V2x-based air-ground cooperative vehicle networking optimization and resource allocation method and system

Abstract

The application discloses a kind of based on V2X's air-ground cooperation vehicle networking height optimization and resource allocation method and system, propose V2U and V2V cooperation framework;Then, with the maximization of UAV-BS coverage radius as target, while maximizing V2U communication rate and;Considering CSI feedback delay, and optimizing UAV-BS height, power control and spectrum allocation, the V2U communication and rate maximization problem formulated is constrained by the capacity and reliability requirements of the proposed cooperation framework;Afterwards, the optimal UAV height is obtained by obtaining the stationary point through derivation, and on this basis, the resource allocation problem is decoupled, and is solved by combining closed-form solution derivation and graph theory.Compared with the prior art, the present application can effectively improve V2U communication and rate by optimizing height and resource allocation, and can solve the problems of base station coverage hole and local traffic overload in urban vehicle networking.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air-ground cooperative vehicle networking communication, and particularly relates to an air-ground cooperative vehicle networking height optimization and resource allocation method and system based on V2X. BACKGROUND

[0002] Low-latency high-reliability vehicle networking communication technology is of great significance to the safety class business application of intelligent transportation systems. The international standard third generation partnership project specially stipulates a higher priority quality of service for latency-sensitive communication scenarios, and the 5G / 6G system open network architecture defines a new type of air interface for direct communication to support the access of low-latency new businesses. Therefore, exploring efficient hybrid heterogeneous vehicle communication technology (vehicle-to-everything, V2X) to promote efficient wireless resource use and diversified wireless access of future vehicle networking has important research significance.

[0003] However, dynamic changes in network topology, vehicle speed, business and many other factors bring many challenges to vehicle networking communication: first, how to achieve seamless coverage in suburban, rural highways and hotspots. An intuitive solution is to deploy ground base stations in a super-dense manner, but this is not economically feasible. Second, how to increase wireless data traffic using effective resource allocation schemes under limited spectrum resources. Third, power allocation should also be reasonably designed for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links to meet capacity and reliability requirements. Finally, in a high-mobility vehicle network, it is difficult to obtain perfect CSI because the channel state information (CSI) of V2V links is only periodically reported to the base station.

[0004] In the face of the above challenges, unmanned aerial vehicles mounted base stations (UAV-BS) equipped with base stations are expected to become an important part of future intelligent transportation systems, which can provide seamless coverage for vehicles on highways with poor cellular infrastructure. UAV-BS can increase the information dimension of vehicle networking and provide a bridge for information interaction for vehicles experiencing link interruption. The combination of UAV-BS and vehicle networking to form air-ground cooperative vehicle networking can improve the coverage range of vehicle networking and further improve the transmission performance of vehicle networking.

[0005] The prior art one studies resource and power allocation in vehicular networks. Specifically, this work employs device-to-device (D2D) communication technology to enable autonomous data exchange among vehicles. In addition, the CSI of V2V links is only reported to the ground base station periodically. This work aims to maximize the total throughput of V2I links while guaranteeing the reliability of each V2V link by delaying CSI feedback. For the above problem, this work designs an algorithm with low complexity to find the optimal spectrum sharing strategy between V2I and V2V links and adjust their transmit power appropriately. Finally, simulation results show that the prior art one can effectively improve the total throughput of V2I links.

[0006] The prior art two studies relay selection in air-ground collaborative vehicular networks and proposes a relay selection scheme based on state transition probability and transmission consumption. Specifically, this work formalizes the relay selection problem as an optimization problem related to state transition probability and transmission consumption, uses opportunistic transmission for transmission, and sets state transition probability to simplify the original problem, reducing the algorithm complexity. Finally, based on Q-learning technology, the proposed multi-objective optimization problem is solved. Compared with existing work, it can effectively improve the transmission success rate and reduce the transmission delay.

[0007] The prior art three studies the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) technology in air base station assisted vehicular networks. The tethered air base station serves as an MEC server to provide MEC services for vehicles. Vehicles use NOMA to transfer their tasks to ABS. For the network considered, the problem of maximizing the average task processing rate is formulated by jointly optimizing the offloading decision, computing resource allocation, and power allocation. To solve this non-convex problem, first, the offloading decision and computing resource allocation are obtained with the help of numerical regularization and convex optimization solver. Then, by considering the decoding threshold of successive interference cancellation technology, the optimal transmit power allocation of vehicles is derived. Finally, simulation results show that the proposed algorithm has significant performance improvement in average task processing rate compared with current work in different urban scenarios.

[0008] However, the prior art has the following problems:

[0009] First, the main goal of prior art one is to enable vehicles to communicate with other vehicles, ground base stations, and pedestrians in real time. This work assumes that ground base stations can provide seamless communication services. In most cases, this assumption is valid. However, in suburban, rural highways, and hotspots, the cellular infrastructure may not be equipped to cover all vehicles. In addition, it is a challenging task to provide ubiquitous coverage for these specific scenarios when base stations experience unexpected hardware failures or damage.

[0010] Second, although prior arts two and three employ aerial base stations or drones to improve connectivity for vehicular networks, these works focus on V2U communication alone. V2U and V2V cooperative modes can further meet the growing demand for vehicle communication. Relying solely on V2U or V2V communication is insufficient to build intelligent transportation systems and smart cities. In addition, these works do not optimize the height of aerial base stations or drones, which is an important factor in improving transmission performance and coverage area.

