STAR-RIS assisted high-speed rail millimeter wave communication system transmission scheduling method

Abstract

The invention provides an STAR-RIS assisted transmission scheduling method for a high-speed rail millimeter wave communication system. The method comprises the following steps: constructing an STAR-RIS-assisted high-speed railway millimeter wave communication system model in the SAGIN, and setting a channel modeling and train moving model of a link in the model; the method comprises the following steps of: setting a target function of a transmission scheduling problem of completing the total number of streams to the maximum extent by considering the movement of a train, signal to interference plus noise ratio constraint, QoS (Quality of Service) requirements of the streams, channel change and a scheduling sequence of the streams; solving the objective function through a coalition game algorithm, and obtaining the total number of successfully scheduled streams in all the unites; and performing priority ranking on all successfully scheduled flows according to the QoS requirements of the flows through a flow scheduling algorithm. According to the method disclosed by the invention, the communication quality of the STAR-RIS assisted high-speed rail train-ground millimeter wave communication system is improved by optimizing inter-flow interference and flow scheduling, the system can be ensured to efficiently work in a complex multi-source interference environment, and the ever-increasing QoS demand of high-speed rail communication is met, so that the ever-increasing QoS demand in the high-speed rail communication is met.

Description

Technical Field

[0001] The present invention relates to the field of millimeter-wave communication technologies, and particularly to a transmission scheduling method for a STAR-RIS assisted high-speed rail millimeter-wave communication system. Background Art

[0002] The application of millimeter-wave technology in high-speed rail communication systems, as an important development direction in the field of wireless communication, is gradually changing the communication pattern of high-speed railways. Millimeter-wave technology has the advantages of large bandwidth and high-speed data transmission, and can provide high-quality mobile broadband services in a high-speed dynamic environment such as high-speed rail, meeting the needs of passengers for high-definition video, real-time data download, and high-capacity Internet services. In high-speed rail communication, the application scenarios of millimeter-wave technology mainly include in-vehicle Wi-Fi networks, vehicle-to-everything (V2X) communication, and high-speed rail signal transmission. First, in in-vehicle Wi-Fi networks, millimeter-wave technology enables the in-train wireless network to have higher bandwidth and lower latency, providing a smoother Internet experience for passengers. Second, in terms of V2X communication, millimeter-wave technology can be used to achieve high-speed data exchange between trains and between trains and ground base stations, ensuring the efficient operation of train control systems and real-time monitoring, which is crucial for ensuring train safety and optimizing operations. Finally, millimeter-wave technology has been widely used in the signal transmission system of high-speed rail, and it can effectively support the transmission of a large amount of monitoring data and control signals, ensuring the safe operation and on-time scheduling of high-speed rail. With the rapid development of integrated circuits and radio frequency technologies, the gain and output power of millimeter-wave communication have been significantly improved, enabling higher data transmission rates and making high-speed rail communication systems more efficient.

[0003] With the continuous evolution of mobile communication technology, SAGIN (Space-Air-Ground Integrated Network) has increasingly become an important development direction for the next-generation communication system. With its advantages such as high bandwidth and low latency, the application potential of millimeter-wave communication in SAGIN is gradually emerging, and it is expected to meet the network requirements of high speed, large capacity, and wide coverage simultaneously. In the "air" layer of SAGIN, that is, in the field of satellite communication, millimeter-wave links can be used to build high-speed data channels between satellites in different orbits (such as GEO, MEO, LEO), and can also achieve two-way high-speed transmission between satellites and the ground. 3GPP proposed in the 38.821 standard that combining millimeter-wave technology with satellite communication can significantly improve the transmission rate and expand the coverage of the network. The reference frequencies of the downlink and uplink can be configured near 20 GHz and 30 GHz respectively, and the maximum channel bandwidth can reach 1 GHz. Such high-bandwidth satellite links provide technical feasibility for data transmission globally and also provide a stable backhaul channel for ground networks. In the "air" layer of SAGIN, flying platforms such as drones and airships carry millimeter-wave communication equipment, which can provide temporary or enhanced network services for ground users or remote areas. Compared with ground base stations, these flying platforms have better flexibility and mobility in emergency rescue, post-disaster reconstruction, or coverage of low-population-density areas. At the same time, the high-bandwidth characteristics of millimeter waves also enable it to complete high-capacity data transmission between air platforms and ground users, suitable for high-speed access or emergency communication scenarios. In the "ground" layer of SAGIN, millimeter waves can become an important supplement to traditional cellular networks, especially in hotspots such as city centers or transportation hubs, where it can provide ultra-high-speed access experiences and provide more generous bandwidth resources for a large number of users.

[0004] To address the adverse effects of high-speed environments and physical obstacles on the propagation of millimeter-wave signals, researchers are exploring emerging technologies such as MR (Mobile Relay) antennas and reconfigurable intelligent surfaces. These solutions can effectively improve signal coverage and quality in the challenging high-speed railway scenario. Different from traditional RIS that can only reflect incident signals, STAR-RIS (Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface) not only has a reflection function but also can transmit signals into the space on the opposite side, thus showing higher flexibility in managing signal paths. Compared with mobile relay devices that actively amplify and forward signals, STAR-RIS consists of an array of passive elements. By dynamically adjusting the phase and amplitude of incident signals, it can simultaneously achieve reflection and transmission, adaptively control the signal direction in multiple dimensions, bypass obstacles, and improve the overall signal quality. In the high-speed railway scenario, if STAR-RIS is deployed on the train window or external surface, signals blocked by metal structures can be effectively reflected and transmitted, significantly enhancing the coverage and connection performance inside and outside the carriage.

[0005] In recent years, Reconfigurable Intelligence Surface (RIS) technology has shown great potential in reducing system transmit power, improving spectral and energy efficiency, and overcoming signal blockage, and has become an important research direction for next-generation wireless networks. Specifically, by jointly optimizing active and passive beamforming, researchers have achieved effective control of transmit power in different application scenarios, improving system capacity and coverage performance. At the same time, to further enhance security and stability, RIS has also demonstrated significant performance gains in physical layer security, preventing channel blockage, and in drone and robot communication systems. However, most existing studies focus on RIS that can only reflect signals, requiring the transceiver to be on the same side of the RIS, resulting in a "half-space" coverage range, which limits the flexibility and application scope of RIS. To address this limitation, the concept of STAR-RIS emerged. STAR-RIS can split the wireless signals incident on the same element into two parts: one part is reflected back into the incident space, and the other part is transmitted into the opposite space. By separately controlling the electric current and magnetic current, the transmission coefficient and reflection coefficient can be independently configured, thus achieving reconfigurable coverage of the entire space. Some engineering prototypes based on metasurface technology have proven the feasibility of this idea. The "intelligent omnidirectional surface" similar to STAR-RIS can also achieve full-space coverage, but its reflection and transmission ends use the same phase shift, with relatively insufficient flexibility. Currently, for issues such as how to effectively design the working mode of STAR-RIS in wireless networks and how to jointly optimize transmission and reflection beamforming, in-depth research is still needed, which is also an important direction for enhancing the application value of STAR-RIS in high-speed railways and other scenarios in the future.

