An energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways

Abstract

This invention provides an energy efficiency optimization method for ultra-reliable low-latency communication (URLC) on high-speed railways. The method includes: establishing a vehicle-to-ground URLLC system; constructing a system power minimization problem under the QoS requirements of URLLC; decomposing the system power minimization problem into a heuristic optimization problem of user terminal bandwidth and a heuristic optimization problem of user terminal power; and using a block coordinate descent algorithm to alternately optimize the bandwidth and power to obtain the energy efficiency of the URLLC system and the optimized transmit power and bandwidth for each user. This invention utilizes the MR (Modular Range) on the train roof to reduce transmission loss in high-speed rail communication, decomposing the optimization problem into two sub-problems, and using the block coordinate descent algorithm to optimize the bandwidth and power allocation for user terminals respectively, thereby maximizing the energy efficiency of RRH (Reliable Low-Latency Communication) and MR under URLLC conditions.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency optimization technology for high-speed rail communication, and in particular to an energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways. Background Technology

[0002] Currently, there is considerable research on energy efficiency optimization in high-speed rail communication. One approach, based on a millimeter-wave HSR (Hierarchical State Routing) communication system with multiple mobile relays (MRs) on the train roof, proposes a dynamic power control scheme for train-to-ground communication, minimizing energy consumption under constraints of data transmission and transmission power budget. Another approach, by embedding power adjustment into the existing communication handover process, studies the impact of power adjustment on handover performance from the perspective of reducing the "uncertainty" in the handover process of high-speed rail communication systems. Results show that handover performance can be improved without increasing additional energy consumption through power adjustment. Further research investigates the application of Non-Orthogonal Multiple Access (NOMA) technology in wireless communication systems in high-speed rail scenarios, addressing the uplink energy efficiency problem of NOMA systems. Considering the service quality and maximum transmission power constraints of user terminals, the objective function is constructed as an optimization model to maximize energy efficiency. Finally, a joint transmission mode selection and power optimization scheme is studied to maximize the energy efficiency of the distributed antenna system in high-speed rail HSR communication. On the one hand, energy efficiency can be maximized by optimizing power to meet the data rate requirements of the train; on the other hand, transmission mode selection strategies can be used to further improve the maximum achievable energy efficiency of the system.

[0003] Ultra-Reliable Low-Latency Communications (URLLC) is one of the three major application scenarios of fifth-generation cellular network technology. URLLC makes 5G qualitatively different from previous generations of mobile communication technology. Its requirement for high reliability and low latency has become a potential driver for a large number of modern applications. URLLC plays an important role in realizing mission-critical applications, such as intelligent factories, vehicle-to-vehicle communication, and remote surgery. These applications have extremely high requirements for communication latency and reliability. The overall expectation of 3GPP for URLLC is: (1) the overall reliability requirement is 1-10^5 (i.e., 99.999%), and the radio latency of the user terminal plane is 1ms; (2) the uplink and downlink user terminal plane transmission latency is less than 0.5ms. In order to ensure strict end-to-end (E2E) latency, it is necessary to consider the transmission latency, queuing latency, encoding and processing latency of the uplink and downlink, as well as the latency in backhaul and routing. To meet the low latency requirements of URLLC, short packet communication becomes necessary, and the main way to reduce latency is to use short frame structures for data packets.

[0004] Resource allocation in URLLC communication is crucial for the rational and efficient use of communication resources and for improving energy efficiency. To address the ultra-high reliability and low latency requirements of emerging URLLC services, a hybrid resource allocation method based on non-orthogonal multiple access (NOMA) technology has been proposed. To improve frequency resource utilization, the system uses NOMA to share URLLC user terminal resources, including both shared and private resources. Another approach considers the design of a resource allocation algorithm for downlink MIMO (Multiple-Input Single-Output) ultra-reliable low-latency communication (URLLC) systems. Resource allocation is optimized to maximize the weighted total system throughput, which is constrained by the number of transmitted bits per URLLC user terminal, packet error probability, and quality of service (QoS) latency.

[0005] The application of URLLC on high-speed rail is an important scenario for the development of vertical services in 5G and a meaningful research direction. However, current research has not fully considered the allocation of URLLC resources in the high-speed rail scenario.

[0006] Most of the existing solutions for optimizing the energy efficiency of high-speed rail communication focus on the two-hop relay train-to-ground model. These solutions do not consider the application of URLLC in high-speed rail and cannot provide a reasonable and effective resource allocation scheme to meet the QoS requirements of different user terminals in the high-speed rail communication system.

[0007] In high-speed rail communication scenarios, additional considerations must be made regarding Doppler shift caused by high-speed movement and fading factors in millimeter-wave vehicle-to-ground communication. This necessitates establishing and proposing a well-founded vehicle-to-ground communication model, utilizing a two-hop MR system model to reduce transmission loss. Furthermore, the varying traffic demands of different user terminals and mobile devices on high-speed trains must be taken into account. Therefore, resource allocation for URLLC communication remains a research challenge in high-speed rail applications. Summary of the Invention

[0008] The embodiments of the present invention provide an energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways, so as to maximize the energy efficiency of RRH and MR under URLLC conditions.

[0009] To achieve the above objectives, the present invention adopts the following technical solution.