[0011] Finally, prior arts two and three assume that perfect CSI can be obtained. However, due to the high mobility of vehicles, the CSI of V2V links is only reported to ground base stations periodically. In this case, the obtained CSI is imperfect, and there is a CSI feedback delay in the vehicle network. Considering this practical factor, the spectrum allocation scheme designed by previous works is not completely suitable for actual vehicular network communication scenarios. In addition, to meet the capacity requirements of V2U links and the reliability requirements of V2V links, power control issues should be studied. By properly allocating transmission power, cross-layer interference between V2U and V2V communication can be reduced. SUMMARY

[0012] The technical problem to be solved by the present application is to provide a V2X-based air-ground cooperative vehicular network height optimization and resource allocation method and system to solve the technical problems of base station coverage holes and local traffic overload in urban vehicular networks.

[0013] The application adopts the following technical solutions:

[0014] A V2X-based air-ground cooperative vehicular network height optimization and resource allocation method, comprising the following steps:

[0015] S1, an air-ground cooperative vehicular network model composed of one unmanned aerial vehicle equipped with a base station and V vehicles is constructed; a V2X communication model for V2U and V2V cooperation and an air-ground path loss model are constructed based on the air-ground cooperative vehicular network model;

[0016] S2, based on the air-ground collaborative V2X model constructed in step S1, the V2X communication model, the air-ground path loss model, the objective function and optimization conditions of the UAV height optimization problem, and the objective function and optimization conditions of the resource allocation problem are constructed;

[0017] S3, the optimal UAV height is obtained by obtaining the stationary point through derivation, and the resource allocation problem obtained in step S2 is decoupled based on this, and the maximum matching is obtained by combining the closed-form solution derivation and graph theory to solve, and the optimal spectrum sharing strategy is obtained.

[0018] Specifically, step S1 is specifically:

[0019] S101, an air-ground collaborative V2X model composed of one UAV-BS and V vehicles is constructed; considering a one-way highway section with a certain length, one independent UAV-BS follows the vehicle to provide service; the vehicle uses LTE-V technology to communicate with the UAV-BS through V2U communication; define V2U communication set is denoted as By using the inter-device communication technology, V2V communication is used to support autonomous data exchange between vehicles; in the air-ground collaborative V2X, there are K V2V communication pairs, K≤V, defined as Define symbol t k and r k Distinguish the sending end and the receiving end of the kth V2V pair;

[0020] S102, define as the transmit power of vehicle v when communicating through V2U, as the transmit power of V2V pair (t k , r k ) when communicating through V2V; define α v,u and h v,u as the large-scale fading and small-scale fading between vehicle v and UAV-BS, define and as the large-scale fading and small-scale fading between vehicles t k and r k ;

[0021] S103, based on the visible distance component and the non-visible distance component of the radio signal transmitted by the UAV-BS, the average path loss between the UAV-BS and the vehicle v is calculated

[0022] Further, in step S102, to ensure the reliability of the V2V link, The probability Pr{·} satisfies the following conditions:

[0023]

[0024] wherein, is the SINR of the k-th V2V pair, is the basic SINR requirement of the V2V link, is the maximum outage probability that can be tolerated.

[0025] Further, in step S103, the average path loss between the UAV-BS and the vehicle v is calculated as follows:

[0026]

[0027] wherein, PL(LoS) is the average path loss of the LoS component, Pr(LoS) is the probability of the occurrence of the LoS component for the ground cooperative V2X, PL(NLoS) is the average path loss of the NLoS component, Pr(NLoS) is the probability of the NLoS component, a and b are environmental parameters, c is the speed of light, f c is the communication frequency, H u is the height of the UAV-BS, is the horizontal projected distance between the vehicle v and the UAV-BS, a LoS is the average shadowing fading of the LoS component, a NLoS is the average shadowing fading of the NLoS component.

[0028] Specifically, in step S2, first, the height (H u ) of the UAV-BS is optimized to maximize the coverage radius of the UAV-BS as Reg(H u );

[0029] Then, by optimizing the power control strategy and the spectrum sharing strategy (χ v,k ), the V2U communication rate and the modeled V2U communication rate and maximization problem are subject to the basic rate requirement of the V2U link and the reliability of the V2V link constraints.

[0030] Further, the coverage radius maximization problem of the UAV-BS is modeled as:

[0031] P1:

[0032] s.t. H u ∈ [H min , H max ]

[0033] wherein, [H min , Hmax H is a height range of the UAV-BS, H max H is a maximum height, H min H is a minimum height; constraint H u H min H max restricts the height of the UAV-BS.

[0034] Further, the V2U communication rate and maximization problem can be modeled as:

[0035] P2:

[0036] s.t.C1.1:

[0037] C1.2:

[0038] C1.3:

[0039] C1.4:

[0040] C1.5:

[0041] C1.6:

[0042] C1.7:

[0043] In P2, P is a power range of V2U communication, P is a maximum power of V2U communication, P is a power range of V2V communication, P is a maximum power of V2V communication; constraints C1.1 and C1.2 guarantee the basic rate requirement and reliability of the air-ground collaborative V2X respectively; constraints C1.3, C1.4 and C1.5 jointly restrict the spectrum reuse scheme, and constraints C1.6 and C1.7 restrict the maximum transmit power of V2U communication and V2V communication respectively.

[0044] Specifically, step S3 is specifically:

[0045] S301, define θ v is an elevation angle between the vehicle v and the UAV-BS, PL th is a path loss threshold; when the UAV-BS provides service for the vehicle v; define the coverage radius of the UAV-BS as Reg(H u ), obtain the optimal height of the UAV-BS, obtain the optimal height of the UAV-BS

[0046] S302, χ v,k For each matching V2U and V2V link, the optimization problem P2 is decoupled into a power control problem and a spectrum allocation problem. Then, focusing on each matching V2U and V2V link, the power control strategy is derived. Based on the obtained power control strategy, the optimal spectrum allocation method is obtained using graph theory.

[0047] Furthermore, step S302 specifically includes:

[0048] S3021. Assume the spectrum allocation is given; for each matched V2U link and V2V link, define and solve the power control problem P3, and derive the optimal power control strategy.

