Intelligent transmissive surface assisted high-speed millimeter wave communication method and system

Abstract

The application provides a kind of intelligent transmission surface assisted high-speed rail millimeter wave communication method and system, belongs to high-speed rail communication technical field.Utilize beamforming technology to obtain the antenna gain of multiple antennas deployed at the transmitting base station;According to the time-varying channel state information, combined with maximum ratio combining, successive convex approximation technique and branch and bound algorithm, the transmitting base station beamforming vector and intelligent transmission surface phase shift matrix are optimized frame by frame;Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization result, local power distribution is carried out within a fixed time interval.The application utilizes beamforming technology to obtain antenna gain, and improves system rate by adjusting the phase of each element of intelligent transmission surface;According to the time-varying channel state information, the transmitting end beamforming vector and IRS phase shift matrix are updated frame by frame;Based on the actual position prediction model and the frame-by-frame optimization result, local power distribution is carried out within a fixed time interval, which effectively improves the overall system throughput.

Description

Technical Field

[0001] This invention relates to the field of high-speed rail communication technology, specifically to a smart transmissive surface-assisted high-speed rail millimeter-wave communication method and system. Background Technology

[0002] High-speed railways not only require faster disaster detection systems to cope with challenging communication environments, but also need to meet the network experience needs of onboard users. This necessitates that high-speed railway communication systems support high-capacity and high-data-rate wireless transmission. Therefore, academia, industry, and standards organizations have shown increasing interest in high-speed rail millimeter-wave communication. In 2014, IEEE 802.15 established the High Rate Rail Communication (HRRC) standards group, validating the effectiveness of high-speed rail millimeter-wave systems through channel measurements, ray-tracing channel modeling, and field tests. 3GPP standardization efforts have advanced in areas such as network architecture, channel models and estimation, frame structure, Doppler compensation, and effective handover, releasing relevant standards for high-speed rail millimeter-wave communication. Research on key technologies for high-speed rail millimeter-wave communication is also in full swing. For example, a network architecture separating the control and user layers is being used. Traditional GSM bands are used to carry control command transmissions for passenger services and data transmissions for train safety operation-related services, while millimeter-wave communication is used to carry passenger user data transmission services. Subsequently, a method was proposed to deploy multiple antenna elements at the transceiver end and utilize beamforming technology to obtain antenna gain, thereby improving the system's capacity. Furthermore, a novel multiple access scheme based on single-carrier technology and orthogonal frequency division multiplexing (OFDM) technology was proposed, and a novel architecture was developed using beamforming and spatial multiplexing techniques to achieve spatial multiplexing gain, effectively alleviating the supply-demand contradiction between limited spectrum resources, low spectrum efficiency, and the demands of broadband mobile communication services.

[0003] Reconfigurable Intelligent Surfaces (RIS) enable intelligent control of the wireless propagation environment and have become a key candidate technology in sixth-generation mobile communication systems. A typical RIS is a plane composed of a large number of low-cost, nearly passive reflective / transmittive elements, each capable of independently controlling the amplitude and / or phase changes of the incident signal. By deploying RIS in a wireless network and intelligently coordinating its elements, the wireless channel between the transmitter and receiver can be flexibly controlled to achieve the desired signal propagation environment. This provides a new approach to fundamentally solve the problems of wireless channel fading and interference, and has the potential to achieve a leap in the throughput and reliability of wireless communication networks.

[0004] As a novel technology, research on RIS in academia is still in its early stages. Introducing RIS into the field of wireless communication brings both new opportunities and challenges. For example, when using RIS to maximize signal-to-noise ratio (SNR) or capacity in multi-user scenarios, the beam design of the system is complex due to resource contention and interference between different users. To address this issue, a beamforming scheme for multi-user systems is proposed, which maximizes system throughput and rate under limited transmitter power while meeting the minimum rate requirements of each user. This scheme simplifies the rate maximization problem by employing a zero-forcing transmission scheme, and then optimizes the base station transmit power and the passive RIS beam through a continuous upper bound minimization method, improving system throughput without requiring additional energy consumption.

[0005] Given the numerous advantages of RIS (Reflection-Based Logic), the academic community has begun exploring its use to improve the performance of high-speed rail mobile communication networks. For example, RIS-assisted schemes are employed to enhance the anti-interference capabilities of railway wireless communication systems requiring ultra-high reliability. Base station beamforming and RIS phase shifting have been optimized using deep reinforcement learning, enhancing the resilience of millimeter-wave high-speed rail mobile networks. A RIS-assisted UAV (Unmanned Aerial Vehicle) scheme has been proposed, providing stable communication services to high-speed rail users while demonstrating superior performance in obstacle avoidance and resource utilization. Most of these studies utilize RIS reflection modes to assist high-speed rail communication, improving system performance to some extent.