[0010] An energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways includes:

[0011] A vehicle-to-ground URLLC system is established. In this system, the RRH communicates with the MR installed on the roof of the high-speed railway car via millimeter waves, and the MR communicates with the user terminal in the car via URLLC.

[0012] The system power consumption minimization problem is constructed based on the received power of MR, system delay and URLLC communication rate to meet the QoS of URLLC.

[0013] The system power consumption minimization problem is decomposed into a heuristic optimization problem of user terminal bandwidth and a heuristic optimization problem of user terminal power.

[0014] The heuristic optimization problem of the user terminal bandwidth is solved by Algorithm 1, the heuristic optimization problem of the user terminal power is solved by Algorithm 2, and Algorithm 3 is used to alternately optimize Algorithm 1 and Algorithm 2 using the Block Coordinate Descent (BCD) algorithm to solve the bandwidth and power optimization problems alternately, thereby obtaining the energy efficiency of the URLLC system and the optimized transmit power and bandwidth of each user.

[0015] Preferably, in the establishment of the vehicle-to-ground URLLC system, the RRH (Railway Reception Hall) communicates with the MR (Mechanical Module) installed on the roof of the high-speed railway train car via millimeter waves, and the MR communicates with the user terminal in the car via URLLC, including:

[0016] A vehicle-to-ground URLLC system is established. In this system, multiple single-frequency networks RRHs transmit and receive signals at the same frequency within a cell. The RRH panels are oriented towards the adjacent railway, with the RRH beam always located on its left and the UE beam on its right. The RRH communicates with the MR located in the middle of the carriage roof via millimeter waves, and the MR communicates with the user terminal via URLLC.

[0017] Preferably, the method of minimizing system power consumption while satisfying the QoS of URLLC, based on the received power of MR, system delay, and URLLC communication rate, includes:

[0018] In a vehicle-to-ground URLLC system, the power of the MR consists of the RRH and the MR's transmit power:

[0019] (twenty two)

[0020] in It is the RRH power, and the MR power consists of the transmit power allocated to each user terminal, that is... , It is an independent and fixed circuit power component. It refers to the power amplifier efficiency. satisfy ,in It is the bandwidth for RRH and MR communication. It depends on the large-scale channel gain of path loss and shadow fading. It is the small-scale channel gain caused by multipath effects; It is the one-sided noise spectral density;

[0021] The model for maximizing system energy efficiency under URLLC QoS is expressed as follows:

[0022] (twenty three)

[0023] in It is the total communication error rate, energy efficiency. Defined as between two positioning points,

[0024] The model for maximizing system energy efficiency is equivalent to the model for minimizing power consumption, i.e.:

[0025] (twenty four)

[0026] The objective function of the power minimization problem model is expressed as:

[0027] (25)

[0028] St

[0029] (25a)

[0030] (25b)

[0031] (25c)

[0032] (25d)

[0033] This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit, It is the transmit power allocated to user k. It is the system's user transmit power threshold. It is the sum of the system's maximum user transmit power. This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth. It is the probability of actively dropping data packets. This is the maximum tolerable error rate required to ensure the overall reliability of URLLC;

[0034] Where (25a) represents the probability of ensuring queuing delay violation and the probability of DL decoding error, i.e., for and The constraint (25b) is to ensure the probability of active packet loss, that is, for The constraints, (25a) and (25b), are to ensure the high reliability and low latency of URLLC, and the maximum transmit power of MR is expressed as... The transmit power allocated to the user terminal should meet the following requirements. A maximum transmit power constraint was introduced for each user terminal. ,in (25d) is the sum of the bandwidth allocated to each user terminal, which should be less than the total bandwidth of MR, i.e. Furthermore, the bandwidth of each user terminal should be less than the bandwidth threshold. .

[0035] Preferably, the objective function of the heuristic optimization problem for user terminal bandwidth is expressed as:

[0036] (26)

[0037] St (26a)

[0038] (26b)

[0039] (26c)

[0040] This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit, This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth, It is the probability of actively dropping data packets. It is the maximum tolerable error rate required to ensure the overall reliability of URLLC.

[0041] Preferably, the objective function of the heuristic optimization problem for the user terminal's transmit power is expressed as:

[0042] (27)

[0043] St (27a)

[0044] (27b)

[0045] (27c).

[0046] Preferably, the heuristic optimization problem of solving the user terminal bandwidth using Algorithm 1 includes:

[0047] After fixing the transmit power allocated by MR to each user terminal, the optimization variable is the bandwidth allocated to the user terminal. The optimization problem (26) and (26a) are transformed into (28), that is:

[0048] (28)

[0049] in .

[0050] Let the right side of equation (26) equal to ,Right now:

[0051] (29)

[0052] Equation (29) has the following properties:

[0053] Property 1: along with Decrease, then increase;

[0054] Property 2: When , It is convex. middle;

[0055] The first step is to calculate the power allocation for K user terminals based on the fixed power allocation for each user terminal. and ,in It utilizes property 1 in Calculated from The inflection point;

[0056] The second step is to analyze user terminal k. If... ,when When, according to property 1, the function In the interval It will change with bandwidth As the value increases, the function value exhibits a phenomenon of first increasing and then decreasing, and... The function reaches its maximum value at point . The function value decreases monotonically over time.