[0049] Optimal power control strategy for:

[0050]

[0051]

[0052] in, This represents the maximum power for V2V communication. This is the maximum power for V2U communication. It is about implicit functions, It is about implicit functions, It is about implicit functions, It is about implicit functions, and As an intermediate auxiliary variable;

[0053] S3022. Substitute the optimal power control strategy into the optimization problem P2, define the spectrum allocation problem P4 and solve it to obtain the optimization problem P5; use the Hungarian algorithm to solve it to obtain the maximum matching, i.e. the optimal spectrum sharing strategy.

[0054] The specific optimization problem P5 is as follows:

[0055] P5:

[0056]

[0057]

[0058]

[0059] wherein V is the number of vehicles in the network, K is the number of V2V communication pairs in the network, χ v,k is the spectrum sharing strategy, and v,k is an intermediate variable, is the set of V2V communication pairs, is the set of V2U communications.

[0060] In a second aspect, embodiments of the present application provide a UAV assisted V2X based cooperative V2X system for height optimization and resource allocation, comprising:

[0061] a construction module configured to construct a UAV assisted cooperative V2X model comprising a UAV equipped with a base station and V vehicles, and to construct a V2X communication model and an air-ground path loss model based on the UAV assisted cooperative V2X model;

[0062] a function module configured to construct an objective function and optimization conditions for the UAV height optimization problem and an objective function and optimization conditions for the resource allocation problem based on the UAV assisted cooperative V2X model, the V2X communication model and the air-ground path loss model constructed by the construction module;

[0063] an allocation module configured to obtain the optimal UAV height by obtaining a stationary point through derivation, and to decouple the resource allocation problem based on the optimal UAV height, and to obtain a maximum matching by solving the decoupled resource allocation problem using closed-form solution derivation and graph theory to obtain the optimal spectrum sharing strategy.

[0064] Compared with the prior art, the present application has at least the following beneficial effects:

[0065] The UAV assisted V2X based cooperative V2X system for height optimization and resource allocation provides low latency and high reliability communication services to users by combining V2U communication and V2V communication using a hybrid heterogeneous V2X communication technology; the UAV height optimization problem is formalized as an optimization problem related to the UAV height, with the goal of maximizing the UAV coverage radius, and the resource allocation problem is formalized as an optimization problem related to power control, spectrum sharing, basic rate requirements of V2U links and reliability of V2V links, with the goal of maximizing the V2U communication rate; the optimal UAV height is obtained by obtaining a stationary point through derivation, and the resource allocation problem is decoupled based on the optimal UAV height, and a maximum matching is obtained by solving the decoupled resource allocation problem using closed-form solution derivation and graph theory to obtain the optimal spectrum sharing strategy, thereby providing seamless coverage for vehicles on highways with poor cellular infrastructure.

[0066] Further, compared with ground-based V2X, air-ground collaborative V2X has greater coverage, stronger situation awareness, and better dynamic reconstruction and disaster recovery capabilities. Especially in terms of large-scale broadcasting, cross-road network traffic situation awareness, rapid and flexible deployment and scheduling, air-ground collaborative V2X has incomparable advantages over ground-based V2X. In addition, V2X communication technology makes the network more flexible, enabling instant communication and dynamic adjustment, multiple connection methods, scalability and adaptability, edge computing support, and flexible application scenarios and services to meet the changing and complex traffic environment requirements. In addition, since V2U communication is based on air-ground collaborative transmission, the air-to-ground path loss needs to be modeled.

[0067] Further, to maximize the coverage radius of the UAV-BS, Reg(H u ), the height of the UAV-BS, H u , needs to be optimized. The reason is that the height of the UAV-BS, H u , will affect the path loss between the UAV and the vehicle, thereby affecting the coverage. In addition, according to Shannon's theorem, optimizing the power control strategy and the spectrum sharing strategy (χ v,k ) can maximize the V2U communication rate and At the same time, to ensure V2X communication, the basic rate requirement of the V2U link and the reliability constraint of the V2V link need to be considered.

[0068] Further, since the UAV height will affect the V2U communication rate and, the optimal UAV height is obtained by taking the derivative to get the stationary point, and on this basis, the resource allocation problem obtained in step S2 is decoupled, combined with closed-form solution derivation and graph theory to solve the maximum matching, and the optimal spectrum sharing strategy is obtained.

[0069] Further, since χ v,k ∈{0, 1}, the V2U communication rate and maximization problem is a mixed integer nonlinear programming (MINLP) problem related to discrete variables (χ v,k ) and continuous variables , and has Non-deterministic polynomial (NP) characteristics. Therefore, to simplify the calculation, first decouple the V2U communication rate and maximization problem into a power control problem and a spectrum allocation problem; then, focusing on each matched V2U and V2V link, the power control strategy is derived; then, based on the obtained power control strategy, the optimal spectrum allocation method is obtained using graph theory.

[0070] It can be understood that the beneficial effects of the above-mentioned second aspect can be referred to the relevant description in the above-mentioned first aspect, which will not be repeated here.

[0071] In summary, in the future-oriented intelligent transportation system application scenario, the introduction of air node assisted vehicle networking is expected to solve the problems of base station coverage holes and local traffic overload in urban vehicle networking. The present application gives the maximum coverage radius and optimal UAV-BS height of UAV-BS in different urban environments. At the same time, compared with the current work, the V2U communication rate and are effectively improved by optimizing the power control strategy and spectrum sharing strategy.

[0072] The technical solutions of the present application will be further described in detail below with the help of drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0073] Figure 1 The flowchart of the present application;

[0074] Figure 2 The schematic diagram of the air-ground collaborative vehicle networking of the present application;

[0075] Figure 3 The schematic diagram of the V2U and V2V cooperative communication mode of the present application;

[0076] Figure 4 The overall scheme framework of the present application.

