Uplink and downlink flexible decoupling joint optimization method and system for cellular Internet of Vehicles

Abstract

The invention discloses an uplink and downlink flexible decoupling joint optimization method and system for a cellular Internet of Vehicles, and provides an efficient and rapid solution for enhancing the communication network performance of the cellular Internet of Vehicles enabled by uplink and downlink decoupling access and coupling access technologies in the future. The method comprises the following steps: constructing an optimization problem of maximizing the throughput of a downlink network based on a cellular Internet of Vehicles system architecture of uplink and downlink flexible decoupling access; wherein the optimization variable comprises a binary decision variable representing that the vehicle is associated with the base station in the downlink, the bandwidth allocated to the vehicle by the base station and the transmitting power; the constraints comprise a downlink rate requirement, a base station bandwidth limit, a transmitting power limit and balanced allocation of bandwidth and power of each vehicle; a series of auxiliary variables, alternative variables and mathematical scaling are introduced, and an original problem is converted into a standard convex optimization problem to be solved. According to the invention, a solution is provided for performance enhancement and access optimization of the cellular Internet of Vehicles under the uplink and downlink flexible decoupling access technology.

Description

Technical Field

[0001] The present invention belongs to the field of communications, relates to cellular vehicle-to-everything (C-V2X) and uplink-downlink decoupled access technologies, and is an uplink-downlink flexible decoupling joint optimization method and system for C-V2X. Background Art

[0002] The C-V2X technology is the main technology to meet the basic service guarantee of current vehicle uplink-downlink communication. However, with the explosive growth of the number of intelligent connected vehicles and the communication service requirements of vehicle users in recent years, C-V2X has gradually evolved into a complex heterogeneous network including various types of base stations such as macro base stations and small base stations. The traditional coupled access technology can no longer meet the current vehicle user requirements. The uplink-downlink decoupled access technology separates the uplink and downlink, enabling the uplink to also select the communication base station with the maximum received power, greatly improving the performance of the communication network. Therefore, researchers regard the uplink-downlink decoupled access technology as an effective solution and apply it to C-V2X.

[0003] Since the uplink-downlink decoupled access technology can greatly improve the uplink communication performance and adapt to the characteristics of heterogeneous networks, it has attracted wide attention in the field of wireless communications.

[0004] After searching the existing literature, it is found that K. Smiljkovikj et al. and Z. Sattar et al. published articles titled "Analysis of the decoupled access for downlink and uplink in wireless heterogeneous networks" and "Spectral efficiency analysis of the decoupled access for downlink and uplink in two-tier network" in the IEEE Wireless Communications Letters in 2015 and the IEEE Transactions on Vehicular Technology in 2019 respectively. These articles are all based on the uplink-downlink decoupled access technology, propose a cellular network framework for uplink-downlink decoupled access, and evaluate the performance of four decoupling modes of uplink-downlink decoupled access.

[0005] After retrieval, it is also found that in order to further study the application of uplink-downlink decoupled access technology in cellular vehicle-to-everything (C-V2X), Kai Yu et al. published an article titled "Deep Reinforcement Learning-Based RAN Slicing for UL / DL Decoupled Cellular V2X" in the 2021 issue of "IEEE Transactions on Wireless Communications". In this article, using deep reinforcement learning tools, the uplink-downlink decoupled access technology was introduced into the field of C-V2X for the first time, and a new solution was provided for vehicle-to-infrastructure forwarding communication using the decoupling technology.

[0006] Although the uplink-downlink decoupled access technology has been preliminarily applied, it is mainly applied at the cell edge, and users in the cell center still mostly use the coupled access technology. Therefore, the method of using only decoupled access throughout the network does not work, and the coupled access method still needs to be considered. Therefore, the hybrid decoupled access mode of decoupled access and coupled access is a solution that urgently needs to be considered.