[0057] According to interval Increase At that time, the millimeter-wave transmission power of RRH and MR communication will decrease. Under the condition of satisfying constraint (26a), find ,when At that time, according to properties 1 and 2, the function In the interval It will change with bandwidth As increases, the function value decreases, let So as to satisfy the constraints of (26a) and (26c);

[0058] Find the optimal bandwidth for user terminal k. Afterwards, use Replace the initial bandwidth of user terminal k and fix the set of user terminals. The bandwidth is adjusted to optimize the bandwidth of user terminal k+1. .

[0059] Preferably, the heuristic optimization problem of solving the user terminal power using Algorithm 2 includes:

[0060] After fixing the bandwidth allocated by the MR to each user terminal, the optimization variable is the MR's transmit power. In fixed In this case, constraints (27b) and (28) restrict... When fixed back, The adjustment affected the results of the optimization problem (27). , The bandwidth allocated to the user terminal by MR is determined;

[0061] Fixed user terminal set The initial power value, for user terminal k, according to the interval Gradually decrease And calculate whether constraints (27b) and (28) are satisfied, in order to find The minimum value is found when the optimal transmit power of user terminal k is determined. Afterwards, use Replace the initial transmit power of user terminal k and a fixed set of user terminals The transmit power is adjusted to optimize the bandwidth of user terminal k+1. .

[0062] Preferably, the step of alternately optimizing Algorithm 1 and Algorithm 2 using Algorithm 3 with the BCD algorithm to alternately solve the bandwidth and power optimization problems, thereby obtaining the energy efficiency of the URLLC system and the optimized transmit power and bandwidth for each user, includes:

[0063] 1 Given the initial power of K user terminals.

[0064] 2 Repeat loop

[0065] 3. At fixed user terminal power Based on this, Algorithm 1 is used to update the bandwidth of the user terminal. ;

[0066] 4. In fixed user terminal bandwidth Based on this, Algorithm 2 is used to update the power of the user terminal. ;

[0067] 5 if then

[0068] 6. Algorithm convergence, output , ,

[0069] 7 else

[0070] 8 , back to the second line

[0071] 9. End if (the current condition is met)

[0072] As can be seen from the technical solutions provided by the embodiments of the present invention above, the embodiments of the present invention establish a model for vehicle-to-ground URLLC communication using MR in a high-speed rail scenario, utilizing the MR on the top of the train to reduce transmission loss in high-speed rail communication; considering how MR allocates bandwidth and power to each user terminal with different network requirements under URLLC communication conditions. For the NP-hard optimization problem, the present invention decomposes the optimization problem into two sub-problems, uses the block coordinate descent method (BCD) to optimize the allocation of bandwidth and power to user terminals respectively, and finally iterates alternately to obtain a global solution, thereby maximizing the energy efficiency of RRH and MR under URLLC conditions.

[0073] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0074] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0075] Figure 1 A schematic diagram of a high-speed rail scenario for RRH (Remote Radio Head) and MR communication provided in an embodiment of the present invention;

[0076] Figure 2 A flowchart illustrating an energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways, provided in an embodiment of the present invention.

[0077] Figure 3 A schematic diagram of a scenario model of a Single Frequency Network (SFN) provided in an embodiment of the present invention;

[0078] Figure 4 A method provided by an embodiment of the present invention and Definition diagram;

[0079] Figure 5 A system delay provided in an embodiment of the present invention A schematic diagram consisting of UL transmission delay, RRH-MR millimeter-wave delay, queuing delay, and DL transmission delay. Detailed Implementation

[0080] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0081] 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, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0082] 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. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0083] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0084] This invention, while satisfying the QoS of URLLC, decomposes the system energy efficiency maximization problem into two sub-problems: heuristic optimization of user terminal bandwidth and heuristic optimization of user terminal transmit power, based on an alternating iterative optimization algorithm and using Block Coordinate Descent (BCD) optimization. By updating the user terminal bandwidth and transmit power through BCD until the algorithm converges, the system effectively allocates the allocated bandwidth and transmit power of MR to the user terminal, thereby improving system energy efficiency.

[0085] A schematic diagram of a high-speed rail scenario for RRH and MR communication provided in this embodiment of the invention is shown below. Figure 1As shown in the left half of the image, the RRH located in the upper left corner communicates with the MR in the middle of the carriage roof via millimeter waves. The right half of the image shows the interior of the carriage, where the MR communicates with the user terminal via URLLC. The MR, acting as an intermediary between the RRH and the user terminal, effectively overcomes the severe penetration loss in high-speed train carriages.

[0086] The processing flowchart of an energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways provided in this embodiment of the invention is as follows: Figure 2 As shown, the processing steps include the following;

[0087] Step S10: Establish a vehicle-to-ground URLLC system. In this system, the RRH communicates with the MR installed on the roof of the high-speed railway train car via millimeter waves, and the MR communicates with the user terminal in the car via URLLC.

[0088] Step S20: Construct a system power minimization problem that satisfies the QoS of URLLC based on the received power of MR, system delay and URLLC communication rate.

[0089] Step S30: Decompose the system power consumption minimization problem into a heuristic optimization problem of user terminal bandwidth and a heuristic optimization problem of user terminal power.