[0077] Figure 5 The maximum coverage radius and optimal UAV-BS height of UAV-BS in different urban environments of the present application.

[0078] Figure 6 The performance comparison diagram of V2U communication and rate comparison of the present application and comparative schemes 1, 2 and 3. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present application will be described clearly and completely below with the help of the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0080] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the existence of described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0081] It should also be understood that the terms used in the specification and the following claims are for the purpose of describing particular embodiments and are not intended to be limiting, as the specific scope of the invention is disclosed in the attached claims. As used in this specification and the appended claims, the singular forms "a," "an" and "the" encompass plural referents unless the context clearly dictates otherwise.

[0082] It should also be further understood that the term "and / or" used in the specification and the following claims, refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations, for example, A and / or B, can mean the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the front and rear associated objects.

[0083] It should be understood that, although the terms first, second, third, etc. can be employed in the embodiments of the present invention to describe a certain range, etc., these ranges should not be limited to these terms. These terms are only used to distinguish the ranges from each other. For example, the first range can also be referred to as the second range, and similarly, the second range can also be referred to as the first range, without departing from the scope of the embodiments of the present invention.

[0084] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".

[0085] Various structural diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These drawings are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and certain details can be omitted. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0086] The application provides a V2X-based air-ground cooperative vehicle networking height optimization and resource allocation method, proposes a V2U and V2V cooperation framework; then, taking the maximization of the coverage radius of the UAV-BS as the target, while maximizing the V2U communication rate and, considering the CSI feedback delay, and optimizing the UAV-BS height, power control and spectrum allocation, the V2U communication and rate maximization problem formulated is constrained by the capacity and reliability requirements of the proposed cooperation framework; then, the optimal UAV height is obtained by taking the derivative to obtain the stationary point, and on this basis, the resource allocation problem is decoupled, and is solved by combining the closed-form solution derivation and graph theory. Compared with the prior art, in the future-oriented intelligent transportation system application scenario, the application introduces an air node assisted vehicle networking, and the V2U communication and rate can be effectively improved by optimizing the height and resource allocation, the problems of base station coverage holes and local traffic overload in urban vehicle networking can be solved, and the maximum coverage radius and optimal UAV-BS height of the UAV-BS in different urban environments are given. At the same time, compared with the current work, the V2U communication rate and are effectively improved.

[0087] Referring to Figure 1 and Figure 4 , the application is a V2X-based air-ground cooperative vehicle networking height optimization and resource allocation method, comprising the following steps:

[0088] S1, referring to Figure 2 , an air-ground cooperative vehicle networking model composed of one unmanned aerial vehicle mounted base station (UAV-BS) and V vehicles is constructed; based on the air-ground cooperative vehicle networking model, a V2X communication model of V2U and V2V cooperation and an air-ground path loss model are constructed;

[0089] S101, an air-ground cooperative vehicle networking model composed of one UAV-BS and V vehicles is constructed; considering a one-way highway section with a certain length, one independent UAV-BS follows the vehicle to provide services; the vehicle uses LTE-V technology to communicate with the UAV-BS for V2U communication. Define as the V2U communication set, denoted as At the same time, by using device-to-device (D2D) communication technology, V2V communication is used to support autonomous data exchange between vehicles; in the air-ground cooperative vehicle networking, it is assumed that there are K V2V communication pairs, K≤V, defined as In addition, symbols t k and r k are defined to distinguish the transmitter (Tx) and receiver (Rx) of the kth V2V pair

[0090] Assume the spectrum for the V2U link has been allocated by the operator; furthermore, the uplink spectrum allocated for V2U communication is shared with the V2V pair; define the spectrum sharing strategy as χ. v,k If the k-th V2V pair is related to the v-th V2V pair If vehicles share the same spectrum, then χ v,k =1, otherwise χ v,k =0; in addition, define H u The height of the UAV-BS; when the height of the UAV-BS is H u At that time, the coverage radius of UAV-BS is Reg(H) u );

[0091] S102, Definition The transmit power for vehicle v during V2U communication. For V2V pairs (t) k ,r k Transmit power during V2V communication; in addition, α is defined respectively. v,u and h v,u For large-scale and small-scale fading between vehicle v and UAV-BS; in addition, define respectively and For vehicle t k and r k Large-scale fading and small-scale fading between;

[0092] Based on the considered V2U and V2V cooperative communication mechanism, the signal-to-interference-plus-noise ratio (SINR) of the v-th vehicle is... Represented as:

[0093]

[0094] Where, σ 2 For noise power; in equation (1), For the sending end t k Large-scale fading between UAV-BS, For the sending end t k Small-scale fading between V2V and UAV-BS; V2V communication can interfere with V2U communication due to spectrum reuse.

[0095] According to equation (1), the transmission rate C of the v-th vehicle is... v,u for:

[0096]

[0097] in, The basic speed requirement for the vth vehicle;

[0098] Furthermore, the SINR of the k-th V2V pair Represented as:

[0099]

[0100] in, For vehicle v and receiver r k Large-scale decay between For vehicle v and receiver r k Small-scale fading between them; similarly, V2U communication can interfere with V2V communication;

[0101] Let the channel state information (CSI) between the vehicle and the UAV-BS be |h v,u | 2 α v,u and Accurate acquisition; however, the CSI between vehicles, i.e. and This needs to be estimated because, in high-speed vehicle-to-everything (V2X) networks, there is a delay in the CSI report from the vehicle link to the UAV-BS; the feedback period is defined as T. delay By using a first-order Gaussian-Markov process, a feedback period T is obtained. delay Modeling the small-scale fading within the range yields:

[0102]

[0103] Where h is the estimated small-scale fading for the current period. For the small-scale decay of the previous period, f c For communication frequency, s rel Let be the relative speed of the vehicles, c be the speed of light, and δ be the channel distribution difference, which follows the law of motion. Distribution; furthermore, in equation (4), J0(·) is a zero-order Bessel function of the first kind, Indicates the maximum Doppler frequency deviation;

[0104] According to equation (4), and They were rewritten as follows:

[0105]

[0106]

[0107] Substituting equations (5) and (6) into equation (3), we get:

[0108]

[0109] Definition The basic SINR requirement for V2V link; if denotes the V2V link between vehicle t k and r k is unreliable and cannot support V2V communication; therefore, to ensure the reliability of the V2V link, we have:

[0110]

[0111] where Pr{·} denotes the probability of , and is the maximum outage probability that can be tolerated.