[0006] Considering the propagation characteristics of millimeter-wave signals and the dynamic nature of railway scenarios, maintaining high-quality onboard services presents numerous challenges. Specifically, millimeter waves, with their short wavelengths and severe path loss, suffer significant penetration loss when passing through solid materials including glass, metal, and trees, making millimeter-wave high-speed rail communication easily obstructed. Furthermore, during movement, if the line-of-sight link between passengers and base stations is blocked by carriages or other obstacles, the Quality of Service (QoS) degrades significantly. To address these issues, many researchers deploy Mobile Relay Stations (MRS) on high-speed trains to combat penetration loss or control cell handover. Installing multiple MRSs on the roof of the high-speed train allows user terminals to connect to the MRS based on their location, and the MRS then establishes a wireless link with the base station, effectively reducing the loss caused by wireless signals penetrating the carriages. While this solution enables multi-user onboard communication, it requires additional hardware costs and energy consumption, and introduces transmission latency. Intelligent Refracting Surfaces (IRS) are small, lightweight, and thin, allowing for flexible deployment at high-speed rail window locations as relays. They assist base stations and users inside the train carriages in establishing communication links without consuming energy. However, maximizing the total transmission rate for in-car users requires effectively controlling interference between multiple users. Simultaneously, it necessitates studying the regular updates of base station beamforming and IRS phase shifts to adapt to time-varying signal transmission between base stations and users. This presents a significant challenge. Summary of the Invention

[0007] The purpose of this invention is to provide a method for dynamically updating the beamforming vector and IRS phase adjustment matrix at the transmitting end, and to optimize power allocation, thereby maximizing the traversal capacity of the intelligent transmission surface-assisted high-speed rail millimeter-wave communication system, in order to solve at least one of the technical problems existing in the background art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] On one hand, the present invention provides a smart transmissive surface-assisted high-speed rail millimeter-wave communication method, comprising:

[0010] Beamforming technology is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station;

[0011] By combining beamforming vectors, an intelligent phase shift optimization model for transmission surfaces is constructed.

[0012] The interior point method is used to solve the phase shift optimization model of the intelligent transmission surface to obtain the optimal phase shift for each user cluster.

[0013] Based on time-varying channel state information, and combining maximum ratio combining (MRT), successive convex approximation techniques and branch-and-bound algorithms, the beamforming vector of the transmitting base station and the phase shift matrix of the intelligent transmission surface are optimized frame by frame.

[0014] Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization results, local power allocation is performed within a fixed time interval.

[0015] Optionally, the distance from the base station (BS) to the track is denoted as D0. The position of the train in frame k is approximately the midpoint of the corresponding line segment. Then, the distance D between the BS and the smart transmission surface in frame k is... k The calculation formula is:

[0016]

[0017] Where D f It represents the distance traveled in a single frame.

[0018] Optionally, the base station is equipped with L antennas, and the intelligent transmitting surface consists of M transmitting elements; during movement, it serves a total of I users, who are evenly distributed into K clusters according to their access time, with N users in each cluster, i.e., I = K * N; the total time t is discretized into K Time Division Multiple Access (TDMA) frames, each with a duration of τ, i.e., t = K * τ; different frames serve users in different clusters, and N users in the same cluster share frame resources; the total transmit power within time t is fixed at P. sum The phase of each transmission element in the IRS changes from having 2 e Selecting from a set of elements, i.e., phase shift Here, 'e' represents the number of quantization bits; note that θ = e. jψ The transmit power over K TDMA frames is denoted as vector p = [P1, ..., P2]. K ] T ;

[0019] The goal is to maximize the system's average throughput over duration t by jointly optimizing base station-side beamforming, IRS phase shifting, and power allocation.

[0020]

[0021] Constraint 1:

[0022] Constraint 2:

[0023] Constraint 3:

[0024] Constraint 4:

[0025] Constraint 5:

[0026] Wherein, constraint 1 indicates that the total power of the system is P. sum This is also the upper limit of power allocated to each cluster; constraint 3 imposes an amplitude constraint on each beamforming vector, constraint 4 specifies the discrete phase set, that is, the phase shift of each IRS element is taken from this set, and constraint 5 indicates that the IRS element maintains unit amplitude.

[0027] Optionally, base station-side beamforming includes:

[0028] Assumption and set Given, then The objective function increases monotonically with the total signal-to-noise ratio of each cluster; optimization This is equivalent to maximizing the signal-to-noise ratio of each cluster, and the optimal beamforming vector can be obtained by solving the following K subproblems:

[0029]

[0030]

[0031] According to the MRT criterion, f is given k The optimal solution expression, i.e.

