CN114630413B - Energy efficiency optimization-oriented C-V2V Internet of vehicles power control method - Google Patents
Energy efficiency optimization-oriented C-V2V Internet of vehicles power control method Download PDFInfo
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- CN114630413B CN114630413B CN202210338734.5A CN202210338734A CN114630413B CN 114630413 B CN114630413 B CN 114630413B CN 202210338734 A CN202210338734 A CN 202210338734A CN 114630413 B CN114630413 B CN 114630413B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/242—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account path loss
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
- H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/38—TPC being performed in particular situations
- H04W52/383—TPC being performed in particular situations power control in peer-to-peer links
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses a C-V2V Internet of vehicles power control method oriented to optimal energy efficiency. The invention mainly aims at an Internet of vehicles communication scene based on the assistance of an edge parking vehicle, and the method comprises the following steps: step one, using vehicle location and channel allocation information to express energy efficiency (EnergyEfficiency, EE) of each user in the network, including parked vehicle roadside units (Parked Cars as Roadside Unit, P-RSU), cellular users and Cellular-vehicle communication pairs (Cellular-Vehicle to Vehicle, C-V2V), describing EE-maximized power control issues; step two, converting the non-convex EE objective function into an equivalent subtraction form, and converting the original power control problem into an iterative solution of a series of strict convex optimization problems; step three, solving a convex optimization problem by utilizing a Lagrangian multiplier method; and step four, introducing a non-cooperative game to obtain 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, can effectively ensure the frequency spectrum efficiency of users, control the interference among users with the same channel, reduce the energy loss and realize the environment-friendly and efficient allocation of the Internet of vehicles resources.
Description
Technical Field
The invention relates to the field of Internet of vehicles, in particular to an energy efficiency-oriented optimal C-V2V Internet of vehicles power control method.
Background
With the rapid development of vehicle technology, the internet of vehicles is faced with an unprecedented low-latency, high-quality-of-service requirement. To achieve reliable internet of vehicles mobile communications, V2V communications and vehicle to edge node communications are two effective schemes. However, cost-wise bracing results in widespread deployment of RSUs is impractical, introducing edge-parked vehicles into urban communication networks, and it is now a potential means for P-RSU assisted Internet of vehicles communication.
In reality, the P-RSU consumes power in the process of providing services such as data transceiving, edge computing and the like, and the parked vehicle is only powered by a storage battery, so that the energy consumption of the P-RSU should be fully considered. Therefore, for the internet of vehicles communication system including the P-RSU, on one hand, to increase the data transmission rate to meet the service quality requirement, and on the other hand, to reduce the energy consumption to achieve green energy saving, EE is becoming a target of great interest. Designing an EE-maximized Internet of vehicles resource allocation scheme requires joint optimization of multi-terminal transmit power, which is typically a non-convex nonlinear programming problem for objective functions. In addition, heterogeneous networks, which contain both C-V2V and cellular users, are the most realistic and instructive models to ease the core network burden.
The existing C-V2V internet of vehicles power control method does not consider the assistance of the edge parking vehicles, and also less considers the energy consumption problem of the edge nodes. The invention provides a C-V2V Internet of vehicles power control method oriented to optimal energy efficiency, which aims at considering the energy consumption of a P-RSU and achieves excellent system EE and spectrum efficiency performance.
