Dynamic transmission optimization method for high-speed rail tunnel millimeter wave communication scene under space-air-ground integrated network

Abstract

The invention provides a dynamic transmission optimization method for a millimeter wave communication scene of a high-speed rail tunnel in a space-air-ground integrated network, and the method comprises the steps: constructing an optimization scheduling model which aims at maximizing the total number of complete streams, and the complete streams are data streams meeting the QoS demands; and solving the optimal scheduling model by using a link selection algorithm to obtain a link combination, evaluating the link combination by using a transmission optimization algorithm based on a graph theory, and determining an optimal link combination based on an evaluation result. According to the method, the communication performance in a multi-network fusion environment is greatly enhanced, smoother and more reliable network service experience is provided for high-speed rail passengers, and the application advantages of the SAGIN in a high-speed rail complex scene are fully reflected.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular, to a dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed rail tunnels under a space-air-ground integrated network. Background Art

[0002] By combining BS, MR, and aerial platforms (such as airships and satellites), constructing a space-air-ground integrated network (SAGIN) has become the core of the solution for high-speed rail communication systems. SAGIN can achieve stable coverage of tunnels and the areas between tunnels, especially showing outstanding performance in remote and complex terrain areas. As the core architecture of the 6G network, SAGIN ensures stable connections by solving the problem of insufficient signals in the high-speed rail environment. Although SAGIN has great potential in expanding the coverage range and improving data transmission efficiency, it still faces serious signal interference problems in high-density device environments and complex scenarios, especially the conflict between the ground and the aerial access networks, and effective solution support is urgently needed.

[0003] Currently, most of the work on high-speed rail communication interference cancellation focuses on the interference of high-speed rail communication supported by ground BS, and the research on high-speed rail communication interference cancellation in SAGIN is still relatively limited. At the same time, there are also few transmission scheduling methods that focus on maximizing the number of scheduled flows in HSR communication in SAGIN by reducing interference. In summary, there is an urgent need for a transmission optimization method that can improve the communication performance of the space-air-ground integrated network. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed rail tunnels under a space-air-ground integrated network.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions.

[0006] In a first aspect, the present invention provides a dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed rail tunnels under a space-air-ground integrated network, which is applied to the space-air-ground integrated network. The method includes:

[0007] Construct an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0008] Use a link selection algorithm to solve the optimization scheduling model to obtain a link combination, and use a transmission optimization algorithm based on graph theory to evaluate the link combination, and determine the optimal link combination based on the evaluation results.

[0009] In some embodiments, the optimization scheduling model is:

[0010]

[0011] In the formula, Q s,r,i,n indicates whether the flow i transmitted from the transmitter s to the receiver r meets the QoS requirement in the nth frame. When Q s,r,i,n = 1, it indicates that the QoS requirement is met; when Q s,r,i,n = 0, it indicates that the QoS requirement is not met. N s represents the total number of transmitters s, represents the total number of flows in the nth frame, N frame is the number of superframes, is the QoS requirement of the flow i in the nth frame, q s,r,i,n is the throughput of the flow i transmitted from the transmitter s to the receiver r in the nth frame. M is the number of time slots, m is the mth time slot, R s,r,i,n,m is the data rate of the flow i transmitted from the transmitter s to the receiver r in the mth time slot of the nth frame. Δt is the time slot duration, T s is the scheduling time of the superframe, w s,r,i,n is the state of the transmitter s scheduling the flow i in the nth frame, a s,r,i,n,m is the state when the transmitter s allocates to the flow i in the mth time slot of the nth frame. b is the interfering flow, a s,r,b,n,m is the interference state of the flow i by the flow b in the mth time slot of the nth frame. N MR is the total number of MRs.

[0012] In some embodiments, the link combination includes a set of transmitter allocation combinations. Solving the optimization scheduling model using the link selection algorithm to obtain the link combination includes:

[0013] Calculating the SINR of the flow i in the nth frame based on the total interference power received by the flow i in the nth frame and the interference power from the interfering flow b, and determining the effective transmitter set according to the comparison result between the SINR and the preset SINR threshold;

[0014] Determining the effective flow set within the current frame according to the effective transmitter set;

[0015] Generating a transmitter allocation combination according to the Cartesian product of the effective flow set and the effective flow set, and sorting the transmitter allocation combination to obtain the set of transmitter allocation combinations.