[0032]

[0033] Optional, intelligent transmission surface phase shift optimization includes:

[0034] Assumption And the set of beamforming vectors is known. The optimization problem of the IRS phase shift is then:

[0035]

[0036]

[0037]

[0038] Since different data frames are transmitted using TDMA mode, the problem can be broken down into the following K independent sub-problems:

[0039]

[0040]

[0041]

[0042] When solving for continuous phase, The unit modulus constraint relaxation in the equation is a convex constraint, and therefore has... It is Θ k The convex quadratic function, approximated using the first-order Qinle expansion, then the point in the r-th iteration... The following relationships hold true:

[0043]

[0044] The optimal phase shift for each cluster is obtained by using the interior-point method.

[0045] Optionally, the quantization interval is defined as... but The search space for M discrete phase shifts has 2 M Possible possibilities; a decision scheme based on a branch and bound algorithm was used, with input... A feasible solution for the discrete phase shift set It then calculates the corresponding throughput; starting from the first element, it calls a branch function to traverse all feasible discrete phase values ​​for all elements, ultimately finding the optimal solution.

[0046] Secondly, the present invention provides an intelligent transmissive surface-assisted high-speed rail millimeter-wave communication system, comprising:

[0047] The acquisition module is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station using beamforming technology;

[0048] A building module is used to combine beamforming vectors to construct an intelligent transmission surface phase shift optimization model;

[0049] The solution module is used to solve the phase shift optimization model of the intelligent transmission surface using the interior point method to obtain the optimal phase shift for each user cluster.

[0050] The optimization module is used to optimize the transmit base station beamforming vector and the intelligent transmission surface phase shift matrix frame by frame based on time-varying channel state information, combined with maximum ratio combining, successive convex approximation technology and branch-bound algorithm.

[0051] The allocation module is used to perform local power allocation within fixed time intervals based on the actual distance between the base station and the smart transmission surface and the frame-by-frame optimization results.

[0052] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the intelligent transmissive surface-assisted high-speed millimeter-wave communication method for high-speed rail as described above.

[0053] Fourthly, the present invention provides a computer program product, including a computer program that, when run on one or more processors, is used to implement the intelligent transmissive surface-assisted high-speed rail millimeter-wave communication method as described above.

[0054] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the intelligent transmissive surface-assisted high-speed millimeter-wave communication method as described above.

[0055] Terminology Explanation:

[0056] Smart Transmitting Surface: Smart transmitting surface is a groundbreaking new technology that integrates a large number of low-cost passive metamaterial units in a two-dimensional plane and uses artificially programmable software to reconfigure the radio wave propagation environment.

[0057] High-speed rail millimeter-wave communication system: The high-speed rail communication system, which operates in the millimeter-wave frequency band, can meet the needs of train-to-ground communication with large data volume and high transmission rate.

[0058] Hybrid Time Division Multiplexing and Non-Orthogonal Multiple Access: A hybrid multiple access technology that groups users into clusters, with users within a cluster using non-orthogonal multiple access and users between clusters using time division multiplexing, thereby effectively reducing interference in the communication system.

[0059] Beamforming: Concentrates antenna energy in a specific direction to increase antenna gain, thereby improving data transmission rate.

[0060] Phase shift optimization: By independently adjusting the phase of each element of the intelligent transmission surface through a controller, the propagation direction of the incident electromagnetic wave signal can be flexibly controlled, thereby improving the communication performance of the system.

[0061] The beneficial effects of this invention are as follows: Considering a high-speed rail millimeter-wave downlink multiple input multiple output (MIMO) communication system assisted by a smart transmission surface, multiple antennas are deployed at the transmitting base station, antenna gain is obtained using beamforming technology, and the system rate is improved by adjusting the phase of each element of the smart transmission surface; the transmitting beamforming vector and IRS phase shift matrix are updated frame by frame according to time-varying channel state information, and maximum ratio combining, successive convex approximation technology and branch and bound algorithm are used in the optimization process; combined with the characteristics of high-speed movement, based on the actual position prediction model and frame-by-frame optimization results, local power allocation is performed within a fixed time interval, which effectively improves the overall throughput of the system.

[0062] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Detailed Implementation

[0063] The embodiments of the present invention are described in detail below.

[0064] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0065] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0066] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated 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, and / or groups thereof.

[0067] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0068] Example 1

[0069] In this embodiment 1, a smart transmissive surface-assisted high-speed rail millimeter-wave communication system is first provided, including:

[0070] The acquisition module is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station using beamforming technology;

[0071] A building module is used to combine beamforming vectors to construct an intelligent transmission surface phase shift optimization model;

[0072] The solution module is used to solve the phase shift optimization model of the intelligent transmission surface using the interior point method to obtain the optimal phase shift for each user cluster.

[0073] The optimization module is used to optimize the transmit base station beamforming vector and the intelligent transmission surface phase shift matrix frame by frame based on time-varying channel state information, combined with maximum ratio combining, successive convex approximation technology and branch-bound algorithm.

