CN114630413A - C-V2V vehicle networking power control method for optimal energy efficiency - Google Patents

Abstract

The invention discloses a C-V2V vehicle networking power control method oriented to energy efficiency optimization. The invention mainly aims at an Internet of vehicles communication scene based on edge parking vehicle assistance, and the method comprises the following steps: the method comprises the steps that firstly, Energy Efficiency (EE) of each user in a network is expressed by utilizing Vehicle position and channel distribution information, and the EE comprises a park car as Roadside Unit (P-RSU), a Cellular user and a Cellular-Vehicle communication pair (C-V2V), so that the power control problem of EE maximization is described; step two, converting a non-convex EE objective function into an equivalent subtraction form, and converting an original power control problem into a series of strict convex optimization problems for iterative solution; thirdly, solving a convex optimization problem by using a Lagrange multiplier method; and step four, introducing a non-cooperative game to obtain the Nash equilibrium of the user transmitting power under the same channel. The invention finally converges to the power control result with optimal energy efficiency through three layers of circulation, thereby effectively ensuring the spectrum efficiency of users, controlling the interference between users in the same channel, reducing the energy loss and realizing green and efficient resource allocation of the Internet of vehicles.

Description

A C-V2V Internet of Vehicles Power Control Method for Optimal Energy Efficiency

technical field

The invention relates to the field of Internet of Vehicles, in particular to a C-V2V Internet of Vehicles power control method for optimal energy efficiency.

Background technique

With the rapid development of vehicle technology, the Internet of Vehicles is facing unprecedented demands for low latency and high service quality. To achieve reliable vehicle networking mobile communication, V2V communication and communication between vehicles and edge nodes are two effective solutions. However, the shackles of cost factors make the widespread deployment of RSUs unrealistic. Introducing edge-parked vehicles into the urban communication network and becoming P-RSU-assisted vehicle networking communication is a promising means at this stage.

In reality, the process of P-RSU providing services such as data transmission and reception and edge computing consumes electric energy, and the parked vehicle is only powered by the battery, and its energy consumption should be fully considered. Therefore, for the Internet of Vehicles communication system including P-RSU, on the one hand, the data transmission rate should be increased to meet the requirements of service quality, and on the other hand, the energy consumption should be reduced to achieve green energy saving, and EE has gradually become a target that has attracted much attention. Designing an IoV resource allocation scheme that maximizes EE requires joint optimization of multi-terminal transmit power, which is usually a nonlinear programming problem with a non-convex objective function. Furthermore, in order to reduce the burden on the core network, a heterogeneous network containing both C-V2V and cellular users is the most realistic and instructive model.

Existing C-V2V IoV power control methods do not consider the assistance of vehicles parked at the edge, nor do they consider the energy consumption of edge nodes. The present invention proposes a C-V2V vehicle networking power control method oriented to the optimal energy efficiency, which focuses on considering the energy consumption of the P-RSU and achieves excellent system EE and spectral efficiency performance.

SUMMARY OF THE INVENTION

The invention discloses a C-V2V vehicle networking power control method for optimal energy efficiency, which is mainly aimed at the vehicle networking communication scenario based on edge parking vehicle assistance. Calculate the EE of network users and describe the power control problem of EE maximization; step 2, transform the non-convex EE objective function into an equivalent subtractive form, and transform the original power control problem into iteratively solve a series of strict convex optimization problems ; Step 3, use the Lagrange multiplier method to solve the convex optimization problem; Step 4, introduce a non-cooperative game to obtain the Nash equilibrium of the user's transmit power under the same channel. The power control result obtained by the invention can effectively ensure the spectral efficiency of users, control the interference between users on the same channel, reduce energy consumption at the same time, and realize green and efficient vehicle networking resource allocation. The specific process is as follows:

表示，另有N个C-V2V通信对(包含发送车辆和接收车辆)用集合

表示。本发明允许C-V2V对复用蜂窝用户占据的正交信道进行直连链路的通信，这会造成蜂窝用户和C-V2V对之间的不可预期干扰，包括层间干扰和层内干扰。本发明的信道增益计算公式为：The P-RSU-based C-V2V vehicle networking system model proposed by the present invention includes one P-RSU and multiple driving vehicle users, in which there are K cellular user vehicles occupying the orthogonal channels provided by the K P-RSUs and the P-RSUs. RSU performs uplink or downlink data communication, and defines the set of cellular users and orthogonal channels