[0006] Currently, a high-speed rail communication interference cancellation solution in the prior art includes: research on reducing interference in high-speed rail communication through transmission scheduling in SAGIN is still relatively limited. Most studies still focus on solving interference problems in high-speed rail communication served by ground BSs. Some solutions have proposed a user equipment-based interference avoidance mechanism, in which macro base station user equipment (MUE) identifies and reports bad resource blocks (RBs) to prevent interference between macro cells and MR cells in the LTE network. Some solutions have also developed a game theory-based dual-objective optimization interference alignment algorithm aimed at improving the throughput and energy efficiency of high-speed rail 5G ultra-dense networks, using a power allocation game model and Nash equilibrium to effectively manage interference. Some solutions have proposed a joint algorithm that combines multi-objective optimal power allocation with partial fixed interference alignment to improve throughput, energy efficiency, and transmission reliability in high-speed rail communication, solving challenges related to interference alignment convergence and optimization objective selection.

[0007] The disadvantages of a high-speed rail communication interference cancellation solution in the above prior art include:

[0008] (1) Currently, the research on high-speed rail millimeter-wave communication systems mainly relies on densely deploying a ground BS network along the line to achieve full coverage of the high-speed rail line. However, due to the complex and variable terrain traversed by the high-speed rail line, the construction of traditional ground base station networks faces huge challenges. In many cases, relying solely on ground base station deployment is not only difficult to construct but also costly, and may be unaffordable. In addition, with the sharp increase in the data requirements of high-speed rail users, it is difficult to meet the communication QoS of all users with only ground base station services. Especially in a high-speed moving environment, as the bandwidth requirements of users continue to grow, traditional ground networks are difficult to effectively cope with.

[0009] (2) Although SAGIN can extend the coverage range to a certain extent and relieve the burden on ground base stations through aerial platforms (such as satellites, drones, airships, etc.), the deployment of aerial platforms also faces challenges. For example, the deployment of airships and drones needs to overcome problems such as altitude changes, signal blockages, and environmental interference. In addition, existing aerial platforms mainly rely on traditional signal transmission technologies, which may not provide the best communication quality and transmission efficiency in some complex scenarios.

[0010] (3) Existing transmission scheduling schemes can be divided into static and dynamic scenarios. In the static scenario, the impact of the scheduling order on the overall system performance is not considered, which may affect the overall efficiency and does not address specific challenges related to the mobile scenario. In the dynamic scenario, the number of scheduled flows is not considered to be increased by reducing interference and optimizing transmission scheduling in the STAR-RIS-assisted mobile scenario. Generally speaking, existing research has not specifically focused on how to reduce interference in transmission scheduling for STAR-RIS-assisted vehicle-ground communication in SAGIN. Summary of the Invention

[0011] Embodiments of the present invention provide a transmission scheduling method for a STAR-RIS-assisted high-speed rail millimeter-wave communication system to effectively improve the communication quality of the STAR-RIS-assisted high-speed rail vehicle-ground millimeter-wave communication system.

[0012] To achieve the above object, the present invention adopts the following technical solutions.

[0013] A transmission scheduling method for a STAR-RIS-assisted high-speed rail millimeter-wave communication system, comprising:

[0014] Construct a STAR-RIS-assisted high-speed railway millimeter-wave communication system model in SAGIN, and set the channel modeling of the links and the train movement model in the model;

[0015] Considering the movement of the train, the signal-to-interference-plus-noise ratio (SINR) constraint, the quality of service (QoS) requirements of the traffic, the channel variation, and the scheduling order of the flows, set up the objective function of the transmission scheduling problem that maximizes the total number of completed flows;

[0016] Solve the objective function through the coalition game algorithm to obtain the total number of successfully scheduled flows in all coalitions;

[0017] According to the QoS requirements of the flows, prioritize all successfully scheduled flows through the traffic scheduling algorithm.

[0018] Preferably, for the construction of the STAR-RIS assisted high-speed railway millimeter-wave communication system model in the SAGIN, set up the channel modeling and train movement model of the links in the model, including:

[0019] The STAR-RIS assisted high-speed railway millimeter-wave communication system model in the SAGIN includes BS-MR, BS-RIS-UE, airship-MR, and airship-RIS-UE links. The MRs are installed on the roofs of the first and last carriages, and the installation height is h MR , and STAR-RISs are installed on other carriages. Each STAR-RIS contains L RIS elements, and there are UEs in each carriage. Both the base station and the airship are equipped with uniform linear arrays (ULAs) with an array size of L ULA , to serve the single-antenna MRs and single-antenna UEs. Each superframe consists of a scheduling phase and a transmission phase. In the scheduling phase, requests from the train MRs and UEs are collected. The transmission phase is divided into M equal time slots (TSs), and the duration of each time slot is Δt;

[0020] For the airship-MR and BS-MR links, use a vector of size L ULA ×1 to represent the channel of flow i in the m-th time slot of the n-th frame, and the representation form is:

[0021]

[0022] where K is the Rice factor, and the line-of-sight (LoS) component and non-line-of-sight (NLoS) component of the channel are expressed as:

[0023]

[0024] where ρ is the value of the reference path loss (PL) at a distance of 1 meter, represents the distance from the l ULA -th antenna of the transmitter s to the receiver r, located in the m-th time slot of the n-th frame, γ is the path loss exponent, is the signal from the l ULA ​The phase offset introduced when an antenna reaches the receiver r represents a small-scale fading channel, the elements of which follow a complex Gaussian distribution

[0025] For the BS-RIS-UE and airship-RIS-UE links, a vector of size L ULA ×1 is used to represent the channel of stream i in the m-th time slot of the n-th frame, denoted as:

[0026]

[0027] where is the Rayleigh-Rician channel from the transmitter s to the STAR-RIS in the m-th time slot of the n-th frame, is the transmission coefficient matrix, where and represent the amplitude and phase offset of the l-th RIS element of the STAR-RIS respectively, is the channel from the STAR-RIS to the UE in the m-th time slot of the n-th frame;

[0028] The power received by the UE for stream i in the m-th time slot of the n-th frame is expressed as:

[0029]

[0030] where is the transmission power of the transmitter s for stream i in the m-th time slot of the n-th frame, G t (θ) and G r (θ) are the antenna gains of the transmitter and receiver respectively. The signal-to-interference-plus-noise ratio SINR of stream i in the m-th time slot of the n-th frame is expressed as:

[0031]

[0032] where is the total interference power received in the m-th time slot of the n-th frame, is the received interference power of stream b in the time slot, N0W is the noise power, W is the bandwidth, N0 is the noise power density. The available data rate of stream i in the n-th frame and time slot is calculated by the Shannon channel capacity formula, expressed as:

[0033]

[0034] where ε ∈ (0,1) is the efficiency of the transceiver design, and the variable w s,r,i,n is a binary indicator indicating whether stream i is scheduled by the transmitter s in the n-th frame. If it is scheduled, then w s,r,i,n= 1, and other transmitters cannot schedule this flow; otherwise, w s,r,i,n = 0, the variable a s,r,i,n,m is a binary indicator indicating whether flow i occupies the resource of the m-th time slot in the n-th frame. If it does, a s,r,i,n,m = 1, and other flows cannot occupy this time slot; otherwise, a s,r,i,n,m = 0. Similarly, a s,r,b,n,m is a binary variable indicating whether flow i receives interference from the flow at the time slot. If there is interference, a s,r,b,n,m = 1; otherwise, a s,r,b,n,m = 0. The throughput of flow i in the n-th frame is calculated as:

[0035]

