Wireless network optimization method, device, system and equipment in high-speed rail scene

Abstract

The invention provides a wireless network optimization method, device, system and equipment in a high-speed rail scene, and the method comprises the steps: determining a base station user group and a satellite user group on a train according to the distance between the position of each carriage on the train and a base station; calculating base station capacity and satellite capacity according to the signal-to-noise ratio of each user group; calculating the total channel capacity of all users on the train according to the base station capacity and the satellite capacity; constructing a target model which takes the maximization of the total channel capacity as a target function and has a power constraint condition and a capacity constraint condition; and solving the target model by using a Lagrange duality method to obtain a base station capacity optimal value and a satellite capacity optimal value. According to the method, a communication system of an air-ground integrated framework is adopted, wide-area coverage of high-speed rail signals is achieved, a multi-hop structural design is not needed, the construction cost is saved, the capacity requirement of high-speed rail users is met, the system capacity is maximized, and the effective utilization rate of resources is improved.

Description

Technical Field

[0001] This invention relates to the field of mobile communication network technology, and in particular to a method, apparatus, system and device for optimizing wireless networks in high-speed rail scenarios. Background Technology

[0002] With the rapid popularization of smartphones, tablets, and other smart mobile devices, mobile wireless multimedia services have become an indispensable part of people's lives. However, in high-speed rail communication network coverage, due to the high speed of trains, train users need to cross different base station cells, resulting in constant signal switching, making it impossible to meet the requirement of "real-time online information." Therefore, how to provide passengers with real-time and effective wireless data services in the context of high-speed train movement is an urgent technical problem to be solved.

[0003] To address the aforementioned issues, existing technologies have introduced intelligent metasurfaces, dynamic metasurfaces, and high-speed rail onboard stations to construct new communication models. Based on these models, optimization techniques are employed to reduce the impact of factors such as handover, signal attenuation, and the Doppler effect, thereby improving the performance of high-speed rail communication systems.

[0004] However, the above methods are all based on terrestrial communication networks and introduce new types of relays (such as smart metasurfaces, dynamic metasurfaces, and high-speed rail vehicle-mounted stations) to improve the performance of high-speed rail communication systems. These methods can expand the coverage of base stations to a certain extent, but the multi-hop transmission structure of relays will introduce additional noise and interference. Dense relay sites need to be built to ensure the coverage of the railway network. This method requires a large investment, especially in sparsely populated areas such as mountainous areas and wilderness. It is evident that the existing high-speed rail communication system is still unable to meet the requirements of wide-area coverage and high performance of high-speed rail signals. Summary of the Invention

[0005] This invention provides a wireless network optimization method, apparatus, system, and equipment for high-speed rail scenarios, which solves the shortcomings of existing high-speed rail communication network optimization, such as the introduction of relays which also introduce noise and interference, and the high construction cost of relay stations. It achieves wide-area coverage of high-speed rail signals without the need for relays and maximizes train channel capacity.

[0006] This invention provides a method for optimizing wireless networks in high-speed rail scenarios, comprising the following steps.

[0007] Based on whether the distance between each carriage on the train and the base station is less than a preset distance, the base station user group and satellite user group on the train are determined. The base station capacity is calculated based on the first signal-to-noise ratio of the base station user group. The satellite capacity is calculated based on the second signal-to-noise ratio of the satellite user group. Based on the base station capacity and the satellite capacity, the total channel capacity of all users on the train is calculated; Construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity. The target model is solved using the Lagrange duality method to obtain the optimal values ​​for the base station capacity and the satellite capacity.

[0008] According to the present invention, a wireless network optimization method for a high-speed rail scenario includes the following steps before calculating the base station capacity based on the first signal-to-noise ratio of the base station user group: The first transmission time is calculated based on the train's trajectory and the location of the base station; the first transmission time is the time required for the base station signal emitted by the base station to be transmitted to the base station user group. Based on the real-time speed of the train and the first transmission time, the first phase offset value of the base station signal after the first transmission time is calculated. The first multicast beamforming vector of the base station is calculated based on the first phase offset value and the first channel matrix; wherein, the first channel matrix is ​​used to describe the channel characteristics between the base station antenna and the base station user group; The first signal-to-noise ratio of the user group of the base station is calculated based on the first multicast beamforming vector of the base station.

[0009] According to the present invention, a wireless network optimization method for a high-speed rail scenario includes calculating the first signal-to-noise ratio of the user group of the base station based on the first multicast beamforming vector of the base station, comprising: Calculate the first effective total signal power of the base station user group based on the first channel matrix; The first signal-to-noise ratio of the base station user group is calculated based on the total power of the first effective signal and the total noise power of the base station user group.

[0010] According to the present invention, a wireless network optimization method for a high-speed rail scenario includes, before calculating the satellite capacity based on the second signal-to-noise ratio of the satellite user group, the following steps are taken: The second transmission time is calculated based on the train's trajectory and the satellite's position; the second transmission time is the time required for the satellite signal emitted by the satellite to be transmitted to the satellite user group. Based on the train's real-time speed and the second transmission time, the second phase offset value of the satellite signal after the second transmission time is calculated. The second groupcast beamforming vector of the satellite is calculated based on the second phase offset value and the second channel matrix; wherein, the second channel matrix is ​​used to describe the channel characteristics between the satellite antenna and the satellite user group; The second signal-to-noise ratio of the satellite user group is calculated based on the second grouping beamforming vector of the satellite.

