A method for allocating wireless resources for high-speed rail communication based on information freshness

Abstract

The present invention provides a method for allocating wireless resources for high-speed rail communication based on information freshness, including: constructing a communication network system architecture between each carriage of the high-speed rail and between each carriage and the base station beside the track, wherein the system includes a high-speed rail and a base station; designing a high-speed rail wireless communication power and spectrum resource allocation algorithm with information freshness service quality requirements, analyzing and formulating information freshness indicators, designing power and spectrum resource allocation optimization problems, solving the optimal power allocation and spectrum allocation solutions, and obtaining the optimal wireless resource allocation strategy. In view of the current situation of heterogeneous service quality requirements of high-speed rail communications and shortage of spectrum resources caused by limited bandwidth, the present invention proposes a power and spectrum resource allocation method with information freshness perception performance, which can improve the throughput of the communication link between the high-speed rail and the base station while meeting the transmission performance of different communication links, and provides an efficient and reliable communication means for high-speed rail wireless resource management.

Description

Technical Field

[0001] The present invention relates to the technical field of high-speed rail communication wireless resource allocation, and in particular to a method for allocating power and spectrum resources in a high-speed rail communication network based on information freshness service quality requirements. Background Art

[0002] High-speed railway (HSR) communications have flourished over the past decade. The growing demand for wireless communications in HSR and the resource shortage caused by limited bandwidth have attracted much attention to Radio Resource Management (RRM). However, due to the heterogeneous QoS requirements of HSR communications, traditional general-purpose network radio resource allocation schemes cannot be effectively applied to HSR scenarios. Therefore, it is crucial to design a resource allocation scheme that meets the more stringent QoS requirements of multiple services in HSR communications.

[0003] Traditional data communications focus more on information transmission latency, while real-time update applications, such as safety-related services in high-speed rail networks, focus on the timeliness of status updates. In order to provide passengers with a good perceptual experience and safety assurance, the information generated should be used more efficiently and intelligently to ensure that the information used is as fresh as possible. Domestic and foreign scholars have conducted a series of studies on improving the performance of real-time services in high-speed rail communications and combining wireless resource allocation schemes.

[0004] In 2014, S.Xu et al. studied the service scheduling algorithm in HSR networks considering the packet delivery rate requirements, and the proposed algorithm outperformed other traditional scheduling algorithms in terms of QoS (Quality of Service) performance. In 2020, L.Yan et al. proposed a high-speed rail network resource allocation scheme for safety services, taking into account the end-to-end delay requirements, and verified that the scheme can always meet the transmission requirements of high-speed rail safety services. However, these works all focus on the performance of data rate, bit error rate and end-to-end delay improvements, but do not mention work on information freshness.

[0005] In order to effectively describe the freshness of information, an end-to-end performance indicator called Age of Information (AoI) is proposed to describe the freshness of information received by the destination. It represents the time since the latest status update received by the destination was generated. In the past few years, AoI has been applied to a series of complex systems, such as energy harvesting networks, queuing systems, and various single-hop and multi-hop wireless networks, but there is a lack of research at home and abroad that takes AoI performance indicators into account in HSR networks.

[0006] Therefore, how to provide a HSR network resource allocation method that takes into account the performance indicators of AoI-QoS and combines power control and spectrum resource management technology to effectively characterize the freshness of data transmitted in the high-speed rail network to meet QoS requirements and improve resource utilization efficiency is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] In view of this, the present invention provides a high-speed railway communication wireless resource allocation method based on information freshness.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] The present invention discloses a method for allocating wireless resources for high-speed rail communication based on information freshness, and constructs a system based on allocating wireless resources for high-speed rail communication, wherein the system includes a high-speed rail and a cellular cell;

[0010] Two types of communication links are set for the high-speed railway, including: T2I communication link and T2T communication link. The T2I communication link is based on macrocellular communication, and the T2T communication link is based on device-to-device communication, i.e., D2D communication. The spectrum resources occupied by the T2I communication link are also reused by the T2T communication link. The T2I communication link is for M carriages to communicate with the base station, and the T2T communication link is for the remaining K carriages to exchange data between carriages based on D2D technology. A wireless access node is provided at the top of the carriage.