[0036] where T s is the scheduling time, and MΔt is the transmission time;

[0037] Approximate the trajectory of the train as a straight line, and the trajectory of the MRs is located on the x-axis. The coordinates of the l ULA -th antenna of the transmitter s are Assume that the initial position of the train is point B(x0, y0, z0), and when MR1 reaches point B, it is marked as the first frame. In the n-th frame, the distance from the l ULA -th antenna of the transmitter s to the MR c in the n-th frame is expressed as:

[0038]

[0039] where d MR is the distance between MR1 and MR C In the n-th frame, the distance from the l ULA -th antenna of the transmitter s to the l RIS -th element in the STAR-RIS of the c-th carriage is expressed as:

[0040]

[0041] where are the coordinates of the l RIS -th element in the STAR-RIS of the c-th carriage. The distance between the UE and the STAR-RIS remains unchanged. The number of frames for the train to pass through BC is expressed as:

[0042]

[0043] Directional antennas are used between the airship and the base station. The directional antenna gain is expressed as:

[0044]

[0045] θ is the included angle between the transmitter and the receiver, θ ml = 2.6·θ -3dB is the main lobe width, θ -3dB is the half-power beam width, the maximum antenna gain G m and the sidelobe gain G sl are expressed as:

[0046]

[0047] G sl = -0.4111ln(θ -3dB ) - 10.579. (14).

[0048] Preferably, considering the movement of the train, the signal-to-interference-plus-noise ratio constraint, the QoS requirements of the traffic, the channel variation, and the scheduling order of the flows, the objective function of the transmission scheduling problem for maximizing the total number of completed flows is set, including:

[0049] If the QoS requirements of a certain flow are met, then the flow is regarded as a completed flow. Define a binary variable Q s,r,i,n to represent whether the transmitter s in the nth frame meets the QoS requirements of the receiver r for the flow i. If it is met, then Q s,r,i,n = 1; otherwise, Q s,r,i,n = 0. The objective function of the transmission scheduling problem for maximizing the total number of completed flows is set as:

[0050]

[0051] where N s is the number of transmitters, is the number of flows in the nth frame;

[0052] Constraint 1: It means that the received signal-to-interference-plus-noise ratio of the MR for the BS-MR and airship-MR links needs to exceed the threshold Γ;

[0053]

[0054] Constraint 2: It means that the condition for the successful scheduling of the flow i in the BS-RIS-UE and airship-RIS-UE links is that the throughput provided by the transmitter s is greater than the QoS requirements of the flow i, where, is the QoS requirement of the flow i in the nth frame;

[0055] Constraint 3: It means that the flow i is scheduled by at most one transmitter within each frame;

[0056] Constraint 4: It means that any transmitter can serve at most one flow in a time slot;

[0057] Constraint 5: It means that the number of interfering flows in each time slot is at most the total number of transmitters minus 1;

[0058] Constraint 6: It means that the number of flows per frame must be less than the total number of user equipments.

[0059] Preferably, solving the objective function through the coalition game algorithm to obtain the total number of successfully scheduled flows in all coalitions includes:

[0060] Define a coalition game for traffic scheduling in the nth frame:

[0061] Definition 1: A transferable utility coalition game for traffic scheduling is defined as a tuple where is the set of players representing flows in the nth frame, and a coalition is a set of flows that use the same resources for scheduling in the nth frame. The utility of coalition is defined as the number of successfully scheduled flows within the nth frame, denoted as;

[0062]

[0063] where, represents the number of flows successfully completed within coalition Within a single time frame, if the airship or base station provides services for the users in a certain carriage, it must complete the flow scheduling for all users in that carriage before continuing to provide services for the users in the next carriage. Define the set of flows of all users in the cth carriage as

[0064] Definition 2: Coalition partition means dividing all the flow sets in the nth frame into mutually exclusive coalitions. Each coalition corresponds to specific resources. Each flow can belong to at most one coalition. The coalition partition needs to satisfy:

[0065]

[0066] where, and represent the flow sets scheduled by the airship and the two base stations respectively. All flows must be assigned to a certain coalition

[0067]

[0068] where, Denote the coalition division in the n-th frame. The flows in the same carriage are regarded as a whole. For any set in the airship coalition and the base station coalition The preference order between them depends on the utility provided by the coalition ;

[0069] Definition 3: Let denote the set of flows in carriage c The preference relationship with respect to the coalitions and is based on the marginal benefit brought by the number of successfully scheduled flows after joining the coalition. The preference relationship is expressed as:

[0070]

[0071] where and respectively represent the number of successfully scheduled flows in the coalitions and in the n-th frame;

[0072] Definition 4: The handover operation describes the redistribution of the set of flows in the carriage among coalitions in the n-th frame to improve the overall system utility. Let the current coalition division be: If the set of flows is redistributed from to then the updated coalition division is expressed as:

[0073]

[0074] In the n-th frame, the total utility of the coalition is expressed as the sum of the utilities of all coalitions, that is:

[0075]

[0076] The goal of the coalition game is to maximize the total utility of the system that is, to maximize the number of successfully scheduled flows. This goal is expressed as:

[0077]

[0078] Within a time frame, the number of possible coalition allocation methods for the set of flows is at most (N s ) C kinds. Determine the association relationship between the user equipment and the airship and the base station through the user association algorithm. The input of the user association algorithm includes: the total number of carriages C, the number of transmitters Ns , an initialized empty set and respectively represent the sets of user equipment associated with the airship, BS1, and BS2 in the n-th iteration of the n-th frame. The output of the user association algorithm is the set of user equipment associated with the airship, BS1, and BS2 in all iterations of each frame; iter The output of the user association algorithm is the set of user equipment associated with the airship, BS1, and BS2 in all iterations of each frame;

[0079] The coalition game algorithm optimizes and adjusts the allocation method of carriages through iteration to maximize the service capabilities of the airship, base station BS1, and BS2, while meeting the QoS requirements of UEs. The input of the coalition game algorithm includes the coalition set and They respectively represent the sets of carriages served by the airship, BS1, and BS2 in the n-th iteration of the n-th frame. The output of the coalition game algorithm is the total number Q of successfully scheduled flows in all coalitions after the final iteration is completed iter The output of the coalition game algorithm is the total number Q of successfully scheduled flows in all coalitions after the final iteration is completed s,r,i,n , during the iteration process and respectively represent the number of flows completed in the previous iteration and the current iteration, while Q A , and respectively represent the number of flows completed by the airship, BS1, and BS2.