Disclosure of Invention
The invention discloses an energy efficiency optimal C-V2V Internet of vehicles power control method, which mainly aims at an Internet of vehicles communication scene based on the assistance of an edge parking vehicle, and comprises the following steps: step one, calculating EE of network users by using vehicle position and channel allocation information and describing EE maximized power control problem; step two, converting the non-convex EE objective function into an equivalent subtraction form, and converting the original power control problem into an iterative solution of a series of strict convex optimization problems; step three, solving a convex optimization problem by utilizing a Lagrangian multiplier method; and step four, introducing a non-cooperative game to obtain Nash equilibrium of the user transmitting power under the same channel. The power control result obtained by the invention can effectively ensure the frequency spectrum efficiency of users, control the interference among users with the same channel, reduce the energy loss and realize the green and efficient allocation of the resources of the Internet of vehicles. The specific process is as follows:
the C-V2V vehicle networking system model based on the P-RSU comprises 1P-RSU and a plurality of running vehicle users, wherein K cellular user vehicles occupy orthogonal channels provided by the K P-RSUs to carry out uplink or downlink data communication with the P-RSU, and a set for the cellular users and the orthogonal channels is defined Indicating that there are N sets for C-V2V communication pairs (including a transmitting vehicle and a receiving vehicle)>And (3) representing. The present invention allows the communication of direct links for C-V2V to multiplex orthogonal channels occupied by cellular users, which can cause unpredictable interference between cellular users and C-V2V pairs, including inter-layer interference and intra-layer interference. The channel gain calculation formula of the invention is:
wherein,beta is fast fading gain, ζ is slow fading gain, alpha is path loss factor, and d is transmission distance. All gains used subsequently can be calculated by substituting the corresponding distances d according to the above formula.
The EE for cellular subscribers and C-V2V can be calculated as follows:
where η refers to the power amplifier efficiency; p is p cir Finger circuit loss;and->Refers to the inter-layer interference of C-V2V to cellular subscriber and P-RSU, < >>Refers to the inter-layer interference of cellular users to C-V2V,/and/or>Refers to intra-layer interference between C-V2V.
The EE-maximized power control problem can be described as follows:
wherein,and->Refers to a power control strategy; c (C) 1 Theta of finger k Defining a constraint; c (C) 2 ，C 3 And C 4 Respectively refers to the transmission power constraint of P-RSU, cellular users and C-V2V; c (C) 5 And C 6 Representing a quality of service constraint.
The problem solving can be divided into the following steps:
1) With cellular users U k For example, the EE objective function is transformed into an equivalent subtractive form as follows:
2) Solving the equivalent convex optimization problem, listing the Lagrangian function, and then solving the dual problem according to the KKT condition to obtain the following optimized power expression:
wherein,and->Is Lagrangian multiplier, respectively corresponding to constraint C 2 ,C 3 And C 5 ；{z} + =max { z,0}. The multipliers are updated according to the gradient method, and the power is recalculated according to the above formula for each update until the change of the iterative multipliers of two adjacent rounds is small enough, and the power converges to a solution of the convex optimization problem.
3) Repeatedly solving the convex optimization problem and according toThe EE value is updated continuously until the value of Ω is close enough to 0, at which point the power control result that maximizes the individual user EE is obtained.
4) For C-V2VV n Solving the power control result in the same way, wherein the optimized power expression is as follows
Wherein,EE objective function value obtained for the previous iteration; />And->Respectively corresponding constraint C 4 And C 6 Lagrangian multiplier of (2)
5) For all users under the same orthogonal channel, introducing non-cooperative game, firstly, assuming that the power of other users is constant, independently optimizing the power of each user, and then repeatedly iterating until the difference between EEs calculated by two adjacent iterations of all users is small enough, and converging to Nash equilibrium of power control.
6) And optimizing the power by adopting the method in the steps 1) to 5) for cellular users and C-V2V under all orthogonal channels, and finally obtaining the power control strategy for maximizing the system EE.
The technical method of the invention has the following advantages:
firstly, the energy consumption of the edge parking vehicle is emphasized, and EE of the P-RSU is put into an optimization target, and meanwhile, an optimization strategy of the P-RSU transmitting power is given. And secondly, controlling interference among users of the same channel by utilizing the non-cooperative game to obtain Nash equilibrium of power control. Finally, the invention realizes green and efficient Internet of vehicles resource allocation through three layers of circularly converging to the power control result with optimal energy efficiency.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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In order to more clearly illustrate the embodiments of the present invention or the technical methods in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained according to the drawings without inventive effort for a person having ordinary skill in the art.
Fig. 1 is a graph of EE versus C-V2V link length.