[0074] The allocation module is used to perform local power allocation within fixed time intervals based on the actual distance between the base station and the smart transmission surface and the frame-by-frame optimization results.

[0075] In this embodiment 1, the above-described system is used to realize a smart transmissive surface-assisted high-speed rail millimeter-wave communication method, including:

[0076] Beamforming technology is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station;

[0077] By combining beamforming vectors, an intelligent phase shift optimization model for transmission surfaces is constructed.

[0078] The interior point method is used to solve the phase shift optimization model of the intelligent transmission surface to obtain the optimal phase shift for each user cluster.

[0079] Based on time-varying channel state information, and combining maximum ratio combining, successive convex approximation techniques and branch-bound algorithms, the beamforming vector of the transmitting base station and the phase shift matrix of the intelligent transmission surface are optimized frame by frame.

[0080] Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization results, local power allocation is performed within a fixed time interval.

[0081] The distance from the base station to the track is denoted as D0. The position of the train in frame k is approximately the midpoint of the corresponding line segment. Then, the distance D between BS and the smart transmission surface in frame k is... k The calculation formula is:

[0082]

[0083] Where D fIt represents the distance traveled in a single frame.

[0084] The base station is equipped with L antennas, and the intelligent transmitting surface consists of M transmitting elements. During movement, it serves I users, who are evenly distributed into K clusters based on their access time. Each cluster contains N users, i.e., I = K * N. The total time t is discretized into K TDMA frames, each with a duration of τ, i.e., t = K * τ. Different frames serve users in different clusters, and the N users in the same cluster share frame resources. The total transmit power within time t is fixed at P. sum The phase of each transmission element in the IRS is selected from a set of 2e elements, i.e., the phase shift. Here, 'e' represents the number of quantization bits; note that θ = e. jψ The transmit power over K TDMA frames is denoted as vector p = [P1, ..., P2]. K ] T ;

[0085] The goal is to maximize the system's average throughput over duration t by jointly optimizing base station-side beamforming, IRS phase shifting, and power allocation.

[0086]

[0087] Constraint 1:

[0088] Constraint 2:

[0089] Constraint 3:

[0090] Constraint 4:

[0091] Constraint 5:

[0092] Wherein, constraint 1 indicates that the total power of the system is P. sum This is also the upper limit of power allocated to each cluster; constraint 3 imposes an amplitude constraint on each beamforming vector, constraint 4 specifies the discrete phase set, that is, the phase shift of each IRS element is taken from this set, and constraint 5 indicates that the IRS element maintains unit amplitude.

[0093] Base station beamforming includes:

[0094] Assumption and set Given, then The objective function increases monotonically with the total signal-to-noise ratio of each cluster; optimization This is equivalent to maximizing the signal-to-noise ratio of each cluster, and the optimal beamforming vector can be obtained by solving the following K subproblems:

[0095]

[0096]

[0097] According to the MRT criterion, f is given k The optimal solution expression, i.e.

[0098]

[0099] Intelligent transmission surface phase shift optimization includes:

[0100] Assumption And the set of beamforming vectors is known. The optimization problem of the IRS phase shift is then:

[0101]

[0102]

[0103]

[0104] Since different data frames are transmitted using TDMA mode, the problem can be broken down into the following K independent sub-problems:

[0105]

[0106]

[0107]

[0108] When solving for continuous phase, The unit modulus constraint relaxation in the equation is a convex constraint, and therefore has... It is Θ k The convex quadratic function, approximated using the first-order Qinle expansion, then the point in the r-th iteration... The following relationships hold true:

[0109]

[0110] The optimal phase shift for each cluster is obtained by using the interior-point method.

[0111] Define the quantization interval as but The search space for M discrete phase shifts has 2 M Possible possibilities; a decision scheme based on a branch and bound algorithm was used, with input... A feasible solution for the discrete phase shift set It then calculates the corresponding throughput; starting from the first element, it calls a branch function to traverse all feasible discrete phase values ​​for all elements, ultimately finding the optimal solution. The alternating optimization method is used to solve the problem. First, fix the transmission power as follows: Initialize f 0 and ψ 0 And calculate the corresponding throughput q 0 Then begin alternating optimization. Note that in each loop, first fix ψ. s Calculated from the beamforming scheme at the base station. Then based on Calculate the discrete phase set. Finally, calculate the optimized system throughput.

[0112] Example 2

[0113] In this embodiment 2, the alternating optimization algorithm is used to update the IRS phase adjustment matrix and the transmitter beamforming vector frame by frame, and the gradient descent method is used to periodically adjust the transmission power, thereby maximizing the user throughput in the carriage without increasing power consumption.