[0016] In some embodiments, evaluating the link combination using the transmission optimization algorithm based on graph theory, and determining the optimal link combination based on the evaluation result, includes:

[0017] When the transmitter allocation combination shows that all flows are served by the same transmitter, calculate the total number of bits transmitted by the transmitter in all time slots within the frame based on the throughput of flow i transmitted from transmitter s to receiver r in the nth frame and the time slot duration, and when the total number of bits meets the QoS requirements of the flow, mark all flows as successfully served;

[0018] When the transmitter allocation combination shows that the flows are served by multiple transmitters, calculate the interference factor of the time slot based on the total interference power, the interference power from interfering flow b, the noise power density, and the bandwidth, and when the interference factor is less than or equal to the interference threshold, update the QoS requirements of the flow, and when the updated QoS requirements of the flow meet the preset requirement threshold, mark the flow as successfully served;

[0019] Obtain the number of successfully served flows, and when the number of successfully served flows meets the preset success threshold, update the number of optimally served flows according to the number of successfully served flows.

[0020] In a second aspect, the present invention further provides a dynamic transmission optimization device for a millimeter-wave communication scenario in a high-speed rail tunnel under a space-air-ground integrated network, which is applied to the space-air-ground integrated network. The device includes:

[0021] A model construction module for constructing an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0022] A solution module for solving the optimization scheduling model using a link selection algorithm to obtain a link combination, and evaluating the link combination using a transmission optimization algorithm based on graph theory, and determining an optimal link combination based on the evaluation result.

[0023] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the methods described above are implemented.

[0024] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the methods described above are implemented.

[0025] In a fifth aspect, the present invention further provides a computer program product including a computer program, and when the computer program is executed by a processor, the methods described above are implemented.

[0026] Advantages of the present invention: The dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed railway tunnels under the integrated space-air-ground network provided by the present invention designs an optimization scheduling model that maximizes the flow to meet QoS requirements. The objective function in the optimization scheduling model defines binary variables to dynamically track the service status of the flow within each frame, maximizing the total number of flows that meet QoS requirements, thereby optimizing the system performance while ensuring transmission integrity. Secondly, the link selection algorithm combines the SINR threshold to dynamically evaluate the association relationship between the transmitter and the receiver, preferentially selects a single transmitter solution that can minimize interference, and at the same time supports dynamic multi-transmitter association to adapt to the train movement scenario. By generating all possible transmitter allocation combinations and reordering, the algorithm effectively reduces potential interference while reducing the computational complexity and improving the scheduling efficiency. Finally, the graph-theory-based transmission optimization algorithm proposed by the present invention models the interference relationship between flows using a conflict graph, dynamically filters low-interference links through an interference threshold, optimizes the scheduling scheme, and maximizes the number of flow schedules. Compared with traditional exhaustive search and transmission scheduling methods with a single optimization objective, the present invention achieves an efficient balance between flow scheduling performance and interference management while reducing the computational complexity, providing a robust solution for high-speed railway communication.

[0027] Additional aspects and advantages of the present invention will be given in part in the following description, which will become apparent from the following description, or can be understood through the practice of the present invention. Description of the Drawings

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0029] Figure 1 Schematic diagram of the integrated space-air-ground network provided for the embodiments of the present invention;

[0030] Figure 2 Schematic flow diagram of the dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed railway tunnels under the integrated space-air-ground network provided for the embodiments of the present invention;

[0031] Figure 3 Schematic diagram of the train movement model provided for the embodiments of the present invention;

[0032] Figure 4 Schematic conflict diagram of interference between flows provided for the embodiments of the present invention. Detailed Embodiments

[0033] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation to the present invention.

[0034] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present invention means the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The phrase "and / or" used herein includes any and all combinations of any of the one or more associated listed items.

[0035] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present invention pertains. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as here.

[0036] The terms in the present invention are defined below:

[0037] Space-Air-Ground Integrated Network (SAGIN): A multi-platform integrated network that integrates satellite systems, high-altitude platform systems (such as airships, drones) and ground communication systems (such as base stations, high-speed rails).

[0038] Millimeter-wave communication system: A communication system operating in the millimeter-wave frequency band, which can meet the wireless communication requirements of high transmission rate and high security.

[0039] Airship-assisted ground-to-train: The airship assists the ground base station in scheduling data streams to high-speed trains.

[0040] QoS requirement: Each data stream has a corresponding data rate requirement.

[0041] Transmission scheduling strategy: The airship or BS network allocates limited network resources to multiple mobile relays (MRs) to meet the QoS requirements of different data streams and improve the overall performance of the system.

[0042] In the existing communication system, there are the following problems:

[0043] (1) For high-speed rail millimeter wave communication systems, communication in tunnel scenarios faces many challenges. At present, research mainly relies on densely deployed ground BS networks along the line to achieve full coverage of high-speed rail lines. However, the complex terrain and long tunnels traversed by high-speed rail lines make the construction of ground networks extremely difficult, especially inside tunnels and in tunnel intervals where signals are difficult to penetrate, and the signal transmission effect of ground BS is significantly reduced. In addition, due to their short wavelength and high directivity, millimeter wave signals are susceptible to reflections from the inner wall of tunnels and obstructions from obstacles, resulting in more serious problems of signal attenuation and multipath interference, further restricting communication quality and coverage.