[0011] Establish a power and spectrum resource allocation optimization problem to optimize the communication node transmission power P of the two types of carriages m , P k and the spectrum reuse indicator variable ρ m,k , solve the optimal power allocation solution and the optimal spectrum reuse matching mode, and maximize the total traversal capacity of the T2I communication link while ensuring the QoS requirements of the two communication links; the power and spectrum resource allocation optimization problem is:

[0012]

[0013] st

[0014] (a)C m (P m ,P k ,ρ m,k )≥C 0

[0015]

[0016] (c)p k [q k (P m ,Pk )]<1

[0017]

[0018]

[0019]

[0020]

[0021] Among them, P m represents the transmission power of the mth IUE communicating from the car to the base station, P k is the transmission power of the kth car-to-car communication device TUE, C m is the ergodic capacity of the mth IUE, is the average age of information (AoI) of the kth TUE, q k represents the signal-to-noise ratio (SINR) outage probability; (a)-(g) are all constraints, constraint (a) is the ergodic capacity constraint of IUEs, C 0 is the minimum capacity that the IUE should meet; constraint (b) is the average AoI constraint for each T2T channel, is the maximum threshold of average AoI; constraint (c) is the stability constraint of the TUE queue system; constraints (d) and (e) are the maximum transmit power constraints of IUEs and TUEs, P T,max and P I,max represent the maximum transmission power of TUEs and IUEs respectively; constraints (f) and (g) mathematically simulate the one-to-one multiplexing relationship between TUEs and IUEs;

[0022] The steps to solve the optimal power and spectrum allocation solution for the system include:

[0023] S1. First, we model the TUE queue system, analyze its average information age AoI, and obtain the mathematical formula of the TUE queue system AoI:

[0024] S2, interruption probability q k The size of is limited by constraints (b) and (c). Constraints (b) and (c) are derived to find q k The final scope of

[0025] S3, establish the power allocation sub-problem, through P m and P k According to the Rayleigh distribution fading of the T2T communication link channel, we can get P m and P k The functional relationship includes the interruption probability qk Variable, based on q obtained in S2 k The final range of the solution is to find the feasible domain of the solution, analyze the value interval of the optimal solution, and find the optimal power allocation solution

[0026] S4. Based on the optimal power allocation solution, the power and spectrum resource allocation optimization problem is transformed into a spectrum allocation sub-problem, and the optimal spectrum reuse matching mode is obtained using the Hungarian algorithm.

[0027] It is understandable that in S2-S3, As the constraint (b) of the optimization problem, this constraint reflects a limit on the age of information transmitted by the T2T communication link, and its purpose is to meet the real-time requirements of the acquired information in practice. The range of the interruption probability of the T2T communication link can be obtained by constraint (b). Since the large-scale fading in the channel of the T2T communication link is Rayleigh fading and follows an exponential distribution, the power optimization variable P can be established through this distribution. m and P k The functional relationship of P m and P k The functional relationship between them can draw the feasible domain of the power allocation sub-problem, which has a direct impact on the solution of the optimal power allocation solution.

[0028] In S4, in solving the optimal spectrum reuse matching mode, based on the obtained optimal power allocation solution The original optimization problem is further transformed into a spectrum allocation sub-problem.

[0029] Preferably, the variable optimization order of the power and spectrum resource allocation optimization problem is to optimize the power variable P first. m and P k Re-optimize the spectrum allocation indicator variable ρ m,k The optimal resource allocation finally obtained is the global optimal one.

[0030] Preferably, M carriages communicate with the base station, forming a single-hop communication structure, and the set of IUEs is The set of TUEs is M mutually orthogonal frequency bands are allocated to M IUEs. The T2T communication link reuses the uplink spectrum resources of M carriages communicating with the base station. The spectrum reuse mode is one-to-one multiplexing, that is, each TUE reuses the spectrum of at most one IUE, and each IUE can only share its spectrum with one TUE.

[0031] Preferably, the head car and the tail car of the high-speed train are selected as IUEs, and the remaining middle cars are selected as TUEs. The T2T transmitters are randomly selected, and the remaining ones are selected as T2T receivers; the T2T transmitters and the T2T receivers are paired according to the size of each pair of corresponding large-scale fading parameter values ​​to form The Hungarian algorithm is used to solve the matching mode with the largest sum of fading values ​​based on the large-scale fading value matrix, and then the best matching TUE pair is selected.

[0032] Preferably, the channel model of the T2I communication link is:

[0033] In the T2T communication link, for the interference channel from the mth IUE to the kth TUE, and the channel between TUE pairs, the base station can only obtain small-scale fading distribution due to the rapid fading changes in the HST scenario. The channel gain between the kth TUE pair is:

[0034]

[0035] Among them, α k is the large-scale fading component including path loss and shadowing, g k is the small-scale fast fading component, exponentially distributed with unit mean, A is the path loss constant, β k is the logarithmic shadow fading random variable, d k is the distance between the kth TUE pair, is the power reduction factor;

[0036] The interference channel from the mth IUE to the kth TUE is:

[0037] h m,k =α m,k g m,k ,

[0038] α m,k is the large-scale fading component including path loss and shadowing, g m,k It is a small-scale fast fading component.