[0090] Step S40: Solve the heuristic optimization problem of the user terminal bandwidth using Algorithm 1, solve the heuristic optimization problem of the user terminal power using Algorithm 2, and use Algorithm 3 to alternately optimize Algorithm 1 and Algorithm 2 using the BCD algorithm to alternately solve the bandwidth and power optimization problems, thereby obtaining the energy efficiency of the URLLC system and the optimized transmit power and bandwidth of each user.

[0091] Since the system power minimization problem in the vehicle-to-ground model proposed in this invention is NP-hard, the method of this invention first decomposes the proposed system power minimization problem into two sub-problems, and then uses the BCD algorithm to alternately solve the bandwidth and power optimization problems, namely the heuristic optimization of user bandwidth and the heuristic optimization of transmit power.

[0092] In Algorithm 1, the first step is to solve the user bandwidth optimization problem proposed in subproblem (1). We first need to fix the transmit power that MR randomly allocates to K users during system initialization. Based on this, the bandwidth allocated to K users by MR is optimized sequentially. In the initialization of the heuristic optimization algorithm for user bandwidth, we fix the transmit power of each user. In addition, the initial random bandwidth for K users also needs to be provided. and utilizing property 1 in Inflection point calculated in the middle The critical bandwidth for which constraint (27a) holds This will serve as the input to Algorithm 1. After heuristic optimization based on user bandwidth, Algorithm 1 will output the optimized system energy efficiency when it converges. and bandwidth for K users .

[0093] Algorithm 2 will address the optimization problem of MR's transmit power allocation to users, as proposed in subproblem (2). The first step is system initialization, initially fixing the bandwidth randomly allocated to K users by MR. Based on this, the transmit power allocated to K users by MR is optimized sequentially. In the initialization of the heuristic optimization algorithm for user transmit power, besides fixing the user bandwidth... In addition, the initial random transmit power for K users also needs to be provided. This will serve as the input to Algorithm 2. After heuristic optimization based on the user's transmit power, Algorithm 2 will output the optimized system energy efficiency when it converges. and bandwidth for K users .

[0094] Algorithm 3 provides the random transmit power of K users. and random initial bandwidth As input, the outputs of Algorithm 1 and Algorithm 2, which are alternately optimized in Algorithm 3, will be used as inputs for Algorithm 2 and Algorithm 1 respectively, achieving the purpose of alternating updates. When the algorithm converges, the output system energy efficiency is... Optimized transmit power for K users and bandwidth .

[0095] A schematic diagram of a scenario model for a Single Frequency Network (SFN) provided in this embodiment of the invention is shown below. Figure 3 As shown, SFN is a deployment method for communication equipment, in which multiple RRHs transmit and receive signals at the same frequency within a single cell. It has advantages such as low interference between adjacent cells, large cell coverage, and low signal switching frequency, making it suitable for high-speed rail scenarios. Figure 3 As shown, this is the only model considered by 3GPP for HSR 30GHz deployment. Figure 3 In the model, v is the speed at which the high-speed train moves. It is the distance between adjacent BBUs. It is the vertical distance between RRH and the rail. It is the height of the MR (Mortar) on the top of the train. This refers to the RRH height. The three RRHs are connected to the same baseband unit via optical fiber. In this model, we assume the RRH panel points towards the adjacent railway, meaning the RRH beam is always on its left and the UE beam is on its right.

[0096] according to Figure 3 The unidirectional HSR SFN scenario model and the link budget calculation formula can be used to determine the received power of MR. Represented as:

[0097] (1)

[0098] in, That is the transmit power of RRH. This is the path loss related to the distance between RRH and MR. Beamforming gain in millimeter-wave communication is determined by the antenna element gain. and composite array radiation gain Composition. Antenna element gain in equation (1) Including the gain of the TX antenna (i.e., the RRH antenna) and RX antenna (i.e., MR) antenna gain The composite array radiation gain in equation (1) Composite array radiation array including RRH and MR , The gain of these four antennas , , , The value depends on the downtilt angle of the connection vectors of the MR and RRH antenna elements. and azimuth .

[0099] The calculation in equation (1) mainly includes three parts: path loss, antenna element gain, and so on. and composite array radiation gain Since the high-speed rail scenario is mostly open terrain, we mainly consider unobstructed line-of-sight transmission here. The path loss calculation method is shown in equations (2), (3), and (4):

[0100] (2)

[0101] (3)

[0102] (4)

[0103] Among them (2)(3)(4) and Definition as follows Figure 4 As shown, Figure 4 Left half of the image This is the RRH height, right half of the image. It is the height of the MR (Mortar) on the top of the train. It is the horizontal distance between RRH and MR. It is the straight-line distance between the top of RRH and the top of MR. In the formula... This represents the average height of buildings in the communication propagation environment, i.e. In equations (2) and (4) It is the distance between breakpoints, and its mathematical expression is:

[0104] (5)

[0105] in, The center frequency is in Hz, and c is the speed of light. In equation (5) This is the center frequency normalized to 1 GHz. In this invention, the antenna element gain... The calculation can be expressed as equations (6), (7) and (8).

[0106] (6)

[0107] (7)

[0108] (8)

[0109] in, It is a vertical 3dB beamwidth. It is a horizontal 3dB beamwidth. It is the sidelobe attenuation in the vertical direction. This represents the maximum attenuation. In equation (6) In the sum (7) These represent the vertical and horizontal cutoff values ​​of the radiated power mode, respectively. In equations (6), (7), and (8)... and They are , The result obtained by transforming from the local coordinate system (LCS) to the spherical coordinate system (GCS).