[0044] (2) In recent years, the data demand of high-speed rail users has exploded, and ground BS services alone may not be able to meet the QoS requirements of all users. SAGIN can effectively supplement the communication capabilities in tunnel scenarios by introducing aerial platforms such as airships. As an important part of the SAGIN aerial network, airships have the following advantages: on the one hand, airships can make up for the lack of ground network coverage between and within tunnels and provide flexible millimeter-wave signal transmission; on the other hand, by sharing the communication load for ground BS, airships can significantly improve the overall transmission efficiency and service quality of the system. Therefore, exploring airship-assisted high-speed rail millimeter-wave tunnel communication systems is an effective way to expand millimeter-wave coverage and improve high-speed rail communication quality.

[0045] (3) In the high-speed rail millimeter wave communication scenario under the SAGIN network, the research on transmission scheduling or flow scheduling is still relatively lacking, which has become a key challenge that needs to be solved urgently. Existing research mainly focuses on aspects such as coverage expansion and network architecture optimization, while there is little research on transmission scheduling strategies that combine the high-speed mobility characteristics of high-speed rail with the high-frequency band characteristics of millimeter waves. In the high-speed rail operation environment, the high-speed movement of trains causes frequent changes in network topology. Traditional scheduling algorithms are difficult to adapt to dynamically changing network conditions and difficult to achieve efficient resource allocation and flow management. In addition, the SAGIN network integrates ground BS, air platforms (such as airships, drones) and satellite communications to form a complex heterogeneous network structure, which further increases the complexity of transmission scheduling. In the millimeter wave frequency band, the high directivity of the signal and the characteristics of being easily blocked by obstacles require the scheduling algorithm to not only consider the dynamic allocation of resources, but also deal with frequent link breakage and reconstruction problems. Therefore, how to design an efficient and robust transmission scheduling strategy in the SAGIN environment to maximize the number of scheduled flows, improve system throughput and meet user QoS requirements is still an important topic that needs to be studied in depth.

[0046] In summary, when the aerial network and the ground base station (BS) provide services to trains simultaneously, co-frequency interference will significantly degrade the system performance and it is difficult to meet the quality of service (QoS) requirements of data streams. Therefore, how to effectively achieve millimeter-wave signal coverage and data stream transmission in the high-speed railway tunnel scenario is an urgent problem to be solved. For this purpose, the present invention provides a dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed railway tunnels under an integrated air-ground-space network. This method aims to maximize the number of scheduled data streams and, through a link selection algorithm based on the signal-to-interference-plus-noise ratio (SINR) threshold, screens out effective link combinations to ensure that the QoS requirements of each data stream are met. Further, to reduce interference between data streams and optimize the scheduling efficiency, the method provided by the present invention also sets an interference threshold through a transmission optimization algorithm based on graph theory, effectively eliminating link combinations with excessive interference, reducing the number of algorithm iterations, and simultaneously achieving an optimal scheduling strategy.

[0047] In addition, since the data rate required in future high-speed railway scenarios is expected to reach 0.5 - 5 Gbps, stringent technical requirements are imposed on existing high-speed railway wireless communication systems. To address this challenge, the present invention focuses on researching millimeter-wave technology with a coverage frequency band of 30 GHz to 300 GHz. This technology has the potential to support high-speed data transmission at the Gbps level in high-speed railway scenarios and provides an effective solution for future high-speed railway communication.

[0048] Although millimeter-wave technology can significantly improve network performance, its short wavelength and directional transmission characteristics make it vulnerable to obstacle shielding in complex scenarios between high-speed railway tunnels and tunnels. The tunnel environment is enclosed and the signal is vulnerable to multipath interference, and it is difficult to deploy ground BSs on a large scale due to terrain restrictions between tunnels. To solve this problem, the present invention has carried out the use of aerial platforms such as airships and low-Earth orbit satellites to provide supplementary coverage between tunnels, especially for areas where BSs cannot be deployed, to achieve flexible and efficient signal transmission. At the same time, to cope with the penetration loss of millimeter-wave signals by the train body of more than 22 dB, MRs are deployed outside the carriage. After receiving and amplifying the external signal, they are forwarded to the inside of the carriage, effectively reducing signal attenuation. By combining the aerial network coverage and transmission optimization strategy, this solution significantly improves the reliability and coverage performance of high-speed railway communication and provides technical support for the efficient and economical deployment of millimeter-wave networks.