[0011] According to the present invention, a wireless network optimization method for a high-speed rail scenario includes calculating the second signal-to-noise ratio of the satellite user group based on the second multicast beamforming vector of the satellite, comprising: Calculate the second effective total signal power of the satellite user group based on the second channel matrix; The second signal-to-noise ratio of the satellite user group is calculated based on the total power of the second effective signal and the total noise power of the satellite user group.

[0012] According to the present invention, a wireless network optimization method for a high-speed rail scenario includes solving the model to be optimized using the Lagrange duality method to obtain the optimal values ​​of the base station capacity and the satellite capacity, comprising: Determine the Lagrange dual form of the model to be optimized; When the partial derivative of the Lagrange dual form with respect to base station power is minimized, the base station power allocation expression with respect to the Lagrange multiplier is obtained; when the partial derivative of the Lagrange dual form with respect to satellite power is minimized, the satellite power allocation expression with respect to the Lagrange multiplier is obtained. Update the Lagrange multiplier based on its subgradient; The base station power is updated based on the updated Lagrange multiplier and the base station power allocation expression; the satellite power is updated based on the updated Lagrange multiplier and the satellite power allocation expression. Return to the step of updating the Lagrange multiplier based on the subgradient of the Lagrange multiplier, until the updated base station power and the updated satellite power satisfy the preset convergence condition, and obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0013] The present invention also provides a wireless network optimization device for high-speed rail scenarios, comprising the following modules: The user group determination module is used to determine the base station user group and satellite user group on the train based on whether the distance between the position of each carriage on the train and the base station is less than a preset distance. The base station capacity calculation module is used to calculate the base station capacity based on the first signal-to-noise ratio of the base station user group. The satellite capacity calculation module is used to calculate the satellite capacity based on the second signal-to-noise ratio of the satellite user group. The total channel capacity calculation module is used to calculate the total channel capacity of all users on the train based on the base station capacity and the satellite capacity. The target model determination module is used to construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity. The capacity optimization calculation module is used to solve the target model using the Lagrange duality method to obtain the base station capacity optimization value and the satellite capacity optimization value.

[0014] The present invention also provides an air-to-ground network combined communication system, the system comprising satellites, base stations, and train user equipment, wherein, The satellite is used to obtain the optimal satellite capacity value through the steps in any of the above embodiments of the wireless network optimization method in a high-speed rail scenario, and to realize communication between the satellite and the satellite user group in the train user equipment based on the optimal satellite capacity value. The base station is used to obtain the optimal base station capacity value through the steps in any of the above embodiments of the wireless network optimization method in a high-speed rail scenario, and to realize communication between the base station and the base station user group in the train user equipment based on the optimal base station capacity value.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the wireless network optimization method for any of the high-speed rail scenarios described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the wireless network optimization method for high-speed rail scenarios as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the wireless network optimization method for high-speed rail scenarios as described above.

[0018] The wireless network optimization method, apparatus, system, and equipment provided by this invention for high-speed rail scenarios determine the base station user group and satellite user group on the train based on whether the distance between each carriage and the base station is less than a preset distance; calculate the base station capacity based on the first signal-to-noise ratio of the base station user group; calculate the satellite capacity based on the second signal-to-noise ratio of the satellite user group; calculate the total channel capacity of all users on the train based on the base station capacity and satellite capacity; construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity; solve the target model using the Lagrange duality method to obtain the optimal values ​​of base station capacity and satellite capacity. This method first leverages the characteristics of high-speed trains—determined direction of motion, stable speed, and predictable trajectory—when traveling on a known track. Users are divided into base station user groups and satellite user groups. An integrated air-ground communication system is adopted, utilizing the wide-area coverage, low free-space loss, and minimal fading caused by multipath effects of satellites to compensate for the shortcomings of terrestrial communication networks. This achieves wide-area coverage without requiring multi-hop structures, saving construction costs and meeting the capacity requirements of high-speed rail users. Furthermore, it optimizes the joint power allocation method under conditions where the maximum power of satellites and base stations is limited while ensuring minimum satellite capacity, maximizing system capacity, improving resource utilization, and ensuring ideal coverage of high-speed rail. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram illustrating the application environment of the wireless network optimization method for high-speed rail scenarios provided by the present invention.

[0021] Figure 2 This is a flowchart illustrating the wireless network optimization method for high-speed rail scenarios provided by the present invention.

[0022] Figure 3 This is a schematic diagram of the calculation process for the first signal-to-noise ratio after considering time delay, provided by the present invention.

[0023] Figure 4 This is a schematic diagram of the calculation process for the second signal-to-noise ratio after considering time delay, provided by the present invention.

[0024] Figure 5This is a schematic diagram of the process for solving the model to be optimized using the Lagrange duality method provided by the present invention.

[0025] Figure 6 This is a comparison chart of system performance under different algorithms provided by this invention.

[0026] Figure 7 This is a schematic diagram of the wireless network optimization device for high-speed rail scenarios provided by the present invention.

[0027] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0029] The following is combined with Figures 1-8 Specific embodiments of the present invention are described below.

[0030] like Figure 1 As shown, Figure 1 This paper illustrates the application environment of a wireless network optimization method in a high-speed rail scenario. Notably, this application proposes an integrated air-ground communication system. This system comprises satellites equipped with M antennas and base stations equipped with N antennas working collaboratively to provide high-speed data information services to high-speed train users. In this system, all users in each carriage are considered as a group of users. The base station provides services to the user group in carriages within its coverage area that is less than a preset distance D from the base station; the satellite provides services to the user group in carriages with poor base station signal reception and those that are farther from the base station. At the I-th base station... Base station user group J under the coverage of (hereinafter referred to as base station I) is denoted as ,in This represents the total number of users within user group J covered by base station I, set Each element in the table represents a user. Similarly, the satellite user group M under satellite coverage is represented as... ,in This represents the total number of users within satellite user group M covered by the satellite.