[0080] Preferably, solving the objective function through the coalition game algorithm to obtain the total number of successfully scheduled flows in all coalitions includes:

[0081] Introduce a threshold ∈ as the convergence criterion of the coalition game algorithm. When the following conditions are met the iteration terminates: At this time is updated to indicating that the system has reached a stable state;

[0082] QoS requirement matrix represents the QoS requirements of all flows. The (i, c) element of the matrix represents the QoS requirement of the i-th flow in carriage c. At the beginning of each time frame, the algorithm initializes the number of completed flows and and simultaneously initializes counters a1, a2, a3, corresponding to the coalitions and respectively, for recording the number of carriages served. k1, k2, k3 respectively represent the carriage numbers currently served by the airship, BS1, and BS2. In each TS, the algorithm checks whether each transmitter meets the QoS requirements of its allocated flows and calculates the number of currently completed flows after each TS is completed, that is If the convergence condition is met then update and terminate the iteration of this frame; otherwise, if then update but do not terminate the iteration and continue to optimize the coalition allocation;

[0083] After all frames are processed, the total completed traffic Q s,r,i,n is given by the sum of for all frames:

[0084] Preferably, the traffic scheduling algorithm prioritizes all successfully scheduled flows according to the QoS requirements of the flows, including:

[0085] Sort the flows according to the QoS requirements of the flows through the traffic scheduling algorithm and schedule the flows in descending order to meet the maximum number of QoS requirements. The input data of the traffic scheduling algorithm is the matrix representing the QoS requirements of the flows of UEs in each carriage, where the rows of the matrix correspond to individual flows and the columns correspond to carriages. The output data of the traffic scheduling algorithm is the sorted matrix O sort , where each column is arranged in ascending order of QoS requirement values, so that the flows with higher QoS requirements gradually move to the bottom of the column. The sorting process traverses all carriages and sorts each column of the matrix independently. Within each carriage, adjacent flows are compared and re-sorted in multiple iterations until all flows are arranged in ascending order of QoS requirement. After sorting, the matrix O sort provides the sorted flow scheduling order for each carriage.

[0086] It can be seen from the technical solutions provided by the embodiments of the present invention described above that the method of the present invention improves the communication quality of the STAR-RIS assisted high-speed rail vehicle-ground millimeter wave communication system by optimizing the interference between flows and traffic scheduling, ensuring that the system can work efficiently and stably in a complex multi-source interference environment and meet the growing QoS requirements of high-speed rail communication. Through the joint optimization of these two-step methods, the embodiments of the present invention can effectively improve the performance of the vehicle-ground communication system, reduce interference and improve traffic scheduling efficiency, so as to meet the growing QoS requirements in high-speed rail communication.

[0087] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for 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.

[0089] Figure 1 This is a processing flowchart of a transmission scheduling method for a STAR - RIS assisted high - speed rail millimeter - wave communication system provided by an embodiment of the present invention;

[0090] Figure 2 This is a model diagram of a high - speed railway communication system combining STAR - RIS in SAGIN provided by an embodiment of the present invention;

[0091] Figure 3 This is a structure diagram of a super - frame provided by an embodiment of the present invention;

[0092] Figure 4 This is a schematic diagram of a train movement model provided by an embodiment of the present invention. Detailed implementation manners

[0093] The following details the implementation manners of the present invention. Examples of the implementation manners are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The implementation manners described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as a limitation of the present invention.

[0094] Those skilled in the art of the present technology can understand that, unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "including" used in the specification of the present invention means the presence of the described 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 embodiment of the present invention states that 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 unit and all combinations of one or more related listed items.

[0095] 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 belongs. 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.

[0096] For the convenience of understanding the embodiments of the present invention, the following will further explain with several specific embodiments in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.

[0097] The embodiments of the present invention maximize the number of successfully scheduled flows by reducing inter-flow interference and optimizing transmission scheduling, while meeting their QoS (Quality of Service) requirements. The embodiments of the present invention first propose an optimization problem, considering multiple key factors, including the movement of trains, signal-to-interference-plus-noise ratio constraints, QoS requirements of traffic, channel variations, and the scheduling order of flows. To solve this optimization problem, the embodiments of the present invention propose a two-step method. In the first step, the embodiments of the present invention introduce a coalition game algorithm to identify the link with the least inter-flow interference in each time slot. This algorithm can dynamically adapt according to the position of the train and the changing channel conditions to ensure that interference is effectively reduced. In the second step, the embodiments of the present invention design a traffic scheduling algorithm to prioritize the flows according to the QoS requirements of the flows, further increasing the number of successfully completed flows.

[0098] The processing flow chart of a transmission scheduling method for a STAR-RIS assisted high-speed railway millimeter-wave communication system provided by the embodiments of the present invention is as Figure 1 shown, including the following processing steps;

[0099] Step S10: Construct a STAR-RIS assisted high-speed railway millimeter-wave communication system model in SAGIN, and set the channel modeling and train movement model of the links in the model.

[0100] Step S20: Considering the movement of trains, signal-to-interference-plus-noise ratio constraints, QoS requirements of traffic, channel variations, and the scheduling order of flows, set the objective function of the transmission scheduling problem for maximizing the total number of completed flows.

[0101] Step S30: Solve the objective function through a coalition game algorithm to obtain the link with the least inter-flow interference identified in each time slot.

[0102] Step S40: Prioritize the flows according to the QoS requirements of the flows through a traffic scheduling algorithm.

[0103] Specifically, the above step S10 includes: The embodiments of the present invention consider a downlink millimeter-wave communication system and propose a model of a high-speed railway communication system combined with STAR-RIS assistance in SAGIN as Figure 2 shown, including BS-MR, BS-RIS-UE, airship-MR, and airship-RIS-UE links, as Figure 2 shown. The train moves at a speed of v tTravel along the track and pass through base stations BS1 and BS2 in sequence. The train has C carriages, and the MRs are installed on the roofs of the first and the last carriages, with the installation height of h MR . STAR-RISs are installed on other carriages, and each STAR-RIS contains L RIS elements. There are UEs in each carriage. The airship is stationary between base stations BS1 and BS2, with the flight height of h A . Both the BS-MR and airship-MR links have sensing capabilities. By analyzing the echo signals, the antennas of the base station and the airship can dynamically align with the target carriage in real time. Both the base station and the airship are equipped with ULAs (Uniform Linear Arrays), with the array size of L ULA to serve the single-antenna MRs and single-antenna UEs.

[0104] An embodiment of the present invention defines N frame superframes as the time period when the train moves from base station BS1 to BS2. Figure 3 FIG. is a structural diagram of a superframe provided by an embodiment of the present invention. As Figure 3 shown, each superframe consists of a scheduling phase and a transmission phase. In the scheduling phase (with a duration of T s ), requests from the train MRs and UEs are collected. The transmission phase is divided into M equal TSs, and the duration of each time slot is Δt, which is used for data transmission. An embodiment of the present invention assumes that the channel conditions remain stable within each frame. In addition, since the train travels along a fixed track in a known direction and at a predictable speed, the Doppler frequency shift between the transmitter and the receiver can be calculated and compensated by the existing technology.

[0105] An embodiment of the present invention uses a vector of size L ULA ×1 at the airship-MR and BS-MR links to represent the channel of flow i in the m-th time slot of the n-th frame, and the representation form is where K is the Rice factor. The line-of-sight (LoS) component and non-line-of-sight (NLoS) component of the channel

[0106]

[0107] can be expressed as: where ρ is the reference path loss (PL), which is the value at a distance of 1 meter.

[0108]

[0109] represents the distance from the l -th antenna of the transmitter s to the receiver r, located in the m-th time slot of the n-th frame. γ is the path loss exponent, ULA and is the phase shift introduced when the signal travels from the \(l\)-th antenna of the transmitter \(s\) to the receiver \(r\). ULA represents the small-scale fading channel, whose elements follow a complex Gaussian distribution For the BS-RIS-UE and airship-RIS-UE links, in the embodiments of the present invention, a vector of size \(L\times1\) is used to represent the channel of stream \(i\) in the \(m\)-th time slot of the \(n\)-th frame, denoted as ULA where is the Rayleigh-Rice channel from the transmitter \(s\) to the STAR-RIS in the \(m\)-th time slot of the \(n\)-th frame.