[0007] In summary, the problems existing in the prior art are as follows: (1) The systematic framework for applying the flexible uplink-downlink decoupled access technology to C-V2X is still lacking. (2) The detailed research on the flexible uplink-downlink decoupled access technology in C-V2X is very scarce. (3) The existing analysis of the uplink-downlink decoupled access technology in C-V2X communication focuses on performance analysis, and general optimization methods are lacking. Currently, no relevant literature has given the resource optimization results of the flexible uplink-downlink decoupled C-V2X. Summary of the Invention

[0008] Object of the Invention: The object of the present invention is to provide a flexible uplink-downlink decoupled joint optimization method and system for C-V2X to enhance the network performance of C-V2X.

[0009] Technical Solution: To achieve the above object, the present invention adopts the following technical solutions:

[0010] A flexible uplink-downlink decoupled joint optimization method for C-V2X includes the following steps:

[0011] Based on the C-V2X system architecture with flexible uplink-downlink decoupled access, a mixed-integer non-linear programming problem for maximizing the downlink network throughput is constructed; wherein the optimization variables include binary decision variables representing the association of vehicles with the base station in the downlink, the bandwidth allocated by the base station to the vehicles, and the base station transmission power; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmission power limit, and the balanced allocation of bandwidth and power.

[0012] A series of auxiliary variables, substitution variables, and mathematical scaling are introduced to transform the original problem into a standard convex optimization problem for solution.

[0013] Furthermore, in the cellular vehicle-to-everything (C-V2X) system architecture with flexible uplink-downlink decoupling access, there are six access modes for the C-V2X: Mode 1: Uplink = Macro Base Station 1, Downlink = Macro Base Station 1; Mode 2: Uplink = Small Cell 1, Downlink = Small Cell 1; Mode 3: Uplink = Macro Base Station 1, Downlink = Macro Base Station 2; Mode 4: Uplink = Small Cell 1, Downlink = Macro Base Station 1; Mode 5: Uplink = Macro Base Station 1, Downlink = Small Cell 1; Mode 6: Uplink = Small Cell 1, Downlink = Small Cell 2.

[0014] Furthermore, the downlink network throughput is expressed as: where is the bandwidth allocated by base station b to vehicle v, and SINR d is the signal-to-interference-plus-noise ratio when the vehicle is associated with the macro base station or small cell in the downlink, B is the number of base stations, which is the sum of the number of small cells and macro base stations, and V is the number of vehicles.

[0015] Furthermore, in the introduction of a series of auxiliary variables, substitution variables, and mathematical scaling, the association variable between the vehicle and the base station is relaxed to a continuous variable ranging from 0 to 1, a penalty term is introduced and a first-order Taylor expansion is performed, and an auxiliary variable is introduced such that and the mathematical identity is used to make an equivalent representation of and then linearized using the first-order Taylor expansion to obtain In the rewritten optimization objective, is rewritten as λ d is a hyperparameter, is the expansion point.

[0016] Furthermore, the introduced constraints are transformed into the following relaxed form:

[0017]

[0018] where is a substitution variable, is the first-order Taylor expansion point, is the downlink transmit power when vehicle v is connected to base station b, is the channel gain between base station b and vehicle b, is the log-normal shadow variance.

[0019] Furthermore, the transformed convex optimization problem is expressed as:

[0020]

[0021] s.t.

[0022]

[0023] where the matrix is the set of optimization variables, and are the set of base stations and vehicles respectively, is the total radio spectrum bandwidth in the downlink, and are the maximum bandwidth limit and maximum transmit power of the base station, is the total transmit power of the base station in the downlink, and are the expansion points.

[0024] An up - and - downlink flexible decoupling joint optimization system for cellular vehicle - to - everything (C - V2X) includes:

[0025] A modeling module, configured to construct a mixed - integer non - linear programming problem for maximizing the downlink network throughput based on the C - V2X system architecture with up - and - downlink flexible decoupling access; where the optimization variables include binary decision variables representing the association of vehicles with base stations in the downlink, the bandwidth allocated by the base station to the vehicles, and the transmit power of the base station; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmit power limit, and the balanced allocation of bandwidth and power;

[0026] A solving module, configured to introduce a series of auxiliary variables, substitution variables, and mathematical scaling to transform the original problem into a standard convex optimization problem for solution.