[0039] Preferably, the channel model of the T2I communication link is:

[0040] The channel gain from the mth IUE to the base station is:

[0041]

[0042] Among them, the large-scale fading parameter G represents the power gain constant, d m,B is the distance between the mth IUE and the base station, is the path loss exponential factor for small-scale fading, gm,B is the small-scale fading component. It is simulated as a Nakagami distribution with coefficients m and Ω, and has

[0043] The interference channel from the kth TUE to the BS is:

[0044] h k,B =|α k,B E[g k,B ]| 2 ,

[0045] α k,B is the large-scale fading parameter, g k,B is the small-scale fading parameter.

[0046] Preferably, considering the co-channel interference generated when the kth TUE reuses the mth IUE spectrum, the base station receives the signal sent by the mth IUE, and the kth TUE receiver receives the signal sent by the kth TUE transmitter. Then the SINR signal-to-interference-plus-noise ratio formula received by the mth IUE at the base station is expressed as:

[0047]

[0048] The SINR signal-to-interference-noise ratio received by the kth TUE receiver is:

[0049]

[0050] Among them, σ 2 is the noise power, ρ m,k =1 means that the kth TUE reuses the spectrum of the mth IUE, otherwise ρ m,k =0.

[0051] Preferably, the modeling of the TUE queue system in S1 includes:

[0052] Real-time incoming or historical data packets in the T2T communication link are stored in an infinite buffer of each TUE transmitter, and the data packets are transmitted under the first-come-first-served (FCFS) rule;

[0053] The queue system of each TUE is modeled as a queue of the form M / G / 1, where M, G, 1 indicates that the arrival process between adjacent updates follows the rate λ k The Poisson process has a geometric distribution of service time and the number of servers is 1;

[0054] If the queue of the kth TUE is empty at the beginning of a time slot, the channel of the kth TUE will not be used in these time slots.

[0055] Preferably, the traversal capacity analysis steps of IUEs are as follows: define p kis the channel service probability of the kth TUE, and the ergodic capacity of the mth IUE is calculated based on the channel service probability p k Expressed as

[0056]

[0057] It should be noted that:

[0058] p k It is a variable that reflects the channel utilization rate, defined according to the characteristics of the queue. In order to maintain the stability of the queue system and enable the data packets in the TUEs queue system to be transmitted normally, the number of data packets generated per unit time cannot exceed the number of data packets that can be processed. Definition And the queue system is stable if and only if p < 1. Where λ is the Poisson distribution arrival rate of the M / G / 1 queue, Indicates the service time of the queue. This condition should always be met.

[0059] Preferably, the specific contents of the arrival interval time and service time of the TUEs queue in the M / G / 1 queue form in S1 include:

[0060] Status update interval X n Follows a Poisson distribution with rate λ k , its first-order moment and second-order moment are expressed as Status update packet service time S n The probability mass function of

[0061]

[0062] Where T is the time slot length, q k is the probability that the status update packet in the queue is not successfully transmitted, that is, the SINR interruption probability. k According to the small-scale fast fading component g k and g m,k An exponential distribution with unit mean gives

[0063]

[0064] Among them, γ 0 is the minimum SINR threshold for the receiver to successfully decode a data packet;

[0065] It needs further explanation:

[0066] Using S n The probability mass function is calculated to have its first and second moments respectively.

[0067]

[0068]

[0069] In the M / G / 1 queue system, the following formula can be defined:

[0070]

[0071] Among them, Y, X, and S represent the interval department time, arrival interval time, and service time respectively.

[0072] Preferably, the analysis steps of the average AoI and channel service probability of TUEs are:

[0073] The concept of age of information AoI is used to measure the freshness of information transmitted by TUEs, which are usually time-sensitive and security-critical. The AoI of the TUE receiver is defined as

[0074] Δ(t)=tu(t), t≥u,

[0075] Where u(t) is the timestamp of the latest update received by the TUE receiver, and t represents the transmission time.

[0076] TUE transmitter at {t n ,n=1,2,...,N}, and transmit the update packet at t' n Moment completed. X n =t n -t n-1 Represents the inter-arrival time between a successfully decoded update and the next generated update. S n =t' n -t n Indicates the service time of the nth status update packet. n =t' n -t' n-1 Indicates the interval time between two consecutive successfully decoded status updates.