[0049] With the increase in the number of device accesses in SAGIN, the diversified QoS requirements pose severe challenges to resource allocation, especially in high-speed rail tunnel scenarios, when ground BS and aerial platforms (such as airships) jointly serve high-speed rail MR, signal coverage overlap areas are prone to inter-stream interference, affecting communication performance. To solve this problem, the method provided by the present invention can maximize the data flow scheduling efficiency and reduce interference within the limited time when the high-speed rail passes through the signal overlap area, significantly improving the performance of the high-speed rail millimeter wave communication system, meeting the high-quality service requirements in high-speed mobile environments, and providing new ideas for resource optimization in complex scenarios.

[0050] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, the present invention considers millimeter wave downlink communication in high-speed railway tunnels supported by SAGIN. Under this communication system, the focus is on scheduling data flows for MRs installed on the top of the train, using airship-MR links and BS-MR links. As the train moves from left to right, it enters the coverage areas of base stations BS1, BS2, and BS3 in turn, where BS2 and BS3 are strategically arranged at the midpoints of two straight tunnels. All BSs are installed at a height of h BS Between the two straight tunnels, since the terrain is not suitable for deploying BS, a height of h is used. A The BS and airship are equipped with L ULA Uniform linear antenna arrays (ULAs) are used to support single-antenna MR. In order to effectively manage the transmission time, N is defined. frame superframes, representing the period from the train's closest point B to BS1 to the end of the second tunnel. Each superframe is divided into a duration of T s The scheduling phase is used to collect data requests from MRs, and the transmission phase consists of M time slots, each of duration Δt, dedicated to data transmission. Therefore, the total duration of each superframe is T frame =T s +MΔt.

[0053] like Figure 2 As shown, the dynamic transmission optimization method for the millimeter wave communication scenario of the high-speed railway tunnel under the integrated air-ground-space network includes the following steps:

[0054] S101, constructing an optimization scheduling model with the goal of maximizing the total number of completed flows, wherein the completed flows are data flows that meet QoS requirements.

[0055] Specifically, the optimized scheduling model is specifically as follows:

[0056]

[0057] In the formula, Q s,r,i,n represents whether the flow i transmitted from the transmitter s to the receiver r meets the QoS requirement in the nth frame. When Q s,r,i,n = 1, it means that the QoS requirement is met; when Q s,r,i,n = 0, it means that the QoS requirement is not met. N s represents the total number of transmitters s, represents the total number of flows in the nth frame, N frame is the number of superframes, is the QoS requirement of the flow i in the nth frame, q s,r,i,n is the throughput of the flow i transmitted from the transmitter s to the receiver r in the nth frame. M is the number of time slots, m is the mth time slot, R s,r,i,n,m is the data rate of the flow i transmitted from the transmitter s to the receiver r in the mth time slot of the nth frame. Δt is the time slot duration, T s is the scheduling time of the superframe, w s,r,i,n is the state of the transmitter s scheduling the flow i in the nth frame, a s,r,i,n,m is the state when the transmitter s allocates to the flow i in the mth time slot of the nth frame. b is the interfering flow, a s,r,b,n,m is the interference state of the flow i by the interfering flow b in the mth time slot of the nth frame. N MR is the total number of MRs.

[0058] The first constraint condition is used to indicate that when the throughput provided by the transmitter s exceeds the QoS threshold of the flow i, it is considered that the flow i is successfully scheduled.

[0059] The second constraint condition is used to indicate that within a single frame, each flow i can be scheduled by at most one transmitter.

[0060] The third constraint condition is used to indicate that each transmitter can serve at most one flow within a single time slot.

[0061] The fourth constraint condition is used to indicate that within a single time slot, the number of interfering flows suffered by the flow i must meet the preset interfering flow number threshold.

[0062] The fifth constraint condition is used to indicate that the total number of schedulable flows in each frame needs to be within a certain value range.

[0063] The above throughput q s,r,i,n and data rate Rs,r,i,n,m etc., can be obtained based on the channel model and physical model under the communication system:

[0064] 1) Channel model

[0065] The communication channels of the BS-MR link and the airship-MR link are described as Rician fading channels. For a specific flow i from transmitter s to receiver r during the m-th time slot of the n-th frame, the channel is represented as a vector h s,r,i,n,m , whose dimension is L ULA ×1. This channel vector consists of a line-of-sight (LoS) component and a non-line-of-sight (NLoS) component, and each component is weighted by the Rician K-factor as follows:

[0066]

[0067] The line-of-sight component of the l ULA -th antenna from transmitter s to receiver r is expressed as:

[0068]

[0069] where [d s,r,i,n,m lULA represents the distance from the l ULA -th antenna of transmitter s to receiver r during the m-th time slot of the n-th frame, and [ψ s,r,i,n,m lULA represents the phase shift introduced during the signal propagation process.