[0027] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the up - and - downlink flexible decoupling joint optimization method for C - V2X.

[0028] Advantageous effects: Compared with the current association strategy based on the nearest distance and the best channel gain and the traditional coupled access method, the flexible decoupling scheme adopted by the present invention can increase the throughput by more than 20% in the downlink. Based on the development of the current up - and - downlink decoupling access technology and the progress of C - V2X, the present invention can more efficiently and reliably enhance the network performance of C - V2X, provide new ideas for the planning and design of future C - V2X with up - and - downlink flexible decoupling access, and promote the application and development of communication technologies in the field of vehicle - to - everything and the distribution management strategy of vehicles and base stations under the up - and - downlink decoupling access technology. Description of the Drawings

[0029] Figure 1It is a scenario diagram of the cellular vehicle-to-everything (C-V2X) with flexible uplink-downlink decoupled access adopted in the embodiments of the present invention.

[0030] Figure 2 It is a convergence diagram of the downlink convex optimization solution process in the embodiments of the present invention.

[0031] Figure 3 It is a diagram of the cumulative distribution function of the downlink rate (V = 10) in the flexible decoupling and coupled access in the embodiments of the present invention.

[0032] Figure 4 It is a comparison diagram of the downlink average rate under the flexible decoupling and coupled access modes in the embodiments of the present invention.

[0033] Figure 5 It is a comparison diagram of the average rate under different base station association strategies in the embodiments of the present invention. Detailed implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further elaborates on the embodiments of the present invention with reference to the accompanying drawings: These embodiments are implemented on the premise of the technical solutions of the present invention, and detailed implementation manners and specific operation processes are given. It should be understood that the specific examples described herein are only used to explain the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0035] A flexible uplink-downlink decoupled joint optimization method for cellular vehicle-to-everything disclosed in the embodiments of the present invention mainly includes: Based on the cellular vehicle-to-everything system architecture with flexible uplink-downlink decoupled access, a mixed-integer non-linear programming problem for maximizing the downlink network throughput is constructed; where the optimization variables include binary decision variables representing the association of vehicles with base stations in the downlink, the bandwidth allocated by the base station to the vehicles, and the transmit power of the base station; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmit power limit, as well as the balanced allocation of bandwidth and power; a series of auxiliary variables, surrogate variables and mathematical scaling are introduced to transform the original problem into a standard convex optimization problem for solution.

[0036] Specifically, the flexible uplink-downlink decoupled joint optimization method for cellular vehicle-to-everything includes the following steps:

[0037] In the first step, a general cellular vehicle-to-everything system framework with flexible uplink-downlink decoupled access is constructed.

[0038] As Figure 1 shown, in this embodiment, a two-layer hybrid access of uplink / downlink decoupling and coupling, that is, a flexible decoupling cellular vehicle-to-everything network model is constructed, including small base stations (SBSs) and macro base stations (MBSs). A central controller collects information of the entire network, including the signal-to-interference-plus-noise ratio and transmission. And Index sets representing the base station and the vehicle respectively. The macro base station set is denoted as The small base station set is denoted as Therefore, and \(B = M+S\). Each base station \(b\) can only serve a limited number of vehicles. The maximum number of vehicles that a macro base station and a small base station can serve in the downlink is

[0039]

[0040] where, and are the maximum values of the number of vehicles that a macro base station and a small base station can serve respectively.