[0077] It needs further explanation:

[0078] Inter-arrival time X n With service time S n They are all independent and identically distributed processes.

[0079] Within the time range of I time slots, there are N successfully decoded state updates. The entire period I is represented by The average AoI can be expressed as the geometric area Q N Indicates that Q N It can be expressed as

[0080]

[0081] For the time domain I where information decoding is completed N times, the average AoI is expressed as,

[0082]

[0083] The average AoI of the kth TUE is derived from the above formulas:

[0084]

[0085] Preferably, S1 combines the power optimization variable P in the optimization problem m and P k The final TUE queue AoI closed expression and the service probability of the TUE channel include:

[0086] Based on the interval department time, arrival interval time, and first-order and second-order moments of service time obtained above, the average AoI of the kth TUE queue is obtained as

[0087]

[0088] The channel service probability is

[0089] Preferably, the constraints (b) and (c) obtained in S2 are related to the optimization variable P m , P k The relevant expression is, where the SINR outage probability q k The size of is limited by constraints (b) and (c), and the average information age of constraints (b) and (c) is and channel service probability p k According to the M / G / 1 queue form of TUE, it is necessary to make a monotonicity judgment on constraints (b) and (c), transform the formula, and calculate q k The final range of the power P m and P k The variable relationship between them is used to derive the optimal power allocation solution.

[0090] Preferably, the power allocation subproblem in S3 temporarily ignores the restriction of constraint (a), and only considers constraints (b), (c), (d), and (e) in the power allocation subproblem. k and g m,k The optimized variable P is obtained by exponential distribution with unit mean. m and P k The functional relationship between them is analyzed by P k is the independent variable, P mThe feasible domain of the solution that satisfies constraints (d) and (e) under the nonlinear function of the dependent variable is derived, and the formula for determining the optimal power allocation solution is obtained.

[0091] Preferably, the optimal ergodic capacity value of IUEs is solved based on the optimal power allocation solution in S3, and then the power and spectrum resource allocation optimization problem is transformed into a weighted bipartite graph matching problem, and the Hungarian algorithm is used to find the optimal IUE-TUE spectrum reuse matching mode.

[0092] Preferably, based on the optimal power allocation solution obtained in S4, the constraints (a), (f), and (g) are jointly considered, and the original problem is further transformed into a spectrum allocation subproblem. The subproblem is formally equivalent to a weighted bipartite graph maximum matching problem. The Hungarian algorithm can effectively solve the problem in polynomial time, and then the optimal spectrum allocation solution is obtained.

[0093] It can be seen from the above technical solution that, compared with the prior art, the beneficial effects of the present invention include:

[0094] The present invention can project the values ​​of various elements based on longitude and latitude area statistics onto a geographic map, and further directly draw them into the statistical grid of the geographic map, and can be widely used in the production of GIS thematic maps. Specifically, the present invention has the following advantages:

[0095] The present invention discloses a method for allocating wireless resources for high-speed rail communication based on information freshness. The present invention addresses the contradiction between the service quality requirements of multiple services in high-speed rail communication and wireless resource management, as well as the shortage of spectrum resources caused by limited bandwidth. It considers the service quality requirements of information freshness in high-speed rail real-time services, and while ensuring the information freshness and reliability of various transmission links in high-speed rail communication, optimally allocates the power and spectrum resources of the system to maximize the total traversal capacity of the communication link with the base station. The present invention takes into account the particularity of the high-speed rail channel and the heterogeneous service quality requirements of various links in the high-speed rail, and the constructed system model is more accurate. It can be verified by simulation results that the method for allocating wireless resources for high-speed rail communication based on information freshness proposed by the present invention can more effectively improve the traversal capacity of the link between the high-speed rail and the base station communication than the traditional method, and at the same time meets the heterogeneous service quality requirements of different communication links, and has more practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work.