[0031] In this application, the advantages of airborne satellites, such as wide-area coverage, low free-space loss, and low fading caused by multipath effects, are used to supplement the deficiencies of ground networks and ensure ideal coverage of high-speed rail.

[0032] Figure 2 This is a flowchart illustrating the wireless network optimization method for high-speed rail scenarios provided by the present invention, as shown below. Figure 2 As shown, the method includes the following steps.

[0033] Step 201: Determine the base station user group and satellite user group on the train based on whether the distance between the position of each carriage on the train and the base station is less than a preset distance.

[0034] Specifically, in the high-speed rail scenario, using the known speed, length, and location information of the high-speed train (i.e., the track position is known), all carriages on the train can be divided into base station user groups and satellite user groups based on the train's current speed and location. Each base station equipped with N antennas provides information transmission to users in N high-speed train carriages; similarly, a satellite equipped with M antennas provides information transmission to users in M ​​carriages.

[0035] Step 202: Calculate the base station capacity based on the first signal-to-noise ratio of the base station user group.

[0036] Shannon's law describes the relationship between channel capacity C and signal-to-noise ratio (SNR). Therefore, base station capacity can be calculated based on the SNR.

[0037] For base station user group J, the received signal comes from four parts: the useful signal sent by the base station to user group J, the interference signal transmitted by the base station to other user groups, the signal transmitted by the satellite to other user groups, and additive white Gaussian noise in the channel. The following expression shows the signal received by base station user group J, which is served by base station I (i.e., the I-th base station). for.

[0038] (1) in, It is the conjugate transpose of the channel matrix h (i.e., the first channel matrix, in which the matrix elements are used to describe the amplitude and phase information of the channel) from base station I to base station user group J; For the beam vector of base station I for base station user group J, optionally, It could also be the base station beam vector after phase shifting has been introduced; This is the signal from base station I to all users in base station user group J; This represents the beam vector of base station I for any base station user group j other than base station user group J. The transmission power between base station I and any base station user group j other than base station user group J; For base station I, signals are sent between any base station user group j other than base station user group J.

[0039] It is the conjugate transpose of the channel matrix g (including amplitude and phase information) from satellite to satellite user group m; It is the satellite beam vector for satellite user group m, which is optional. It can also be a satellite beam vector that introduces phase shift; Let be the transmission power between the satellite and the m-th satellite user group; The signal from the satellite to satellite user group m; It is additive white Gaussian noise.

[0040] Therefore, the signal-to-noise ratio (SNR) of base station user group J (i.e., the first SNR) can be obtained. As shown in the following formula: (2) Therefore, according to Shannon's law, the base station capacity of base station I... for: (3) Step 203: Calculate the satellite capacity based on the second signal-to-noise ratio of the satellite user group.

[0041] Similarly, the signals received by satellite user group K come from four parts: useful signals sent by the satellite to satellite user group K, signals sent by the satellite to other satellite user groups, signals transmitted by the base station to other user groups, and additive white Gaussian noise in the channel.

[0042] Therefore, the signal received by satellite user group K As shown in the following formula.

[0043] (4) in, The conjugate transpose of the channel matrix g from satellite to satellite user group K; It is the satellite beam vector for satellite user group K, which is optional. It can also be a satellite beam vector that introduces phase shift; This represents the transmission power between the satellite and the Kth satellite user group. The signal from the satellite to satellite user group K; This represents the beam vector of the satellite for any satellite user group m other than satellite user group K; The transmission power between the satellite and any satellite user group m other than satellite user group K; The signal given by the satellite to any satellite user group m other than satellite user group K.

[0044] It is the conjugate transpose of the channel matrix (including amplitude and phase information) from the base station to the satellite user group; Let be the beam vector of the base station for user group j; Let be the transmission power between the base station and the j-th base station user group; This is the signal from the base station to user group j. It is additive white Gaussian noise.

[0045] It can be seen that the signal-to-noise ratio of satellite user group K is... (i.e., the second signal-to-noise ratio) is shown in the following formula.

[0046] (5) Therefore, satellite capacity for.

[0047] (6) Step 204: Calculate the total channel capacity of all users on the train based on the base station capacity and the satellite capacity.

[0048] Specifically, according to the above formulas (3) and (6), the total channel capacity of all users on the train is .

[0049] ; (7) Step 205: Construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity.

[0050] Specifically, due to the different channel conditions among users and the potential co-channel interference, it is crucial to allocate transmission power reasonably between the satellite and the base station in order to balance signal and interference and achieve optimal system performance, i.e., maximize system capacity. However, since the satellite will cause interference to all base station users, if only the total system capacity is maximized, the satellite system capacity may be too small, failing to guarantee communication services for users in carriages not covered by the base station. In view of this situation, in order to provide the highest possible quality communication service to all high-speed rail carriage users, a constraint condition limiting the minimum satellite capacity is proposed to protect the performance of the satellite system and maximize the capacity of the satellite-base station joint communication system while ensuring the communication quality of all carriage users. Therefore, the target model can be represented by the following formulas (8)-(12).