[0110] In equation (8): It is the gain attenuation of an antenna element. It is the maximum directional gain of an antenna element.

[0111] Composite array radiation gain It can be represented as:

[0112] (9)

[0113] in, It refers to the number of antenna elements on the panel. It is the number of antenna elements with the same polarization in each column. In equation (9) and These are the weight and superposition vectors, respectively, expressed as equation (10) and equation (11).

[0114] (10)

[0115] (11)

[0116] in It is an imaginary number. For wavelength, The distance between antenna elements in the vertical direction is generally set as... In equation (11) and Beam direction, including the beam direction of RRH. and MR beam direction and .

[0117] The focus of this invention is to find a bandwidth and power allocation scheme with maximizing energy efficiency as the optimization objective for user terminals with different network requirements, while satisfying the latency and reliability constraints of URLLC. Since there has been considerable research on the beam alignment problem in high-speed railways, we assume that the beams between RRH and MR are always aligned, and therefore do not use this assumption in this invention. and As a research variable to simplify the research model of this invention, we assume that an MR is located in the middle of the top of the train, establish an RRH to communicate with the MR, and analyze the bandwidth and power resource allocation schemes of different user terminals.

[0118] URLLC communication rate. The communication capacity described by Shannon's formula is widely used to express the rates of traditional communication services. However, in URLLC, due to the use of a short packet structure, the impact of decoding errors is more pronounced and cannot be ignored. The mathematical relationship between achievable communication rate, transmission delay, and decoding error probability in short packet communication differs from that in traditional long packet communication. Furthermore, the MR and the user terminal inside the carriage are relatively stationary; therefore, even during high-speed train operation, it is an interference-free single-antenna system affected by quasi-static flat fading channels. Thus, the maximum achievable rate within the short packet length range of user terminal k can be accurately approximated as:

[0119] (12)

[0120] in and It is MR to user terminal The bandwidth and power allocated during DL transmission. It depends on the path loss and the large-scale channel gain of the shadow. It is the small-scale channel gain caused by multipath effects; It is the number of bytes in a data packet; It is the one-sided noise spectral density. It is the time available for DL ​​transmission within a frame. It is a user terminal The probability of transmission errors, It is the reciprocal of the Gaussian Q-function. It is channel dispersion.

[0121] (13)

[0122] When the signal-to-noise ratio at the receiver is higher than 5 dB, in equation (13) The approximate value is approximately 1, which is satisfied most of the time in URLLC communication. On the other hand, V < 1 in the case of low signal-to-noise ratio. However, if we substitute V = 1 into the URLLC rate expression (12), we obtain the lower bound of the achievable rate of URLLC. If this lower bound is applied to subsequent resource allocation studies, the reliability and latency requirements can be met.

[0123] Communication traffic model: Time is applied to the system model in units of frames, where This represents the duration of each frame. Assume user terminal k has... An app that connects to the network, i.e., an application that consumes data, and in a frame, with probability... The activation of each app is independent and identically distributed. Therefore, the data packet arrival process for each user terminal is modeled as an average arrival rate of Poisson process of packets / frames

[28] Assume the total number of apps on all user terminals. The set of conforms to a Gaussian distribution.

[0124] QoS (Quality of Service) requirements: Figure 5 This is a schematic diagram of system delay provided for an embodiment of the present invention. For example... Figure 5 As shown, system delay Composed of UL transmission delay, RRH-MR millimeter-wave delay, queuing delay, and DL transmission delay, it can be expressed as: Since the packet size is very small (e.g., 20 bytes), we assume that the UL and DL transmissions of a packet can be completed within a single frame with a given error probability without retransmission. For simplicity, we assume the millimeter-wave delay is... .

[0125] Therefore, to ensure system latency, queuing latency is denoted as...

[0126] (14)

[0127] If the packet queuing delay is greater than the delay limit If the packet is not found, it is discarded. The probability of queuing delay violation is expressed as... To meet the queuing delay requirements of limited transmission power, an active packet loss mechanism can be applied. The probability of actively dropping data packets is expressed as... Then, the above three probabilities should satisfy the condition. To ensure the overall reliability of URLLC, among which It is a user terminal The probability of transmission error (DL transmission error rate), and has been derived from it. Subtract the UL transmission error rate.

[0128] To ensure "queue delay and queue delay violation probability, i.e. and DL transmission error probability To meet URLLC requirements, we used the concept of effective bandwidth. If the probability of queuing delay violation is small in URLLC communication, then effective bandwidth can be used to analyze the queuing delay at the transmitter in a Poisson process. Since the queuing delay in URLLC is typically shorter than the channel coherence time, the service rate is constant. For a Poisson arrival process... The effective bandwidth can be expressed as:

[0129] (15)

[0130] In order to meet the requirements for ensuring queuing delays The constant packet service rate should not be lower than the effective bandwidth. That is:

[0131] (16)

[0132] Substituting (12) into (16), we get

[0133] (17)

[0134] The signal-to-noise ratio is defined as follows: .