[0041] The uplink-downlink decoupled access technology allows the uplink and downlink of vehicle users to access a base station separately, thus separating the uplink from the downlink access to the base station, and then effectively improving the communication conditions of the link. While the coupled access requires users to access the same base station. Therefore, there are six access modes for the cellular vehicle-to-everything network with flexible uplink-downlink decoupling access:

[0042] Mode 1: Uplink = Macro base station 1, Downlink = Macro base station 1

[0043] Mode 2: Uplink = Small base station 1, Downlink = Small base station 1

[0044] Mode 3: Uplink = Macro base station 1, Downlink = Macro base station 2

[0045] Mode 4: Uplink = Small base station 1, Downlink = Macro base station 1

[0046] Mode 5: Uplink = Macro base station 1, Downlink = Small base station 1

[0047] Mode 6: Uplink = Small base station 1, Downlink = Small base station 2

[0048] When the vehicle is connected to the base station \(b\), the base station is activated in the downlink with transmit power The maximum transmit powers of the macro base station, the small base station and the vehicle are denoted as

[0049] The channel gain between the base station and the vehicle, for the downlink is modeled as Here, represents the composite channel power gain between the base station and the vehicle considering Rayleigh fading and log-normal shadowing When, the fading component follows an exponential distribution with a mean of 1 / μ, \(\gamma\sim exp(1 / \mu)\). The shadowing component follows a log-normal distribution, that is where \(\omega\) d represents \(\chi\) d the mean in dB, \(\delta\)d Denote χ d Standard deviation in dB

[0050] Downlink path loss between the base station and the vehicle is a function of the distance between the base station and the vehicle. Here, consider an Urban Macro (UMa) scenario path model. The details are given on page 28 of 3GPP_TR_38.901 and are omitted here.

[0051] The downlink received power is where is a binary decision variable, defined as

[0052]

[0053] Assume that the vehicle can only access one base station in uplink or downlink, and there is

[0054] Assume that different frequency bands are used for uplink and downlink respectively. In the downlink, the total interference I at a typical vehicle d is composed of interference from macro base stations and small base stations.

[0055] Therefore, when the vehicle is associated with a macro base station or a small base station in the downlink, the signal-to-interference-plus-noise ratio is

[0056]

[0057] where is the received noise power, and I d is expressed as

[0058]

[0059] Based on the Shannon-Hartley theorem, the transmission rate of the vehicle in the downlink is given by the following formula:

[0060]

[0061] where is the bandwidth allocated by base station b to vehicle v.

[0062] In the second step, based on the flexible decoupling system architecture, construct a mixed-integer non-linear programming problem that maximizes the downlink network throughput.

[0063] Since the uplink and downlink in the flexible decoupling architecture do not interfere with each other in different frequency bands, the downlink total throughput can be optimized separately. The optimization objective is to maximize the downlink cumulative throughput of all vehicles. In this embodiment, the optimization problem is expressed as follows:

[0064]

[0065] In the optimization problem, is the total radio spectrum bandwidth in the downlink, is the total transmit power of the base stations in the downlink.

[0066] Constraint 1 ensures that the uplink and downlink communications of each vehicle meet the QoS requirements, that is, the downlink rate of each vehicle is required to reach at least To avoid the extreme situation where most of the bandwidth and power are allocated to a single vehicle during the multi-vehicle and multi-base station optimization process, this paper sets a maximum bandwidth limit and maximum transmit power for each base station in the downlink to each vehicle. This ensures an even distribution of bandwidth and power to conform to the actual scenario.

[0067] This optimization problem is a mixed integer non-linear programming problem and is NP-hard. Therefore, it is usually difficult to find the optimal solution. The following aims to obtain an efficient sub-optimal solution.

[0068] Step 3: Introduce a series of auxiliary variables, substitution variables, and mathematical scaling to transform the original problem into a standard convex optimization problem. The specific transformation steps are as follows:

[0069] (1) Associated variable and objective function optimization

[0070] Due to the discrete nature of the associated variable first relax to a continuous variable ranging from 0 to 1. Therefore,

[0071] At the same time, to maintain the binary nature of the associated variable, introduce a penalty term Therefore, can be rewritten as:

[0072]

[0073] where λ d is a hyperparameter.

[0074] Although the penalty term is a concave function, this penalty term is a quadratic function of one variable, and its maximum point is at . To guide to converge to 0 or 1, perform the following first-order Taylor expansion on it:

[0075]

[0076] where is the expansion point.

