Air-ground cooperative vehicle-mounted ad hoc network time delay optimization method and system

Abstract

The invention discloses an air-ground cooperative vehicle-mounted self-organizing network time delay optimization method and system. An air-ground cooperative vehicle-mounted self-organizing network architecture is provided; then, taking the minimization of the total network delay as a target, and jointly optimizing channel allocation and power control; in consideration of the influence of large-scale vehicle access on network transmission delay, the NOMA technology is introduced into the proposed network architecture so as to improve the data transmission rate; afterwards, a closed-form solution of an optimal power control strategy is derived, a channel allocation problem is decoupled into a maximum weighted independent set searching problem on this basis, and solution is carried out by combining closed-form solution derivation and iterative optimization. Through joint optimization of channel allocation and power control, network transmission time delay can be effectively reduced, the low-time-delay and high-reliability transmission problem of vehicle-mounted communication in mountainous areas, rural roads and hot spots is solved, and diversified service requirements in the scene of the Internet of Vehicles are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of air-ground collaborative vehicle-mounted self-organizing network communication, and specifically relates to an air-ground collaborative vehicle-mounted self-organizing network delay optimization method and system. Background Art

[0002] With the rapid development of intelligent transportation systems, vehicle-mounted communication technology has become the key to achieving efficient information interaction between vehicles. However, existing vehicle-mounted communication technologies are mainly focused on communication between ground vehicles, and there are relatively few studies on vehicle-mounted self-organizing networks for air-ground collaboration. In actual scenarios, the driving environment of vehicles is complex and changeable, and traditional communication methods are difficult to meet the real-time communication needs of vehicles during high-speed driving. At the same time, with the continuous improvement of the intelligence level of vehicles, higher requirements are put forward for the rate, reliability and latency of data transmission. In addition, the limited channel resources and the interference problem between vehicles also bring challenges to vehicle-mounted communication. Building an air-ground collaborative vehicle-mounted self-organizing network has become a very potential solution. By introducing aerial platforms such as solar drones and forming a collaborative network with ground vehicles, the communication coverage can be effectively expanded and the service time can be improved. At the same time, with the help of advanced communication technologies such as NOMA technology, the spectrum utilization rate can be improved to meet the communication needs of more vehicles. In addition, through reasonable resource allocation and optimization algorithms, the performance and service quality of the network can be further improved, providing strong support for the comprehensive development of intelligent transportation.

[0003] Prior art 1 is dedicated to optimizing the communication link between the vehicle and the roadside unit (RSU) in the vehicle network to reduce the data transmission delay from the vehicle to the roadside unit. Specifically, in order to improve the stability and transmission efficiency of the link, prior art 1 optimizes the transmission power and channel allocation of the vehicle.

[0004] The second existing technology proposes an air-ground collaborative vehicle network architecture, which assists the ground vehicle subnet in information transmission by combining air-to-air and air-to-ground communications, and optimizes the power of NOMA transmission. Specifically, drones can be sent to hot spots to collect information and transmit it to ground vehicles. In addition, when the link of the ground vehicle subnet is interrupted, drones can be used as temporary relays to ensure network connectivity.

[0005] The existing technology of three-air-ground integrated vehicle network has intermittent connectivity characteristics and uses the "store-carry-forward" opportunistic information transmission method to transmit data. On this basis, a relay selection scheme is designed by jointly considering the state transition probability and transmission consumption, and the spectrum allocation of NOMA transmission is optimized, which can effectively reduce transmission delay and routing overhead.

[0006] However, the existing technology still has shortcomings: First, the existing technology 1 considers the power optimization and resource allocation between vehicles, but ignores the mobility of drones, and provides access and relay services for vehicles in the form of drone hovering. Compared with ground base stations, drones can follow the movement of vehicles and relay the wireless signals of base stations by planning tracks, thereby establishing a more reliable data connection link. In addition, the existing technology 1 does not comprehensively consider the impact of channel allocation, power control, decoding threshold and channel estimation on the total transmission delay of the Internet of Vehicles.

[0007] Secondly, although the existing technologies 2 and 3 use aerial base stations or drones to improve the connectivity of the Internet of Vehicles, they do not consider the differences in latency requirements for different services in the Internet of Vehicles. Due to the high mobility of vehicles and drones, latency needs to be considered according to specific services in actual Internet of Vehicles applications. In addition, these works do not consider channel state information, which is an important factor in improving the transmission performance and coverage area of ​​the air-ground integrated Internet of Vehicles.

[0008] Finally, although the introduction of non-orthogonal multiple access technology in existing technologies 2 and 3 can improve data transmission rate and spectrum utilization, it will also increase the reception complexity of enabling continuous interference cancellation technology. The impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in air-ground collaborative vehicle-mounted self-organizing networks should be further analyzed. In addition, in order to increase coverage area and endurance, solar-powered drones should be considered to improve flight efficiency and wider range of surveillance and communication. Summary of the invention

[0009] In order to overcome the above shortcomings, the present invention provides an installation and use method of a shield segment pore water pressure gauge with automatic alarm. The technical solution adopted by the present invention is: A method for optimizing time delay of an air-ground collaborative vehicle-mounted self-organizing network comprises the following steps: S1. Construct an air-ground collaborative vehicle-mounted self-organizing network model including 1 solar-powered UAV and V intelligent networked vehicles, and construct a V2V channel allocation model and an intelligent networked vehicle power control model based on the air-ground collaborative vehicle network model; S2. Based on the air-ground cooperative vehicle-mounted self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model constructed in step S1, the objective function and optimization conditions of the network total delay minimization problem are constructed; S3, based on the objective function and optimization conditions of the network total delay minimization problem constructed in step S2, the problem is iteratively solved by closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy; S4. Based on the optimal channel allocation strategy and power allocation strategy iteratively solved in step S3, analyze the impact of orthogonal multiple access and non-orthogonal multiple access on the data transmission of the air-ground collaborative vehicle-mounted self-organizing network, and derive the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle-mounted self-organizing network.