Indicates that there are also N C-V2V communication pairs (including sending vehicles and receiving vehicles) using a set

express. The present invention allows the C-V2V pair to perform direct link communication on the orthogonal channels occupied by the multiplexed cellular users, which will cause unpredictable interference between the cellular users and the C-V2V pair, including inter-layer interference and intra-layer interference. The channel gain calculation formula of the present invention is:

为路损常数，β为快衰落增益，ζ为慢衰落增益，α为路损因子，d为传输距离。后续用到的所有增益都可根据上式代入相应距离d计算。in,

is the path loss constant, β is the fast fading gain, ζ is the slow fading gain, α is the path loss factor, and d is the transmission distance. All subsequent gains can be calculated by substituting the corresponding distance d according to the above formula.

The EE for cellular users and C-V2V can be calculated as follows:

和

指C-V2V对蜂窝用户及P-RSU的层间干扰，

指蜂窝用户对C-V2V的层间干扰，

指C-V2V之间的层内干扰。Among them, η refers to the power amplifier efficiency; p _cir refers to the circuit loss;

and

Refers to the interlayer interference of C-V2V to cellular users and P-RSU,

Refers to the interlayer interference of cellular users to C-V2V,

Refers to intra-layer interference between C-V2V.

The EE maximizing power control problem can be described as follows:

和

指功率控制策略；C₁指的θ_k定义约束；C₂，C₃和C₄分别指P-RSU，蜂窝用户和C-V2V的发射功率约束；C₅和C₆代表服务质量约束。in,

and

refers to the power control strategy; C ₁ refers to θ _k to define constraints; C ₂ , C ₃ and C ₄ refer to the transmit power constraints of P-RSU, cellular users and C-V2V, respectively; C ₅ and C ₆ represent QoS constraints.

Solving this problem can be divided into the following steps:

1) Taking the cellular user U _k as an example, the EE objective function is transformed into the following equivalent subtraction form:

2) Solve the above equivalent convex optimization problem. After listing the Lagrangian function, solve the dual problem according to the KKT condition, and obtain the following optimal power expression:

和

为拉格朗日乘子，分别对应约束C₂,C₃和C₅；{z}⁺＝max{z,0}。根据梯度法更新乘子，每次更新都根据上式重新计算功率，直到相邻两轮迭代乘子的变化足够小，此时功率收敛到凸优化问题的解。in,

and

are Lagrange multipliers, corresponding to constraints C ₂ , C ₃ and C ₅ respectively; {z} ⁺ =max{z,0}. The multiplier is updated according to the gradient method, and the power is recalculated according to the above formula for each update until the changes of the multipliers in the adjacent two iterations are small enough, at which time the power converges to the solution of the convex optimization problem.

不断更新EE值，直到Ω的值足够接近0，此时得到使单个用户EE最大的功率控制结果。3) Iteratively solve the convex optimization problem, and according to

The EE value is continuously updated until the value of Ω is sufficiently close to 0, at which time the power control result that maximizes the EE of a single user is obtained.

4) Solve the power control result in the same way for C-V2VV _n , and its optimized power expression is

为上一轮迭代得到的EE目标函数值；

和

分别为对应约束C₄和C₆的拉格朗日乘子in,

is the EE objective function value obtained from the previous iteration;

and

are the Lagrange multipliers corresponding to constraints _C4 and _C6 , respectively

5) For all users in the same orthogonal channel, a non-cooperative game is introduced, first assuming that the power of other users is constant and optimizing the power of each user individually, and then repeating iteratively until the difference between the EEs calculated by all users in two adjacent rounds of iterations is equal to is small enough to converge to a power-controlled Nash equilibrium.

6) The methods in steps 1) to 5) are used to optimize the power for cellular users and C-V2V in all orthogonal channels, and finally a power control strategy that maximizes the system EE is obtained.