[0070] Similarly, the non-line-of-sight component is expressed as:

[0071]

[0072] where represents the small-scale fading channel, and each of its elements follows a complex Gaussian distribution

[0073] 2) Physical model

[0074] The communication channels of the BS-MR link and the airship-MR link are described as Rician fading channels. For a specific flow i from transmitter s to receiver r during the m-th time slot of the n-th frame, the received power is modeled as:

[0075]

[0076] where represents the power transmitted by transmitter s for flow i during the m-th time slot of the n-th frame. The antenna gains of the transmitter and the receiver are represented as G​​t (θ) and G r (θ).

[0077] The SINR of stream i in the m-th time slot of the n-th frame is defined as:

[0078]

[0079] where, represents the total interference power received by stream i during the m-th time slot of the n-th frame. Here, P s,r,b,n,m is the interference power from stream b, W represents the bandwidth, and N 0 is the noise power density.

[0080] The achievable data rate R of stream i in the m-th time slot of the n-th frame s,r,i,n,m is:

[0081]

[0082] where, ε represents the transceiver efficiency and ranges within the interval [0, 1]. When the transmitter s schedules stream i in the n-th frame, w s,r,i,n takes the value of 1, otherwise w s,r,i,n is 0. Similarly, when the transmitter s allocates to stream i in the m-th time slot of the n-th frame, a s,r,i,n,m takes the value of 1, otherwise a s,r,i,n,m is 0. When stream i is interfered by stream b during the m-th time slot of the n-th frame, a s,r,b,n,m takes the value of 1, otherwise a s,r,b,n,m is 0.

[0083] The throughput q of stream i in the n-th frame s,r,i,n is calculated as:

[0084]

[0085] where, T frame = T s + MΔt, which is the total duration of the superframe, corresponding to the transmission rate achieved by the transmitter s using M time slots.

[0086] S102. Solve the optimized scheduling model using the link selection algorithm to obtain a link combination, and evaluate the link combination using a transmission optimization algorithm based on graph theory. Determine the optimal link combination based on the evaluation results.

[0087] Since the optimized scheduling model in S101 is a non - linear integer programming problem, its objective function contains the logarithmic representation of the achievable data rate for each flow. The transmission scheduling scheme is affected by multiple factors, such as the number of transmitters, interference between flows, QoS requirements of flows, and train speed. Due to the complexity of directly solving this problem, therefore, this step proposes to combine link selection and a graph - theory - based transmission optimization algorithm for model solution to efficiently solve the above - mentioned optimization problem.

[0088] Among them, using the link selection algorithm to solve the optimized scheduling model to obtain a link combination, including the following sub - steps:

[0089] Calculate the SINR of flow i in the nth frame based on the total interference power received by flow i in the nth frame and the interference power from interfering flow b, and determine the set of effective transmitters according to the comparison result between the SINR and the preset SINR threshold;

[0090] Determine the set of effective flows within the current frame according to the set of effective transmitters;

[0091] Generate a transmitter assignment combination according to the Cartesian product of the set of effective flows and the set of effective flows, and sort the transmitter assignment combination to obtain a set of transmitter assignment combinations.

[0092] Specifically, the link selection algorithm aims to determine the effective transmitter - receiver associations within each frame. This algorithm systematically evaluates all possible transmitter - MR combinations, giving priority to configurations where a single transmitter can serve all MRs simultaneously. This approach simplifies the association process and reduces potential interference. When an MR enters the coverage area of transmitter s, it will establish an association with transmitter s. Once the association is established, the MR will upload its QoS requirements during scheduling. According to these requirements, transmitter s allocates time slots to efficiently manage the downlink data transmission to the MR.

[0093] Schematically, as Figure 3 shown, when the train moves from point B to the exit of tunnel 2, the MR may have the following three association states: single - transmitter association, where all MRs are only connected to a single transmitter s, thus eliminating interference; multi - transmitter association, where the MRs are simultaneously within the coverage areas of multiple transmitters and can be associated with multiple transmitters simultaneously; dynamic re - association, where the MR dynamically switches associations between different transmitters as the train moves, thus ensuring optimal connectivity and performance.

[0094] By efficiently selecting the appropriate transmitters, the link selection algorithm lays the foundation for the subsequent transmission scheduling algorithm. The link selection algorithm determines the set of effective transmitters for each flow i according to the SINR threshold Γ, expressed as:

[0095]

[0096] Among them, represents the set of all transmitters. Specifically, s = 1 represents the airship, s = 2 represents the base station BS1, s = 3 represents the base station BS2, and s = 4 represents the base station BS3. In other embodiments, the number and numbering of the transmitters in the transmitter set are determined according to the actual situation.