[0051] ; (8) ; (9) ; (10) ; (11) ;(12) Wherein, equation (8) is the objective function. It is a constraint on the minimum satellite capacity, i.e., satellite capacity. Must be in minimum capacity above, It is a limitation on the maximum transmission power of a base station, representing the total power of any base station I. Must be at maximum base station power the following; This is a limit on the satellite's maximum transmission power, representing the satellite's total power. Must be at maximum satellite power the following; This indicates that the power from the base station to the base station user group J is greater than or equal to 0, and the power from the satellite to the satellite user group K is greater than or equal to 0.

[0052] Step 206: Solve the target model using the Lagrange duality method to obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0053] The Lagrange duality method is an important technique in optimization theory used to solve constrained optimization problems. It simplifies the solution process by transforming the primal problem into a dual problem, and is particularly widely used in convex optimization.

[0054] Specifically, the above proposes an optimization problem to maximize system capacity under the constraint of limiting the minimum power of the satellite, as shown in equations (8)-(12). The Lagrange dual method can be used to solve the target model, and finally obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0055] It is worth mentioning that, due to non-convex functions The existence of (as shown in Equation 3) makes the optimization problem a non-convex optimization problem. Since the non-convex optimization problem has high complexity in the optimization process, the continuous convex approximation method is adopted to overcome this difficulty and reduce the complexity of optimization. By using the continuous convex approximation method, some convex functions are used to approximate the non-convex functions in Equation (8) at feasible points, thereby transforming the non-convex problem into a series of convex subproblems. The subproblems are iterated to finally obtain the optimal solution.

[0056] In the above embodiment, the base station user group and satellite user group on the train are determined based on whether the distance between each carriage and the base station is less than a preset distance; the base station capacity is calculated based on the first signal-to-noise ratio of the base station user group; the satellite capacity is calculated based on the second signal-to-noise ratio of the satellite user group; the total channel capacity of all users on the train is calculated based on the base station capacity and the satellite capacity; an objective function is constructed with the goal of maximizing the total channel capacity, and an objective model is constructed with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity; the objective model is solved using the Lagrange duality method to obtain the optimal values ​​of the base station capacity and the satellite capacity. This method first leverages the characteristics of high-speed trains—determined direction of motion, stable speed, and predictable trajectory—when traveling on a known track. Users are divided into base station user groups and satellite user groups. An integrated air-ground communication system is adopted, utilizing the wide-area coverage, low free-space loss, and minimal fading caused by multipath effects of satellites to compensate for the shortcomings of terrestrial communication networks. This achieves wide-area coverage without requiring multi-hop structures, saving construction costs and meeting the capacity requirements of high-speed rail users. Furthermore, it optimizes the joint power allocation method under conditions where the maximum power of satellites and base stations is limited while ensuring minimum satellite capacity, maximizing system capacity, improving resource utilization, and ensuring ideal coverage of high-speed rail.

[0057] In one embodiment, the step 201 described above further includes a step of calculating the first signal-to-noise ratio, such as... Figure 3 As shown, Figure 3 A schematic diagram illustrating the calculation process of the first signal-to-noise ratio considering time delay is shown, including the following steps: Step 301: Calculate the first transmission time based on the train's trajectory and the location of the base station. The first transmission time The time required for the base station signal emitted by the base station to be transmitted to the base station user group.

[0058] Specifically, the position of the carriage can be determined based on the train's trajectory. Based on the carriage's position and the base station's position, the time it takes for the base station signal to travel from the base station to the train carriage can be calculated. .

[0059] Step 302, based on the train's real-time speed V and the first transmission time The base station signal was calculated after the first transmission time. The first phase offset value after .

[0060] Specifically, the first phase offset value The calculation formula is shown below: ; (13) Where T is the receiving period after considering signal processing and transmission, and c is the speed of light.

[0061] Step 303, based on the first phase offset value And the first channel matrix, calculate the first multicast beamforming vector of base station I to base station user group J. The first channel matrix is ​​used to describe the channel characteristics between the base station antenna and the base station user group.

[0062] Specifically, in a satellite-terrestrial joint beamforming network, each base station equipped with N antennas provides information transmission to users in N high-speed train carriages during beamforming. Base station I transmits signals to all N carriages. As shown in equation (14).

[0063] (14) in, It is the beam vector of base station I for base station user group J. ; This refers to the transmission power of base station I to base station user group J. It is the multicast signal of all users in user group J of the base station, where, .

[0064] In traditional point-to-point transmission, beamforming vectors are based on Maximum Ratio Transmission (MRT). It is designed as shown in equation (15).

[0065] (15) in, The ith element is the conjugate transpose of the first channel matrix, and the denominator is... Let Frobenius norm be the first channel matrix.

[0066] When this beamforming vector is extended to point-to-multipoint transmission, the beamforming vector based on the maximum transmission ratio... As shown in equation (16).

[0067] (16) in, This refers to the channel from base station I to base station user group J, i.e., the first channel matrix. .

[0068] Compared to ordinary mobile communication systems, communication systems for high-speed railways have inherent characteristics. This invention utilizes the fact that, compared to the randomness of the movement direction of ordinary mobile communication users, the movement direction of high-speed trains on known tracks is deterministic, their speed is relatively stable, and their trajectory is predictable. Furthermore, the train control system can obtain the train's position and speed information in real time, providing prior information to the high-speed railway mobile communication system. This prior information can be used to design more accurate beamforming vectors.

