阅读说明：本技术 一种基于有限状态马尔科夫的宽带无线信道建模方法 (Broadband wireless channel modeling method based on finite state Markov ) 是由 李璐 张嘉驰 于 2021-08-23 设计创作，主要内容包括：本发明提供了一种基于有限状态马尔科夫的宽带无线信道建模方法,通过消除接收信号中收发间距离的影响,对主径和两个邻近主径共同建模,采用Lloyd-Max量化器确定每个抽头不同状态的信噪比范围,进而实现车联网宽带无线信道的建模。本发明提供的方法,弥补了现有马尔科夫信道模型中信道状态严重依赖于距离,导致每个状态范围内信道非平稳这一缺陷；同时将窄带模型提升为宽带系统,提高了车联网无线信道建模的准确性。(The invention provides a finite state Markov-based broadband wireless channel modeling method, which is used for jointly modeling a main path and two adjacent main paths by eliminating the influence of the distance between receiving and transmitting in a received signal, and determining the signal-to-noise ratio range of each tap in different states by adopting a Lloyd-Max quantizer so as to realize the modeling of a broadband wireless channel of the Internet of vehicles. The method provided by the invention overcomes the defect that the channel state in the existing Markov channel model depends on the distance seriously, so that the channel in each state range is not stable; meanwhile, a narrow-band model is improved to be a wide-band system, and the accuracy of modeling of the wireless channel of the Internet of vehicles is improved.)

1. A finite state Markov-based wideband wireless channel modeling method is characterized by comprising the following steps:

s1, based on the channel impulse response collected in the scene of Internet of vehicles, carrying out first-order linear fitting on the receiving power and the distance between the receiving and transmitting ends, and eliminating the influence of the distance between the receiving and transmitting ends;

s2, constructing the main path and the tap adjacent to the main path in the channel impulse response executed in the step S1 into a plurality of signal-to-noise ratio sets, carrying out interval division on each signal-to-noise ratio set, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set;

s3, based on the initial state of the main path and the tap near the main path, the channel state probability and the channel transition probability of each SNR set, and the influence of the receiving and sending distance on the receiving power, the data of the vehicle networking broadband wireless channel is obtained through a first-order N state Markov model.

2. The method according to claim 1, wherein step S1 includes:

s11 passing formula

Eliminating large-scale fading influence in received power so as to eliminate the influence of transmitting and receiving intervals; in the formula (I), the compound is shown in the specification,representing the transmitted power, in dBm,which represents the received power at a transmit-receive spacing, d, in dBm,representing the received power with the effect of eliminating the transmit-receive spacing in dBm, and the parameter n representsPath loss fitting exponent, f₀Representing the carrier frequency, d₀Indicates the reference position distance, FSPL (f)₀,d₀) Is shown at a reference position d₀In dB, expressed as

FSPL(f₀,d₀)＝20log₁₀(4πd₀c/f₀) (2)；

Wherein c represents the speed of light;

s12 passing formula

Eliminating the influence of the distance between the transmitting ends and calculating the signal-to-noise ratio of each multipath tap; in the formula, h^raw(d, tau) represents the originally collected CIR at different positions, tau represents the time delay dimension independent variable, the symbol | | · | | represents the absolute value,the received power for the linear scale, which eliminates the effect of large scale fading, can be expressed asN₀Is the noise power.

3. The method according to claim 2, wherein step S2 includes:

s21, based on the channel impulse response executed in step S1, takes the tap with the largest signal-to-noise ratio in each frame snapshot as the tap of the main path;

s22 passing formula

Obtaining the time delay coordinate tau of the main diameter tap_LOS；

S23 obtaining two taps adjacent to the main diameter based on the taps of the main diameter and passing through the formula

τ₁＝(τ_LOS+1/B) (5) and τ₂＝(τ_LOS+2/B) (6)

Obtaining the time delay coordinates of the two taps adjacent to the main diameter; wherein B is the bandwidth;

s24 expressed by Nakagami-m function

Acquiring signal-to-noise ratio set distribution of the tap of the main path and the tap adjacent to the main path; in the formula (I), the compound is shown in the specification,for the expectation of the signal-to-noise ratio of each path tap, gamma function is represented by gamma, and the parameter m corresponding to each tap set is obtained by fitting;

s25 dividing SNR of each tap into N continuous non-overlapping ranges, and setting nth SNR range as [ gamma ]_n-1,Γ_n) The signal-to-noise ratio falling within this range is considered as state s_n；

S26 passing formula

Carrying out quantization operation on the range of the signal-to-noise ratio of each tap; passing through type

Calculating the mean square error of the range of the signal-to-noise ratio of each tap after quantization; in the formula (I), the compound is shown in the specification,for the quantization level of the nth quantization interval, f (gamma) represents the distribution function of the signal-to-noise ratio, D_kRepresenting the mean square error obtained by the k iteration calculation;

s27 repeating substep S26 until the difference of mean square errors of two adjacent iterations is less than a preset error delta, obtaining the range [ gamma ] of signal-to-noise ratio of all the divided taps_n-1,Γ_n),n＝1,2,…,N-1；

S28 passing formula

Calculating to obtain the channel state probability; wherein num {. represents a state s_nNumber of occurrences, γ_tRepresents the SNR obtained by the t-th sampling, if gamma_t∈[Γ_n-1,Γ_n) Then, the status is considered as s_n；

S29 passing formula

Calculating the channel transition probability, where s_nAnd s_jRepresenting two different states, p_n,jRepresenting a state s_nTransition to State s_jThe probability of (c).