[0112] The radio signal transmitted by the UAV-BS includes two parts, i.e., the line-of-sight (LoS) component and the non-line-of-sight (NLoS) component; the probability of the appearance of the LoS component in the air-ground collaborative V2X network is Pr(LoS):

[0113]

[0114] where a and b are environmental parameters, which are constants; in formula (9), is the horizontal projection distance between vehicle v and the UAV-BS, which is calculated as:

[0115]

[0116] where x u and x v are the X-axis coordinates of the UAV-BS and vehicle v, and y u and y v are the Y-axis coordinates of the UAV-BS and vehicle v.

[0117] In addition, the probability of the NLoS component is defined as Pr(NLoS); since Pr(LoS)+Pr(NLoS)=1, we have:

[0118]

[0119] The average path loss of the LoS component is PL(LoS):

[0120]

[0121] where a LoS is the average shadow fading of the LoS component, and the result of formula (12) is in dB.

[0122] Moreover, the average path loss of the NLoS component, PL(NLoS), is:

[0123]

[0124] where a NLoS is the average shadowing fading of the NLoS component, the result of equation (13) is in dB.

[0125] According to equations (9)-(13), the average path loss between the UAV-BS and the vehicle v, is calculated as:

[0126]

[0127] S2, based on the air-ground collaborative V2X model constructed in step S1, the V2X communication model, the air-ground path loss model, the objective function and optimization conditions of the UAV height optimization problem, and the objective function and optimization conditions of the resource allocation problem are constructed;

[0128] The performance of the air-ground collaborative V2X is highly dependent on the height (H u ) of the UAV-BS, the power control strategy and the spectrum sharing strategy (χ v,k ).

[0129] Specifically, first, the height (H u ) of the UAV-BS is optimized to maximize the coverage radius of the UAV-BS, Reg(H u );

[0130] Then, by optimizing the power control strategy and the spectrum sharing strategy (χ v,k ), the V2U communication rate and are maximized, where the modeled V2U communication rate and maximization problem is subject to the basic rate requirement of the V2U link and the reliability of the V2V link constraint.

[0131] In addition, since accurate CSI is difficult to obtain in air-ground collaborative V2X, CSI feedback delay, i.e., imperfect CSI, is considered.

[0132] The coverage radius maximization problem of the UAV-BS is modeled as:

[0133]

[0134] In P1, [H min , H max ] is the height range of the UAV-BS, where H maxHmax min Hmin u ∈ [H min ,H max ] limits the height of UAV-BS;

[0135] The V2U communication rate and maximization problem is modeled as:

[0136]

[0137] In P2, denotes the power range of V2U communication, is the maximum power of V2U communication; in addition, denotes the power range of V2V communication, is the maximum power of V2V communication; constraints C1.1 and C1.2 guarantee the basic rate requirement and reliability of air-ground collaborative V2V respectively; constraints C1.3, C1.4 and C1.5 jointly limit the spectrum reuse scheme.

[0138] Specifically, the spectrum of each V2U link can only be reused to one V2V pair; at the same time, each V2V pair can only access the spectrum of one V2U link. Constraints C1.6 and C1.7 limit the maximum transmit power of V2U communication and V2V communication respectively.

[0139] S3, the optimal UAV height is obtained by taking the derivative to get the stationary point, and the resource allocation problem is decoupled based on this, combined with closed-form solution derivation and graph theory to obtain maximum matching, and the optimal spectrum sharing strategy is obtained.

[0140] S301, define θ v is the elevation angle between vehicle v and UAV-BS, and we have:

[0141]

[0142] Substituting equation (17) into equation (14), we have:

[0143]

[0144] Define PL th as the path loss threshold; in order to ensure the quality of service of vehicles, when , the UAV-BS provides service for vehicle v; therefore, the coverage radius of UAV-BS is Reg(H u ) is defined as:

[0145]

[0146] Since Reg(H u ) is related to Hu implicit function, thus it is necessary to obtain the unique stationary point numerically; according to the stationary point, the optimal height of the UAV-BS, i.e., the maximum coverage radius, can be obtained;

[0147]

[0148] For we have:

[0149]

[0150] Define opt as the optimal elevation angle; when v = 0, we have opt , To achieve we have:

[0151]

[0152] For a given path loss threshold PL th , we have:

[0153]

[0154] where Reg max is the maximum coverage radius of the UAV-BS;

[0155] According to equation (23), the optimal height of the UAV-BS is:

[0156]

[0157] S302, since v,k ∈ {0,1}, the optimization problem P2 is a mixed integer nonlinear programming problem related to discrete variables (χ v,k ), continuous variables and has non-deterministic polynomial characteristics; to simplify the calculation, first decouple P1 into power control problem and spectrum allocation problem; then, focusing on each matched V2U and V2V link, the power control strategy is derived as shown in Figure 3 ; after that, based on the obtained power control strategy, the optimal spectrum allocation method is obtained using graph theory.