[0077] Since The function is not a concave function, and convex optimization tools cannot be used to optimize it. To maximize an objective function, its maximum value can be approximately found by maximizing its lower bound. Therefore, an auxiliary variable is introduced such that

[0078]

[0079] Therefore is relaxed to

[0080] In convex optimization, maximizing an objective function requires the function itself to be concave. To meet this requirement, the following mathematical transformation is performed

[0081] Using the mathematical identity 4xy = (x + y) 2 -(x - y) 2 , can be equivalently expressed as

[0082]

[0083] Since this function is still not concave, it is linearized using first-order Taylor expansion

[0084]

[0085] where and are the expansion points. Therefore is transformed into a concave function

[0086] Since the form of the above equation is too complex, for ease of representation, it is denoted as

[0087] Therefore can be rewritten as

[0088]

[0089] (2) Downlink radio resource allocation

[0090] After performing the above mathematical transformation, the constraint is transformed into the following two constraints

[0091]

[0092] where the first constraint is concave, while the second constraint is not

[0093] To handle the second constraint involving power and continuous variable it is transformed into the following relaxed form

[0094]

[0095] where is an alternative variable, and are the first-order Taylor expansion points. After transformation, the constraint condition becomes a concave constraint with respect to and as follows: The detailed derivation is as follows:

[0096] First, expand the SINR d term and perform the following operations.

[0097]

[0098] where, in (a)

[0099] Since the concavity and convexity of the above formula are uncertain. To solve this problem, an alternative variable is introduced such that

[0100] Substitute Equation into the above formula, and the above formula is directly transformed into the difference between two concave functions with respect to and The DC-Programming method can be used.

[0101] Then perform the first-order Taylor expansion to obtain its lower bound as follows:

[0102]

[0103] In step (a), the first half is in the form of ln(x) and is concave. For the second half, perform the first-order Taylor expansion at the point to obtain the lower bound in (b). The derivation is complete.

[0104] The introduced variable satisfies

[0105]

[0106] This formula is not concave, and the first-order Taylor expansion can be used to approximately linearize it to obtain

[0107]

[0108] Through the above mathematical processing, the constraint is transformed into a concave constraint, and the successive convex approximation can be used for further calculation.

[0109] Substitute the upper and lower bounds obtained above into the optimization objective problem, and the following convex optimization problem can be obtained:

[0110]

[0111]

[0112] where the matrix is the set of optimization variables.

[0113] The above uplink and downlink maximum throughput problem has been transformed into a standard convex optimization problem, which can be solved by the CVX toolbox, and can be optimized and solved in combination with the successive convex approximation algorithm.

[0114] The specific settings of the relevant parameters are shown in Table 1.

[0115] Table 1 System settings

[0116]

[0117]

[0118] Figure 2 Shows the solution convergence of the objective function in the downlink based on the successive convex approximation algorithm. By comparing the convergence processes under different vehicle densities, it can be found that this optimization method exhibits efficient convergence ability under different network scales. Specifically, the value of the system utility function rises rapidly in the initial iteration stage, then gradually approaches the steady state, and finally converges within 4 to 5 iterations. The experiment verified the stability of the results through repeated runs, and the deviation of the convergence trajectories of different repeated trials is extremely small, indicating that the optimization method is less sensitive to the initial conditions.

[0119] Figure 3 Presents the cumulative distribution function of the downlink rate of 10 vehicles in the scenarios of flexible decoupled access and coupled access. As can be seen from the figure, the cumulative distribution function curve of the downlink rate of flexible decoupled access is located to the right of the coupled access curve. This means that among the vehicles using flexible decoupled access, a higher proportion of vehicle users can achieve higher downlink rates, indicating that flexible decoupled access has a positive impact on improving the downlink rate. The cumulative distribution function curve of coupled access to the macro base station is always more to the right than that of coupled access to the small base station. At the same time, in the case of flexible decoupled access, there is an intersection point between the curves corresponding to the macro base station and the small base station. This is mainly because flexible decoupled access breaks the restriction that the uplink and downlink must be accessed through the same base station, and fully exploits the potential performance advantages of the small base station due to its lower transmission power but closer distance to the user.