[0114] A smart transmissive surface assists a millimeter-wave high-speed rail communication system, where a multi-antenna trackside base station provides services to multiple onboard users. Since millimeter-wave direct links are easily blocked during high-speed movement, smart transmissive surfaces are deployed on the train windows to assist communication from the ground to users inside the carriages. If the train's speed is v and the cell's coverage radius is R, then the train passes through the cell in time t = 2R / v.

[0115] To reduce interference between users and improve communication quality, hybrid time-division multiplexing and non-orthogonal multiple access are employed during mobility. Time is discretized into non-overlapping data frames, and each data frame has a very short duration. Therefore, it can be assumed that the vehicle-mounted user remains relatively stationary within a frame, and users within the frame simultaneously access the base station using non-orthogonal multiple access.

[0116] Since each frame is very short, it is reasonable to assume that the communication distance between the base station and the train remains constant within a frame and varies with the frame. Therefore, considering the above motion model, where the distance from the base station to the track is denoted as D0, and the train's position in frame k can be approximated as the midpoint of the corresponding line segment, the distance D between the BS and IRS in frame k is... k The calculation formula is:

[0117]

[0118] Where D f It represents the distance traveled in a single frame.

[0119] Consider a quasi-static fast fading channel where the Channel State Information (CSI) remains constant within a frame but is updated with frame changes. The channel gain from the BS to the IRS and from the IRS to the user is modeled as Ricean fading, considering both line-of-sight (LoS) and non-line-of-sight (NLoS) components. Here, the channel gain matrix of BS-IRS in frame k is expressed as:

[0120]

[0121] Where K f Represents Rice's K-factor. This represents the Loss component, while This represents the NLoS component. Specifically,

[0122]

[0123] Where α k (·,·) represents the angular response. and denoted by , h0 represents the horizontal angle of arrival and the elevation angle of arrival from the l-th BS antenna to the m-th IRS element, β1 represents the path loss at a distance of 1m, and β1 represents the path loss exponent.

[0124] In addition, the NLoS component matrix Each element of the follows a complex Gaussian distribution with a mean of 0 and a variance of 1.

[0125] Meanwhile, in frame k, the channel gain matrix from the IRS to the user is: With d k,i Let β represent the distance from IRS to user i, and β2 represent the path loss index within the carriage. and Let the horizontal and pitch departure angles of the m-th IRS element from user i be represented, then the LosS component... It can be represented as

[0126]

[0127] Each element of its NLoS component also follows a complex Gaussian distribution with a mean of 0 and a variance of 1.

[0128] Assuming we can obtain the ideal CSI from BS to IRS and from IRS to the user, and the transmitted signal, BS-end beamforming vector, and IRS phase shift matrix are expressed as s k，j , The signal received at user i can be represented as

[0129]

[0130] Where P k w represents the transmission power in frame k. i This represents additive white Gaussian noise at user i, with a mean of 0 and a variance of σ. 2 .

[0131] Assuming the user in frame k uses Non-Orthogonal Multiple Access (NOMA), then the Signal-to-Interference-Noise Ratio (SINR) at user i is:

[0132]

[0133] The total reachable data rate in frame k is:

[0134]

[0135] Before addressing the problem of maximizing the system's average throughput, let's review the key system configurations and parameters. Note that the BS is equipped with L antennas, and the IRS consists of M transmission elements. During operation, the system serves I users, who are evenly distributed into K clusters based on their access time. Each cluster contains N users, i.e., I = K * N. The total time t is discretized into K TDMA frames, each with a duration of τ, i.e., t = K * τ. Different frames serve users in different clusters, and the N users within the same cluster share frame resources. Furthermore, the total transmit power within time t is fixed at P. sum The phase of each transmission element in the IRS changes from having 2 e Selecting from a set of elements, i.e., phase shift Here, 'e' represents the number of quantization bits; note that θ = e. jψ Furthermore, the transmit power over K TDMA frames is denoted as a vector p = [P1, ..., P2]. K ] T .

[0136] The objective is to maximize the system's average throughput over duration t by jointly optimizing base station-side beamforming, IRS phase shifting, and power allocation. This problem is defined as follows:

[0137]

[0138] Where constraint 1 represents the total power of the system as P. sumThis is also the upper limit of power allocated to each cluster. Constraint 3 imposes an amplitude constraint on each beamforming vector, constraint 4 specifies the discrete phase set, i.e., the phase shift of each IRS element is taken from this set, and constraint 5 indicates that the IRS elements maintain unit amplitude.

[0139] Because the optimization variables in the objective function are coupled, and constraint 5 is non-convex, it is not possible to directly apply the solution to the problem. The solution is then performed. Therefore, this embodiment proposes a two-step optimization algorithm. The first step uses maximum ratio combining for beamforming design and optimizes the IRS phase shift, while the second step adjusts the power allocated to each frame based on the optimization results of the first step to maximize the system's average throughput.