[0010] Further optimization, the step S1 is specifically as follows: S101. Construct an air-ground collaborative vehicle-mounted self-organizing network model. Intelligent networked vehicles travel in a convoy, and solar drones follow the convoy. Each intelligent networked vehicle is equipped with a single antenna, works in half-duplex mode, and has T tasks to be transmitted to the solar drone. There are K channels in the network. Define the vth The tth intelligent connected vehicle The task is v t , where v t Size Bit; The T tasks to be transmitted to the solar-powered drone are: T={1,...,T}, The K channels are: K={1,...,K} The bandwidth of each channel is B0Hz; S102. In the air-ground cooperative vehicle-mounted self-organizing network, each intelligent connected vehicle is assigned a channel for data transmission. Different intelligent connected vehicles use a non-orthogonal multiple access mechanism to share K channels. For intelligent connected vehicles multiplexed on the same channel, the solar drone uses continuous interference elimination technology to sequentially decode overlapping signals. To obtain the maximum spectrum efficiency, the decoding order is in descending order of channel power gain. The channel allocation strategy is If the kth is assigned to the vth intelligent connected vehicle, then otherwise S103, determine P as the power control strategy, For the kth The small-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV on the channel and the large-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV are defined as α v , the power control strategy is: Where P v is the transmission power of the vth intelligent connected vehicle for data transmission. The vth intelligent connected vehicle transmits data v t Transmission delay T to solar-powered drone v for: Among them, γ th is the decoding threshold for successful execution of continuous interference cancellation technology in actual NOMA applications, β(·) is the indicator function, β(·)∈{0,1}; if β(·)=1, otherwise β(·)=0; in this case, T v It is defined as infinity, which means that the channel allocation and power control strategies at this time are not feasible.

[0011] Further optimization, the step S102 is specifically as follows: the signal to noise ratio of the vth intelligent connected vehicle on the kth channel It is expressed as: Where j is the jth channel occupying the kth channel at the same time. Intelligent connected vehicles, P j is the transmission power of the jth intelligent connected vehicle for data transmission, α v is the large-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV; For the kth The small-scale fading component between the vth intelligent connected vehicle and the solar drone on the channel; α j and are the large-scale fading and small-scale fading components between the j-th intelligent connected vehicle and the solar-powered UAV, P N is the noise power; The achievable data rate R of the vth intelligent connected vehicle v : R v =B0log2(1+γ v ), Among them, B0 represents the channel bandwidth from solar drone to intelligent connected vehicle; γ v is the signal-to-interference-noise ratio of the vth intelligent connected vehicle,

[0012] Further optimization, the step S2 is specifically: constructing the total delay by combining channel allocation, power control, decoding threshold and channel estimation Optimization problem, the optimization problem is: in, represents that the kth channel is allocated to the vth intelligent connected vehicle; P max is the maximum transmission power of intelligent connected vehicles. In P1, constraint C1 limits each intelligent connected vehicle to occupy only one channel. Constraints C2 and C3 jointly define that each channel can accommodate at most two intelligent connected vehicles. Constraint C4 represents the transmission power range of each intelligent connected vehicle.

[0013] Further optimization, step S3 is specifically as follows: S301, decoupling the optimization problem P1 into a power control problem P2 determined by a given channel allocation strategy and a channel allocation problem P3 determined by a given power control strategy; S302: There are three situations in the optimization problem P2 in the air-ground cooperative vehicle self-organizing network: A. On the kth channel, both the vth and jth intelligent connected vehicles can use the non-orthogonal multiple access mechanism for data transmission, that is, and B. On the kth channel, only one intelligent connected vehicle can transmit data, that is, or At this point, the data transmission mode degenerates into an orthogonal multiple access mechanism; C. On the kth channel, no data is transmitted, that is, and S303, equivalently transforming the optimization problem P3 into the problem of finding the maximum weighted independent set in the weighted undirected graph G, using a heuristic algorithm to solve until convergence, and obtaining an optimized channel allocation strategy.

[0014] Further optimization, in step S301, given the channel allocation strategy S, the power control problem P2 model is constructed as: Among them, (v,j,k) means that the vth and jth intelligent connected vehicles occupy the kth channel at the same time; In addition, it is assumed that the size of each data to be transmitted is the same, that is, Given the power control strategy P, the channel allocation problem model is constructed as:

[0015] Further optimization, in step S302, the optimal power control strategy (P v ,P j ) * for:

[0016] Further optimization, step S303 is specifically as follows: Define G = {A, B, C} as a weighted undirected graph, where A is the vertex set, B is the edge set, and C is the weight set of all vertices; in G, the ath The combination of vertices introduced (v a ,j a ,ka ), where v a ∈V,j a ∈V,v a ≠j a And k a ∈K; Assume a v ∈A and a j ∈A; in this case, if and only if the ath v and a j When the vertices contain 1-2 identical intelligent connected vehicles or the same channels, the a v and a j There is an edge between the vertices, represented by (a v ,a j ).

