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

C-V2V vehicle networking power control method for optimal energy efficiency Download PDF

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CN114630413A
CN114630413A CN202210338734.5A CN202210338734A CN114630413A CN 114630413 A CN114630413 A CN 114630413A CN 202210338734 A CN202210338734 A CN 202210338734A CN 114630413 A CN114630413 A CN 114630413A
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CN114630413B (en
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秦鹏
伏阳
王淼
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North China Electric Power University
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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
    • 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
    • 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
    • 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]
    • 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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  • Computer Networks & Wireless Communication (AREA)
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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

C-V2V vehicle networking power control method for optimal energy efficiency
Technical Field
The invention relates to the field of vehicle networking, in particular to a C-V2V vehicle networking power control method for optimizing energy efficiency.
Background
With the rapid development of vehicle technologies, the internet of vehicles is faced with unprecedented demands for low latency and high service quality. To achieve reliable mobile communication in the internet of vehicles, V2V communication and vehicle to edge node communication are two effective solutions. However, cost implications have led to impractical wide deployment of RSUs, the introduction of edge parked vehicles into the urban communication network, and the advent of P-RSU assisted internet communication is a promising approach at this stage.
In the real situation, the process of providing services such as data transceiving, edge calculation and the like by the P-RSU consumes electric energy, and the parked vehicle is only powered by the storage battery, and the energy consumption of the parked vehicle is fully considered. Therefore, for the car networking communication system including the P-RSU, EE gradually becomes a target of much attention, 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. Designing an EE-maximized vehicle networking resource allocation scheme requires joint optimization of multi-terminal transmit power, which is generally a non-convex non-linear programming problem of an objective function. Furthermore, to reduce the core network burden, a heterogeneous network containing both C-V2V and cellular users is the most realistic and instructive model.
The existing C-V2V Internet of vehicles power control method does not consider the assistance of parking vehicles at the edge, and also considers less the problem of energy consumption of edge nodes. The invention provides a C-V2V vehicle networking power control method oriented to optimal energy efficiency, which emphasizes the consideration of P-RSU energy consumption and achieves excellent system EE and spectral efficiency performance.
Disclosure of Invention
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 one, calculating the EE of the network user by using the vehicle position and the channel distribution information and describing the power control problem of the EE maximization; 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 power control result obtained by the invention can effectively ensure the spectrum efficiency of the user, control the interference between the users in the same channel, reduce the energy loss and realize green and efficient vehicle networking resource allocation. The specific process is as follows:
the C-V2V vehicle networking system model based on P-RSU comprises 1P-RSU and a plurality of driving 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
Figure BDA0003574630790000021
Figure BDA0003574630790000022
That is, there are N sets of C-V2V communication pairs (including the sending vehicle and the receiving vehicle)
Figure BDA0003574630790000023
And (4) showing. The present invention allows the communication of direct links by C-V2V to orthogonal channels occupied by multiplexed 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 as follows:
Figure BDA0003574630790000024
wherein,
Figure BDA0003574630790000025
is a path loss constant, beta is a fast fading gain, zeta is a slow fading gain, alpha is a path loss factor, and d is a transmission distance. All gains subsequently used can be calculated by substituting the above formula into the corresponding distance d.
The EE for cellular users and C-V2V can be calculated as follows:
Figure BDA0003574630790000026
Figure BDA0003574630790000027
wherein eta refers to powerAmplifier efficiency; p is a radical ofcirRefers to circuit loss;
Figure BDA0003574630790000028
and
Figure BDA0003574630790000029
refers to the inter-layer interference of C-V2V to cellular users and P-RSUs,
Figure BDA00035746307900000210
refers to the inter-layer interference of the cellular user to C-V2V,
Figure BDA00035746307900000211
refers to the intralayer interference between C-V2V.
The EE maximization power control problem can be described as follows:
Figure BDA00035746307900000212
Figure BDA0003574630790000031
Figure BDA0003574630790000032
Figure BDA0003574630790000033
Figure BDA0003574630790000034
Figure BDA0003574630790000035
Figure BDA0003574630790000036
wherein,
Figure BDA0003574630790000037
and
Figure BDA0003574630790000038
a power control strategy; c1Theta of fingerkDefining a constraint; c2，C3And C4Transmit power constraints referring to P-RSU, cellular users, and C-V2V, respectively; c5And C6Representing a quality of service constraint.
The problem solving can be divided into the following steps:
1) by cellular users UkFor example, the EE objective function is transformed into the equivalent subtraction form as follows:
Figure BDA0003574630790000039
2) solving the equivalent convex optimization problem, and solving the dual problem according to KKT conditions after listing Lagrange functions to obtain the following optimized power expression:
Figure BDA00035746307900000310
Figure BDA00035746307900000311
wherein,
Figure BDA00035746307900000312
and
Figure BDA00035746307900000313
for lagrange multipliers, respectively corresponding to constraints C2,C3And C5；{z}+Max { z,0 }. Updating multipliers according to a gradient method, wherein each updating is rootedAnd recalculating the power according to the formula until the change of the iteration multipliers of the two adjacent rounds is small enough, and then converging the power to the solution of the convex optimization problem.
3) Solving the convex optimization problem repeatedly according to
Figure BDA00035746307900000314
The EE value is continuously updated until the value of Ω is close enough to 0, at which point a power control result is obtained that maximizes EE for a single user.
4) For C-V2VVnSolving the power control result by the same method, wherein the optimized power expression is
Figure BDA0003574630790000041
Wherein,
Figure BDA0003574630790000042
the EE objective function value obtained in the previous iteration is obtained;
Figure BDA0003574630790000043
and
Figure BDA0003574630790000044
respectively corresponding constraints C4And C6Of lagrange multiplier
5) And (3) introducing a non-cooperative game to all users under the same orthogonal 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 between the EE calculated by two adjacent iterations of all the users is small enough, and then converging to the Nash equilibrium of power control.
6) The power is optimized by adopting the method in steps 1) to 5) for all the cellular users and C-V2V under the orthogonal channels, and finally, a power control strategy for maximizing the EE is obtained.
The technical method of the invention has the following advantages:
firstly, the energy consumption of the edge parking vehicle is considered emphatically, the EE of the P-RSU is put into an optimization target, and meanwhile, an optimization strategy of the P-RSU transmitting power is given. Secondly, interference among co-channel users is controlled by using a non-cooperative game, and Nash balance of power control is obtained. Finally, the invention converges to the power control result with optimal energy efficiency through three-layer circulation, thereby realizing green and efficient vehicle networking resource allocation.
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 in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a graph of EE versus link length for C-V2V.
Fig. 2 is a graph of spectral efficiency versus link length of C-V2V.
Fig. 3 is a graph of EE versus the number of orthogonal channels.
Fig. 4 is a graph of spectral efficiency versus the number of orthogonal channels.
Detailed Description
The invention provides a C-V2V vehicle networking power control method oriented to energy efficiency optimization, and the embodiment is described in detail below with reference to the accompanying drawings.
The concrete implementation scene of the invention is that the Beijing princess grave overpass is positioned at 116.32 degrees of the west longitude of 39.91 degrees in the North latitude. The P-RSU in the system model is located in the top left of the scene in the department of Cui, cellular users and C-V2V pairs to select from vehicles traveling on the road.
The overall simulation time of the invention is 500s, wherein the real-time positions of all vehicles are extracted once every 0.5s and input into the power control algorithm, and the algorithm is operated to obtain the simulation result. Simulation parameters are selected as follows: the logarithm of the C-V2V is 4-12, the number of orthogonal channels is 4-12, the length of a C-V2V link is 20-60 m, and parameters of channel gain are calculated
Figure BDA0003574630790000051
Is 10-2Beta is exponential distribution with parameter 1, zeta is logarithmic normal distribution with mean value of 0 and standard deviation of 8dB, alpha is 4, power upper limit is 23dBm, power amplifier efficiency is 35%, circuit loss is 20dBm, noise power is-144 dBm, lower limit of spectral efficiency is 0.5,1]Are uniformly distributed.
The specific implementation steps are as follows:
1) calculating energy efficiency of each cellular user and C-V2V according to the parameters
Figure BDA0003574630790000052
And
Figure BDA0003574630790000053
2) a power control algorithm is employed for all users in each orthogonal channel (K1, …, K).
3) And entering a loop of the non-cooperative game, wherein the mark of the loop is that the difference of EE obtained by two adjacent iterations of all the users under the channel is small enough.
4) And entering a loop for converting the problem into an equivalent subtraction form, listing an equivalent objective function, and iteratively updating the EE of the user until the equivalent objective function is close to 0.
5) And entering a cycle of solving the equivalent convex optimization problem, and continuously calculating the optimization power along with the updating of the Lagrange multiplier until a solution of the convex optimization problem is found.
6) The power control result obtained after the end of the triple cycle is the optimum result for maximizing the system EE.
Fig. 1 and 2 show the average EE and spectral efficiency versus C-V2V link length, respectively, and it can be seen that all curves are in a downward trend because an increase in C-V2V communication distance decreases the channel gain, thereby degrading system performance. Compared with 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 effectively control the interference among users, ensure the receiving intensity of useful signals, meet the service quality requirement, reduce the energy consumption of the users and realize green energy conservation. The 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 relation between the average EE and the spectrum efficiency relative to the number of orthogonal channels, respectively, and as K increases, C-V2V can find a proper orthogonal channel more easily, so that the system EE and the spectrum efficiency performance are improved. Due to reasonable power control, the performance of the proposed method is significantly better than the other two reference methods. 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 that of the maximum power method and the random power method respectively.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention covered by this disclosure is not limited to the particular combination of features described above, but also encompasses other technical means formed by any combination of the above features or their equivalents without departing from the scope of the inventive concept. Such as those described above, are interchangeable with other features disclosed in this disclosure (but not limited to) having similar functionality.