[0097] Figure 1 A flow chart of a method for allocating wireless resources for high-speed rail communication based on information freshness provided by an embodiment of the present invention;

[0098] Figure 2 A schematic diagram of a high-speed rail communication system model with two communication links in a high-speed rail communication wireless resource allocation method based on information freshness provided by an embodiment of the present invention;

[0099] Figure 3 A schematic diagram of the evolution of instantaneous information age over time in a method for allocating wireless resources for high-speed rail communications based on information freshness provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0100] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0101] The embodiment of the present invention discloses a method for allocating wireless resources for high-speed rail communication based on information freshness, such as Figure 1 As shown, the specific steps are as follows:

[0102] (1) Communication system construction

[0103] like Figure 2 As shown in FIG. 1 , a high-speed train is in a cellular cell, and each carriage is equipped with a wireless access node on the top. There are two types of communication links in the high-speed train, in which M carriages communicate with the base station (T2I), forming a one-on-one communication structure. These M carriages are called infrastructure user equipment (IUEs), and the remaining K carriages perform carriage-to-carriage data exchange based on D2D technology (T2T), which are called carriage user equipment (TUEs); the set of IUEs is defined as The set of TUEs is

[0104] It should be noted that the network range covered by a base station is a cellular cell. The system model of this embodiment is carried out under the condition that only one cell is considered for coverage. If multiple cells are considered for coverage, inter-cell switching is required.

[0105] For the selection of IUEs, based on the actual situation, only the head and tail carriages of the high-speed train are selected as IUEs, and the remaining middle carriages are defined as IUEs. Due to T2T communication, the T2T transmitter is randomly selected, and the remaining ones are selected as T2T receivers; the T2T transmitters and receivers are paired according to the size of each pair of corresponding large-scale fading parameter values ​​to form The Hungarian algorithm is used to solve the optimal matching solution for the large-scale fading value matrix, and then the optimal TUE matching pair is selected.

[0106] All communication units are matched with only a single antenna. M mutually orthogonal frequency bands have been allocated to M IUEs. The spectrum reuse relationship between IUEs and TUEs is one-to-one multiplexing, that is, each TUE needs to reuse the spectrum of an IUE, and each IUE can only share its spectrum with one TUE.

[0107] (2) Construction of T2I and T2T communication link channel models

[0108] In the T2T communication link, for the interference channel from the mth IUE to the kth TUE, and the channel between TUE pairs, the base station can only obtain small-scale fading distribution due to the rapid fading changes in the HST scenario. The channel gain between the kth TUE pair is defined as

[0109]

[0110] Among them, α k is the large-scale fading component including path loss and shadowing, g k is the small-scale fast fading component, exponentially distributed with unit mean, A is the path loss constant, β k is the logarithmic shadow fading random variable, d k is the distance between the kth TUE pair, is the power attenuation factor. Similarly, the interference channel from the mth IUE to the kth TUE is defined as h m,k =α m, k g m,k , α m,k is the large-scale fading component including path loss and shadowing, g m,k It is a small-scale fast fading component.

[0111] In a T2T communication link, the channel gain from the mth IUE to the base station is defined as

[0112]

[0113] Among them, the large-scale fading parameter G represents the power gain constant, d m,B is the distance between IUEm and the base station, is the path loss exponential factor for small-scale fading, g m,B The small-scale fading component can be modeled as a Nakagami distribution with coefficients m and Ω, and Similarly, the interference channel from the kth TUE to the BS is h k,B =|α k,B E[g k,B ]| 2 , α k,B is the large-scale fading parameter, g k,B is the small-scale fading parameter.

[0114] (3) Analysis of SINR at the receiving end of T2T and T2I links

[0115] Considering the co-channel interference generated when the kth TUE reuses the spectrum of the mth IUE, the BS receives the signal sent by the mth IUE, and the kth TUE receiver receives the signal sent by the kth TUE transmitter. Then the SINR received by the mth IUE at the BS can be given by the following formula:

[0116]

[0117] The SINR signal-to-noise ratio received by the kth TUE receiver is

[0118]

[0119] Among them, σ 2 is the noise power, ρ m,k =1 means that the kth TUE reuses the spectrum of the mth IUE, otherwise ρ m,k =0.

[0120] (4) Analysis of the TUEs Queue System

[0121] Incoming or backlogged packets will be stored in an infinite buffer of each TUE sender, and these packets are transmitted under the first-come, first-served (FCFS) rule. The queuing system of each TUE can be modeled as a queue of the form M / G / 1, where M,G,1 indicates that the arrival process between adjacent updates follows a rate of λ kThe service time is geometrically distributed and the number of servers is 1. Note that if the queue of the kth TUE is empty at the beginning of a time slot, the channel of the kth TUE will not be used in these time slots, so the kth TUE will not cause interference to the mth IUE. Define p k is the channel service probability of the kth TUE. k It is a variable that reflects the channel utilization rate, defined according to the characteristics of the queue. In order to maintain the stability of the queue system and enable the data packets in the TUEs queue system to be transmitted normally, the number of data packets generated per unit time cannot exceed the number of data packets that can be processed. Definition And the queue system is stable if and only if p < 1. Where λ is the Poisson distribution arrival rate of the M / G / 1 queue, Indicates the service time of the queue. This condition should always be met.