[0017] The weight c of the a-th vertex a for: c a =T v ((P v ,P j ) * ,k a )+T j ((P v ,P j ) * ,k a ) Calculated as:

[0018] Further optimization, step S4 is specifically as follows: For a given P v and P j , the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground cooperative vehicle ad hoc network Calculated as:

[0019] A time delay optimization system for air-ground collaborative vehicle-mounted self-organizing network, comprising: The network module builds an air-ground collaborative vehicle-mounted self-organizing network model consisting of 1 solar-powered UAV and V intelligent networked vehicles; based on the air-ground collaborative vehicle network model, a V2V channel allocation model and an intelligent networked vehicle power control model are built; The construction module constructs the objective function and optimization conditions for minimizing the total network delay based on the constructed air-ground cooperative vehicle self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model; The optimization module, based on the objective function and optimization conditions of the constructed network total delay minimization problem, it iteratively solves the problem through closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy; The performance analysis module analyzes the impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in the air-ground collaborative vehicle self-organizing network based on the iterative optimal channel allocation strategy and power allocation strategy, and derives the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle self-organizing network.

[0020] The beneficial effects of the present invention are: 1. The present invention proposes an air-ground collaborative vehicle-mounted self-organizing network architecture, with the goal of minimizing the total network delay, and jointly optimizes channel allocation and power control. Considering the impact of large-scale vehicle access on network transmission delay, the NOMA technology is introduced into the proposed network architecture to improve the data transmission rate. After that, the closed-form solution of the optimal power control strategy is derived, and on this basis, the channel allocation problem is decoupled into the problem of finding the maximum weighted independent set, which is solved by combining the closed-form solution derivation and iterative optimization. Finally, compared with the existing mechanism, the present invention can achieve delay optimization, that is, reduce the total task processing delay; 2. The present invention introduces solar-powered drones that can fly at higher altitudes and have a wider field of view and coverage. Drones can serve as aerial base stations to communicate with intelligent networked vehicles, fill in the blind spots of ground base stations, and ensure unimpeded communication between vehicles and between vehicles and the outside world in remote areas, mountainous areas, or complex terrains such as highways. Especially on some mountain roads, ground base station signals may not cover all areas, resulting in interrupted communication between vehicles. Through air-ground collaboration, drones can provide signal coverage over these areas to ensure safe driving and communication of vehicles. 3. The application of non-orthogonal multiple access (NOMA) technology is the key to improving data transmission efficiency in air-ground collaboration. NOMA allows multiple intelligent connected vehicles to transmit data simultaneously on the same channel. Through reasonable power allocation and signal processing, spectrum utilization is improved. Compared with traditional orthogonal multiple access (OMA), NOMA can support more vehicles to communicate under the same spectrum resources, thereby increasing the capacity of the system. In addition, by jointly optimizing channel allocation and power control strategies, air-ground collaboration can dynamically allocate resources according to the channel conditions and business needs of the vehicles, further improving data transmission efficiency and reducing transmission delay; 4. When intelligent connected vehicles and solar-powered drones are moving at high speed, the channel state will keep changing, which brings great challenges to communication. The air-ground collaboration uses a first-order Gauss-Markov process to estimate the small-scale fading component, which can obtain channel state information in a timely manner and adjust communication parameters based on this information to adapt to complex dynamic environments. At the same time, the maneuverability of the drone allows it to flexibly adjust its position and posture according to the vehicle's position and channel conditions to optimize the communication link and improve communication quality; 5. Through the iterative optimization framework, the optimal power control strategy is derived based on channel allocation, making resource allocation more reasonable and efficient. When the channel conditions change, the network performance can be kept stable by recalculating the optimal power control strategy and channel allocation, ensuring the reliability and timeliness of data transmission. The solution to the maximum weighted independent set problem can minimize the total delay of the system and improve the data transmission efficiency and overall performance of the air-ground collaborative network. In addition, performance analysis proves that the probability of non-orthogonal multiple access mechanism in air-ground collaborative vehicle-mounted self-organizing network is better than that of orthogonal multiple access mechanism; In summary, the present invention introduces the collaboration between solar-powered drones and ground vehicles to effectively expand the communication coverage and overcome the limitations of ground communications. Air-ground collaboration can improve channel quality and reduce the impact of channel fading caused by vehicle movement and environmental changes. A total delay optimization problem model is constructed, and a variety of main control factors affecting the transmission delay are comprehensively considered. The non-convex planning problem is converted into power control and channel allocation sub-problems for iterative solution to achieve the minimization of the total delay. Considering the high-speed movement characteristics of vehicles, the first-order Gauss-Markov process is used to estimate channel fading, which can adjust the transmission strategy in time according to changes in vehicle position and speed, and can still maintain good communication performance in complex and changeable vehicle networking scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of the present invention; Figure 2 It is a schematic diagram of the air-ground collaborative vehicle-mounted self-organizing network of the present invention; Figure 3 A performance comparison diagram comparing the total task processing latency of the embodiment of the present invention with Comparative Example 1, Comparative Example 2, Comparative Example 3, Comparative Example 4 and a benchmark solution; Figure 4 A performance comparison diagram comparing the average task processing delay of an embodiment of the present invention with Comparative Example 1, Comparative Example 2, Comparative Example 3, Comparative Example 4 and a benchmark example. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned purposes, features and advantages of the present invention, the present invention is described in detail below in conjunction with specific embodiments. The following embodiments are implemented based on the technical solutions of the present invention, and detailed implementation methods and specific operating procedures are given. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the following embodiments.