[0135] To ensure the probability of active packet loss Meets URLLC requirements. It can be approximated as

[0136] (18)

[0137] in and The mathematical expression is as follows

[0138] (19)

[0139] (20)

[0140] in . It is approximately the probability of active packet loss. The upper limit, therefore As a measure of the probability of active packet loss QoS constraints. It can be represented as

[0141] (twenty one)

[0142] It refers to the number of antennas. It is the gain threshold that meets the delay requirements. It is approximately the probability of active packet loss. The upper limit, It is the probability of active packet loss. Approximation, This is the maximum achievable rate of short packet transmission for user k. and It refers to the bandwidth and power allocated when MR transmits data to the user via DL. It is the large-scale channel gain that depends on path loss and shadowing; it is the number of bytes in a data packet. It is the one-sided noise spectral density. It is the time available for DL ​​transmission within a frame. It is the probability of transmission errors by the user. It is the reciprocal of the Gaussian Q-function. It is channel dispersion. It is the process of Poisson's arrival. The effective bandwidth, It is the probability of actively dropping data packets. It is the maximum tolerable error rate required to ensure the overall reliability of URLLC.

[0143] Assuming the number of antennas There are enough to meet the requirements of URLLC. It is to ensure An approximate optimal combination of packet loss / error probabilities.

[0144] Energy efficiency model: The power of the URLLC high-speed rail communication model utilizing millimeter wave and MR consists of the transmit power of RRH and MR.

[0145] (twenty two)

[0146] in It is the RRH power, and the MR power consists of the transmit power allocated to each user terminal, that is... , It is an independent and fixed circuit power component. It is the power amplifier efficiency

[29] . satisfy ,in It is the bandwidth for RRH and MR communication. It depends on the large-scale channel gain of path loss and shadow fading. It is the small-scale channel gain caused by multipath effects; It is the one-sided noise spectral density.

[0147] Therefore, the energy efficiency model can be written as:

[0148] (twenty three)

[0149] in It is the total communication error rate, energy efficiency. Defined as the distance between two positioning points (i.e., the distance between them). (i.e., time) Inside (of which) The communication rates of all user terminals and their energy consumption ratios relative to BS and MR. To simplify the analysis, when studying resource allocation, the high-speed rail travel distance is considered to be every [missing information - likely a number]. The bandwidth and power of the user terminal are optimized once.

[0150] Optimization problem: This invention optimizes resource allocation to improve energy efficiency. While maximizing the QoS requirements, it is also necessary to meet them. This is because the numerator in (23), i.e., the communication rate of user terminal k, is based on... The activation process of each app is determined by a Poisson process, which is almost unaffected by resource allocation. Furthermore, due to the high reliability and low latency requirements of URLLC, Therefore, "maximizing energy efficiency" is equivalent to "minimizing power consumption," that is:

[0151] (twenty four)

[0152] Therefore, the optimization problem can be expressed as:

[0153] (25)

[0154] St

[0155] (25a)

[0156] (25b)

[0157] (25c)

[0158] (25d)

[0159] This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit. It is the transmit power allocated to user k. It is the system's user transmit power threshold. It is the sum of the system's maximum user transmit power. This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth. It is the probability of actively dropping data packets. It is the maximum tolerable error rate required to ensure the overall reliability of URLLC.

[0160] Where (25a) represents the probability of ensuring queuing delay violation and the probability of DL decoding error, i.e., for and The constraint (25b) is to ensure the probability of active packet loss, that is, for The constraints. In summary, (25a) and (25b) are to ensure the high reliability and low latency of URLLC. We express the maximum transmit power of MR as... Then, the transmit power allocated to the user terminal should meet the following requirements. Under the constraint (25c), the power allocated to each user terminal depends on the channels of other user terminals. Therefore, it is difficult to obtain the average transmit power of each user terminal in a closed-loop configuration. To facilitate optimization, we introduce a maximum transmit power constraint for each user terminal. ,in (25d) is the sum of the bandwidth allocated to each user terminal, which should be less than the total bandwidth of MR, i.e. Furthermore, the bandwidth of each user terminal should be less than the bandwidth threshold. .

[0161] In order to effectively solve the proposed system power consumption minimization problem, we decompose the optimization problem (25) into two sub-problems: heuristic optimization of user terminal bandwidth and heuristic optimization of user terminal power.

[0162] Problem 1: Heuristic Optimization of User Terminal Bandwidth: With the transmit power allocated to user terminals by MR remaining constant, we use a heuristic algorithm to study the bandwidth allocated to each user terminal. Our optimization problem is to find a scheme for how MR allocates bandwidth to K user terminals, with the optimization objective being to minimize the system's power consumption. Therefore, when variable P is fixed, the optimization problem (25) can be written as...

[0163] (26)

[0164] St (26a)

[0165] (26b)

[0166] (26c)

[0167] This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit. This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth. It is the probability of actively dropping data packets. It is the maximum tolerable error rate required to ensure the overall reliability of URLLC.

[0168] Question 2: Heuristic Optimization of User Terminal Transmit Power: With the bandwidth allocated to user terminals by the MR remaining constant, a heuristic algorithm is used to study the amount of power transmitted by the MR to each user terminal. Our optimization problem is to find a scheme for how the MR allocates transmit power to K user terminals, with the optimization objective being to minimize the system's power consumption. Therefore, when the variable W is fixed, the optimization problem (25) can be written as...