[0122] (5) Analysis of traversal capacity of IUEs

[0123] The ergodic capacity of the mth IUE in the high-speed railway communication system is calculated based on the channel service probability p k Expressed as

[0124]

[0125] (6) Analysis of Average AoI and Channel Service Probability of TUEs

[0126] TUE transmitter at {t n ,n=1,2,...,N}, and transmit the update packet at t' n Done. X n =t n -t n-1 Represents the inter-arrival time between a successfully decoded update and the next generated update. S n =t' n -t n Indicates the service time of the nth status update packet. n =t' n -t' n-1 Indicates the time between two consecutive successfully decoded status updates. n With service time S n They are all independent and identically distributed processes.

[0127] Within the time range of I time slots, there are N successfully decoded state updates. The entire period I is represented by like Figure 3 As shown, the average AoI can be expressed as the geometric area Q N Indicates that Q NIt can be expressed as

[0128]

[0129] For the time domain I where information decoding is completed N times, the average AoI is expressed as,

[0130]

[0131] The average AoI of the kth TUE is derived from the above formulas:

[0132]

[0133] Status update interval X n Follows a Poisson distribution with rate λ k , its first-order moment and second-order moment are expressed as Status update packet service time S n The probability mass function of

[0134]

[0135] Where T is the time slot length, q k is the probability that the status update packet in the queue is not successfully transmitted, that is, the SINR interruption probability. k According to the small-scale fast fading component g k and g m,k An exponential distribution with unit mean gives

[0136]

[0137] Among them, γ 0 is the minimum SINR threshold for the receiver to successfully decode a data packet;

[0138] Using S n The probability mass function is calculated to have its first and second moments respectively.

[0139]

[0140]

[0141] In the M / G / 1 queue system, the following formula can be defined:

[0142]

[0143] Among them, Y, X, and S represent the interval department time, arrival interval time, and service time respectively.

[0144] Furthermore, S1 combines the power optimization variable P in the optimization problem m and P kThe final TUE queue AoI closed expression and the service probability of the TUE channel include:

[0145] Based on the interval department time, arrival interval time, and first-order and second-order moments of service time obtained above, the average AoI of the kth TUE queue is obtained as

[0146]

[0147] And the channel service probability is

[0148] Combined with the high-speed rail communication system and transmission link queue model constructed earlier, the optimization problem is analyzed:

[0149] (1) Construction of the overall optimization problem

[0150] Given the total ergodic capacity expression of IUEs and various constraints, we can optimize the variable power allocation P m , P k , power allocation indicator variable ρ m,k , the overall optimization problem of power and spectrum resource allocation is constructed as follows:

[0151]

[0152] st

[0153] (a)C m (P m ,P k ,ρ m,k )≥C 0

[0154]

[0155] (c)p k [q k (P m ,P k )]<1

[0156]

[0157]

[0158]

[0159]

[0160] (2) Power allocation optimization for any IUE-TUE multiplexing pair

[0161] We consider an arbitrary reuse pattern, i.e., the kth TUE reuses the spectrum of the mth IUE. Problem (P1) can be simplified to a power allocation problem (P2). Note that there is no IUE capacity constraint (a) in problem (P2), which will be explained in the spectrum allocation problem. The constructed power allocation optimization problem is:

[0162]

[0163] st

[0164]

[0165] (b)p k,Nr [q k (P m ,P k )]<1,

[0166] (c)0≤P k ≤P T,max ,

[0167] (d)0≤P m ≤P I,max

[0168] Among them, constraints (a) and (b) are

[0169]

[0170]

[0171] For average The expression of q k The first derivative of the correlation

[0172]

[0173] The derivative of the average AoI is greater than 0, which is a function of q k It is easy to deduce that constraints (a) and (b) are equivalent to and in It is an equation The solution, It is necessary to use the binary search method to evaluate in the range (0,1), and then transform constraints (a) and (b) into The power allocation optimization problem can be further simplified as

[0174]

[0175] st

[0176]

[0177] (b)0≤P k ≤P T,max ,

[0178] (c)0≤P m ≤P I,max

[0179] The optimal solution formula for problem (P2.1) is

[0180]

[0181]

[0182] Among them, the function f(·), the function value f -1 (P I,max ) is solved as follows:

[0183] By the formula and formula Deform, get The function f(·) is monotonically increasing in the feasible domain. Then, by A binary search within the range can be used to find f -1 (P I,max ), where is the minimum power allocation value that TUE can obtain in the feasible domain of the optimization problem, and

[0184] The maximum traversal capacity of a single IUE is calculated as follows:

[0185]

[0186] At this point, the optimal power allocation for a single IUE-TUE multiplexing pair has been solved.