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:
Figure FDA0003574630780000011
Figure FDA0003574630780000012
Figure FDA0003574630780000013
Figure FDA0003574630780000014
Figure FDA0003574630780000015
Figure FDA0003574630780000016
Figure FDA0003574630780000017
wherein,
Figure FDA0003574630780000021
refers to the power control strategy of cellular users and P-RSUs,
Figure FDA0003574630780000022
refer to the power control strategy of C-V2V; u shapekAnd VnRespectively 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;
Figure FDA0003574630780000023
and
Figure FDA0003574630780000024
represents UkAnd VnA set of (a); theta.theta.kRepresents UkAnd a binary indicator of the direction of data transmission between the P-RSU and the P-RSU, thetak1 means P-RSU to UkSending data, otherwise thetak＝0；C1Finger thetakThe definition of (3); c2，C3And C4Transmit power constraints referring to P-RSU, cellular user and C-V2V respectively,
Figure FDA0003574630780000025
and
Figure FDA0003574630780000026
is the upper power limit; c5And C6On behalf of the quality of service constraints, the service quality constraint,
Figure FDA0003574630780000027
and
Figure FDA0003574630780000028
the spectral efficiency of cellular users and C-V2V respectively,
Figure FDA0003574630780000029
and
Figure FDA00035746307800000210
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:
Figure FDA00035746307800000211
where omega is the objective function in equivalent form,
Figure FDA00035746307800000212
energy consumption; t is the number of iteration rounds;
Figure FDA00035746307800000213
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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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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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