Fig. 2 is a graph of spectral efficiency versus C-V2V link length.
Fig. 3 is a graph of EE versus the number of orthogonal channels.
Fig. 4 is a graph showing the variation of spectral efficiency with respect to the number of orthogonal channels.
Detailed Description
The invention provides a C-V2V Internet of vehicles power control method oriented to optimal energy efficiency, and embodiments are described in detail below with reference to the accompanying drawings.
The concrete implementation scene of the invention is Beijing princess common-component overpass, which is positioned in the North latitude 39.91 degrees and the Western longitude 116.32 degrees. The P-RSU in the system model is located in the left-upper part of the scene, and the cellular user and the C-V2V pair pick up vehicles running on the road.
The overall simulation time of the invention is 500s, wherein the real-time positions of all vehicles are extracted every 0.5s and input into a power control algorithm, and the algorithm is operated to obtain a simulation result. The simulation parameters were selected as follows: the logarithm of C-V2V is 4-12, the number of orthogonal channels is 4-12, the length of C-V2V link is 20-60 m, and the parameters of channel gain are calculatedIs 10 -2 Taking the exponential distribution with the parameter of 1 as beta, taking the logarithmic normal distribution with the mean value of ζ being 0 and the standard deviation being 8dB, the alpha being 4, the upper power limit being 23dBm, the efficiency of a power amplifier being 35%, the circuit loss being 20dBm, the noise power being-144 dBm, and the lower spectral efficiency limit being [0.5,1 ]]Is a uniform distribution of (c).
The specific implementation steps are as follows:
1) Calculating energy efficiency of each cellular user and C-V2V according to parametersAnd->
2) A power control algorithm is employed for all users under each orthogonal channel (k=1, …, K).
3) Entering a loop of a non-cooperative game, wherein the mark of the end of the loop is that the difference between EEs obtained by two adjacent iterations of all users under the channel is small enough.
4) A loop is entered that converts the problem into an equivalent subtractive form, lists the equivalent objective function, and iteratively updates the user's EE until the equivalent objective function approaches 0.
5) And (3) entering a loop for solving the equivalent convex optimization problem, and continuously calculating the optimization power along with the updating of the Lagrangian multiplier until the solution of the convex optimization problem is found.
6) The power control result obtained after the end of the triple cycle is the optimal result that maximizes system EE.
Fig. 1 and 2 show the average EE and spectral efficiency versus C-V2V link length, respectively, and all curves can be seen to be decreasing since an increase in C-V2V communication distance reduces channel gain, thereby degrading system performance. From the comparison between the methods, the EE performance of the proposed method is obviously superior to that of the maximum power method and the random power method, because reasonable power optimization can not only effectively control the interference among users and ensure the receiving strength of useful signals, but also reduce the energy consumption of the users and realize green energy conservation. Numerical results show that at a C-V2V link length of 20m, the EE of the proposed algorithm is 75.4% and 58.8% higher than the maximum power method and the random power method, respectively.
Fig. 3 and 4 show the relationship between average EE and spectral efficiency versus the number of orthogonal channels, respectively, and as K increases, C-V2V is easier to find the appropriate orthogonal channel, thus improving system EE and spectral efficiency performance. The performance of the proposed method is significantly better than the other two reference methods due to reasonable power control. The 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 the maximum power method and the random power method, respectively.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other technical approaches which may be made by any combination of features described above or equivalents thereof without departing from the spirit of the invention. Such as those described above, are provided in the present disclosure in place of, but not limited to, features having similar functions.