The technical method of the present invention has the following advantages:

First, the energy consumption of edge-parked vehicles is emphatically considered, and the EE of P-RSU is put into the optimization objective, and the optimization strategy of P-RSU transmit power is given. Secondly, a non-cooperative game is used to control the interference between co-channel users, and a power-controlled Nash equilibrium is obtained. Finally, the present invention converges to the power control result with the optimal energy efficiency through the three-layer cycle, so as to realize the green and efficient resource allocation of the Internet of Vehicles.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

In order to more clearly illustrate the embodiments of the present invention or the technical methods in the prior art, the accompanying drawings required in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some of the present invention. In the embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Figure 1 shows the variation curve of EE versus C-V2V link length.

Figure 2 is a graph of spectral efficiency versus C-V2V link length.

Fig. 3 is the variation curve of EE relative to the number of orthogonal channels.

Fig. 4 is the variation curve of spectral efficiency with respect to the number of orthogonal channels.

Detailed ways

The present invention proposes a C-V2V Internet of Vehicles power control method for optimal energy efficiency, and the embodiments are described in detail below with reference to the accompanying drawings.

The specific implementation scene of the present invention is Beijing Gongzhufen overpass, which is located at 39.91° north latitude and 116.32° west longitude. The P-RSU in the system model is located at the Cuiwei Department Store in the upper left of the scene, and the cellular user and C-V2V pair are picked from vehicles driving on the road.

为10^-2，β取参数为1的指数分布，ζ取均值为0标准差为8dB的对数正态分布，α为4，功率上限为23dBm，功率放大器效率为35％，电路损耗为20dBm，噪声功率为-144dBm，频谱效率下限取[0.5,1]的均匀分布。The overall simulation time of the present invention is 500s, wherein the real-time positions of all vehicles are extracted every 0.5s and input to the power control algorithm, and the algorithm is run to obtain the simulation results. The simulation parameters are selected as follows: the logarithm of C-V2V is 4 to 12, the number of orthogonal channels is 4 to 12, the length of the C-V2V link is 20 to 60m, and the parameters for calculating the channel gain

is 10 ^-2 , β is an exponential distribution with a parameter of 1, ζ is a log-normal distribution with a mean value of 0 and a standard deviation of 8dB, α is 4, the upper power limit is 23dBm, the power amplifier efficiency is 35%, and the circuit loss is 20dBm , the noise power is -144dBm, and the lower limit of the spectral efficiency is a uniform distribution of [0.5, 1].

The specific implementation steps are as follows:

和

1) Calculate the energy efficiency of each cellular user and C-V2V according to the parameters

and

2) Adopt a power control algorithm for all users under each orthogonal channel (k=1, . . . , K).

3) Enter the non-cooperative game loop. The sign of the end of the loop is that the difference between the EEs obtained by all users under the channel in two adjacent iterations is small enough.

4) Enter the loop that converts the problem into the equivalent subtractive form, list the equivalent objective functions, and iteratively update the user's EE until the equivalent objective function approaches 0.

5) Enter the cycle of solving the equivalent convex optimization problem, and continuously calculate the optimization power with the update of the Lagrange multiplier until the solution of the convex optimization problem is found.

6) The power control result obtained after the triple cycle is over is the optimal result that maximizes the system EE.

Figures 1 and 2 show the relationship between the average EE and spectral efficiency versus the C-V2V link length, respectively. It can be seen that all the curves show a downward trend. The reason is that the increase of the C-V2V communication distance reduces the channel gain, which reduces the system. performance. From the comparison between the methods, the EE performance of the proposed method is significantly better than that of the maximum power method and the random power method. This is because reasonable power optimization can not only effectively control the interference between users, but also ensure the reception strength of useful signals and satisfy the quality of service. It can also reduce user energy consumption and achieve green energy saving. Numerical results show that when the C-V2V link length is 20m, the EE of the proposed algorithm is 75.4% and 58.8% higher than that of the maximum power method and the random power method, respectively.