[0120] Figure 4 Shows the average downlink rate when accessing the macro base station and the small base station under different numbers of vehicles. At Figure 4In this case, whether accessing a macro base station or a small base station, the downlink average rate of flexible decoupled access is higher than that of coupled access. In addition, the number of vehicles has a relatively small impact on the average rate of flexible decoupled access. When the number of vehicles is large, the uplink average rate of accessing a small base station is higher than that of accessing a macro base station. This is because the uplink rate is more significantly affected by channel conditions (especially path loss). Therefore, although the number of vehicles increases and interference increases, the small base station is closer to the vehicles compared to the macro base station. So, the signal-to-interference-plus-noise ratio (SINR) when accessing a small base station is generally higher, resulting in better performance under high vehicle loads.

[0121] Figure 5 The performance impacts of different base station association strategies on the downlink average rate are compared. The experiments cover four strategies: the uplink and downlink flexible decoupled access scheme proposed in the present invention, the association strategy based on the nearest distance, the association strategy based on the best channel gain, and the traditional coupled access method. The results show that the flexible decoupled scheme exhibits significant advantages in the downlink. Its core mechanism is to allow the uplink and downlink to independently select the optimal base station - the downlink preferentially accesses the base station with stronger coverage ability to enhance the signal strength, while the uplink selects the base station with less interference to reduce resource conflicts. This decoupled design significantly improves the spectrum utilization efficiency through dynamic resource allocation and interference co-management.

[0122] Based on the same inventive concept, an uplink and downlink flexible decoupled joint optimization system for a cellular vehicle-to-everything (C-V2X) network disclosed in an embodiment of the present invention includes:

[0123] A modeling module, configured to construct a mixed-integer non-linear programming problem for maximizing the downlink network throughput based on the architecture of a cellular vehicle-to-everything network with uplink and downlink flexible decoupled access; wherein the optimization variables include binary decision variables representing the association of vehicles with base stations in the downlink, the bandwidth allocated by the base station to the vehicles, and the transmit power of the base station; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmit power limit, and the balanced allocation of bandwidth and power;

[0124] A solving module, configured to introduce a series of auxiliary variables, substitution variables, and mathematical scaling to transform the original problem into a standard convex optimization problem for solution.

[0125] The present invention also discloses a computer program product, including a computer program, which implements the steps of the uplink and downlink flexible decoupled joint optimization method for a cellular vehicle-to-everything network when executed by a processor.

[0126] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or a controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or the controller, the steps of the method of the present invention are implemented. The program codes can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or a server. Where the present invention is not described in detail, it is the well-known technology to those skilled in the art.

[0127] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for jointly optimizing the flexible decoupling of uplink and downlink for cellular vehicle-to-everything, characterized in that Including the following steps: Based on the cellular vehicle-to-everything (C-V2X) system architecture with flexible uplink-downlink decoupled access, construct a mixed-integer non-linear programming problem to maximize the downlink network throughput; Among them, the optimization variables include binary decision variables representing the association between vehicles and base stations in the downlink, the bandwidth allocated by the base station to the vehicle, and the transmit power of the base station; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmit power limit, and the balanced allocation of bandwidth and power; Introduce a series of auxiliary variables, surrogate variables, and mathematical scaling to transform the original problem into a standard convex optimization problem for solution.

2. The method for jointly optimizing the flexible uplink-downlink decoupling for cellular vehicle-to-everything according to claim 1, wherein In the cellular vehicle-to-everything (C-V2X) system architecture with flexible uplink-downlink decoupled access, there are six access modes for the cellular vehicle-to-everything (C-V2X): Mode 1: Uplink = Macro Base Station 1, Downlink = Macro Base Station 1; Mode 2: Uplink = Small Base Station 1, Downlink = Small Base Station 1; Mode 3: Uplink = Macro Base Station 1, Downlink = Macro Base Station 2; Mode 4: Uplink = Small Base Station 1, Downlink = Macro Base Station 1; Mode 5: Uplink = Macro Base Station 1, Downlink = Small Base Station 1; Mode 6: Uplink = Small Base Station 1, Downlink = Small Base Station 2.