Claims (4)
1. The invention discloses an energy efficiency optimal C-V2V Internet of vehicles power control method, which mainly aims at an Internet of vehicles communication scene based on the assistance of an edge parking vehicle, and comprises the following steps:
step 1, calculating the frequency spectrum efficiency of K cellular user vehicles and N C-V2V communication pairs by using vehicle position information and channel allocation information, dividing the frequency spectrum efficiency by energy consumption to obtain energy efficiency, and describing the EE maximized power control problem;
step 2, converting a non-convex EE objective function into an equivalent subtraction form, converting an original power control problem into an iteration solution for a series of strict convex optimization problems, enabling EE to continuously approach an optimal value along with the iteration, and converging the equivalent subtraction form of the objective function to 0 under the condition that the maximum power control result of a single user EE is achieved;
step 3, solving a 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 Lagrange multiplier converges to an optimal solution of the convex optimization problem;
and 4, introducing a non-cooperative game to obtain Nash equilibrium of the transmitting power of the users under the same channel, and iteratively optimizing the power of each user until the difference between EEs calculated by two adjacent iterations of all users is small enough, wherein the power optimization result at the moment can effectively control the interference among the users and achieve EEs satisfactory to all users.
2. The energy-efficiency-optimized C-V2V internet of vehicles power control method according to claim 1, wherein the EE-maximized power control problem in step 1 can be described as follows:
wherein,refers to the power control strategy of the cellular subscriber and the P-RSU, < >>Refers to a power control strategy of C-V2V; u (U) k And V n Representing a kth cellular user/orthogonal channel and an nth C-V2V, respectively; k and N represent the total number of cellular users and C-V2V, respectively; />And->Represents U k And V n Is a collection of (3); θ k Representing U k Binary indicator factor, θ, of data transmission direction with P-RSU k =1 refers to P-RSU to U k Send data, vice versa k ＝0；C 1 Finger theta k Defining constraints of (2); c (C) 2 ，C 3 And C 4 Respectively P-RSU, cellular subscriber and C-V2V transmit power constraint,/->And->Is the upper power limit; c (C) 5 And C 6 Representing quality of service constraints,)>And->Spectral efficiency of cellular users and C-V2V, respectively, +.>And->Is the lower spectral efficiency limit.
3. The EE-maximized power control problem according to claim 2, the solving steps of:
first, taking the cellular user as an example, the EE objective function is transformed into the equivalent subtractive form as follows:
where Ω is an objective function of equivalent form,is energy consumption; t is the iteration round number; />EE value for the previous round; secondly, solving the optimization problem of the transformation of the objective function into an equivalent subtraction form through a Lagrange multiplier method to obtain a power optimization expression and an update formula of the multiplier; finally, non-cooperative game is introduced to obtain Nash equilibrium of the transmitting power of the users under the same channel, the power of each user is optimized independently under the assumption that the power of other users is constant, and then iteration is repeated until the difference between EE calculated by two adjacent iterations of all users is small enough.
4. According to the method for solving the EE maximized power control problem in claim 3, finally, three layers of circulation convergence are adopted to obtain the EE optimized power control result, so that the spectrum efficiency of users can be effectively ensured, the interference among co-channel users is controlled, the energy loss is reduced, and the environment-friendly and efficient Internet of vehicles resource allocation is realized.
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| CN109905918A (en) * | 2019-02-25 | 2019-06-18 | 重庆邮电大学 | A kind of NOMA honeycomb car networking dynamic resource scheduling method based on efficiency |
| US10440668B1 (en) * | 2018-11-07 | 2019-10-08 | Ford Global Technologies, Llc | Vehicle platooning management and power control with LTE/5G V2X communications |
| CN112260730A (en) * | 2020-10-15 | 2021-01-22 | 东南大学 | C-V2V broadband large-scale MIMO pilot frequency multiplexing channel acquisition method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US10440668B1 (en) * | 2018-11-07 | 2019-10-08 | Ford Global Technologies, Llc | Vehicle platooning management and power control with LTE/5G V2X communications |
| CN109905918A (en) * | 2019-02-25 | 2019-06-18 | 重庆邮电大学 | A kind of NOMA honeycomb car networking dynamic resource scheduling method based on efficiency |
| CN112260730A (en) * | 2020-10-15 | 2021-01-22 | 东南大学 | C-V2V broadband large-scale MIMO pilot frequency multiplexing channel acquisition method |
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