[0023] The present invention provides a method for optimizing the delay of an air-ground collaborative vehicle-mounted self-organizing network, and proposes an air-ground collaborative vehicle-mounted self-organizing network architecture; then, with the goal of minimizing the total network delay, the channel allocation and power control are jointly optimized; considering the impact of large-scale vehicle access on network transmission delay, the NOMA technology is introduced into the proposed network architecture to improve the data transmission rate; then, the closed-form solution of the optimal power control strategy is derived, and on this basis, the channel allocation problem is decoupled into the problem of finding the maximum weighted independent set, and the closed-form solution derivation and iterative optimization are combined to solve it. Compared with the prior art, the present invention can effectively reduce the network transmission delay by optimizing channel allocation and power control, solve the problem of low-latency and high-reliability transmission of vehicle-mounted communications in mountainous areas, rural roads and hot spots, and meet the business needs in the vehicle networking scenario.

[0024] See also Figure 1 The present invention provides a method for optimizing the delay of an air-ground collaborative vehicle-mounted self-organizing network, comprising the following steps: S1. Construct an air-ground collaborative vehicle-mounted self-organizing network model consisting of 1 solar-powered UAV and V intelligent networked vehicles; construct a V2V channel allocation model and an intelligent networked vehicle power control model based on the air-ground collaborative vehicle network model; S101. Construct an air-ground collaborative vehicle-mounted self-organizing network model, which includes 1 solar-powered UAV and V intelligent networked vehicles. The intelligent networked vehicles travel in a convoy, and the solar-powered UAV follows the convoy. Each intelligent networked vehicle is equipped with a single antenna, works in half-duplex mode, and has T tasks to be transmitted to the solar-powered UAV, defined as T = {1, ..., T}. At the same time, consider that there are K channels in the network, defined as K = {1, ..., K}, and the bandwidth of each channel is B0 Hz. Define the vth The tth intelligent connected vehicle The task is v t , data v t Size Bit; According to step S101, the method constructs an air-ground collaborative vehicle-mounted self-organizing network model consisting of 1 solar-powered UAV and V intelligent networked vehicles; S102. In the air-ground collaborative vehicle-mounted self-organizing network, each intelligent connected vehicle is assigned a channel for data transmission. Different intelligent connected vehicles share K channels using the non-orthogonal multiple access (NOMA) mechanism. For intelligent connected vehicles multiplexed on the same channel, the solar drone uses the successive interference cancellation (SIC) technology to sequentially decode the overlapping signals. To obtain maximum spectral efficiency, the optimal decoding order is in descending order of channel power gain (CPG). Definition is the channel allocation strategy; if the kth channel is allocated to the vth intelligent connected vehicle, then otherwise Therefore, the signal-to-interference-noise ratio of the vth intelligent connected vehicle on the kth channel is It is expressed as: Where j is the jth channel occupying the kth channel at the same time. Intelligent connected vehicles, P j is the transmission power of the jth intelligent connected vehicle for data transmission, α j and are the large-scale fading and small-scale fading components between the j-th intelligent connected vehicle and the solar-powered UAV, P N is the noise power; According to formula (1), the achievable data rate R of the vth intelligent connected vehicle is v : R v =B0log2(1+γ v ), (2) Among them, γ v is the signal-to-interference-noise ratio of the vth intelligent connected vehicle, According to step S102, the method constructs a V2V channel allocation model.

[0025] S103. Definition is the power control strategy, where P v is the transmission power of the vth intelligent connected vehicle for data transmission; definition For the kth The small-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV on the channel is defined as α. v The vth intelligent connected vehicle sends data v t Transmission delay T to solar-powered drone v for: Among them, γ this the decoding threshold for successful execution of continuous interference cancellation technology in actual NOMA applications, β(·) is the indicator function, β(·)∈{0,1}; if β(·)=1, otherwise β(·)=0; in this case, T v It is defined as infinity, which means that the channel allocation and power control strategies at this time are not feasible.

[0026] According to step S103, the method constructs a power control model for an intelligent connected vehicle. S2. Based on the air-ground cooperative vehicle-mounted self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model constructed in step S1, the objective function and optimization conditions of the network total delay minimization problem are constructed; By jointly considering channel allocation, power control, decoding threshold and channel estimation, the total delay The optimization problem is modeled as: Among them, P max is the maximum transmission power of intelligent connected vehicles; in P1, constraint C1 limits each intelligent connected vehicle to occupy only one channel; constraints C2 and C3 jointly define that each channel can accommodate a maximum of two intelligent connected vehicles; constraint C4 gives the transmission power range of each intelligent connected vehicle.

[0027] According to step S2, the method constructs the objective function and optimization conditions of the delay optimization problem. S3. Based on the objective function and optimization conditions of the network total delay minimization problem constructed in step S2, the problem is iteratively solved through closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy, which specifically includes the following sub-steps.