4. The method according to claim 3, wherein step S3 includes:

s31 generating a state sequence of the primary path and the two secondary paths at different positions by a Markov model of a first order N state based on the initial state of the tap of the primary path, the initial state of the tap adjacent to the primary path, the channel state probability and the channel transition probability_t,1,s_t,2,s_t,3],t＝1,2,…}；

S32 is based on the state sequence { [ S ]_t,1,s_t,2,s_t,3]And t is 1,2, … }, and is combined with the influence factor of the transmitting and receiving distance on the received power by the formula

A sequence of vehicle networking broadband wireless channel data is obtained.

Technical Field

The invention relates to the technical field of wireless communication, in particular to a broadband wireless channel modeling method based on finite state Markov.

Background

The vehicle networking communication technology has important significance for improving traffic safety, reducing congestion, improving traffic efficiency and the like, and accurate cognitive radio channels provide theoretical support for early-stage design, link simulation and later-stage network optimization of a vehicle networking communication system. The current wireless channel modeling method for the Internet of vehicles mainly takes theoretical model construction as a mainstream, and typical wireless channel modeling theoretical models are ray tracing method, random channel model based on geometry and the like. The theoretical method can provide a closed expression form of the vehicle networking channel, but the method is a simulation of an actual wireless transmission environment and cannot truly restore the actual transmission condition of the vehicle networking radio waves.

The finite state Markov chain channel model is based on the actual measured channel data and uses a discrete time Markov chain to approximate the attenuation of the wireless channel. The model models the set of all possible channel attenuations with a finite set of channel states by discretizing the time and channel signal-to-noise ratios. However, the conventional finite state markov chain channel modeling method has the following disadvantages: on one hand, the channel state is severely restricted by the distance between the receiving end and the transmitting end, namely the channel state still contains the influence of the distance between the receiving end and the transmitting end; on the other hand, the method is only suitable for simulation of a narrow-band communication system, the bandwidth of the wireless communication system of the Internet of vehicles can reach 20MHz, and the model cannot be directly applied to modeling of wireless channels of the Internet of vehicles. Therefore, the invention mainly researches a vehicle networking broadband wireless channel modeling method based on finite state Markov.

Disclosure of Invention

The embodiment of the invention provides a finite state Markov-based broadband wireless channel modeling method, which is used for solving the problems in the prior art.

In order to achieve the purpose, the invention adopts the following technical scheme.

A finite state Markov-based wideband wireless channel modeling method comprises the following steps:

s1, based on the channel impulse response collected in the scene of Internet of vehicles, carrying out first-order linear fitting on the receiving power and the distance between the receiving and transmitting ends, and eliminating the influence of the distance between the receiving and transmitting ends;

s2, constructing the main path and the tap adjacent to the main path in the channel impulse response executed in the step S1 into a plurality of signal-to-noise ratio sets, carrying out interval division on each signal-to-noise ratio set, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set;

s3, based on the initial state of the main path and the tap near the main path, the channel state probability and the channel transition probability of each SNR set, and the influence of the receiving and sending distance on the receiving power, the data of the vehicle networking broadband wireless channel is obtained through a first-order N state Markov model.

Preferably, step S1 includes:

s11 passing formula

Eliminating large-scale fading influence in received power so as to eliminate the influence of transmitting and receiving intervals; in the formula (I), the compound is shown in the specification,representing the transmitted power, in dBm,which represents the received power at a transmit-receive spacing, d, in dBm,representing the received power without the effect of the transmit-receive spacing in dBm, the parameter n representing the path loss fitting index, f₀Representing the carrier frequency, d₀Indicates the reference position distance, FSPL (f)₀,d₀) Is shown at a reference position d₀In dB, expressed as

FSPL(f₀,d₀)＝20log₁₀(4πd₀c/f₀) (2)；

Wherein c represents the speed of light;

s12 passing formula

Eliminating the influence of the distance between the transmitting ends and calculating the signal-to-noise ratio of each multipath tap; in the formula, h^raw(d, tau) represents the originally collected CIR at different positions, tau represents the time delay dimension independent variable, the symbol | | · | | represents the absolute value,the received power for the linear scale, which eliminates the effect of large scale fading, can be expressed asN₀Is the noise power.