[0097] In the set of valid transmitters only the flows with at least one valid transmitter will be considered for scheduling. According to this constraint, the set of valid flows

[0098]

[0099] The number of flows to be scheduled in the nth frame is denoted as The core of link selection is to generate all possible transmitter assignment combinations for valid flows Each combination is a unique assignment, where each flow in the nth frame is associated with one of the transmitters in Formally, the set of all valid combinations

[0100]

[0101] Among them, represents the Cartesian product. The total number of combinations in frame n is:

[0102]

[0103] The link selection algorithm aims to optimize the assignment of multiple flows among the available transmitters, namely the airship, base station BS1, base station BS2, and base station BS3. For each frame n, first calculate the SINR received by each MR from each transmitter and filter out those MRs that fail to meet the minimum threshold Γ. This ensures that only the links with sufficient SINR are considered. Next, determine the set of flows that are still valid within the current frame and generate all possible transmitter assignment combinations by performing the Cartesian product of the sets of valid transmitters for each flow. These assignments are stored in which represents the complete set of potential link configurations. Each combination explicitly specifies the transmitter selected for each flow under the SINR constraint. After generating the combinations are reordered to give priority to single-transmitter solutions. Specifically, it does this by performing to combine the set of single-transmitter combinations with the set of multi-transmitter combinations Connect them so that all single - transmitter assignments are arranged before the combinations that require multiple transmitters. This prioritization enables subsequent resource allocation or scheduling processes to quickly identify cases where all flows can be served by a single transmitter, thereby potentially reducing computational overhead.

[0104]

[0105]

[0106] After obtaining the above - mentioned link combinations, use a graph - theory - based transmission optimization algorithm to evaluate the link combinations, and determine the optimal link combination based on the evaluation results, including the following sub - steps:

[0107] When the transmitter assignment combination shows that all flows are served by the same transmitter, calculate the total number of bits transmitted by the transmitter in all time slots within the frame according to the throughput of flow i from transmitter s to receiver r in the nth frame and the time - slot duration, and when the total number of bits meets the QoS requirements of the flow, mark all flows as successfully served;

[0108] When the transmitter assignment combination shows that flows are served by multiple transmitters, calculate the interference factor of the time slot according to the total interference power, the interference power from interfering flow b, the noise power density, and the bandwidth, and when the interference factor is less than or equal to the interference threshold, update the QoS requirements of the flow, and when the updated QoS requirements of the flow meet the preset demand threshold, mark the flow as successfully served;

[0109] Obtain the number of successfully served flows, and when the number of successfully served flows meets the preset success threshold, update the number of best - served flows according to the number of successfully served flows.

[0110] Specifically, to avoid exhaustive evaluation of all possible link combinations, the present invention introduces a graph - theory - based transmission optimization algorithm with an interference threshold. This method can maximize the number of scheduled flows while ensuring QoS requirements are met and effectively solve the inter - flow interference problem. In addition, the algorithm simplifies the calculation process by systematically narrowing the possible transmitter assignment range using a conflict graph.

[0111] In the high - speed railway tunnel communication scenario in SAGIN, the interference situation can be divided into three main types, such as Figure 4As shown. Unidirectional interference means that one communication link interferes with another link, but the reverse interference is not significant. Bidirectional interference means that two communication links interfere with each other, resulting in a decline in the performance of both sides. Multi-flow interference is a more complex situation, involving three or more communication links that interfere with each other. The proposed algorithm preferentially selects non-interfering or minimally interfering link combinations by efficiently modeling interference relationships and applying interference thresholds. Compared with exhaustive combination generation, this method not only reduces the computational complexity but also improves the network performance by maintaining a balance between flow maximization and interference management.

[0112] The transmission optimization algorithm based on graph theory starts from initializing the optimal number of service flows for the current frame to be zero. This variable is used to track the maximum number of flows successfully served without violating the interference constraints. For each frame n processed in sequence, the algorithm evaluates all possible combinations of transmitter assignments where k represents the combination index, including all valid assignments of transmitters to flows in frame n.

[0113] For each combination if all flows are served by the same transmitter s, the algorithm calculates the total number of bits B transmitted by transmitter s in all time slots M within the frame s , with the formula If B s meets or exceeds the sum of the QoS requirements of all valid flows, that is:

[0114]

[0115] where, is the QoS requirement matrix of the flows, then the algorithm will mark all the flows in s,r,i,n as successfully served, that is, for each flow i, set Q

[0116] When the flows are served by different transmitters in

[0117]

[0118] If the interference exceeds the threshold ∈ s , the algorithm skips the further evaluation of flow s in the current frame.

[0119] Based on the above steps, a graph is obtained, where each link is represented as a vertex, and the edges between the vertices represent the potential interference between the corresponding links. For each time slot where the interference is within the acceptable range, Algorithm 2 deducts the number of transmitted bits from the remaining QoS requirements of flow i, that is:

[0120] O(n, i) = O(n, i) - b s ,

[0121] where b s = q s,r,i,n ×Δt is the amount of data transmitted in a time slot. If O(n, i) ≤ 0, the QoS requirements of the flow are fully satisfied, and the algorithm marks it as successfully served and sets Q s,r,i,n = 1.