[0028] S301, the optimization problem P1 is a mixed integer and non-convex programming, which is difficult to solve directly. Decouple the optimization problem P1 into two sub-problems, namely, the power control problem P2 determined by a given channel allocation strategy and the channel allocation problem P3 determined by a given power control strategy; Given a channel allocation strategy S, the power control problem can be expressed as: The definition (v, j, k) indicates that the vth and jth intelligent connected vehicles occupy the kth channel at the same time; except In addition, it is assumed that the size of each data to be transmitted is the same, that is, Given a power control strategy P, the channel allocation problem can be modeled as: S302: For the optimization problem P2, three situations in the air-ground collaborative vehicle self-organizing network are considered: Case 1: On the kth channel, both the vth and jth intelligent connected vehicles can use the non-orthogonal multiple access mechanism for data transmission, that is, and Case 2: On the kth channel, only one intelligent connected vehicle can transmit data, that is, or At this point, the data transmission mode degenerates into an orthogonal multiple access mechanism; Case 3: No data is transmitted on the kth channel, that is and First, to satisfy and We can get: Then, for the vth and jth intelligent connected vehicles, we can get: Since the delay is only affected by the achievable data rate, the essence of minimizing the delay is to maximize the rate. According to equations (7) and (8), we can get: in, According to formula (9), the optimal power control strategy (P v ,P j ) * for:

[0029] S303, equivalently transforming the optimization problem P3 into the problem of finding the maximum weighted independent set in the weighted undirected graph G, using a heuristic algorithm to solve until convergence, and obtaining an optimized channel allocation strategy.

[0030] Define G = {A, B, C} as a weighted undirected graph, where A is the vertex set, B is the edge set, and C is the weight set of all vertices; in G, the ath The combination of vertices introduced (v a ,j a ,k a ), where v a ∈V,j a ∈V,v a ≠j a And k a ∈K; Assume a v ∈A and a j∈A; in this case, if and only if the ath v and a j When the vertices contain the same intelligent connected vehicles (1 or 2) or the same channel, the a v and a j There is an edge between the vertices, represented by (a v ,a j ); The weight c of the a-th vertex a It can be expressed as: c a =T v ((P v ,P j ) * ,k a )+T j ((P v ,P j ) * ,k a ). (11) can be calculated as: Among them, H G (a) is the neighbor node of the a-th vertex in the weighted undirected graph G. For the a′th (a′∈{H G (a)∪{a}}) vertex in subgraph G m The number of neighbor nodes in .

[0031] According to step S3, the method iteratively solves the problem through closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy.

[0032] S4. Based on the optimal channel allocation strategy and power allocation strategy iteratively solved in step S3, analyze the impact of orthogonal multiple access and non-orthogonal multiple access on the data transmission of the air-ground collaborative vehicle-mounted self-organizing network, and derive the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle-mounted self-organizing network, which specifically includes the following sub-steps.

[0033] S401, using orthogonal multiple access technology, consider that there is only the jth intelligent connected vehicle on the kth channel; in this case, a dedicated time slot is allocated to the jth intelligent connected vehicle. The minimum time interval of each time slot is θ t,j for

[0034] S402. If non-orthogonal multiple access technology is used, the vth intelligent connected vehicle may be allowed to use the kth channel for data transmission at the same time; it should be noted that even if the vth and jth intelligent connected vehicles use non-orthogonal multiple access technology to share the kth channel, the vth intelligent connected vehicle cannot reduce the transmission performance of the jth intelligent connected vehicle; otherwise, if orthogonal multiple access technology is used, the kth channel cannot be shared by the vth and jth intelligent connected vehicles; In this case, R v The following constraints must be met: For a given P v and P j , the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground cooperative vehicle ad hoc network It can be calculated as: in Φ(·) is the Gaussian probability integral.

[0035] S403. The proof of formula (14) is as follows: According to formula (13), the probability can be further rewritten as: Based on formula (15), using algebraic operations, we can get: To successfully implement the SIC technique, this paper has an implicit constraint, namely In this case, equation (16) can be composed of two cases: Case 1: Case 2: In case 1, we have: Similarly, in case 2, we have: and therefore, It consists of two parts, which can be obtained: Since the constraint of the optimal power control strategy is P v ≥P j , so ρ v ≥ρ jIn this case, according to the order statistics, and The joint probability density function of It can be expressed as: To simplify the notation, let and

[0036] Based on the binomial expansion, Pr{κ1} can be further rewritten as: In formula (23), ε1(P v ,P j )=exp{-ε2(P v ,P j )x-ε3(P v ,P j )x 2},in and To facilitate the calculation of Gaussian probability integral, further rewrite equation (23) to obtain: in, Similarly, by using the joint probability density function Pr{κ2} can be expressed as: in, Substituting equations (26) and (28) into equation (20), we can obtain equation (14), thus completing the proof.

[0037] A time delay optimization system for an air-ground collaborative vehicle-mounted self-organizing network. The system can be used to implement the above-mentioned time delay optimization method for an air-ground collaborative vehicle-mounted self-organizing network. Specifically, the time delay optimization system for an air-ground collaborative vehicle-mounted self-organizing network includes a network module, a construction module, an optimization module and a performance analysis module.