[0069] Because there is a time delay in the processing and transmission of signals in the uplink and downlink, the position of the high-speed train is displaced during this period. Therefore, the first multicast beamforming vector of the base station based on the maximum ratio transmission while considering the phase offset is shown in equation (17).

[0070] (17) Where V is the train's speed at this moment, and T is the reception period after considering signal processing and transmission. It is the time it takes for the signal to travel from the base station to the train. This is the first phase offset value.

[0071] Step 304: Calculate the first signal-to-noise ratio of the user group of the base station based on the first multicast beamforming vector of the base station.

[0072] In detail, step 304 includes: calculating the first effective total signal power of the base station user group J based on the first channel matrix h. The first signal-to-noise ratio of the base station user group is calculated based on the total power of the first effective signal and the total noise power of the base station user group.

[0073] The calculation method is shown in formula (2) above. Substituting formula (17) into formula (2) above will yield the first signal-to-noise ratio. Further details will not be provided here.

[0074] In the above embodiments, considering the time delay in the processing and transmission of communication signals between users and base stations in the uplink and downlink, and the displacement of the high-speed train during this period, the multicast beamforming vector of the base station based on the maximum ratio transmission needs to consider the phase offset. Beamforming of the base station signal based on this phase offset is beneficial to obtaining a more directional enhanced signal, providing reliable communication signal support for the base station user group on the train.

[0075] In one embodiment, such as Figure 4 As shown, Figure 4 The diagram illustrates the calculation process for the second signal-to-noise ratio considering time delay. Prior to step 203 above, the process includes the following steps: Step 401: Calculate the second transmission time based on the train's trajectory and the satellite's position; the second transmission time is the time required for the satellite signal emitted by the satellite to be transmitted to the satellite user group.

[0076] Specifically, since the train's trajectory is known, the position of the carriage can be determined. Based on the carriage's position and the satellite's position, the time it takes for the satellite signal to travel from the satellite to the satellite user group Q on the train can be calculated. That is, the second transmission time.

[0077] Step 402, based on the train's real-time speed V and the second transmission time The satellite signal was calculated after the second transmission time. The second phase offset value ; ; (18) Step 403, based on the second phase offset value The second channel matrix is ​​used to calculate the second groupcast beamforming vector of the satellite; wherein the second channel matrix is ​​used to describe the channel characteristics between the satellite antenna and the satellite user group.

[0078] Specifically, similar to the analysis of the base station multicast beamforming vector above, the transmission signal of the satellite (including M antennas) is as shown in the following formula.

[0079] ; (19) in, It is the beam vector of the satellite to satellite user group m. It is the transmission power from satellite to satellite user group m. It is the transmission signal from the satellite to satellite user group m.

[0080] The satellite is based on ZFBF (Zero-Forcing Beamforming), and also considers a second set of beamforming vectors for phase offset. (The symbol is a whole, in which the specific symbols are meaningless) as shown in the following formula (20).

[0081] ; (20) Among them, the second channel matrix Representation matrix The conjugate transpose of . These represent the channels from the satellite to different satellite users; D is the second channel matrix. The normalized diagonal matrix, It is the second phase offset value.

[0082] Step 404: Calculate the second signal-to-noise ratio of the satellite user group based on the second group beamforming vector of the satellite (as shown in Formula 20 above) (the formula for calculating the second signal-to-noise ratio is shown in Formula 5 above).

[0083] Specifically, step 404 includes: calculating the second effective total signal power of the satellite user group based on the second channel matrix. (As shown in the first term on the right side of the equal sign in Formula (4) above); calculate the second signal-to-noise ratio of the satellite user group based on the total power of the second effective signal and the total noise power of the satellite user group (as shown in the sum of the second, third and fourth terms on the right side of the equal sign in Formula (4) above); as shown in Formula (5) above, it will not be repeated here.

[0084] Considering the time delay in the processing and transmission of communication signals between users and satellites in the uplink and downlink, and the displacement of the high-speed train during this period, the multicast beamforming vector based on zero-forcing beamforming (ZFBF) needs to take phase offset into account. Beamforming of satellite signals based on this phase offset is beneficial to obtaining a more directional enhanced signal, providing reliable communication signal support for satellite user groups on the train.

[0085] In one embodiment, such as Figure 5 As shown, Figure 5 A flowchart illustrating the process of solving the model to be optimized using the Lagrange duality method is shown, namely, step 206 above includes the following steps.

[0086] Step 501: Determine the Lagrange dual form of the model to be optimized.

[0087] The constraints refer to the conditions in formulas (9) to (12) above. ~ .

[0088] It should be noted that the Lagrange duality method is a mathematical tool for solving constrained optimization problems, particularly suitable for handling optimization problems with equality or inequality constraints. It simplifies the solution process by introducing Lagrange multipliers, transforming the original problem into a dual problem.

[0089] The proposed optimization model in this application is shown in formulas (8) to (12) above, which is an optimization problem of maximizing system capacity under the constraint of limiting the minimum power of the satellite. Although the optimization problem involves... ~ It is either convex or linear, but because the objective function (Equation 8) contains a non-convex function... , As shown in Equation 3 above, this makes the optimization problem a non-convex optimization problem. Before determining the Lagrange multiplier, the non-convex function is approximated by the logarithmic approximation method shown in Equation (21).

[0090] (twenty one) in, , These are approximate parameters.

[0091] Logarithmic approximation is an optimization technique that approximates the objective function or constraints by introducing a logarithmic function. It is often used to transform non-convex problems into convex problems or to simplify complex nonlinear problems, making them easier to solve. Since the logarithmic function is concave, taking the logarithm of some non-convex functions may transform them into convex functions, thereby simplifying the optimization problem. In this application, the above... In Take the logarithm to transform it into a convex function.