[0122] After processing all iterations of the current frame, the number of successfully served flows F served is:

[0123]

[0124] If the algorithm updates By iterating over all frames, the algorithm ensures efficient resource allocation and achieves a balance between interference management and QoS satisfaction.

[0125]

[0126]

[0127] As can be seen from the above, the transmission optimization algorithm based on graph theory contains multiple nested loops. The outermost loop traverses N frame frames. For each frame, the algorithm needs to evaluate combinations. Within each combination, the algorithm traverses M time slots and processes valid flows for each time slot. The number of operations of these nested loops grows linearly with the number of frames, the number of iterations per frame, the number of time slots, and the number of valid flows. Therefore, the overall computational complexity of the algorithm is

[0128] In summary, the dynamic transmission optimization method for the millimeter-wave communication scenario in high-speed rail tunnels under the space-air-ground integrated network provided by the embodiments of this application has the following advantages:

[0129] (1) For the high-speed rail tunnel communication scenario in SAGIN, the present invention proposes an optimization problem, aiming to maximize the number of flows that meet the QoS requirements through efficient transmission scheduling. This optimization problem combines key factors such as inter-flow interference, QoS requirements, and time slot allocation, and is modeled based on the time division multiple access (TDMA) framework. The algorithm quantifies the problem objective by defining a binary variable Q s,r,i,n to identify whether a certain flow meets the QoS requirements within a specific frame.

[0130] (2) A link selection algorithm is proposed to allocate optimal links for base stations (BS1, BS2, and BS3) and airships. The algorithm calculates the signal-to-interference-plus-noise ratio (SINR) and filters out links that do not meet the threshold, generating a set of valid flows and transmitter combinations. At the same time, it gives priority to single-transmitter solutions to reduce potential interference. On this basis, a transmission optimization algorithm based on graph theory is further proposed. By introducing an interference threshold, a conflict graph is constructed to model the interference between flows, and link combinations with excessive interference are excluded, effectively reducing the iterative complexity.

[0131] (3) The transmission optimization algorithm based on graph theory iterates frame by frame, evaluates all possible link combinations frame by frame, and calculates the interference factor of the flows in each time slot After ensuring that the interference level is below the threshold, it continues to evaluate and allocate resources. The algorithm determines whether a flow is successfully served by gradually reducing the remaining QoS requirements. The overall computational complexity depends on the number of frames N frame , the number of iterations , the number of time slots M, and the number of valid flows Its complexity is Compared with exhaustive search, the transmission optimization algorithm based on graph theory achieves a good balance between resource allocation efficiency and flow scheduling performance.

[0132] Embodiment 2

[0133] Based on Embodiment 1, this Embodiment 2 provides a dynamic transmission optimization device for millimeter-wave communication scenarios in high-speed rail tunnels in the space-air-ground integrated network. The dynamic transmission optimization device for millimeter-wave communication scenarios in high-speed rail tunnels in the space-air-ground integrated network corresponds to the above-mentioned dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed rail tunnels in the space-air-ground integrated network, and specifically includes:

[0134] A model construction module for constructing an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0135] A solving module for solving the optimization scheduling model using the link selection algorithm to obtain link combinations, and evaluating the link combinations using the transmission optimization algorithm based on graph theory, and determining the optimal link combination based on the evaluation results.

[0136] For specific details, refer to the description in the part of the dynamic transmission optimization method for millimeter-wave communication scenarios in high-speed rail tunnels in the space-air-ground integrated network, which will not be elaborated here.

[0137] Embodiment 3

[0138] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor. The processor and the memory communicate with each other. The memory stores program instructions executable by the processor. The processor invokes the program instructions to execute a dynamic transmission optimization method for the millimeter-wave communication scenario in a high-speed rail tunnel under a space-air-ground integrated network. The method includes the following process steps:

[0139] Construct an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0140] Use a link selection algorithm to solve the optimization scheduling model to obtain a link combination, and use a transmission optimization algorithm based on graph theory to evaluate the link combination, and determine the optimal link combination based on the evaluation results.

[0141] Embodiment 4

[0142] Embodiment 4 of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a dynamic transmission optimization method for the millimeter-wave communication scenario in a high-speed rail tunnel under a space-air-ground integrated network. The method includes the following process steps:

[0143] Construct an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0144] Use a link selection algorithm to solve the optimization scheduling model to obtain a link combination, and use a transmission optimization algorithm based on graph theory to evaluate the link combination, and determine the optimal link combination based on the evaluation results.