[0110]

[0111] is the transmission coefficient matrix, where and represent the amplitude and phase shift of the \(l\)-th element of the STAR-RIS, respectively. and is the channel from the STAR-RIS to the UE in the \(m\)-th time slot of the \(n\)-th frame. RIS The power received by the UE for stream \(i\) in the \(m\)-th time slot of the \(n\)-th frame can be expressed as where

[0112] is the transmit power of the transmitter \(s\) for stream \(i\) in the \(m\)-th time slot of the \(n\)-th frame, \(G(\theta)\) and \(G(\theta)\) are the antenna gains of the transmitter and receiver, respectively. The signal-to-interference-plus-noise ratio (SINR) of stream \(i\) in the \(m\)-th time slot of the \(n\)-th frame can be expressed as:

[0113]

[0114] where is the total interference power received in the \(m\)-th time slot of the \(n\)-th frame, t is the received interference power of stream \(b\) in the time slot, \(N_0W\) is the noise power, \(W\) is the bandwidth, and \(N_0\) is the noise power density. The available data rate of stream \(i\) in the \(n\)-th frame and time slot can be calculated by the Shannon channel capacity formula and can be expressed as r where \(\epsilon\in(0,1)\) is the efficiency of the transceiver design. The variable \(w\)

[0115]

[0116] where is a binary indicator indicating whether stream \(i\) is scheduled by the transmitter \(s\) in the \(n\)-th frame. If it is scheduled, then \(w\) is the received interference power of stream \(b\) in the time slot, \(N_0W\) is the noise power, \(W\) is the bandwidth, and \(N_0\) is the noise power density. The available data rate of stream \(i\) in the \(n\)-th frame and time slot can be calculated by the Shannon channel capacity formula and can be expressed as

[0117]

[0118] where \(\epsilon\in(0,1)\) is the efficiency of the transceiver design. The variable \(w\) s,r,i,n is a binary indicator indicating whether stream \(i\) is scheduled by the transmitter \(s\) in the \(n\)-th frame. If it is scheduled, then \(w\) s,r,i,n= 1, and other transmitters cannot schedule this flow; otherwise, w s,r,i,n = 0. The variable a s,r,i,n,m is a binary indicator indicating whether flow i occupies the resource of the m-th time slot in the n-th frame. If it occupies, then a s,r,i,n,m = 1, and other flows cannot occupy this time slot; otherwise, a s,r,i,n,m = 0. Similarly, a s,r,b,n,m is a binary variable indicating whether flow i receives interference from flow . If there is interference, then a s,r,b,n,m = 1; otherwise, a s,r,b,n,m = 0. Finally, the throughput of flow i in the n-th frame can be calculated as:

[0119]

[0120] where T s is the scheduling time and MΔt is the transmission time.

[0121] Figure 4 is a schematic diagram of a train movement model provided by an embodiment of the present invention. To facilitate the analysis of the distance between receiver r and transmitter s, the embodiment of the present invention approximates the trajectory of the train as a straight line, and the trajectory of MRs is located on the x-axis, as Figure 3 shown. The coordinates of the l ULA -th antenna of transmitter s are Assume that the initial position of the train is point B(x0, y0, z0), and it is marked as the first frame when MR1 reaches point B. In the n-th frame, the distance from the l ULA -th antenna of transmitter s to MR c in the n-th frame can be expressed as:

[0122]

[0123] where d MR is the distance between MR1 and MR C . In the n-th frame, the distance from the l ULA -th antenna of transmitter s to the l RIS -th element in the STAR-RIS of carriage c is expressed as:

[0124]

[0125] where are the coordinates of the l RIS -th element in the STAR-RIS of carriage c. In addition, the embodiment of the present invention assumes that the UE in the carriage remains stationary, so the distance between the UE and the STAR-RIS remains unchanged. The number of frames for the train to pass through BC can be expressed as:

[0126]

[0127] Directional antennas play an important role in millimeter-wave communication. Embodiments of the present invention consider using directional antennas between an airship and a base station. Embodiments of the present invention assume that these directional antennas can achieve perfect beam alignment during the movement of a train. Then, embodiments of the present invention adopt a directional antenna model, and the antenna gain can be expressed as:

[0128]

[0129] where θ is the angle between the transmitter and the receiver, and θ ml = 2.6·θ -3dB is the main lobe width, and θ -3dB is the half-power beam width. The maximum antenna gain G m and the sidelobe gain G sl can be expressed as:

[0130]

[0131] G sl = -0.4111ln(θ -3dB ) - 10.579. (14)

[0132] Specifically, the above step S20 includes:

[0133] Embodiments of the present invention consider the problem of inter-stream interference transmission scheduling in a STAR-RIS assisted vehicle-to-ground communication system in SAGIN. The goal of embodiments of the present invention is to schedule as many flows as possible that meet the QoS requirements of UEs by reducing signal interference and optimizing traffic scheduling. If the QoS requirements of a certain flow are met, then this flow is regarded as a completed flow. Embodiments of the present invention define a binary variable Q s,r,i,n to represent whether the transmitter s in the nth frame meets the QoS requirements of the receiver r for the flow i. If it is met, then Q s,r,i,n = 1; otherwise, Q s,r,i,n = 0. Set the objective function of the transmission scheduling problem that maximizes the total number of completed flows as:

[0134]

[0135] where N s is the number of transmitters, is the number of flows in the nth frame.

[0136] Constraint 1: It means that the received signal-to-interference-plus-noise ratio of the MR for the BS-MR and airship-MR links needs to exceed the threshold Γ to ensure that the positions of the front and rear of the vehicle head can be effectively located for these two links. That is:

[0137]

[0138] Constraint 2: It is shown that the condition for the successful scheduling of flow \(i\) in the BS-RIS-UE and airship-RIS-UE links is that the throughput provided by the transmitter \(s\) is greater than the QoS requirement of flow \(i\). Among them, is the QoS requirement of flow \(i\) in the \(n\)th frame.

[0139] Constraint 3: It is shown that flow \(i\) is scheduled by at most one transmitter within each frame.

[0140] Constraint 4: It is shown that any transmitter can serve at most one flow in a time slot.

[0141] Constraint 5: It is shown that the number of interfering flows in each time slot is at most the total number of transmitters minus 1.

[0142] Constraint 6: It is shown that the number of flows per frame must be less than the total number of user equipments.

[0143] Problem P1 is a non-linear integer programming problem, whose objective function is non-linear and contains a logarithmic function for modeling the available data rate of each flow. The transmission scheduling scheme is affected by various factors, including interference between flows, QoS requirements of flows, train speed, etc. Due to the complexity of direct solution, the embodiments of the present invention propose to use a coalition game algorithm and a traffic scheduling algorithm to solve the problem.

[0144] When solving problem P1, the embodiments of the present invention first propose a coalition game algorithm to reduce interference between flows with low complexity. Then, the embodiments of the present invention introduce a traffic scheduling algorithm for maximizing the number of flows completed in STAR-RIS assisted train-ground communication.

[0145] The coalition game algorithm is widely used in non-linear integer programming problems because it can achieve Nash stable equilibrium. The purpose of the coalition game algorithm is to reduce interference between flows and increase the number of completed flows within each frame by selecting appropriate links for user equipments.