[0140] The first step involves jointly optimizing beamforming and IRS phase shift. Here, the power allocated to each frame is fixed at the average power, i.e. Then the problem Simplified to

[0141]

[0142] In this approach, the phase shift of each transmission element is relaxed to a continuous variable with a value range of [0, 2π]. Due to the high complexity of jointly optimizing base station beamforming and IRS phase shift, further... The problem is decomposed into two sub-problems. Each problem optimizes only one set of variables while keeping the other set of variables unchanged. Then, an alternating algorithm is used to optimize the transmitter beamforming and IRS phase shift until convergence.

[0143] In this embodiment, beamforming at the base station includes:

[0144] Assumption and set Given, then The objective function increases monotonically with the total signal-to-noise ratio of each cluster. Therefore, optimization... This is equivalent to maximizing the signal-to-noise ratio of each cluster, and the optimal beamforming vector can be obtained by solving the following K subproblems.

[0145]

[0146] Therefore, f can be given according to the MRT criterion. k The optimal solution expression, i.e.

[0147]

[0148] In this embodiment, the IRS phase shift design includes the following:

[0149] Assumption And the set of beamforming vectors is known. The optimization problem of IRS phase shift then simplifies to:

[0150]

[0151] Since different data frames are transmitted using TDMA mode, this problem can be decomposed into the following K independent sub-problems:

[0152]

[0153] The solution to this problem is also divided into two steps: first, solve for the optimal continuous phase, and then solve for the optimal discrete phase.

[0154] When solving for continuous phase, The unit modulus constraint relaxation in the equation is a convex constraint, and therefore has... at the same time, It is Θ k A convex quadratic function can be approximated using a first-order Taylor expansion. Therefore, the point in the r-th iteration... The following relationships are established.

[0155]

[0156] therefore, This can be approximated as a convex problem, and the optimal phase shift for each cluster can be obtained using the interior-point method.

[0157] Continuous phase shifts are difficult to achieve in practice. In this embodiment, the optimal phase shift is further quantized into discrete values. The quantization interval is defined as... but Therefore, the search space for M discrete phase shifts has 2 M There are several possibilities. To select the optimal discrete phase shift set, this invention utilizes a decision scheme based on a branch and bound algorithm. Input A feasible solution for the discrete phase shift set Then calculate the corresponding throughput. Next, starting from the first element, call the branch function to traverse all feasible discrete phase values ​​for all elements, ultimately finding the optimal solution.

[0158] In this embodiment, an alternating optimization method is used to solve the problem. First, fix the transmission power as follows: Initialize f 0 and ψ 0 And calculate the corresponding throughput q 0 Then begin alternating optimization. Note that in each loop, first fix ψ. s Update the beamforming vector at the base station to obtain Then based on Optimize the discrete phase set. Finally, calculate the optimized system throughput. The following algorithm flowchart summarizes the specific execution steps:

[0159]

[0160] Power Allocation and System Throughput Maximization: Inter-frame power allocation is performed based on the optimization results of beamforming and IRS phase shift. The specific optimization problem is as follows:

[0161]

[0162] Where p l =[P ρ+1 , ..., P ρ+l ] T Let represent the power allocation vector within l frames, and ρ represent the number of clusters for which power allocation has been performed. Clearly, this is a convex optimization problem, solvable using the Lagrange multiplier method. The Lagrange function can be expressed as...

[0163]

[0164] Where λ=[λ ρ+1 ,...,λ ρ+l ] T , β=[β ρ+1 , ..., θ ρ+l ] T μ are all Lagrange multipliers.

[0165] According to the KKT conditions, solve for P. k The expression after the r-th iteration is

[0166]

[0167] To obtain the optimal value, the Lagrange multipliers are updated using gradient descent, hence we have

[0168]

[0169] in er indicates the step size.

[0170] The following algorithm flowchart summarizes the specific execution steps of the above power allocation:

[0171]

[0172] Example 3

[0173] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute a smart transmissive surface-assisted high-speed rail millimeter-wave communication method, the method including the following steps:

[0174] Beamforming technology is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station;

[0175] By combining beamforming vectors, an intelligent phase shift optimization model for transmission surfaces is constructed.

[0176] The interior point method is used to solve the phase shift optimization model of the intelligent transmission surface to obtain the optimal phase shift for each user cluster.

[0177] Based on time-varying channel state information, and combining maximum ratio combining, successive convex approximation techniques and branch-bound algorithms, the beamforming vector of the transmitting base station and the phase shift matrix of the intelligent transmission surface are optimized frame by frame.

[0178] Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization results, local power allocation is performed within a fixed time interval.

[0179] Example 4

[0180] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements a smart transmissive surface-assisted high-speed millimeter-wave communication method. The method includes the following steps:

[0181] Beamforming technology is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station;

[0182] By combining beamforming vectors, an intelligent phase shift optimization model for transmission surfaces is constructed.