[0092] If the approximate parameters are chosen as shown in equations (22)-(23): ; (twenty two) ; (twenty three) The above parameters and In the following text, it is abbreviated as and Similarly, approximate parameters can be obtained from formulas (22)-(23). , The form will not be elaborated here.

[0093] The logarithmic approximation method shown in equation (21) is used to approximate... and Function, and at the same time , By performing variable substitution, the lower boundary of the model to be optimized can be obtained as shown in equation (24): (twenty four) in, (25) (26) Finally, the objective function is replaced by its lower boundary, and after variable transformation, the convex optimization approximation subproblem of the original model to be optimized is obtained, as shown in formula group (27): (27) Now, since the logarithmic summation function is a convex function, the transformed optimization problem can also be proven to be a standard convex optimization problem. However, the subproblem (27) is only a lower bound approximation of the original optimization problem. In order to obtain the optimal solution of the original optimization objective, it is necessary to repeatedly update the approximate parameters in equations (22)-(23) with the results of the convex subproblem, and use the updated parameters in the next iteration of optimization calculation until the result converges.

[0094] This application uses the Lagrange duality method to solve the transformed convex subproblem, and the Lagrange duality form of equation (27) is given by... As shown in equation (28) below.

[0095] (28) Among them, the introduction of restrictions , , Lagrange multiplier , , Its dual function is shown in equation (29).

[0096] (29) Step 502: When the partial derivative of the Lagrange dual form with respect to the base station power is minimized, the base station power allocation expression with respect to the Lagrange multiplier is obtained; when the partial derivative of the Lagrange dual form with respect to the satellite power is minimized, the satellite power allocation expression with respect to the Lagrange multiplier is obtained.

[0097] Specifically, by solving , The optimal power allocation expression for the problem to be optimized (28) can be obtained, as shown in equations (30) and (31).

[0098] (30) (31) Where L represents the total number of base stations providing services to high-speed trains at the current moment, defined as follows: This means that for x, when x > 0, the value is x, and when x < 0, the value is 0; and it is defined as: ; (32) Step 503: Update the Lagrange multiplier according to the subgradient of the Lagrange multiplier.

[0099] Subgradient is an important concept in optimization theory, mainly used to deal with optimization problems of non-differentiable functions. For a convex function f, if the function f is not differentiable at a point x, then the subgradient of the function f at that point can be used as a substitute for the gradient.

[0100] Since the optimal solution for power allocation is in the form of Lagrange multipliers, and in equation (29) Since it is not differentiable, the subgradient method shown in equation (33) is used to calculate the Lagrange multiplier.

[0101] ; ; ; (33) in, It is the number of iterations. It is the size of the iteration step.

[0102] Step 504: Update the base station power according to the updated Lagrange multiplier and the base station power allocation expression; update the satellite power according to the updated Lagrange multiplier and the satellite power allocation expression.

[0103] Specifically, the base station power and satellite power are updated according to the algorithm shown in Table 1 below.

[0104] Step 505: Return to the step of updating the Lagrange multiplier based on the subgradient of the Lagrange multiplier until the updated base station power and the updated satellite power satisfy the preset convergence condition, and obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0105] Specifically, as shown in Table 1 above, the optimal values ​​for base station capacity and satellite capacity were finally obtained after multiple iterations.

[0106] The above embodiment first initializes the power of the base station and satellite. In the outer loop of the iteration, the approximate parameters are updated based on the results of the previous iteration, and the original problem is transformed into a convex subproblem using a logarithmic approximation method. Then, the convex subproblem is solved using the Lagrange dual method in the inner loop. In this way, by iteratively solving the transformed convex subproblem, the result eventually converges to the optimal solution of the initial problem. This method is a joint optimization allocation method for power under the conditions that the maximum power of the satellite and the base station are respectively limited while ensuring the minimum capacity of the satellite. It optimizes the signal-to-noise ratio of each user group and maximizes the system capacity. It has been verified that this method is superior to non-iterative algorithms, and the comparison results are as follows. Figure 6 As shown, Figure 6 A graph comparing the system performance under different algorithms.

[0107] The wireless network optimization device for high-speed rail scenarios provided by the present invention is described below. The wireless network optimization device for high-speed rail scenarios described below and the wireless network optimization method for high-speed rail scenarios described above can be referred to in correspondence.

[0108] like Figure 7 As shown, Figure 7 A schematic diagram of a wireless network optimization device for a high-speed rail scenario is shown. The device includes the following modules: User group determination module 701 is used to determine the base station user group and satellite user group on the train based on whether the distance between the position of each carriage on the train and the base station is less than a preset distance; The base station capacity calculation module 702 is used to calculate the base station capacity based on the first signal-to-noise ratio of the base station user group. The satellite capacity calculation module 703 is used to calculate the satellite capacity based on the second signal-to-noise ratio of the satellite user group. The total channel capacity calculation module 704 is used to calculate the total channel capacity of all users on the train based on the base station capacity and the satellite capacity. The target model determination module 705 is used to construct an objective function with the goal of maximizing the total channel capacity, and an objective model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity. The capacity optimal value calculation module 706 is used to solve the target model using the Lagrange duality method to obtain the base station capacity optimal value and the satellite capacity optimal value.