[0169] (27)

[0170] St (27a)

[0171] (27b)

[0172] (27c)

[0173] Optimization Algorithm

[0174] A. Heuristic algorithm for user terminal bandwidth

[0175] After fixing the transmit power allocated by MR to each user terminal, the only optimization variable is the bandwidth allocated to the user terminal. The optimization problem is shown in (26). (26a) can be transformed mathematically to obtain (28), that is...

[0176] (28)

[0177] in .

[0178] Analysis (26) reveals that fixing In the case of (26a) It has a binding effect, while (26b) does not have a direct restriction. We let the right side of equation (26) equal to ,Right now

[0179] (29)

[0180] Equation (29) has the following properties:

[0181] Property 1: along with First, strictly reduce, then strictly increase.

[0182] Property 2. When , It is strictly convex. middle.

[0183] The first step is to calculate the power allocation for K user terminals based on the fixed power allocation for each user terminal. and ,in It utilizes property 1 in Calculated from The turning point.

[0184] The second step is to analyze user terminal k. If... ,when When, according to property 1, the function In the interval It will change with bandwidth As the value increases, the function value exhibits a phenomenon of first increasing and then decreasing, and... The function reaches its maximum value at that point. The time function value is monotonically decreasing. According to (12) and MR-RRH communication, when we follow the interval Increase At that time, the millimeter-wave transmission power of RRH and MR communication will decrease, and under the condition of satisfying constraint (26a), find .when At that time, according to properties 1 and 2, the function In the interval It will change with bandwidth As increases, the function value decreases, therefore we let To satisfy the constraints (26a) and (26c), the optimal bandwidth for user terminal k is found. After that, we used Replace the initial bandwidth of user terminal k and fix the set of user terminals. The bandwidth is adjusted to optimize the bandwidth of user terminal k+1. Algorithm 1 summarizes the steps of the heuristic algorithm for user terminal bandwidth.

[0185] Algorithm 1: Heuristic Algorithm for User Terminals

[0186] 1. Given the initial power of K user terminals. And calculate the initial bandwidth value of user terminal k. and (29) bandwidth inflection point value , , .

[0187] 2. repeat

[0188] 3. For k in K do

[0189] 4. Fixed user terminal set bandwidth value

[0190] 5. If

[0191] 6. If then

[0192] 7. For

[0193] 8. Under the condition of satisfying constraint (26a), calculate

[0194] 9. End for

[0195] 10. Else

[0196] 11. For

[0197] 12. Under the condition of satisfying constraint (26a), calculate

[0198] 13. End for

[0199] 14. End if

[0200] 15. End if

[0201] 16. If

[0202] 17.

[0203] 18. End if

[0204] 19. Find the way The bandwidth value of the smallest user terminal k , and make

[0205] 20. Endfor

[0206] twenty one, Return to the second line

[0207] 22. Until

[0208] B. Heuristic Algorithm for User Terminal Transmit Power

[0209] After fixing the bandwidth allocated by the MR to each user terminal, the only optimization variable is the MR's transmit power. The optimization problem is shown in (27). Analysis reveals that, under a fixed... In this case, constraints (27b) and (28) restrict... When fixed back, The adjustment directly affected the results of the optimization problem (27). But it had no effect. ,because The bandwidth allocated to the user terminal by the MR (Mean Access Registry) is determined. Therefore, the heuristic algorithm steps for user terminal power are as follows:

[0210] The first step is to allocate bandwidth to the optimized user terminals. Fixed, and fixed user terminal set The initial power value. The second step, for user terminal k, is to calculate the power value according to the interval... Gradually decrease And calculate whether constraints (27b) and (28) are satisfied, in order to find The minimum value. Finding the optimal transmit power for user terminal k. After that, we used Replace the initial transmit power of user terminal k and a fixed set of user terminals The transmit power is adjusted to optimize the bandwidth of user terminal k+1. Algorithm 2 summarizes the steps of the heuristic algorithm for user terminal power.

[0211] Algorithm 2: Heuristic Algorithm for User Terminal Transmit Power

[0212] 1. Give the optimized user terminal bandwidth allocation obtained by Algorithm 1. , , .

[0213] 2 repeat

[0214] 3 For k in K do

[0215] 4 For

[0216] 5. Under the condition of satisfying constraints (27b) and (28), calculate

[0217] 6 End for

[0218] 7. Find the The minimum transmit power value of user terminal k and make

[0219] 8 End for

[0220] 9 Return to the second line

[0221] 10 Until

[0222] C. Resource allocation optimization algorithm for user terminal bandwidth and transmit power based on BCD heuristic algorithm

[0223] Next, we update the user terminal bandwidth and transmit power using BCD until the algorithm converges, as shown in Algorithm 3.

[0224] Algorithm 3: Resource Allocation Optimization of User Terminal Bandwidth and Transmit Power Based on Heuristic Algorithm by BCD

[0225] 1 Given the initial power of K user terminals.