[0183] The interior point method is used to solve the phase shift optimization model of the intelligent transmission surface to obtain the optimal phase shift for each user cluster.

[0184] Based on time-varying channel state information, and combining maximum ratio combining, successive convex approximation techniques and branch-bound algorithms, the beamforming vector of the transmitting base station and the phase shift matrix of the intelligent transmission surface are optimized frame by frame.

[0185] Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization results, local power allocation is performed within a fixed time interval.

[0186] Example 5

[0187] Embodiment 5 of the present invention provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute a smart transmission surface-assisted high-speed rail millimeter-wave communication method, the method comprising the following steps:

[0188] Beamforming technology is used to obtain the antenna gain of multiple antennas deployed at the transmitting base station;

[0189] By combining beamforming vectors, an intelligent phase shift optimization model for transmission surfaces is constructed.

[0190] The interior point method is used to solve the phase shift optimization model of the intelligent transmission surface to obtain the optimal phase shift for each user cluster.

[0191] Based on time-varying channel state information, and combining maximum ratio combining, successive convex approximation techniques and branch-bound algorithms, the beamforming vector of the transmitting base station and the phase shift matrix of the intelligent transmission surface are optimized frame by frame.

[0192] Based on the actual distance between the base station and the intelligent transmission surface and the frame-by-frame optimization results, local power allocation is performed within a fixed time interval.

[0193] In summary, the intelligent transmissive surface-assisted high-speed rail millimeter-wave communication method and system described in this invention considers an intelligent transmissive surface-assisted high-speed rail millimeter-wave downlink MIMO communication system. Multiple antennas are deployed at the transmitting base station, antenna gain is obtained using beamforming technology, and the system rate is improved by adjusting the phase of each element of the intelligent transmissive surface. The transmitting beamforming vector and IRS phase shift matrix are updated frame-by-frame based on time-varying channel state information. Maximum ratio combining, successive convex approximation techniques, and branch-and-bound algorithms are used in the optimization process. Considering the characteristics of high-speed movement, based on the actual position prediction model and frame-by-frame optimization results, local power allocation is performed within fixed time intervals, effectively improving the overall throughput of the system. Deploying an IRS on the train window to assist downlink communication in the millimeter-wave high-speed rail communication system effectively overcomes the link congestion problem and improves the vehicle-to-ground communication rate. Clustering technology and hybrid TDMA-NOMA technology are used to eliminate multi-user transmission interference. A low-complexity two-step scheme is proposed to maximize the throughput of the onboard communication system and improve the onboard service quality. In the first stage, beamforming and IRS phase shift at the transmitter are optimized alternately frame by frame; in the second stage, based on the optimization results of the first stage, local power allocation is performed using KKT conditions, which ultimately improves the overall throughput.

[0194] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0195] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent transmissive surface assisted high-iron millimeter wave communication, characterized in that, The method comprises: obtaining, by using a beamforming technology, antenna gains of multiple antennas deployed at a transmitting base station; constructing, in combination with a beamforming vector, an intelligent transmissive surface phase shift optimization model; solving the intelligent transmissive surface phase shift optimization model by using an interior point method to obtain optimal phase shifts for each user cluster; optimizing, frame by frame, a transmitting base station beamforming vector and an intelligent transmissive surface phase shift matrix in combination with maximum ratio combining, successive convex approximation technology and branch and bound algorithm according to time-varying channel state information; Based on the actual distance between the base station and the intelligent transmissive surface and the frame-by-frame optimization results, local power allocation is performed at fixed time intervals; wherein the base station is equipped with an antenna, and the intelligent transmissive surface is composed of transmissive elements; during movement, a total of users are served, which are evenly distributed into clusters according to access time, and each cluster has users, i.e. ; the total time is discretized into TDMA frames, and the duration of each frame is , i.e. ; different frames serve users in different clusters, and the users in the same cluster share frame resources; the total transmission power within the time is fixed at , and the phase of each transmissive element of the IRS is selected from a set with elements, i.e. phase shift , , where represents the number of quantization bits, and note that ; the transmission power on the TDMA frames is recorded as a vector ; The goal is to maximize the system average throughput within a time duration by jointly optimizing the base station end beamforming, IRS phase shift, and power allocation: ； ； ； ； ； ； where constraint 1 represents the total power of the system is which is also the upper bound of the power allocated to each cluster; constraint 3 imposes an amplitude constraint on each beamforming vector, constraint 4 specifies a discrete phase set, i.e., the phase shift of each IRS element is taken from the set, and constraint 5 represents that the IRS elements maintain unit amplitude; the base station end beamforming comprises: Assume and the set is known, then the objective function increases monotonically with the total signal-to-noise ratio of each cluster; optimizing is equivalent to maximizing the signal-to-noise ratio of each cluster, in which case the optimal beamforming vector can be obtained by solving the following sub-problems: ； The optimal solution expression is given according to the MRT criterion, i.e. ; and ; the intelligent transmissive surface phase shift optimization comprises: Assume and a set of beamforming vectors The optimization problem for the IRS phase shifts is then ； ； ； Since different data frames are transmitted in TDMA mode, the problem is decomposed into the following independent sub-problems: ； ； ； When solving for continuous phase, The unit modulus constraint relaxation in the equation is a convex constraint, and therefore has... ; yes A convex quadratic function, approximated using a first-order Taylor expansion, then at the th... Points in the next iteration The following relationships hold true: ； The optimal phase shift of each cluster is obtained by using the interior point method as .