3. The method for jointly optimizing the flexible uplink-downlink decoupling for cellular vehicle-to-everything according to claim 1, wherein The downlink network throughput is expressed as: where is the bandwidth allocated by base station b to vehicle v, and SINR d is the signal-to-interference-plus-noise ratio of the vehicle when associated with the macro base station or small base station in the downlink, B is the number of base stations, which is the sum of the number of small base stations and macro base stations, and V is the number of vehicles.

4. A method for jointly optimizing the flexible uplink-downlink decoupling for cellular vehicle-to-everything (C-V2X), as claimed in claim 3, wherein In the introduction of a series of auxiliary variables, alternative variables, and mathematical scaling, the association variable between the vehicle and the base station is relaxed to a continuous variable ranging from 0 to 1, a penalty term is introduced and the first-order Taylor expansion is performed, and an auxiliary variable is introduced such that and using mathematical identities for after equivalent representation and then using the first-order Taylor expansion for linearization to obtain Rewrite in the optimization objective as λ d is a hyperparameter, is the expansion point.

5. A method for jointly optimizing the flexible uplink-downlink decoupling for cellular vehicle-to-everything (C-V2X), according to claim 4, characterized in that The introduced constraints are transformed into the following relaxation form: wherein is an alternative variable, is the first-order Taylor expansion point, is the downlink transmission power when vehicle v is connected to base station b, is the channel gain between base station b and vehicle b, log-normal shadowing variance.

6. The flexible uplink-downlink decoupling joint optimization method for cellular vehicle-to-everything network according to claim 5, characterized in that The transformed convex optimization problem is expressed as: s.t. where the matrix is the set of optimization variables, and are the base station and vehicle sets respectively, is the total radio spectrum bandwidth in the downlink, and are the base station maximum bandwidth limit and maximum transmit power, is the total transmit power of the base station in the downlink, and are the expansion points.

7. A flexible uplink-downlink decoupling and joint optimization system for cellular vehicle-to-everything, characterized in that Including: A modeling module for constructing a mixed-integer non-linear programming problem to maximize the downlink network throughput based on the cellular vehicle-to-everything (C-V2X) system architecture with flexible uplink-downlink decoupled access; Among them, the optimization variables include binary decision variables representing the association between vehicles and base stations in the downlink, the bandwidth allocated by the base station to the vehicle, and the transmit power of the base station; the constraints include the downlink rate requirements of each vehicle, the base station bandwidth limit and transmit power limit, and the balanced allocation of bandwidth and power; A solving module for introducing a series of auxiliary variables, surrogate variables, and mathematical scaling to transform the original problem into a standard convex optimization problem for solution.

8. A flexible uplink-downlink decoupling and joint optimization system for cellular vehicle-to-everything (C-V2X) according to claim 7, characterized in that The downlink network throughput is expressed as: where is the bandwidth allocated by base station b to vehicle v, and SINR d is the signal-to-interference-plus-noise ratio when the vehicle is associated with the macro base station or small base station in the downlink, B is the number of base stations, which is the sum of the number of small base stations and macro base stations, and V is the number of vehicles.

9. The flexible uplink-downlink decoupling and joint optimization system for cellular vehicle-to-everything according to claim 8, characterized in that, In the introduction of a series of auxiliary variables, substitution variables, and mathematical scaling, the association variable between the vehicle and the base station is relaxed to a continuous variable ranging from 0 to 1, a penalty term is introduced and the first-order Taylor expansion is performed, and an auxiliary variable is introduced such that and using mathematical identities for after equivalent representation and linearization using the first-order Taylor expansion, we get Rewrite in the optimization objective as λ d is a hyperparameter, is the expansion point.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it realizes the steps of a flexible uplink-downlink decoupled joint optimization method for cellular vehicle-to-everything (C-V2X) according to any one of claims 1-6.

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