[0226] 2 Repeat

[0227] 3. At fixed user terminal power Based on this, Algorithm 1 is used to update the bandwidth of the user terminal. ;

[0228] 4. In fixed user terminal bandwidth Based on this, Algorithm 2 is used to update the power of the user terminal. ;

[0229] 5 if then

[0230] 6 output , ,

[0231] 7 else

[0232] 8 , back to the second line

[0233] 9 end if

[0234] In summary, this invention aims to enhance the reliability of data transmission in vehicle-to-ground communication systems, reduce latency for high-speed rail user terminals, and promote the application and development of URLLC. It proposes a vehicle-to-ground URLLC model equipped with relay equipment, and, under the QoS constraints of URLLC, addresses the problem of maximizing system energy efficiency by alternately optimizing user terminal bandwidth and transmission power. Furthermore, it rationally and effectively allocates the bandwidth and transmission power of the MR (Mean Access Provider) to user terminals based on the configuration of different network applications, thereby fully utilizing the resources of the high-speed railway communication system.

[0235] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0236] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0237] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0238] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An energy efficiency optimization method for ultra-reliable low-latency communication in high-speed railways, characterized in that, include: A vehicle-to-ground URLLC system is established. In this system, the RRH communicates with the MR installed on the roof of the high-speed railway car via millimeter waves, and the MR communicates with the user terminal in the car via URLLC. The system power consumption minimization problem is constructed based on the received power of MR, system delay and URLLC communication rate to meet the QoS of URLLC. The system power consumption minimization problem is decomposed into a heuristic optimization problem of user terminal bandwidth and a heuristic optimization problem of user terminal power. The heuristic optimization problem of the user terminal bandwidth is solved by Algorithm 1, and the heuristic optimization problem of the user terminal power is solved by Algorithm 2. Algorithms 1 and 2 are alternately optimized using the Block Coordinate Descent (BCD) algorithm to obtain the energy efficiency of the URLLC system, as well as the optimized transmit power and bandwidth of each user. The model for maximizing system energy efficiency under URLLC QoS is expressed as follows: （23） in It is the total communication error rate, energy efficiency. Defined as being between two positioning points; The model for maximizing system energy efficiency is equivalent to the model for minimizing power consumption, i.e.: （24） The objective function of the power minimization problem model is expressed as: (25) St (25a) (25b) (25c) (25d) This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit, It is the transmit power allocated to user k. It is the system's user transmit power threshold. It is the sum of the system's maximum user transmit power. This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth, It is the probability of actively dropping data packets. This is the maximum tolerable error rate required to ensure the overall reliability of URLLC; The objective function of the heuristic optimization problem for user terminal bandwidth is expressed as: (26) S.t. (26a) (26b) (26c) This is the maximum achievable rate of short packet transmission for user k. It is the process of Poisson's arrival. The effective band, It is approximately the probability of active packet loss. The upper limit, This is the bandwidth allocated to user k. It is the system's user bandwidth threshold. The sum of the system's maximum user bandwidth, It is the probability of actively dropping data packets. This is the maximum tolerable error rate required to ensure the overall reliability of URLLC; The objective function of the heuristic optimization problem for the user terminal transmit power is expressed as: (27) S.t. (27a) (27b) (27c)； The heuristic optimization problem of solving the user terminal bandwidth using Algorithm 1 includes: After fixing the transmit power allocated by MR to each user terminal, the optimization variable is the bandwidth allocated to the user terminal. Based on the fixed power allocation for user terminals, calculate the power of K user terminals. and ,in Is Calculated from The inflection point; Analyze user terminal k, and find the condition that it satisfies constraint (26a). ,make To satisfy the constraints of (26a) and (26c); to find the optimal bandwidth for user terminal k. Afterwards, use Replace the initial bandwidth of user terminal k and fix the set of user terminals. bandwidth; The heuristic optimization problem of solving the user terminal power using Algorithm 2 includes: Fixed user terminal set The initial power value, for user terminal k, according to the interval Gradually decrease Calculate whether constraints (27b) and (28) are satisfied, and find The minimum value is found when the optimal transmit power of user terminal k is determined. Afterwards, use Replace the initial transmit power of user terminal k and a fixed set of user terminals The transmit power is optimized to improve the bandwidth of user terminal k+1. .

2. The method according to claim 1, characterized in that, The aforementioned vehicle-to-ground URLLC system, in which the RRH (Railway Reception Hall) communicates with the MR (Mechanical Module) installed on the roof of the high-speed railway train carriage via millimeter waves, and the MR communicates with the user terminal in the carriage via URLLC, including: A vehicle-to-ground URLLC system is established. In this system, multiple single-frequency networks RRHs transmit and receive signals at the same frequency within a cell. The RRH panels are oriented towards the adjacent railway, with the RRH beam always located on its left and the UE beam on its right. The RRH communicates with the MR located in the middle of the carriage roof via millimeter waves, and the MR communicates with the user terminal via URLLC.

3. The method according to claim 1, characterized in that, The heuristic optimization problem of the user terminal bandwidth is solved using Algorithm 1, and the heuristic optimization problem of the user terminal power is solved using Algorithm 2. Algorithms 1 and 2 are then alternately optimized using the Block Coordinate Descent (BCD) algorithm to obtain the energy efficiency of the URLLC system, as well as the optimized transmit power and bandwidth for each user. This includes: S1. Given the initial power of K user terminals. ; S2. Under fixed user terminal power Based on this, the bandwidth of the user terminal is updated using Algorithm 1. ; S3. In fixed user terminal bandwidth Based on this, the power of the user terminal is updated using Algorithm 2. ; S4. Determine if the condition is satisfied. If so, output , , ;otherwise, Repeat the above process until the algorithm ends.

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