[0038] The network module builds an air-ground collaborative vehicle-mounted self-organizing network model consisting of 1 solar-powered UAV and V intelligent networked vehicles; based on the air-ground collaborative vehicle network model, a V2V channel allocation model and an intelligent networked vehicle power control model are built; The construction module constructs the objective function and optimization conditions for minimizing the total network delay based on the constructed air-ground cooperative vehicle self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model; The optimization module, based on the objective function and optimization conditions of the constructed network total delay minimization problem, it iteratively solves the problem through closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy; The performance analysis module analyzes the impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in the air-ground collaborative vehicle self-organizing network based on the iterative optimal channel allocation strategy and power allocation strategy, and derives the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle self-organizing network.

[0039] The present invention also includes a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the air-ground collaborative vehicle-mounted self-organizing network delay optimization method, including: A model of an air-ground cooperative vehicle-mounted self-organizing network consisting of 1 solar-powered UAV and V intelligent connected vehicles is constructed; a V2V channel allocation model and an intelligent connected vehicle power control model are constructed based on the air-ground cooperative vehicle network model; based on the constructed air-ground cooperative vehicle-mounted self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model, the objective function and optimization conditions of the network total delay minimization problem are constructed; based on the constructed objective function and optimization conditions of the network total delay minimization problem, the problem is iteratively solved by closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy. Based on the iteratively solved optimal channel allocation strategy and power allocation strategy, the impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in the air-ground cooperative vehicle-mounted self-organizing network is analyzed, and the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground cooperative vehicle-mounted self-organizing network is derived.

[0040] The present invention also includes a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended 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 here can be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0041] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the air-ground collaborative vehicle-mounted self-organizing network delay optimization method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: A model of an air-ground cooperative vehicle-mounted self-organizing network consisting of 1 solar-powered UAV and V intelligent connected vehicles is constructed; a V2V channel allocation model and an intelligent connected vehicle power control model are constructed based on the air-ground cooperative vehicle network model; based on the constructed air-ground cooperative vehicle-mounted self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model, the objective function and optimization conditions of the network total delay minimization problem are constructed; based on the constructed objective function and optimization conditions of the network total delay minimization problem, the problem is iteratively solved by closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy. Based on the iteratively solved optimal channel allocation strategy and power allocation strategy, the impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in the air-ground cooperative vehicle-mounted self-organizing network is analyzed, and the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground cooperative vehicle-mounted self-organizing network is derived.

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] The air-ground collaborative vehicle-mounted self-organizing network delay optimization method of the present invention is specifically as follows: By jointly considering channel allocation, power control, decoding threshold and channel estimation, the total delay optimization problem is modeled as a mixed integer and non-convex programming problem P1. Constraint C1 in P1 limits each intelligent connected vehicle to occupy only one channel, constraints C2 and C3 jointly define that each channel can accommodate a maximum of two intelligent connected vehicles, and constraint C4 gives the transmission power range of each intelligent connected vehicle.

[0044] Then, given the channel allocation, the closed-form solution of the optimal power control strategy is derived. At this time, the power control problem is expressed as P2. By introducing a combination to represent the relationship between the intelligent connected vehicle and the channel, the formulas that satisfy different conditions are discussed separately, and then the optimal power control strategy on the kth channel is obtained.

[0045] Afterwards, assuming a given power control strategy, the channel allocation problem is modeled as P3. By defining it as a weighted undirected graph, the original problem is equivalently transformed into the problem of finding the maximum weighted independent set in a weighted undirected graph. A heuristic algorithm is designed to solve it. In the algorithm, the vertex set is obtained by calculating the relevant equations, and the optimal vertex is selected. It is continuously updated until the conditions are met and the optimal channel allocation and corresponding power control strategy are output, ultimately achieving the optimal allocation of communication and computing resources in the air-ground collaborative network to reduce latency.

[0046] The technical effects of the present invention are described in detail below in conjunction with simulation.

[0047] This experiment evaluates the performance of the proposed total delay optimization scheme through simulation experiments and compares it with four state-of-the-art schemes, as shown in the following comparative examples 1-4. At the same time, the orthogonal multiple access mechanism is used as a benchmark scheme for comparison. The specific steps are as follows: The same network parameters are: The number of intelligent connected vehicles is [8,80], the number of channels in the network is [4,40], the maximum transmission power of intelligent connected vehicles is 30dBm, the bandwidth of each channel is 180kHz, the size of each data to be transmitted is 1MB, the decoding threshold for successful execution of continuous interference cancellation technology in actual NOMA applications is [20,50]dB, the noise power is -174dBm / Hz, the coverage range of the drone is 2km, the signal frequency is 2.1GHz, the vehicle moving speed is [60,100]km / h, the channel feedback delay is 1ms, and the three-dimensional path loss model is used to characterize the path loss between the drone and the vehicle 20log 10 (d[m])+20log 10 (f[Hz])-147.55+η1h1+η2h2.

[0048] The following two aspects of data are collected: 1. Total transmission delay; 2. Average transmission delay.

[0049] The results are for the simulation 5×10 4 The average value after running.

[0050] The performance of the present invention is compared with that of Comparative Example 1, Comparative Example 2, Comparative Example 3, Comparative Example 4 and the benchmark example. Figure 3 and Figure 4 The specific comparison scheme is as follows:

[0051] Comparative Example 1 Time division multiple access technology is used to allow multiple users to use the same frequency in different time slots.

[0052] Comparative Example 2 Drones are used for network coverage, and NOMA technology is used for information transmission.