[0109] In one embodiment, the base station capacity calculation module 702 is further configured to: Based on the train's trajectory and the base station's location, a first transmission time is calculated; the first transmission time is the time required for the base station signal emitted by the base station to be transmitted to the base station user group; based on the train's real-time speed and the first transmission time, a first phase offset value of the base station signal after the first transmission time is calculated; based on the first phase offset value and a first channel matrix, a first multicast beamforming vector of the base station is calculated; wherein, the first channel matrix is ​​used to describe the channel characteristics between the base station antenna and the base station user group; based on the first multicast beamforming vector of the base station, a first signal-to-noise ratio of the base station user group is calculated.

[0110] In one embodiment, the base station capacity calculation module 702 is further configured to: The first effective total signal power of the base station user group is calculated based on the first channel matrix; the first signal-to-noise ratio of the base station user group is calculated based on the first effective total signal power and the total noise power of the base station user group.

[0111] In one embodiment, the satellite capacity calculation module 703 is further configured to: Based on the train's trajectory and the satellite's position, a second transmission time is calculated; the second transmission time is the time required for the satellite signal emitted by the satellite to be transmitted to the satellite user group; based on the train's real-time speed and the second transmission time, a second phase offset value of the satellite signal after the second transmission time is calculated; based on the second phase offset value and the second channel matrix, a second group broadcast beamforming vector of the satellite is calculated; wherein, the second channel matrix is ​​used to describe the channel characteristics between the satellite antenna and the satellite user group; based on the second group broadcast beamforming vector of the satellite, a second signal-to-noise ratio of the satellite user group is calculated.

[0112] In one embodiment, the satellite capacity calculation module 703 is further configured to: The second effective total signal power of the satellite user group is calculated based on the second channel matrix; the second signal-to-noise ratio of the satellite user group is calculated based on the second effective total signal power and the total noise power of the satellite user group.

[0113] In one embodiment, the above-mentioned capacity optimal value calculation module 706 is further configured to: Determine the Lagrange dual form of the model to be optimized; When the partial derivative of the Lagrange dual form with respect to base station power is minimized, the base station power allocation expression with respect to the Lagrange multiplier is obtained; when the partial derivative of the Lagrange dual form with respect to satellite power is minimized, the satellite power allocation expression with respect to the Lagrange multiplier is obtained. Update the Lagrange multiplier based on its subgradient; The base station power is updated based on the updated Lagrange multiplier and the base station power allocation expression; the satellite power is updated based on the updated Lagrange multiplier and the satellite power allocation expression. Return to the step of updating the Lagrange multiplier based on the subgradient of the Lagrange multiplier, until the updated base station power and the updated satellite power satisfy the preset convergence condition, and obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0114] This application also provides a joint air-ground network communication system, such as... Figure 1 As shown, the system includes satellites, base stations, and train user equipment. The satellite is used to obtain the optimal satellite capacity value through any of the wireless network optimization methods in the high-speed rail scenario described in the above embodiments, and to realize communication between the satellite and the satellite user group in the train user equipment based on the optimal satellite capacity value. The base station is used to obtain the optimal base station capacity value through any of the wireless network optimization methods in the high-speed rail scenario described in the above embodiments, and to realize communication between the base station and the base station user group in the train user equipment based on the optimal base station capacity value.

[0115] Figure 8 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 8 As shown, the electronic device may include: a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a wireless network optimization method for a high-speed rail scenario. This method includes: determining the base station user group and satellite user group on the train based on whether the distance between each carriage and the base station is less than a preset distance; calculating the base station capacity based on the first signal-to-noise ratio of the base station user group; calculating the satellite capacity based on the second signal-to-noise ratio of the satellite user group; calculating the total channel capacity of all users on the train based on the base station capacity and the satellite capacity; constructing an objective function with the goal of maximizing the total channel capacity, and a target model constrained by the base station transmission power being less than a preset first power, the satellite transmission power being less than a preset second power, and the satellite capacity being greater than or equal to a preset third capacity; and solving the target model using the Lagrange duality method to obtain the optimal values ​​for the base station capacity and the satellite capacity.

[0116] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0117] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the wireless network optimization method for high-speed rail scenarios provided by the above methods. The method includes: determining the base station user group and satellite user group on the train based on whether the distance between the position of each carriage on the train and the base station is less than a preset distance; calculating the base station capacity based on the first signal-to-noise ratio of the base station user group; calculating the satellite capacity based on the second signal-to-noise ratio of the satellite user group; calculating the total channel capacity of all users on the train based on the base station capacity and the satellite capacity; constructing an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity; and solving the target model using the Lagrange duality method to obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

[0118] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the wireless network optimization method for high-speed rail scenarios provided by the above methods. The method includes: determining the base station user group and satellite user group on the train based on whether the distance between each carriage on the train and the base station is less than a preset distance; calculating the base station capacity based on a first signal-to-noise ratio of the base station user group; calculating the satellite capacity based on a second signal-to-noise ratio of the satellite user group; calculating the total channel capacity of all users on the train based on the base station capacity and the satellite capacity; constructing an objective function with the goal of maximizing the total channel capacity, and a target model constrained by the base station transmission power being less than a preset first power, the satellite transmission power being less than a preset second power, and the satellite capacity being greater than or equal to a preset third capacity; and solving the target model using the Lagrange duality method to obtain the optimal values ​​of the base station capacity and the satellite capacity.