[0187] (3) Spectrum reuse optimization

[0188] is the maximum ergodic capacity of a single IUE under the condition that the QoS requirements of all TUEs (i.e., the average AoI requirement and the queue stability requirement) are met. In order to ensure that this value also satisfies constraint (a) of problem (P1), the following expression is defined to select a feasible IUE-TUE spectrum reuse pair that can meet the QoS requirements of the T2I link.

[0189]

[0190] in, represents the optimal ergodic capacity value of the mth IUE, and then by simplifying the problem (P1), it is transformed into a spectrum allocation optimization problem. The transformed problem is

[0191]

[0192] st

[0193]

[0194]

[0195] The solution process of problem (P3) is:

[0196] From a formal point of view, this problem is a typical weighted bipartite graph maximum matching problem, which can be effectively solved in polynomial time using the Hungarian algorithm. of Traverse the capacity matrix to perform one-to-one matching on the edge weights to find the maximum sum of the pairings. The result after solving will return a size of A vector, each column in the vector represents the column value corresponding to the original matrix, and the final result is the result that can achieve the optimal spectrum reuse.

[0197] The above is a detailed introduction to the high-speed rail communication wireless resource allocation method based on information freshness provided by the present invention. In this embodiment, specific examples are used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0198] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined in the present embodiments may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown in the present embodiment, but will conform to the widest range consistent with the principles and novel features disclosed in the present embodiment.

Claims

1. A method for allocating wireless resources for high-speed rail communication based on information freshness, characterized in that: Constructing a system for allocating wireless resources based on high-speed rail communication, the system comprising a high-speed rail and a cellular cell; Two types of communication links are set for the high-speed railway, including: T2I communication link and T2T communication link. The T2I communication link is based on macrocellular communication, and the T2T communication link is based on device-to-device communication, i.e., D2D communication. The spectrum resources occupied by the T2I communication link are also reused by the T2T communication link. The T2I communication link is for M carriages to communicate with the base station, and the T2T communication link is for the remaining K carriages to exchange data between carriages based on D2D. A wireless access node is provided at the top of the carriage. Establish a power and spectrum resource allocation optimization problem to optimize the communication node transmission power P of the two types of carriages m , P k and the spectrum reuse indicator variable ρ m,k , solve the optimal power allocation solution and the optimal spectrum reuse matching mode, and maximize the total traversal capacity of the T2I communication link while ensuring the QoS requirements of the two communication links; the power and spectrum resource allocation optimization problem is: st (a)C m (P m ,P k ,ρ m,k )≥C0 (b) (c)p k [q k (P m ,P k )]<1 (d) (e) (f) (g) Among them, P m represents the transmission power of the mth IUE communicating from the car to the base station, P k is the transmission power of the kth car-to-car communication device TUE, C m is the ergodic capacity of the mth IUE, is the average information age of the kth TUE, i.e., AoI, q k represents the signal-to-noise ratio (SINR) outage probability; (a)-(g) are all constraints, constraint (a) is the ergodic capacity constraint of IUEs, C0 is the minimum capacity that IUE should meet; constraint (b) is the average AoI constraint of each T2T channel, is the maximum threshold of average AoI; constraint (c) is the stability constraint of the TUE queue system; constraints (d) and (e) are the maximum transmit power constraints of IUEs and TUEs, P T,max and P I,max represent the maximum transmission power of TUEs and IUEs respectively; constraints (f) and (g) mathematically simulate the one-to-one multiplexing relationship between TUEs and IUEs; The steps to solve the optimal power and spectrum allocation solution for the system include: S1. First, we model the TUE queue system, analyze its average information age AoI, and obtain the mathematical formula of the TUE queue system AoI: S2, interruption probability q k The size of is limited by constraints (b) and (c). Constraints (b) and (c) are derived to find q k The final scope of S3. Establish the power allocation sub-problem. According to the Rayleigh distribution fading of the T2T communication link channel, we get P m and P k The functional relationship includes the interruption probability q k Variable, based on q obtained in S2 k Find the feasible domain of the solution, analyze the value interval of the optimal solution, and find the optimal power allocation solution S4. Based on the optimal power allocation solution, the power and spectrum resource allocation optimization problem is transformed into a spectrum allocation sub-problem, and the optimal spectrum reuse matching mode is obtained using the Hungarian algorithm.