2. The intelligent transmissive surface assisted high-iron millimeter wave communication method of claim 1, wherein, The distance from the base station to the track is denoted as The position of the train in the frame is approximated as the midpoint of the respective line segment, then the distance between the base station and the intelligent transmissive surface in the frame is calculated as wherein represents the distance advanced in a frame.

3. The intelligent transmissive surface assisted high-iron millimeter wave communication method of claim 1, wherein, The quantization interval is defined as , , then ; The search space of the discrete phase shifts has possibilities; a decision scheme based on a branch-and-bound algorithm is used, which takes as input and a feasible solution of the set of discrete phase shifts and computes the corresponding throughput; The branch function is called starting from the first element to traverse all feasible discrete phase values for all elements to finally find the optimal solution .

4. An intelligent transmissive surface assisted high-iron millimeter wave communication system, characterized by, The method comprises: an acquisition module configured to obtain, by using a beamforming technology, antenna gains of multiple antennas deployed at a transmitting base station; a construction module configured to construct, in combination with a beamforming vector, an intelligent transmissive surface phase shift optimization model; a solution module configured to solve the intelligent transmissive surface phase shift optimization model by using an interior point method to obtain optimal phase shifts for each user cluster; an optimization module configured to optimize, frame by frame, a transmitting base station beamforming vector and an intelligent transmissive surface phase shift matrix in combination with maximum ratio combining, successive convex approximation technology and branch and bound algorithm according to time-varying channel state information; The distribution module is used for local power distribution in a fixed time interval based on the actual distance between the base station and the intelligent transmissive surface and the frame-by-frame optimization result; wherein the base station is equipped with an antenna, and the intelligent transmissive surface is composed of transmissive elements; during movement, a total of users are served, which are evenly distributed into clusters according to access time, and each cluster has users, i.e. ; the total time is discretized into TDMA frames, and the duration of each frame is , i.e. ; different frames serve users in different clusters, and the users in the same cluster share frame resources; the total transmission power in the time is fixed as , and the phase of each transmissive element of the IRS is selected from a set with elements, i.e. phase shift , , wherein represents the number of quantization bits, and it is noted that ; the transmission power on the TDMA frames is recorded as a vector ; The goal is to maximize the system average throughput within a time duration by jointly optimizing the base station end beamforming, IRS phase shift, and power allocation: ； ； ； ； ； ； where constraint 1 represents the total power of the system is which is also the upper bound of the power allocated to each cluster; constraint 3 imposes an amplitude constraint on each beamforming vector, constraint 4 specifies a discrete phase set, i.e., the phase shift of each IRS element is taken from this set, and constraint 5 represents that the IRS elements maintain unit amplitude; the base station end beamforming comprises: Assume and the set is known, then The objective function of is equivalent to maximizing the signal-to-noise ratio of each cluster, in which case the optimal beamforming vector can be obtained by solving the following sub-problems: ； According to the MRT criteria, The optimal solution expression, i.e. ; the intelligent transmissive surface phase shift optimization comprises: Assume and given a set of beamforming vectors then the optimization problem for the IRS phase shifts is ； ； ； Since different data frames are transmitted in TDMA mode, the problem is decomposed into the following independent sub-problems: ； ； ； Solving the unit modulus constraint in (1) as a convex constraint, we have ; ; is a convex quadratic function of , using a first order Taylor expansion for approximation, the following relation holds at the point in the th iteration: ； The optimal phase shift of each cluster is obtained by using the interior point method as .

5. A non-transitory computer-readable storage medium, comprising: The non-transitory computer readable storage medium is configured to store computer instructions, which, when executed by a processor, implement the high-speed rail millimeter wave communication method assisted by an intelligent transmissive surface according to any one of claims 1-3.

6. A computer program product, characterised in that, The computer program, when running on one or more processors, is configured to implement the high-speed rail millimeter wave communication method assisted by an intelligent transmissive surface according to any one of claims 1-3.

7. An electronic device, comprising: The electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected with the memory, and the computer program is stored in the memory; when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions for implementing the high-speed rail millimeter wave communication method assisted by an intelligent transmissive surface according to any one of claims 1-3.

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