[0053] Comparative Example 3 Drones were used for network coverage and the power of NOMA transmission was optimized.

[0054] Comparative Example 4 Drones were used for network coverage and spectrum allocation for NOMA transmission was optimized.

[0055] Benchmark Example The Orthogonal Multiple Access (OMA) mechanism is taken as a benchmark example.

[0056] See also Figure 3 , which gives the relationship between the total transmission delay and the number of intelligent networked vehicles. From this figure, it can be seen that under different numbers of intelligent networked vehicles, the performance of the present invention is significantly better than the other five comparison schemes, that is, it has the lowest total transmission delay. Taking the number of intelligent networked vehicles of 24 as an example, the total transmission delay of the present invention is reduced by 47.54% relative to the comparison scheme. Taking the number of intelligent networked vehicles of 40 as an example, the total transmission delay of the present invention is reduced by 34.83% relative to the comparison scheme. Taking the number of intelligent networked vehicles of 56 as an example, the total transmission delay of the present invention is reduced by 12.48% relative to the comparison scheme. Taking the number of intelligent networked vehicles of 72 as an example, the total transmission delay of the present invention is reduced by 18.88% relative to the comparison scheme. Taking the number of intelligent networked vehicles of 88 as an example, the total transmission delay of the present invention is reduced by 22.99% relative to the baseline scheme.

[0057] See also Figure 4 , which gives the relationship between the average transmission delay and the number of intelligent networked vehicles. From this figure, it can be seen that as the number of intelligent networked vehicles increases, the average transmission delay of the present invention increases slowly, always remains at a low level, and is significantly lower than that of comparative examples 1-4 and the orthogonal multiple access mechanism.

[0058] In summary, the present invention introduces the collaboration between solar-powered drones and ground vehicles to effectively expand the communication coverage and overcome the limitations of ground communications. Air-ground collaboration can improve channel quality and reduce the impact of channel fading caused by vehicle movement and environmental changes. A total delay optimization problem model is constructed, and a variety of main control factors affecting the transmission delay are comprehensively considered. The non-convex planning problem is converted into power control and channel allocation sub-problems for iterative solution to achieve the minimization of the total delay. Considering the high-speed movement characteristics of vehicles, the first-order Gauss-Markov process is used to estimate channel fading, which can adjust the transmission strategy in time according to changes in vehicle position and speed, and can still maintain good communication performance in complex and changeable vehicle networking scenarios.

[0059] The above shows and describes the main features, methods of use, basic principles and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements according to actual conditions, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the delay of an air-ground collaborative vehicle-mounted self-organizing network, characterized in that: The following steps are involved: S1. Construct an air-ground collaborative vehicle-mounted self-organizing network model including 1 solar-powered UAV and V intelligent networked vehicles, and construct a V2V channel allocation model and an intelligent networked vehicle power control model based on the air-ground collaborative vehicle network model; S2. Based on the air-ground cooperative vehicle-mounted self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model constructed in step S1, the objective function and optimization conditions of the network total delay minimization problem are constructed; S3, based on the objective function and optimization conditions of the network total delay minimization problem constructed in step S2, the problem is iteratively solved by closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy; S4. Based on the optimal channel allocation strategy and power allocation strategy iteratively solved in step S3, analyze the impact of orthogonal multiple access and non-orthogonal multiple access on the data transmission of the air-ground collaborative vehicle-mounted self-organizing network, and derive the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle-mounted self-organizing network.

2. The method for optimizing the time delay of an air-ground collaborative vehicle-mounted self-organizing network according to claim 1, characterized in that: Step S1 is specifically as follows: S101. Construct an air-ground collaborative vehicle-mounted self-organizing network model. Intelligent networked vehicles travel in a convoy, and solar drones follow the convoy. Each intelligent networked vehicle is equipped with a single antenna, works in half-duplex mode, and has T tasks to be transmitted to the solar drone. There are K channels in the network. Define the vth The tth intelligent connected vehicle The task is v t , where v t Size Bit; The T tasks to be transmitted to the solar-powered drone are: T={1,...,T}, The K channels are: K={1,...,K} The bandwidth of each channel is B0Hz; S102. In the air-ground cooperative vehicle-mounted self-organizing network, each intelligent connected vehicle is assigned a channel for data transmission. Different intelligent connected vehicles use a non-orthogonal multiple access mechanism to share K channels. For intelligent connected vehicles multiplexed on the same channel, the solar drone uses continuous interference elimination technology to sequentially decode overlapping signals. To obtain the maximum spectrum efficiency, the decoding order is in descending order of channel power gain. The channel allocation strategy is If the kth is assigned to the vth intelligent connected vehicle, then otherwise S103, determine P as the power control strategy, For the kth The small-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV on the channel and the large-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV are defined as α v , the power control strategy is: Where P v is the transmission power of the vth intelligent connected vehicle for data transmission. The vth intelligent connected vehicle transmits data v t Transmission delay T to solar-powered drone v for: Among them, γ th is the decoding threshold for successful execution of continuous interference cancellation technology in actual NOMA applications, β(·) is the indicator function, β(·)∈{0,1}; if β(·)=1, otherwise β(·)=0; in this case, T v It is defined as infinity, which means that the channel allocation and power control strategies at this time are not feasible.