[0158] S3021, assuming that the spectrum allocation has been given; for each matched V2U and V2V link, the power control problem is defined as:

[0159]

[0160]

[0161] To solve the optimization problem P3, one needs to satisfy Thus, rewriting (7) gives

[0162]

[0163] where Γ and Φ are two independent exponential random variables with unit mean. P3 is solved by considering the following two cases:

[0164] Case 1:

[0165] From Case 1, we have

[0166]

[0167]

[0168] Substituting (29) into (28), we have

[0169]

[0170]

[0171]

[0172] Under Case 1, P3 is rewritten as

[0173]

[0174] Case 2:

[0175]

[0176]

[0177] Substituting (35) into (34), we have

[0178]

[0179]

[0180]

[0181]

[0182] Under Case 2, P3 is rewritten as

[0183]

[0184] For the optimization problems P3(a) and P3(b), the optimal power control strategy can be derived

[0185]

[0186]

[0187]

[0188] In the equations (41) and (44), is the implicit function of , is the implicit function of ; therefore, we have:

[0189]

[0190] In addition, is the implicit function of , is the implicit function of ; therefore, we have:

[0191]

[0192] In the equation (45), when the implicit function Θ is expressed as:

[0193]

[0194] In the equation (46), when the implicit function Ξ is expressed as:

[0195]

[0196] S3022, the optimal power control strategy, i.e. equations (41) and (44), is substituted into the optimization problem P2, and the spectrum allocation problem is expressed as:

[0197]

[0198] To solve the optimization problem P4, a new parameter is introduced and expressed as:

[0199]

[0200]

[0201] Substituting equation (50) into the optimization problem P4, we have:

[0202]

[0203] In the optimization problem P5, since the new variable Delta v,k is adopted, the constraint is omitted; the optimization problem P5 is a bipartite graph matching problem, and the Hungarian algorithm is adopted to obtain the maximum matching, that is, the optimal spectrum sharing strategy.

[0204] In another embodiment of the present application, a V2X-based air-ground collaborative vehicle networking height optimization and resource allocation system is provided, which can be used to implement the above-mentioned V2X-based air-ground collaborative vehicle networking height optimization and resource allocation method. Specifically, the V2X-based air-ground collaborative vehicle networking height optimization and resource allocation system includes a construction module, a function module, and a distribution module.

[0205] The construction module constructs an air-ground collaborative vehicle networking model composed of one unmanned aerial vehicle equipped with a base station and V vehicles; based on the air-ground collaborative vehicle networking model, a V2X communication model of V2U and V2V cooperation and an air-ground path loss model are constructed.

[0206] The function module constructs the objective function and optimization conditions of the unmanned aerial vehicle height optimization problem and the objective function and optimization conditions of the resource allocation problem based on the air-ground collaborative vehicle networking model, the V2X communication model, and the air-ground path loss model constructed by the construction module.

[0207] The distribution module obtains the optimal UAV height by obtaining the stationary point through derivation, and on this basis, decouples the resource allocation problem obtained by the function module, solves it by combining the closed-form solution derivation and graph theory to obtain the maximum matching, and obtains the optimal spectrum sharing strategy.

[0208] In still another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the V2X-based air-ground cooperative vehicle networking optimization and resource allocation method, which comprises:

[0209] An air-ground cooperative vehicle networking model composed of one unmanned aerial vehicle equipped with a base station and V vehicles is constructed; a V2X communication model of V2U and V2V cooperation and an air-ground path loss model are constructed based on the air-ground cooperative vehicle networking model; based on the constructed air-ground cooperative vehicle networking model, V2X communication model and air-ground path loss model, a target function and optimization condition of the unmanned aerial vehicle optimization problem and a target function and optimization condition of the resource allocation problem are constructed; the optimal UAV height is obtained by obtaining the stationary point through derivation, and the obtained resource allocation problem is decoupled on this basis, and the maximum matching is obtained by solving the closed-form solution derivation and graph theory to obtain the optimal spectrum sharing strategy.

[0210] In another embodiment of the present application, the present application also provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a terminal device, used for storing programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the terminal device, and of course can also include the expansion storage medium supported by the terminal device. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory or a non-volatile memory such as at least one disk memory.

[0211] The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the V2X-based air-ground collaborative vehicle networking optimization and resource allocation method in the above embodiments; the one or more instructions in the computer readable storage medium are loaded and executed by the processor as follows:

[0212] An air-ground collaborative vehicle networking model composed of one unmanned aerial vehicle equipped with a base station and V vehicles is constructed; a V2X communication model of V2U and V2V cooperation and an air-ground path loss model are constructed based on the air-ground collaborative vehicle networking model; based on the constructed air-ground collaborative vehicle networking model, V2X communication model and air-ground path loss model, a target function and optimization condition of the unmanned aerial vehicle optimization problem and a target function and optimization condition of the resource allocation problem are constructed; the optimal UAV height is obtained by obtaining the stationary point through derivation, and the obtained resource allocation problem is decoupled based on this, and the maximum matching is obtained by solving the closed-form solution and graph theory to obtain the optimal spectrum sharing strategy.

[0213] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0214] The experiment simulates a V2X-based air-ground cooperative vehicle networking height optimization and resource allocation method and an existing mechanism based on the same network parameters to verify the superiority of the method; the specific steps are as follows:

[0215] The same network parameters are: the number of vehicles is 50, the number of V2V pairs is 50, the height interval of UAV-BS is 0-1000m, the maximum transmission power of V2U is 30dBm, the maximum transmission power of V2V is 30dBm, the transmission power of UAV-BS is 33dBm, the basic rate requirement of the vehicle is 1bps / Hz, the basic SINR requirement of the V2V link is 6dB, the tolerable maximum interruption probability is 0.1%, the vehicle speed is 60km / h, and the channel bandwidth is 10MHz.

[0216] The following two aspects of data are counted:

[0217] 1. The maximum coverage radius of UAV-BS;

[0218] 2. V2U communication rate and.