[0146] The key of the coalition game lies in allowing the flows within each carriage to join the coalition through strategies. By forming coalitions, the game framework helps to reduce interference and optimize traffic scheduling. Next, the embodiments of the present invention formally define the coalition game for the traffic scheduling of the \(n\)th frame.

[0147]

Definition 1

[0148]

[0149] where represents the number of flows successfully completed within the coalition . Within a single time frame, if an airship or a base station serves the users in a certain carriage, it must complete the flow scheduling for all users in that carriage before continuing to serve the users in the next carriage. Define the set of flows of all users in the c-th carriage as . It should be noted that the coalition describes the set of flows scheduled by the airship or the base station, rather than the airship or the base station itself. This scheduling constraint ensures that the resources of the current carriage can be fully utilized before entering the next carriage, thereby improving the resource allocation efficiency and traffic management ability. On this basis, the embodiments of the present invention introduce the concept of coalition partitioning to formally describe the grouped scheduling method of flows

[0150]

Definition 2

[0151]

[0152] where and represent the sets of flows scheduled by the airship and two base stations respectively. At the same time, all flows must be assigned to a certain coalition

[0153]

[0154] where represents the coalition partitioning in the n-th frame. To evaluate the marginal benefit of different coalitions in allocating flows, the embodiments of the present invention define a preference order. First, since the transmitter must complete the flow scheduling of the current carriage before moving to the next carriage, the flows in the same carriage are regarded as a whole. Second, for any set its preference order between the airship coalition and the base station coalition depends on the utility provided by the coalition for . Based on this, the embodiments of the present invention define a preference relationship to ensure optimal scheduling decisions within each carriage

[0155]

Definition 3

[0156]

[0157] where and respectively represent the number of successfully scheduled flows in the coalition and in the nth frame. Based on the above preference order, the embodiments of the present invention can further perform coalition switching operations to ensure that the flows of each carriage are allocated to the coalition with the maximum utility, thereby optimizing resource scheduling.

[0158]

Definition 4

[0159]

[0160] It should be noted that the updated coalition division needs to meet the preference relation conditions to ensure that the re-divided coalition can improve the total utility of the system. In addition, the switching operation between the base station BS2 and the airship or BS1 is similar to formula (19). In the nth frame, the total utility of the coalition is expressed as the sum of the utilities of all coalitions, that is

[0161]

[0162] The main goal of the coalition game is to maximize the total utility of the system That is, to maximize the number of successfully scheduled flows.

[0163] This goal can be expressed as

[0164]

[0165] Due to the limited number of transmitters, each transmitter can schedule at most N in one TS sIn addition, in each time slot, the scheduled flows must come from different carriages. Therefore, in a time frame, the flow set The maximum number of ways to allocate a union is (N s ) C In order to determine how the user equipment in different carriages associate with the airship and the base station in each time frame, the embodiment of the present invention summarizes the user association algorithm as follows (see Algorithm 1). The input of the algorithm includes: the total number of carriages C, the number of transmitters N s , initialized empty collection and Respectively represent the nth iter The output of the algorithm is the set of user devices associated with the airship, BS1, and BS2 in all iterations of each frame. Define the matrix It contains all possible ways to associate user devices. In the nth frame, each row c and column n of the matrix iter The element at n represents the iter The union selected by the user device in the cth car during the round iteration. n The values ​​in are defined as follows: 1 indicates that the user equipment is associated with airship A, 2 indicates that the user equipment is associated with base station BS1, and 3 indicates that the user equipment is associated with base station BS2. The algorithm traverses each iteration n iter , initialize variable p = n iter -1, and calculate the nth iter During round iteration, the union of user devices in carriage c selects S n (c,n iter )=mod(p,N s )+1. Subsequently, p is updated by integer division: Finally, according to S n (c,n iter ), add the user equipment in compartment c to the corresponding consortium set: n (c,n iter )=1, then the user equipment in c is added with 1, indicating that airship A is associated with the user equipment in this compartment. n (c,n iter )=2, then the user equipment in c joins Indicates that base station BS1 is associated with the user equipment in this compartment. n (c,n iter )=3, then the user equipment in c joins Indicates that base station BS2 is associated with the user equipment in this compartment. However, directly using Algorithm 1 to exhaustively search The optimal union of will lead to high computational complexity, specifically To reduce the computational complexity brought by the exhaustive algorithm, an optimization algorithm based on coalition game is proposed in an embodiment of the present invention. Its core idea is to approximate the Nash equilibrium by setting a threshold ∈ during the process of selecting the optimal coalition, so as to improve the computational efficiency. In addition, to meet the QoS requirements of flow i, the number of TCs required by flow i within coalition s can be expressed as:

[0166]

[0167]

[0168]

[0169] The coalition game algorithm aims to maximize the service capabilities of the airship, base stations BS1 and BS2 by iteratively optimizing and adjusting the allocation method of carriages, while meeting the QoS requirements of UEs (see Algorithm 2). The inputs of this algorithm include the coalition set and which represent the sets of carriages served by the airship, BS1 and BS2 respectively in the n-th round of iteration in the n-th frame. The output of the algorithm is the total number Q of successfully scheduled flows in all coalitions after the final iteration is completed iter s,r,i,n . During the iteration process, and represent the number of flows completed in the previous iteration and the current iteration respectively, while Q A , and represent the number of flows completed by the airship, BS1 and BS2 respectively. To ensure the convergence of the algorithm, a threshold ∈ is introduced as the convergence criterion. When the following conditions are met, the iteration terminates: At this time, is updated to indicating that the system has reached a stable state. In addition, the QoS requirement matrix represents the QoS requirements of all flows, where the (i, c) element of the matrix represents the QoS requirement of the i-th flow in carriage c. At the beginning of each time frame, the algorithm initializes the number of completed flows and and simultaneously initializes counters a1, a2, a3, corresponding to the coalitions and respectively, to record the number of carriages served. In addition, k1, k2, k3 represent the carriage numbers currently served by the airship, BS1 and BS2 respectively. In each TC, the algorithm checks whether each transmitter meets the QoS requirements of its allocated flows, and calculates the number of currently completed flows after each TC is completed, that is If the convergence condition is met, then is updated and the iteration of this frame is terminated. Otherwise, if​ Then update but do not terminate the iteration and continue to optimize the coalition allocation. After all frames are processed, the total completed traffic Q s,r,i,n from all frames is given by the sum of:

[0170] This algorithm uses ∈ as the convergence criterion and dynamically adjusts the coalition during the iteration process to reduce the computational overhead and optimize the system resource allocation. In Algorithm 2, the outer for loop executes N frame rounds of iteration, and the number of executions of the inner for loop is where N iter (∈) represents the number of iterations determined by the parameter ∈. Therefore, the total computational complexity of this algorithm is

[0171]

[0172]

[0173]

[0174]

[0175] 2) Flow scheduling algorithm:

[0176] The QoS completion degree of the flow is mainly affected by the throughput provided by the transmitter to the UEs and the number of TSs allocated by the transmitter to the UEs. Among them, the main factors affecting the throughput of the UEs include the distance between the transmitter and the receiver and the channel quality, while the QoS requirements of the flow are determined by the service types required by the UEs. Both of these are objective factors and are relatively fixed. In contrast, the number of TSs allocated by the transmitter to the UEs is an adjustable parameter. In the case where the distance between the transmitter and the receiver is large and the channel quality is poor, it is particularly critical to reasonably allocate TSs to optimize the QoS guarantee ability. The embodiments of the present invention do not consider the flow priority differences caused by different service types. Therefore, the flows are only sorted according to the QoS requirements of the flows and scheduled in descending order to meet the maximum number of QoS requirements. Algorithm 3 describes the scheduling process of the flows. The input matrix represents the QoS requirements of the flows of the UEs in each carriage, where the rows of the matrix correspond to individual flows and the columns correspond to carriages. The output of the algorithm is the sorted matrix O sort , where each column is arranged in ascending order of the QoS requirement values, so that the flows with higher QoS requirements gradually move to the bottom of the column. The sorting process traverses all carriages and sorts each column of the matrix independently. Inside each carriage, adjacent flows are compared and re-sorted in multiple iterations until all flows are arranged in ascending order of QoS requirements. After sorting, the matrix O sortA sorted flow scheduling order is provided for each carriage, preferentially satisfying the flows with lower QoS requirements to ensure the fairness of resource allocation and scheduling efficiency. The computational complexity of Algorithm 3 can be analyzed as follows. The outermost for loop traverses all carriages and performs C iterations in total. Inside each carriage, the second-layer for loop traverses all flows and performs iterations for each carriage. Inside this loop, the innermost for loop performs the sorting operation. During each iteration, this loop traverses the position of the current flow in the column, and the upper bound of the loop decreases from to 1, where i is the index of the current flow in the second-layer loop. Therefore, for the flow scheduling sorting within a carriage, the total number of executions of the innermost loop can be expressed as: This result represents the computational complexity of sorting the flows within a single carriage. Considering the computational contributions of all c carriages, the total computational complexity of the entire algorithm can be expressed as This complexity indicates that as the number of carriages c and the number of flows within each carriage increase, the computational overhead of flow scheduling will increase accordingly. Therefore, in practical applications, optimizing the sorting algorithm can effectively reduce the computational complexity and improve the scheduling efficiency.

[0177]

[0178] In summary, the embodiments of the present invention aim to solve the problems of dynamic interference suppression and transmission scheduling faced in the high-speed rail millimeter-wave communication system assisted by STAR-RIS and achieve the following main objectives: First, by comprehensively considering the high-speed movement of the train, the QoS requirements of the flows, and the interference level, a transmission scheduling optimization model for the space-air-ground integrated network scenario is constructed; Second, based on the dynamic user association and coalition game method, taking into account the interference situation and time-varying characteristics of different links, an adaptive scheduling algorithm is proposed to reduce the interference between flows and improve the resource utilization efficiency; Finally, by performing QoS priority sorting and flexible time slot allocation for the flows, the number of successfully schedulable flows and the overall throughput are further improved, realizing the effective scheduling of multi-link and multi-time slot resources in the high-speed mobile environment. While the present invention improves the QoS guarantee ability and reduces interference, it also provides innovative ideas and technical support for the cross-layer optimization and resource management of future high-speed railway communication systems in the space-air-ground network environment.

[0179] 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.

[0180] As can be seen from the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention, 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 storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0181] 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 the device or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments. The device 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 can 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. Those of ordinary skill in the art can understand and implement it without creative work.

[0182] 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 STAR-RIS-assisted high-speed rail millimeter wave communication system transmission scheduling method, characterized in that: include: Construct a STAR-RIS-assisted high-speed railway millimeter wave communication system model in SAGIN, and set up the channel modeling and train movement model of the link in the model; Considering the movement of trains, signal-to-interference-to-noise ratio constraints, QoS requirements of traffic, channel changes, and the scheduling order of flows, the objective function of the transmission scheduling problem is set to maximize the total number of completed flows; Solving the objective function by using a coalition game algorithm to obtain the total number of flows successfully scheduled within all coalitions; All successfully scheduled flows are prioritized according to their QoS requirements through the traffic scheduling algorithm.

2. The method according to claim 1, characterized in that The construction of the STAR-RIS-assisted high-speed railway millimeter wave communication system model in SAGIN, setting the channel modeling of the link and the train movement model in the model, includes: The STAR-RIS-assisted high-speed railway millimeter-wave communication system model in SAGIN includes BS-MR, BS-RIS-UE, airship-MR and airship-RIS-UE links. MR is installed on the roof of the first and last carriages at a height of h. MR , STAR-RIS are installed on other carriages, each STAR-RIS contains L RIS components, each car has UE, base station and airship are equipped with uniform linear array ULA, the array size is L ULA , to serve single-antenna MRs and single-antenna UEs, each superframe consists of a scheduling phase and a transmission phase, in which requests from train MRs and UEs are collected, and the transmission phase is divided into M equal TSs, each time slot duration is Δt; For airship-MR and BS-MR links, use a size of L ULA ×1 vector To represent the channel of stream i in the mth time slot of the nth frame, the representation is: Among them, K is the Rice factor, the channel The line-of-sight LoS component and non-line-of-sight NLoS component are expressed as: Where ρ is the reference path loss PL at a distance of 1 meter, represents the lth ULA The distance from the antenna to the receiver r, located in the mth time slot of the nth frame, γ is the path loss exponent, is the signal from the transmitter s ULA The phase offset introduced from the antenna to the receiver r is represents a small-scale fading channel, whose elements follow a complex Gaussian distribution For BS-RIS-UE and airship-RIS-UE links, use a size of L ULA ×1 vector To represent the channel of stream i in the mth time slot of the nth frame, it is expressed as: in, is the Rayleigh-Rician channel from transmitter s to STAR-RIS in the mth time slot of the nth frame, is the transmission coefficient matrix, where and They represent the first RIS The amplitude and phase offset of each element, is the channel from STAR-RIS to UE in the mth time slot of the nth frame; The power received by the UE for stream i in the mth time slot of the nth frame is expressed as: in, is the transmission power of transmitter s for stream i in the mth time slot of the nth frame, G t (θ) and G r (θ) are the antenna gains of the transmitter and the receiver respectively. The signal-to-interference-plus-noise ratio (SINR) of stream i in the mth time slot of the nth frame is expressed as: in, is the total interference power received in the mth time slot of the nth frame, is the received interference power of stream b in the nth time slot, N0W is the noise power, W is the bandwidth, N0 is the noise power density, and the available data rate of stream i in the nth time slot of the nth frame is calculated by the Shannon channel capacity formula, which is expressed as: Where ε∈(0,1) is the efficiency of the transceiver design and the variable w s,r,i,n is a binary indicator indicating whether stream i is scheduled by transmitter s in frame n. If so, then w s,r,i,n = 1, and other transmitters cannot schedule this flow; otherwise, w s,r,i,n =0, variable a s,r,i,n,m is a binary indicator indicating whether stream i occupies the resources of the mth time slot of the nth frame. If so, a s,r,i,n,m =1, other flows cannot occupy this time slot; otherwise, a s,r,i,n,m = 0, similarly, a s,r,b,n,m is a binary variable indicating whether stream i receives stream i in the first time slot. If there is interference, then a s,r,b,n,m =1; otherwise, a s,r,b,n,m =0, the throughput of stream i in the nth frame is calculated as: Among them, T s is the scheduling time, MΔt is the transmission time; The train trajectory is approximated as a straight line, and the trajectory of MRs is located on the x-axis. ULA The coordinates of the antennas are Assume that the initial position of the train is point B (x0, y0, z0), and when MR1 arrives at point B, it is marked as the first frame. In the nth frame, the lth transmission of transmitter s ULA antennas to MR in the nth frame c The distance is expressed as: Among them, d MR It is MR1 ​​and MR C The distance between the transmitter s and the lth ULA The first antenna to the STAR-RIS in car c RIS The distance between the elements is expressed as: in It is the first in the STAR-RIS in the C carriage. RIS The coordinates of the elements are calculated, the distance between UE and STAR-RIS remains unchanged, and the number of frames that the train passes through BC is expressed as: A directional antenna is used between the airship and the base station. The directional antenna gain is expressed as: θ is the angle between the transmitter and the receiver, θ ml =2.6·θ -3dB is the main lobe width, θ -3dB is the half-power beamwidth, the maximum antenna gain G m and sidelobe gain G sl It is expressed as: G sl =-0.4111ln(θ -3dB )-10,579.(14).