3. The method for optimizing the time delay of an air-ground collaborative vehicle-mounted self-organizing network as claimed in claim 2, characterized in that: Step S102 is specifically as follows: Signal-to-interference-noise ratio of the vth intelligent connected vehicle on the kth channel It is expressed as: Where j is the jth channel occupying the kth channel at the same time. Intelligent connected vehicles, P j is the transmission power used by the jth intelligent connected vehicle for data transmission; α y is the large-scale fading component between the vth intelligent connected vehicle and the solar-powered UAV; For the kth The small-scale fading component between the vth intelligent connected vehicle and the solar drone on the channel; α j and are the large-scale fading and small-scale fading components between the j-th intelligent connected vehicle and the solar-powered UAV, P N is the noise power; The achievable data rate R of the vth intelligent connected vehicle v : R v =B0log2(1+γ v ), Among them, B0 represents the channel bandwidth from solar drone to intelligent connected vehicle; γ v is the signal-to-interference-noise ratio of the vth intelligent connected vehicle, 4. The method for optimizing the time delay of an air-ground collaborative vehicle-mounted self-organizing network according to claim 1, characterized in that: Step S2 is specifically as follows: The total delay is constructed by combining channel allocation, power control, decoding threshold and channel estimation Optimization problem, the optimization problem is: in, represents that the kth channel is allocated to the vth intelligent connected vehicle; P max is the maximum transmission power of intelligent connected vehicles. In P1, constraint C1 limits each intelligent connected vehicle to occupy only one channel. Constraints C2 and C3 jointly define that each channel can accommodate at most two intelligent connected vehicles. Constraint C4 represents the transmission power range of each intelligent connected vehicle.

5. The method for optimizing the time delay of an air-ground cooperative vehicle-mounted self-organizing network according to claim 1, characterized in that: Step S3 is specifically as follows: S301, decoupling the optimization problem P1 into a power control problem P2 determined by a given channel allocation strategy and a channel allocation problem P3 determined by a given power control strategy; S302: There are three situations in the optimization problem P2 in the air-ground cooperative vehicle self-organizing network: A. On the kth channel, both the vth and jth intelligent connected vehicles can use the non-orthogonal multiple access mechanism for data transmission, that is, and B. On the kth channel, only one intelligent connected vehicle can transmit data, that is, or At this point, the data transmission mode degenerates into an orthogonal multiple access mechanism; C. On the kth channel, no data is transmitted, that is, and S303, equivalently transforming the optimization problem P3 into the problem of finding the maximum weighted independent set in the weighted undirected graph G, using a heuristic algorithm to solve until convergence, and obtaining an optimized channel allocation strategy.

6. The method for optimizing the time delay of an air-ground cooperative vehicle-mounted self-organizing network according to claim 5, characterized in that: In step S301, given the channel allocation strategy S, the power control problem P2 model is constructed as: Among them, (v,j,k) means that the vth and jth intelligent connected vehicles occupy the kth channel at the same time; In addition, it is assumed that the size of each data to be transmitted is the same, that is, Given the power control strategy P, the channel allocation problem model is constructed as:

7. The air-ground collaborative vehicle-mounted self-organizing network delay optimization method according to claim 5 is characterized in that: In step S302, the optimal power control strategy (P v ,P j ) * for:

8. The air-ground collaborative vehicle-mounted self-organizing network delay optimization method according to claim 5 is characterized in that: Step S303 is specifically as follows: Define G = {A, B, C} as a weighted undirected graph, where A is the vertex set, B is the edge set, and C is the weight set of all vertices; in G, the ath The combination of vertices introduced (v a ,j a ,k a ), where v a ∈V,j a ∈V,v a ≠j a And k a ∈K; Assume a v ∈A and a j ∈A; in this case, if and only if the ath v and a j When the vertices contain 1-2 identical intelligent connected vehicles or the same channels, the a v and a j There is an edge between the vertices, represented by (a v ,a j ); The weight c of the a-th vertex a for: c a =T v ((P v ,P j ) * ,k a )+T j ((P v ,P j ) * ,k a ) Calculated as:

9. The performance analysis method of the air-ground cooperative vehicle-mounted self-organizing network according to claim 1 is characterized in that: Step S4 is specifically as follows: For a given P v and P j , the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground cooperative vehicle ad hoc network Calculated as:

10. A time delay optimization system for air-ground collaborative vehicle-mounted self-organizing network, characterized in that: include: The network module builds an air-ground collaborative vehicle-mounted self-organizing network model consisting of 1 solar-powered UAV and V intelligent networked vehicles; based on the air-ground collaborative vehicle network model, a V2V channel allocation model and an intelligent networked vehicle power control model are built; The construction module constructs the objective function and optimization conditions for minimizing the total network delay based on the constructed air-ground cooperative vehicle self-organizing network model, V2V channel sharing model and intelligent connected vehicle power control model; The optimization module, based on the objective function and optimization conditions of the constructed network total delay minimization problem, it iteratively solves the problem through closed-form solution derivation and weighted undirected graph method to obtain the optimal channel allocation strategy and power control strategy; The performance analysis module analyzes the impact of orthogonal multiple access and non-orthogonal multiple access on data transmission in the air-ground collaborative vehicle self-organizing network based on the iterative optimal channel allocation strategy and power allocation strategy, and derives the probability that the non-orthogonal multiple access mechanism is better than the orthogonal multiple access mechanism in the air-ground collaborative vehicle self-organizing network.

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