Figures 3 and 4 show the relationship between average EE and spectral efficiency relative to the number of orthogonal channels, respectively. With the increase of K, it is easier for C-V2V to find suitable orthogonal channels, so the system EE and spectral efficiency performance are improved. Due to reasonable power control, the performance of the proposed method significantly outperforms the other two baseline methods. Numerical results show that when the number of orthogonal channels is 12, the EE of the proposed algorithm is 59.4% and 43.6% higher than that of the maximum power method and the random power method, respectively.

The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical method formed by the specific combination of the above-mentioned technical features, and should also cover the above-mentioned technical features without departing from the inventive concept. or other technical methods formed by any combination of its equivalent features. For example, a technical method formed by replacing the above features with the technical features disclosed in the present disclosure (but not limited to) with similar functions.

Claims (4)

1. The invention discloses a C-V2V vehicle networking power control method oriented to energy efficiency optimization, which mainly aims at a vehicle networking communication scene based on edge parking vehicle assistance, and comprises the following steps:

step 1, calculating the spectral efficiency of each user by using vehicle position information and channel allocation information, and dividing the spectral efficiency by energy consumption to obtain energy efficiency so as to describe the power control problem of EE maximization;

step 2, converting a non-convex EE target function into an equivalent subtraction form, converting an original power control problem into an iterative solution of a series of strict convex optimization problems, wherein EE is continuously close to an optimal value along with the progress of iteration, and the condition of ending the iteration is that the equivalent subtraction form of the target function converges to 0, so that the maximum power control result of the EE of a single user is achieved;

step 3, solving the convex optimization problem by using a Lagrange multiplier method, listing a Lagrange function corresponding to the problem, solving a dual problem according to Karush-Kuhn-Tucker (KKT) conditions to obtain an expression of optimized power, and iteratively updating the Lagrange multiplier until the optimal solution of the convex optimization problem is converged;

and 4, introducing a non-cooperative game to obtain Nash balance of user transmitting power under the same channel, and iteratively optimizing the power of each user until the difference between the EE calculated by two adjacent iterations of all the users is small enough, which represents that the power optimization result can effectively control the interference between the users and achieve the EE which enables all the users to be satisfied.

2. The optimal energy efficiency oriented C-V2V Internet of vehicles power control method according to claim 1, wherein the EE maximization power control problem in step 1 can be described as follows:

wherein,

refers to the power control strategy of cellular users and P-RSUs,

refer to the power control strategy of C-V2V; u shape_kAnd V_nRespectively represent the kth cellular user/orthogonal channel and the nth C-V2V; k and N represent the total number of cellular users and C-V2V, respectively;

and

represents U_kAnd V_nA set of (a); theta.theta._kRepresents U_kAnd a binary indicator of the direction of data transmission between the P-RSU and the P-RSU, theta_k1 means P-RSU to U_kSending data, otherwise theta_k＝0；C₁Finger theta_kThe definition of (3); c₂，C₃And C₄Transmit power constraints referring to P-RSU, cellular user and C-V2V respectively,

and

is the upper power limit; c₅And C₆On behalf of the quality of service constraints, the service quality constraint,

and

the spectral efficiency of cellular users and C-V2V respectively,

and

the lower spectral efficiency limit.

3. The EE maximization power control problem according to claim 2, which is solved by the steps of:

first, taking cellular users as an example, the EE objective function is transformed into the equivalent subtraction form as follows:

where omega is the objective function in equivalent form,

energy consumption; t is the number of iteration rounds;

is the EE value of the previous round; secondly, solving the equivalent convex optimization problem by a Lagrange multiplier method to obtain a power optimization expression and an update formula of the multiplier; and finally, introducing a non-cooperative game to obtain Nash equilibrium of the user transmitting power under the same channel, firstly assuming that the power of other users is constant, independently optimizing the power of each user, and then repeatedly iterating until the difference of the EE calculated by two adjacent iterations of all the users is small enough.

4. According to the method, the optimal power control result of EE is finally converged through three layers of circulation, so that the spectrum efficiency of users can be effectively ensured, the interference among users in the same channel is controlled, the energy loss is reduced, and the green and efficient vehicle networking resource allocation is realized.

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