3. The method according to claim 2, characterized in that The objective function of the transmission scheduling problem of maximizing the total number of completed flows is set by considering the movement of trains, signal-to-interference-to-noise ratio constraints, QoS requirements of traffic, channel changes, and the scheduling order of flows, including: If the QoS requirements of a flow are met, the flow is considered to be a completed flow and a binary variable Q is defined. s,r,i,n To indicate whether the transmitter s meets the QoS requirement of the receiver r for flow i in the nth frame, if so, Q s,r,i,n =1; otherwise, Q s,r,i,n =0, and the objective function of the transmission scheduling problem that maximizes the total number of completed flows is set as: Where N s is the number of transmitters, is the number of streams in the nth frame; Constraint 1: indicates that the BS-MR and airship-MR links require that the MR's received signal-to-interference-noise ratio must exceed a threshold Γ; Constraint 2: The condition for the successful scheduling of flow i in the BS-RIS-UE and airship-RIS-UE links is that the throughput provided by transmitter s is greater than the QoS requirement of flow i, where, is the QoS requirement of the nth frame flow i; Constraint 3: Indicates that flow i is scheduled by at most one transmitter in each frame; Constraint 4: It means that any transmitter can serve at most one flow in one time slot; Constraint 5: It means that the number of interference flows in each time slot is at most the total number of transmitters minus 1; Constraint 6: Indicates that the number of streams per frame must be less than the total number of user devices.

4. The method according to claim 3, characterized in that The method of solving the objective function by using the coalition game algorithm to obtain the total number of flows successfully scheduled in all coalitions includes: Define the coalition game for traffic scheduling in the nth frame: Definition 1: The transferable utility coalition game of traffic scheduling is defined as a tuple in is the set of players representing the stream in frame n, an alliance is a group of flows that are scheduled using the same resources in the nth frame. The utility of is defined as the number of flows successfully scheduled within the nth frame, expressed as; in, Represents a union The number of flows that are successfully completed within a time frame. In a single time frame, if the airship or base station provides services for users in a certain compartment, it must complete the flow scheduling of all users in the compartment before continuing to provide services to users in the next compartment. The flow set of all users in the cth compartment is defined as Definition 2: Union partitioning refers to the collection of all flows in the nth frame Divide into mutually exclusive unions. Each union corresponds to specific resources. Each flow can belong to at most one union. The division of the union must meet the following requirements: in, and Represents the flow sets scheduled by the airship and the two base stations respectively. All flows must be assigned to a certain union in, represents the union partition in the nth frame, the flow of the same compartment is regarded as a whole, for any set Its airship consortium and base station complex The preference order between them depends on whether the union is the utility provided; Definition 3: Let represents the flow set in compartment c For the consortium and The preference relationship is based on the marginal benefit brought by the number of flows successfully scheduled after joining the consortium. The preference relationship is expressed as: in, and Respectively represent that in the nth frame, the union and The number of successfully scheduled flows within; Definition 4: The switching operation describes the flow set in the carriage in the nth frame Redistribute among the federations to improve the overall utility of the system. Suppose the current federation is divided into: If the flow collection from Reassign to The updated union is divided into It is expressed as: In the nth frame, the total utility of the coalition is expressed as the sum of the utilities of all coalitions, that is: The goal of the coalition game is to maximize the total utility of the system That is, to maximize the number of successfully scheduled flows. The goal is expressed as: In a time frame, the flow set The maximum number of ways to allocate a union is (N s ) C The association relationship between the user equipment, the airship and the base station is determined by a user association algorithm, and the input of the user association algorithm includes: the total number of carriages C, the number of transmitters N s , initialized empty collection and Respectively represent the nth iter The set of user devices associated with the airship, BS1 and BS2 in the round iteration The output of the user association algorithm is the set of user devices associated with the airship, BS1 and BS2 in all iterations of each frame; The consortium game algorithm adjusts the allocation mode of the carriages through iterative optimization to maximize the service capacity of the airship, base stations BS1 and BS2, while meeting the QoS requirements of UEs. The input of the consortium game algorithm includes the consortium set and They represent the nth iter The set of carriages served by the airship, BS1 and BS2 in the round iteration. The output of the consortium game algorithm is the total number of flows successfully scheduled in all consortia after the final iteration is completed. s,r,i,n , during the iteration process, and represent the number of flows completed in the previous iteration and the current iteration, respectively, and Q A , and denote the number of flows completed by the airship, BS1 and BS2 respectively.

5. The method according to claim 4, characterized in that The method of solving the objective function by using the coalition game algorithm to obtain the total number of flows successfully scheduled in all coalitions includes: The threshold ∈ is introduced as the convergence criterion of the coalition game algorithm. When the following conditions are met, Iteration termination: At this point, Updated to It indicates that the system has reached a stable state; QoS Requirements Matrix represents the QoS requirements of all flows, where the (i,c)th element of the matrix represents the QoS requirements of the i-th flow in car c. At the beginning of each time frame, the algorithm initializes the number of completed flows and At the same time, the counters a1, a2, and a3 are initialized, corresponding to the unions respectively. and It is used to record the number of cars served, k1, k2, k3 represent the car numbers currently served by the airship, BS1 and BS2 respectively. In each TS, the algorithm checks whether each transmitter meets the QoS requirements of its assigned flow, and calculates the current number of completed flows after completing each TS, i.e. If the convergence condition is met Update And terminate the iteration of this frame; otherwise, if Update But the iteration is not terminated, and the optimization of the union allocation continues; After all frames are processed, the total completed flow Q s,r,i,n From all frames The sum gives:

6. The method according to claim 4, characterized in that The traffic scheduling algorithm prioritizes all successfully scheduled flows according to the QoS requirements of the flows, including: The traffic scheduling algorithm is used to sort the flows according to their QoS requirements and schedule the flows in descending order to meet the QoS requirements of the maximum number of flows. The input data of the traffic scheduling algorithm is the matrix represents the QoS requirements of the flows of UEs in each car, where the rows of the matrix correspond to individual flows and the columns correspond to cars. The output data of the traffic scheduling algorithm is the sorted matrix O sort , where each column is sorted in ascending order of QoS requirement value, so that the flows with higher QoS requirements are gradually moved to the bottom of the column. The sorting process traverses all carriages and sorts each column of the matrix independently. In each carriage, adjacent flows are compared and reordered in multiple iterations until all flows are arranged in ascending order according to QoS requirements. After the sorting is completed, the matrix O sort A sorted flow scheduling order is provided for each carriage.

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