2. The high-speed rail communication wireless resource allocation method based on information freshness according to claim 1 is characterized in that: M carriages communicate with the base station, forming a single-hop communication structure. The set of IUEs is The set of TUEs is M mutually orthogonal frequency bands are allocated to M IUEs. The T2T communication link reuses the uplink spectrum resources of M carriages communicating with the base station. The spectrum reuse mode is one-to-one multiplexing, that is, each TUE reuses the spectrum of at most one IUE, and each IUE can only share its spectrum with one TUE.

3. The high-speed rail communication wireless resource allocation method based on information freshness according to claim 2 is characterized in that: The head and tail carriages of the high-speed train are selected as IUEs, and the remaining middle carriages are selected as TUEs. The T2T transmitters are randomly selected, and the remaining ones are selected as T2T receivers. The T2T transmitters and T2T receivers are paired according to the values ​​of each pair of corresponding large-scale fading parameters to form The Hungarian algorithm is used to solve the matching mode with the largest sum of fading values ​​based on the large-scale fading value matrix, and then the best matching TUE pair is selected.

4. The high-speed rail communication wireless resource allocation method based on information freshness according to claim 3 is characterized in that: The channel model of the T2I communication link is: In the T2T communication link, for the interference channel from the mth IUE to the kth TUE, and the channel between the TUE pair, the channel gain between the kth TUE pair is: Among them, α k is the large-scale fading component including path loss and shadowing, g k is the small-scale fast fading component, exponentially distributed with unit mean, A is the path loss constant, β k is the logarithmic shadow fading random variable, d k is the distance between the kth TUE pair, is the power reduction factor; The interference channel from the mth IUE to the kth TUE is: h m,k =a m,k g m,k , α m,k is the large-scale fading component including path loss and shadowing, g m,k It is a small-scale fast fading component.

5. The high-speed rail communication wireless resource allocation method based on information freshness according to claim 4 is characterized in that: The channel model of the T2I communication link is: The channel gain from the mth IUE to the base station is: Among them, the large-scale fading parameter G represents the power gain constant, d m,B is the distance between the mth IUE and the base station, is the path loss exponential factor for small-scale fading, g m,B is the small-scale fading component and is modeled as a Nakagami distribution with coefficients m and Ω, and The interference channel from the kth TUE to the BS is: h k,B =|a k,B E[g k,B ]| 2 , α k,B is the large-scale fading parameter, g k,B is the small-scale fading parameter.

6. The high-speed railway communication wireless resource allocation method based on information freshness according to claim 5 is characterized in that: The SINR signal-to-interference-plus-noise ratio formula received by the mth IUE at the base station is expressed as: The SINR signal-to-interference-noise ratio received by the kth TUE receiver is: Among them, σ 2 is the noise power, ρ m,k =1 means that the kth TUE reuses the spectrum of the mth IUE, otherwise ρ m,k =0.

7. The high-speed railway communication wireless resource allocation method based on information freshness according to claim 1 is characterized in that: The modeling of the TUE queue system in S1 includes: Real-time incoming or historical data packets in the T2T communication link are stored in an infinite buffer of each TUE transmitter, and the data packets are transmitted under a first-come, first-served rule; The queue system of each TUE is modeled as a queue of the form M / G / 1, where M, G, 1 indicates that the arrival process between adjacent updates follows the rate λ k The Poisson process has a geometric distribution of service time and the number of servers is 1; If the queue of the kth TUE is empty at the beginning of a time slot, the channel of the kth TUE will not be used in these time slots.

8. The high-speed railway communication wireless resource allocation method based on information freshness according to claim 6 is characterized in that: Define p k is the channel service probability of the kth TUE, and the ergodic capacity of the mth IUE is calculated based on the channel service probability p k Expressed as 9. The high-speed railway communication wireless resource allocation method based on information freshness according to claim 7 is characterized in that: The average information age of constraints (b) and (c) in S2 and channel service probability p k Analyze and formulate the M / G / 1 queue form of TUE to determine the interruption probability q k The value range of the power P m and P k The variable relationship between them is used to derive the optimal power allocation solution.

10. The high-speed railway communication wireless resource allocation method based on information freshness according to claim 1, characterized in that: Based on the optimal power allocation solution in S3, the optimal ergodic capacity value of IUEs is solved, and then the power and spectrum resource allocation optimization problem is transformed into a weighted bipartite graph matching problem, and the Hungarian algorithm is used to find the optimal IUE-TUE spectrum reuse matching mode.

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