[0119] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0121] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing wireless networks in a high-speed rail scenario, characterized in that, include: Based on whether the distance between each carriage on the train and the base station is less than a preset distance, the base station user group and satellite user group on the train are determined. The base station capacity is calculated based on the first signal-to-noise ratio of the base station user group. The satellite capacity is calculated based on the second signal-to-noise ratio of the satellite user group. Based on the base station capacity and the satellite capacity, the total channel capacity of all users on the train is calculated; Construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity. The target model is solved using the Lagrange duality method to obtain the optimal values ​​for the base station capacity and the satellite capacity.

2. The wireless network optimization method for high-speed rail scenarios according to claim 1, characterized in that, Before calculating the base station capacity based on the first signal-to-noise ratio of the base station user group, the following steps are included: The first transmission time is calculated based on the train's trajectory and the location of the base station; the first transmission time is the time required for the base station signal emitted by the base station to be transmitted to the base station user group. Based on the real-time speed of the train and the first transmission time, the first phase offset value of the base station signal after the first transmission time is calculated. The first multicast beamforming vector of the base station is calculated based on the first phase offset value and the first channel matrix; wherein, the first channel matrix is ​​used to describe the channel characteristics between the base station antenna and the base station user group; The first signal-to-noise ratio of the user group of the base station is calculated based on the first multicast beamforming vector of the base station.

3. The wireless network optimization method for high-speed rail scenarios according to claim 2, characterized in that, The step of calculating the first signal-to-noise ratio of the user group of the base station based on the first multicast beamforming vector of the base station includes: Calculate the first effective total signal power of the base station user group based on the first channel matrix; The first signal-to-noise ratio of the base station user group is calculated based on the total power of the first effective signal and the total noise power of the base station user group.

4. The wireless network optimization method for high-speed rail scenarios according to claim 1, characterized in that, Before calculating the satellite capacity based on the second signal-to-noise ratio of the satellite user group, the following steps are included: The second transmission time is calculated based on the train's trajectory and the satellite's position; the second transmission time is the time required for the satellite signal emitted by the satellite to be transmitted to the satellite user group. Based on the train's real-time speed and the second transmission time, the second phase offset value of the satellite signal after the second transmission time is calculated. The second groupcast beamforming vector of the satellite is calculated based on the second phase offset value and the second channel matrix; wherein, the second channel matrix is ​​used to describe the channel characteristics between the satellite antenna and the satellite user group; The second signal-to-noise ratio of the satellite user group is calculated based on the second grouping beamforming vector of the satellite.

5. The wireless network optimization method for high-speed rail scenarios according to claim 4, characterized in that, The step of calculating the second signal-to-noise ratio of the satellite user group based on the second grouping beamforming vector of the satellite includes... Calculate the second effective total signal power of the satellite user group based on the second channel matrix; The second signal-to-noise ratio of the satellite user group is calculated based on the total power of the second effective signal and the total noise power of the satellite user group.

6. The wireless network optimization method for high-speed rail scenarios according to claim 1, characterized in that, The step of solving the model to be optimized using the Lagrange duality method to obtain the optimal values ​​for the base station capacity and the satellite capacity includes: Determine the Lagrange dual form of the model to be optimized; When the partial derivative of the Lagrange dual form with respect to base station power is minimized, the base station power allocation expression with respect to the Lagrange multiplier is obtained; when the partial derivative of the Lagrange dual form with respect to satellite power is minimized, the satellite power allocation expression with respect to the Lagrange multiplier is obtained. Update the Lagrange multiplier based on its subgradient; The base station power is updated based on the updated Lagrange multiplier and the base station power allocation expression; the satellite power is updated based on the updated Lagrange multiplier and the satellite power allocation expression. Return to the step of updating the Lagrange multiplier based on the subgradient of the Lagrange multiplier, until the updated base station power and the updated satellite power satisfy the preset convergence condition, and obtain the optimal value of the base station capacity and the optimal value of the satellite capacity.

7. A wireless network optimization device for high-speed rail scenarios, characterized in that, include: The user group determination module is used to determine the base station user group and satellite user group on the train based on whether the distance between the position of each carriage on the train and the base station is less than a preset distance. The base station capacity calculation module is used to calculate the base station capacity based on the first signal-to-noise ratio of the base station user group. The satellite capacity calculation module is used to calculate the satellite capacity based on the second signal-to-noise ratio of the satellite user group. The total channel capacity calculation module is used to calculate the total channel capacity of all users on the train based on the base station capacity and the satellite capacity. The target model determination module is used to construct an objective function with the goal of maximizing the total channel capacity, and a target model with the constraints that the base station transmission power is less than a preset first power, the satellite transmission power is less than a preset second power, and the satellite capacity is greater than or equal to a preset third capacity. The capacity optimization calculation module is used to solve the target model using the Lagrange duality method to obtain the base station capacity optimization value and the satellite capacity optimization value.

8. A joint air-ground network communication system, characterized in that, The system includes satellites, base stations, and train user equipment, wherein, The satellite is used to obtain the optimal value of satellite capacity through the wireless network optimization method in the high-speed rail scenario as described in any one of claims 1 to 6, and to realize communication between the satellite and the satellite user group in the train user equipment based on the optimal value of satellite capacity. The base station is configured to obtain the optimal base station capacity value using the wireless network optimization method for high-speed rail scenarios as described in any one of claims 1 to 6, and to achieve communication between the base station and the base station user group in the train user equipment based on the optimal base station capacity value.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the wireless network optimization method for high-speed rail scenarios as described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the wireless network optimization method for high-speed rail scenarios as described in any one of claims 1 to 6.

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