[0219] The results are the average values after 5000 simulations.

[0220] Please refer to Figure 5 , which gives the maximum coverage radius of UAV-BS and the optimal UAV-BS height in different urban environments. From the figure, the optimal UAV-BS height in different urban scenarios can be observed. It can be found that the maximum coverage radius of UAV-BS is about 800m in the suburban scenario, 630m in the dense urban scenario, and 400m in the high-rise urban scenario. With the increase of urban building density, the maximum coverage radius of UAV-BS gradually decreases. As mentioned above, since the method considers the one-way rural highway scenario, the data of the suburban scenario is used in the simulation. Figure 6

[0221] Please refer to Figure 6 , which shows the comparison of the present invention and three existing technologies in terms of V2U communication and rate. The present invention gives the maximum coverage radius of UAV-BS and the optimal UAV-BS height in different urban environments; then, the technical effects of the present invention in terms of V2U communication and rate are compared with three existing technologies; the specific comparison scheme is as follows:

[0222] In comparison scheme 1, NOMA technology and air base station are used to form air-ground cooperative vehicle networking, and the transmission power is optimized.

[0223] In comparison scheme 2, the transmission power and spectrum are optimized, and the rate and reliability requirements and CSI delay are considered.

[0224] ​In the comparative scheme 3, the explicit expression of the optimal transmit power is derived, and the Ford-Fulkerson algorithm is used to optimize the spectrum sharing scheme.

[0225] Since the comparative scheme 2 and the comparative scheme 3 use ground base stations to provide communication services for vehicles, in this case, the transmission mode is called V2I communication. It is observed that as the number of vehicles increases, the V2U / V2I communication and rate continue to increase.

[0226] It is worth noting that the present application is superior to the other 3 comparative schemes in terms of V2U communication and rate. In addition, compared with ground vehicle networking, air-ground collaborative vehicle network has better transmission performance. This is because the limited height of the ground base station is more likely to cause NLoS transmission of the vehicle. In this case, the radio signal may be interfered, which will not meet the capacity requirement. In contrast, vehicles cooperate with UAV-BS through heterogeneous V2U communication to improve LoS transmission to improve the data exchange process between vehicles and UAV-base stations. Simulation results show that compared with the traditional base station-vehicle cooperation framework, the UAV-vehicle cooperation framework proposed by the present application has an advantage in transmission rate, providing better services for various applications of vehicle networking.

[0227] In summary, the air-ground collaborative vehicle networking based on V2X in the present application is highly optimized and resource allocation method and system, which uses UAV-BS to realize larger coverage, stronger situation awareness capability and better dynamic reconstruction and disaster recovery capability, and introduces V2X communication technology to make the network have higher flexibility, can through instant communication and dynamic adjustment, multiple connection modes, scalability and adaptability, edge computing support and flexible application scenarios and services, meet the changing and complex traffic environment demand. On this basis, the coverage radius of UAV-BS is maximized, and the V2U communication rate is maximized. The present application has an important promoting effect on intelligent transportation systems, through data exchange and information sharing, can improve traffic efficiency, enhance safety, optimize traffic flow, etc., provide more intelligent, efficient and convenient solutions for urban traffic management and travel.

[0228] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, any modification made according to the technical idea of the present application on the basis of the technical scheme, falls within the protection scope of the claims of the present application.

Claims

1. A method for optimizing the height and allocating resources in a V2X-based air-to-ground cooperative vehicle network, characterized in that, Includes the following steps: S1, Construct a drone equipped with a base station and V A vehicle-to-everything (V2X) communication model consisting of several vehicles; and an air-to-ground path loss model based on the V2X model for V2U and V2V collaboration. S2. Based on the air-ground cooperative vehicle network model, V2X communication model, and air-ground path loss model constructed in step S1, construct the objective function and optimization conditions for the UAV altitude optimization problem, and the objective function and optimization conditions for the resource allocation problem. S3. Obtain the optimal UAV altitude by differentiating the stationary point. Based on this, decouple the resource allocation problem obtained in step S2, and solve it using closed-form derivation and graph theory to obtain the maximum matching, thus obtaining the optimal spectrum sharing strategy, specifically: S301, Definition For vehicles v The elevation angle between the UAV-BS and the UAV-BS This is the path loss threshold; when At that time, UAV-BS is a vehicle v Provide services; define the coverage radius of UAV-BS as... To obtain the optimal altitude of the UAV-BS, the optimal altitude of the UAV-BS was obtained. ; S302, The optimization problem P2 is decoupled into a power control problem and a spectrum allocation problem. Then, focusing on each matched V2U and V2V link, a power control strategy is derived. Based on the obtained power control strategy, the optimal spectrum allocation method is obtained using graph theory, as follows: S3021. Assume the spectrum allocation is given; for each matched V2U link and V2V link, define and solve the power control problem P3, and derive the optimal power control strategy. ; Optimal power control strategy for: in, This represents the maximum power for V2V communication. This is the maximum power for V2U communication. It is about implicit functions, It is about implicit functions, It is about implicit functions, It is about implicit functions, and As an intermediate auxiliary variable; S3022. Substitute the optimal power control strategy into optimization problem P2, and define the spectrum allocation problem. The optimization problem P5 is obtained by solving it; the maximum matching, i.e. the optimal spectrum sharing strategy, is obtained by using the Hungarian algorithm. The specific optimization problem P5 is as follows: in, The number of vehicles in the network. The number of V2V communication pairs in the network. For spectrum sharing strategy, As an intermediate variable, For V2V communication pairs, This is a V2U communication set.

