is compared. Specifically, a scenario consisting of 10 users is considered, and the maximum transmit power of each FS ranges from 1 W to 100 W.

[0163] Figure 5The performance of the proposed water-filling power allocation method, the Nelder-Mead method, and the average power allocation method were compared. It can be seen that the data rate gradually increases with the number of users, and the proposed power allocation method outperforms the 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 for each user.

[0164] Secondly, the proposed fixed-site frequency matching method based on alternating optimization is compared with other benchmark methods in terms of sum and rate performance. Specifically, a scenario is considered where the maximum transmit power of each FS is 100W and the number of users ranges from 1 to 25.

[0165] Figure 5 (a) and Figure 5 (b) describes the variation of sum rate and average sum rate with the number of users. It can be seen that the sum rate increases with the number of users because more resource blocks are utilized. On the other hand, the performance of the proposed method decreases more slowly with the number of users than other benchmark methods. Meanwhile, the proposed fixed-station-frequency matching method outperforms methods based on signal-to-noise ratio greedy algorithms, and the saturation region corresponding to the proposed method is larger than the number of users corresponding to saturation performance. This is because the proposed method can alleviate congestion on the same resource block by multiple users, such as... Figure 6 As shown. Meanwhile, in multi-user scenarios, it has t max The performance of the proposed method with ∞ is better than that with t max The method with a value of 1 is better because it can perform more rounds of optimization, resulting in a higher system performance and speed. Furthermore, when there are few users, the distance-based fixed-station-frequency selection method performs worse than the signal-to-noise ratio (SNR)-based method. This is because, within the shortwave signal coverage area, closer areas do not necessarily have a higher SNR.

[0166] The embodiments described above do not constitute a limitation on the scope of protection of this application.

Claims

1. A skywave cooperative transmission method for air-to-ground communication, characterized in that, The method includes: Step 1: Establish a skywave cooperative transmission model for air-to-ground communication; Step two: Establish an optimization problem to maximize user speed; Step 3: Modify the optimization problem and establish sub-problems based on a two-layer structure; Step 4: Solve the fixed station power allocation problem using the adaptive Nelder-Mead algorithm; Step 5: Establish the matching problem between fixed stations and operating frequencies under the optimal power allocation condition; 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. Step 7: Matching fixed stations and operating frequencies based on alternating optimization, including: First, assign the fixed station and frequency point corresponding to the maximum channel gain to the first user; for each subsequent user, select the fixed station and operating frequency point after considering the performance of the fixed station power allocation. Once a round is completed, a new round begins with the first user, until the fixed station and frequency selections of all users in a certain round cease to be updated, or the maximum number of rounds t is reached. max End of time; The process is as follows: Step 7-1: First, the initialization process is performed; in this stage, based on the instruction to preprocess resource blocks according to channel quality, a set is obtained. Applicable to each n belonging to the set And each f belongs to the set Set the variable ∈ = 0; where the set This represents all users matching a fixed station n and a frequency f; the set Represents all fixed stations; set Indicates all available frequencies; Step 7-2: Perform a loop, with the loop ranging from t=1 to t0. max ; Step 7-2-a: At the beginning of each iteration of the loop, reset variable ∈ to 0; Step 7-2-b: For each sub-loop of m from 1 to M, calculate the signal-to-noise ratio of each resource block after power allocation; identify the current optimal pairing (n). t ,f t ); Step 7-2-c: If the current iteration t is greater than 1, and the currently obtained optimal pairing (n) t f t Pairing with the previous iteration (n) t-1 f t-1 If the values ​​of m and m are different, then the set needs to be updated; the update operation is to add the current m to the set. Right now and from the set Remove m from the middle, that is 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 no change has occurred in this complete loop, and the entire loop is terminated at this time; Step 7-3, finally the result set As output.

2. The method according to claim 1, characterized in that, Step 1: Establish a skywave cooperative transmission model for air-to-ground communication, including: Let the locations of the N fixed stations be α1, α2, ... α N The locations of M aircraft users are l1, l2, ... l M For the nth fixed station, when the operating frequency is f n At that time, the channel gain location distribution G(α) formed on the ground surface n ,ξ,f n ), where ξ is the receiver position, and let the transmit power from fixed station n to user m be 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 Let f be 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) Let the noise power be N0; the signal-to-noise ratio of the signal received by user m from fixed station n at frequency point f is: in and S is a temporary variable. n,m (f) represents the energy of the signal. Interference from co-frequency signals from other fixed stations, This refers to interference from co-channel signals transmitted from this fixed station to other users; N0 represents 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 two, establish an optimization problem to maximize user speed, including: The optimization problem for maximizing the target is to achieve the goal of maximizing user rate by optimizing user-fixed station matching and fixed station transmit power, under the constraints of maximum transmit 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 follows: Where P ：，： For the transmit power matrix, P n,m A non-zero value indicates that user m is matched with fixed station n and is allocated power P. n,m Constraint C1 indicates that each user can only be matched with one fixed station operating frequency, constraint C2 indicates that the transmit power of each fixed station must be less than the maximum value, and constraint C3 indicates that the transmit 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 top layer; the bottom layer is used to optimize the transmit power allocation under the given frequency-fixed station selection scheme, and the top layer optimizes the frequency-fixed station selection scheme through the system and rate returned by the bottom layer; Given a fixed station and operating frequency selection scheme, the fixed station transmit power allocation problem is expressed as: Considering that each user is matched with one fixed station and uses one operating frequency, the fixed station transmit power allocation problem P2 simplifies to: It is clear that 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 may be interference from other signals. To transform P2 into an unconstrained form, we define: Therefore, P2.1 is rewritten as follows:

5. The method according to claim 1, characterized in that, Step four involves using the adaptive Nelder-Mead algorithm to solve the fixed-site power allocation problem, including: In the Nelder-Mead algorithm, the inputs are the fixed stations and the frequency allocation scheme. The output is a power distribution scheme. The method is as follows: Step 4-1, Initialization: Step 4-2, set the loop n = 1:N, and set another loop inside it. Power allocation is performed for each user in the system. Step 4-3, settings To optimize the objective; Step 4-4, Setting To optimize the 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 operating frequencies under the optimal power allocation condition, including: Based on a transmit power allocation scheme with given fixed station selection and operating frequency settings, the achievable rate of user m with selected fixed station n is defined as follows: The optimization problem for the two discrete variables, fixed station and operating frequency allocation, can be expressed as: Where constraint C1 indicates that only one fixed station and one operating frequency can be matched for one user, and constraint C2 indicates that all users are assigned a corresponding fixed station; substituting constraint C2 into the optimization objective, P3 can be simplified to:

7. The method according to claim 2, characterized in that, Step 6: Preprocess the resource blocks according to 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 loop 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 Using Top-k sorting, obtain the top m resource blocks and their ranking, and save the results to [location]. 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.

Images