[0145] Embodiment 5

[0146] Embodiment 5 of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements a dynamic transmission optimization method for the millimeter-wave communication scenario in a high-speed rail tunnel under a space-air-ground integrated network. The method includes the following process steps:

[0147] Construct an optimization scheduling model with the goal of maximizing the total number of completed flows, where the completed flows are data flows that meet the QoS requirements;

[0148] Use a link selection algorithm to solve the optimization scheduling model to obtain a link combination, and use a transmission optimization algorithm based on graph theory to evaluate the link combination, and determine the optimal link combination based on the evaluation results.

[0149] Those of ordinary skill in the art can understand that the drawings are only schematic diagrams of an embodiment, and the modules or processes in the drawings are not necessarily essential for implementing the present invention.

[0150] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for method or system embodiments, since they are basically similar to method embodiments, they are described relatively simply. For the relevant parts, reference can be made to the description of the method embodiments. The method and system embodiments described above are only illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0151] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A dynamic transmission optimization method for millimeter wave communication scenarios in high-speed railway tunnels under an integrated air-ground-space network, characterized in that: Applied to an air-ground integrated network, the method comprises: Constructing an optimization scheduling model with the goal of maximizing the total number of completed flows, wherein the completed flows are data flows that meet QoS requirements; The optimization scheduling model is solved by using a link selection algorithm to obtain a link combination, and the link combination is evaluated by using a transmission optimization algorithm based on graph theory, and the optimal link combination is determined based on the evaluation result.

2. The method according to claim 1, characterized in that The optimization scheduling model is: In the formula, Q s,r,i,n Indicates whether the flow i transmitted from transmitter s to receiver r meets the QoS requirement in the nth frame. s,r,i,n =1, it means that the QoS requirement is met; When Q s,r,i,n =0, indicating that the QoS requirement is not met, N s represents the total number of transmitters s, Indicates the total number of streams in the nth frame, N frame is the number of superframes, is the QoS requirement of flow i in frame n, q s,r,i,n is the throughput of stream i transmitted from transmitter s to receiver r in the nth frame, M is the number of time slots, m is the mth time slot, and R s,r,i,n,m is the data rate of stream i transmitted from transmitter s to receiver r in the mth time slot of the nth frame, Δt is the time slot duration, T s is the scheduling time of the superframe, w s,r,i,n is the state of the transmitter s scheduling stream i in the nth frame, a s,r,i,n,m is the state of transmitter s when the mth time slot in the nth frame is assigned to stream i, b is the interference stream, a s,r,b,n,m is the interference state of stream i in the mth time slot of the nth frame by stream b, N MR is the total number of MRs.

3. The method according to claim 1, characterized in that The link combination includes a transmitter allocation combination set, and the link selection algorithm is used to solve the optimization scheduling model to obtain the link combination, including: Calculate the SINR of stream i in the nth frame according to the total interference power received by stream i in the nth frame and the interference power from the interference stream b, and determine the effective transmitter set according to the comparison result between the SINR and the preset SINR threshold; Determine a valid stream set in a current frame according to the valid transmitter set; A transmitter allocation combination is generated according to a Cartesian product of the effective stream set and the effective stream set, and the transmitter allocation combinations are sorted to obtain a transmitter allocation combination set.

4. The method according to claim 3, characterized in that The link combination is evaluated by using a transmission optimization algorithm based on graph theory, and an optimal link combination is determined based on the evaluation result, including: When the transmitter allocation combination shows that all flows are served by the same transmitter, the total number of bits transmitted by the transmitter in all time slots in the frame is calculated according to the throughput and time slot duration of flow i transmitted from the transmitter s to the receiver r in the nth frame, and when the total number of bits meets the QoS requirements of the flow, all flows are marked as successfully served; When the transmitter allocation combination shows that the flow is served by multiple transmitters, the interference factor of the time slot is calculated according to the total interference power, the interference power from the interference flow b, the noise power density and the bandwidth, and when the interference factor is less than or equal to the interference threshold, the QoS requirement of the flow is updated, and when the QoS requirement of the updated flow meets the preset requirement threshold, the flow is marked as successfully served; The number of successfully served flows is obtained, and when the number of successfully served flows meets a preset success threshold, the number of best service flows is updated according to the number of successfully served flows.

5. A dynamic transmission optimization device for high-speed railway tunnel millimeter wave communication scenarios under an air-ground integrated network, characterized in that: Applied to an air-ground integrated network, the device comprises: A model building module, used to build an optimization scheduling model with the goal of maximizing the total number of completed flows, wherein the completed flows are data flows that meet QoS requirements; The solution module is used to solve the optimization scheduling model by using a link selection algorithm to obtain a link combination, and to evaluate the link combination by using a transmission optimization algorithm based on graph theory, and to determine the optimal link combination based on the evaluation result.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: The device stores a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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