2. The method for height optimization and resource allocation of V2X-based air-to-ground cooperative vehicle-to-everything (V2X) network according to claim 1, characterized in that, Step S1 is as follows: S101, Construct a UAV-BS and V A vehicle-to-ground cooperative vehicle network model consisting of several vehicles; considering a one-way highway segment of a certain length, with one independent UAV-BS following the vehicle to provide services; the vehicle uses LTE-V technology to communicate with the UAV-BS via V2U; defining... For V2U communication sets, denoted as By utilizing device-to-device (V2V) communication technology, V2V communication is used to support autonomous data exchange between vehicles; in air-to-ground cooperative vehicle networks, there are... K One V2V communication pair, Defined as , define symbols and Distinguish the first k indivual( The V2V pair's sender and receiver; S102, Definition For vehicles v Transmit power during V2U communication For V2V pairs Transmit power during V2V communication; definition and For vehicles v Large-scale fading and small-scale fading between UAV-BS are defined. and For vehicles and Large-scale fading and small-scale fading between; S103. Based on the line-of-sight and non-line-of-sight components of the radio signals transmitted by UAV-BS, the relationship between UAV-BS and the vehicle is calculated. v Average path loss between .

3. The method for height optimization and resource allocation of V2X-based air-to-ground cooperative vehicle-to-everything (V2X) network according to claim 2, characterized in that, In step S102, the reliability of the V2V link is ensured. probability The following conditions must be met: in, For the first k SINR of V2V pairs For the basic SINR requirements of V2V links, This represents the maximum tolerable interruption probability.

4. The method for height optimization and resource allocation of V2X-based air-to-ground cooperative vehicle-to-everything (V2X) network according to claim 2, characterized in that, In step S103, the UAV-BS and the vehicle v Average path loss between The calculation is as follows: in, The average path loss of the Loss component. The probability of Loss of Space (LoS) components appearing in air-to-ground cooperative vehicle networks. The average path loss for the NLoS component. The probability of the NLoS component. a and b For environmental parameters, At the speed of light, For communication frequency, For the height of UAV-BS, For vehicles v Horizontal projection distance between and UAV-BS The average shadow fading of the Loss component, The average shadow fading is represented by the NLoS component, where LoS is the visible distance and NLoS is the non-visible distance.

5. The method for height optimization and resource allocation of V2X-based air-to-ground cooperative vehicle-to-everything (V2X) network according to claim 1, characterized in that, In step S2, the height of the UAV-BS is first optimized. To maximize the coverage radius of UAV-BS ; Then, by optimizing the power control strategy and spectrum sharing strategy Maximize V2U communication speed and , The modeled V2U communication rate and maximization problem are subject to the basic rate requirements of the V2U link. Reliability of V2V links constraint.

6. The method for height optimization and resource allocation of V2X-based air-to-ground cooperative vehicle-to-everything (V2X) network according to claim 5, characterized in that, The problem of maximizing the coverage radius of UAV-BS is modeled as follows: in, For the height range of UAV-BS, For maximum height, Minimum height; constraint Limit the height of UAV-BS.

7. The method for optimizing the height and allocating resources of V2X-based air-to-ground cooperative vehicle-to-everything network according to claim 6, characterized in that, The V2U communication rate maximization problem can be modeled as follows: In P2, Indicates the power range of V2U communication. This is the maximum power for V2U communication. Indicates the power range of V2V communication. The maximum power for V2V communication is defined by constraints C1.1 and C1.2, which respectively ensure the basic rate requirements and reliability of air-to-ground cooperative vehicle networks. Constraints C1.3, C1.4, and C1.5 together limit the spectrum reuse scheme, while constraints C1.6 and C1.7 limit the maximum transmission power for V2U and V2V communication, respectively.

8. A V2X-based air-to-ground cooperative vehicle-to-everything (V2X) highly optimized and resource-allocation system, characterized in that, include: Building blocks, constructing a drone equipped with a base station and V A vehicle-to-ground cooperative vehicle network model consisting of several vehicles; Based on the air-ground cooperative vehicle-to-everything (V2X) communication model and air-ground path loss model, a V2U and V2V collaborative V2X communication model and an air-ground path loss model are constructed. The function module, based on the air-ground cooperative vehicle network model, V2X communication model, and air-ground path loss model built by the building module, constructs the objective function and optimization conditions for the UAV altitude optimization problem and the resource allocation problem. The allocation module obtains the optimal UAV altitude by differentiating the stationary points. Based on this, it decouples the resource allocation problem obtained from the function module, and combines closed-form solution derivation and graph theory to obtain the maximum matching, thus deriving the optimal spectrum sharing strategy, specifically: definition For vehicles v The elevation angle between the UAV-BS and the UAV-BS This is the path loss threshold; when At that time, UAV-BS is a vehicle v Provide services; define the coverage radius of UAV-BS as... To obtain the optimal altitude of the UAV-BS, the optimal altitude of the UAV-BS was obtained. ; The optimization problem P2 is decoupled into a power control problem and a spectrum allocation problem. Then, focusing on each matched V2U and V2V link, a power control strategy is derived. Based on the obtained power control strategy, the optimal spectrum allocation method is obtained using graph theory, as follows: Assuming the spectrum allocation is given, for each matched V2U and V2V link, define and solve the power control problem P3, and derive the optimal power control strategy. ; Optimal power control strategy for: in, This represents the maximum power for V2V communication. This is the maximum power for V2U communication. It is about implicit functions, It is about implicit functions, It is about implicit functions, It is about implicit functions, and As an intermediate auxiliary variable; Substituting the optimal power control strategy into optimization problem P2, we define the spectrum allocation problem. The optimization problem P5 is obtained by solving it; the maximum matching, i.e. the optimal spectrum sharing strategy, is obtained by using the Hungarian algorithm. The specific optimization problem P5 is as follows: in, The number of vehicles in the network. The number of V2V communication pairs in the network. For spectrum sharing strategy, As an intermediate variable, For V2V communication pairs, This is a V2U communication set.

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