Preferably, step S2 includes:

s21, based on the channel impulse response performed in step S1, takes the tap with the largest signal-to-noise ratio in each frame snapshot as the tap of the main path;

s22 passing formula

Obtaining the time delay coordinate tau of the main diameter tap_LOS；

S23 obtaining two taps adjacent to the main diameter based on the taps of the main diameter and passing through the formula

τ₁＝(τ_LOS+1/B) (5) and τ₂＝(τ_LOS+2/B) (6)

Obtaining the time delay coordinates of the two taps adjacent to the main diameter; wherein B is the bandwidth;

s24 expressed by Nakagami-m function

Acquiring signal-to-noise ratio set distribution of taps of the main path and taps adjacent to the main path; in the formula (I), the compound is shown in the specification,for the expectation of the signal-to-noise ratio of each path tap, gamma function is represented by gamma, and the parameter m corresponding to each tap set is obtained by fitting;

s25 dividing SNR of each tap into N continuous non-overlapping ranges, and setting nth SNR range as [ gamma ]_n-1,Γ_n) The signal-to-noise ratio falling within this range is considered as state s_n；

S26 passing formula

Andcarrying out quantization operation on the range of the signal-to-noise ratio of each tap; passing through type

Calculating the mean square error of the range of the signal-to-noise ratio of each tap after quantization; in the formula (I), the compound is shown in the specification,for the quantization level of the nth quantization interval, f (gamma) represents the distribution function of the signal-to-noise ratio, D_kRepresenting the mean square error obtained by the k iteration calculation;

s27 repeating substep S26 until the difference of mean square errors of two adjacent iterations is less than a preset error delta, obtaining the range [ gamma ] of signal-to-noise ratio of all the divided taps_n-1,Γ_n),n＝1,2,…,N-1；

S28 passing formula

Calculating to obtain the channel state probability; wherein num {. represents a state s_nNumber of occurrences, γ_tRepresents the SNR obtained by the t-th sampling, if gamma_t∈[Γ_n-1,Γ_n) Then, the status is considered as s_n；

S29 passing formula

Calculating a channel transition probability, where s_nAnd s_jRepresenting two different states, p_n,jRepresenting a state s_nTransition to State s_jThe probability of (c).

Preferably, step S3 includes:

s31 generating a state sequence of the primary path and the two secondary paths at different positions by a Markov model of a first order N state based on the initial state of the tap of the primary path, the initial state of the tap adjacent to the primary path, the channel state probability and the channel transition probability { [ S ]_t,1,s_t,2,s_t,3],t＝1,2,…}；

S32 is based on the state sequence { [ S ]_t,1,s_t,2,s_t,3]And t is 1,2, … }, and is combined with the influence factor of the transmitting and receiving distance on the received power by the formula

A sequence of vehicle networking broadband wireless channel data is obtained.

According to the technical scheme provided by the embodiment of the invention, the invention provides the broadband wireless channel modeling method based on the finite state Markov, the influence of the distance between receiving and transmitting in the received signal is eliminated, the main path and two adjacent main paths are jointly modeled, the signal-to-noise ratio range of each tap in different states is determined by adopting the Lloyd-Max quantizer, and further the modeling of the broadband wireless channel of the Internet of vehicles is realized. The method provided by the invention overcomes the defect that the channel state in the existing Markov channel model depends on the distance seriously, so that the channel in each state range is not stable; meanwhile, a narrow-band model is improved to be a wide-band system, and the accuracy of modeling of the wireless channel of the Internet of vehicles is improved.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a process flow diagram of a finite state Markov-based wideband wireless channel modeling method according to the present invention;

FIG. 2 is a diagram illustrating a conventional state range division;

FIG. 3 is a schematic diagram of state interval division for eliminating distance factors in a finite state Markov-based wideband wireless channel modeling method according to the present invention;

fig. 4 is a schematic diagram of power correlation-based quasi-stationary interval calculation in a finite state markov-based wideband wireless channel modeling method according to the present invention;

fig. 5 is a schematic diagram of a finite state markov-based vehicle networking broadband wireless channel modeling method in the finite state markov-based broadband wireless channel modeling method provided by the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.

The invention provides a vehicle networking broadband wireless channel modeling method based on finite state Markov, which aims to solve the problems that the existing finite state Markov chain channel modeling method is too coarse in state range division, so that the modeling precision is low, and on the other hand, the existing finite state Markov chain channel modeling method cannot be applied to broadband vehicle networking channel modeling.

Referring to fig. 1, the present invention provides a finite state markov-based wideband wireless channel modeling method, including:

s1, based on Channel Impulse Response (CIR) collected in the scene of Internet of vehicles, carrying out first-order linear fitting on the receiving power and the distance between the receiving and transmitting ends, and eliminating the influence of the distance between the receiving and transmitting ends;

s2, constructing the main path and the tap adjacent to the main path in the channel impulse response executed in the step S1 into a plurality of signal-to-noise ratio sets, carrying out interval division on each signal-to-noise ratio set, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set;

s3 based on the initial state of the main path and the tap near the main path, the channel state probability and the channel transition probability of each SNR set, and the influence factor of the receiving and sending distance on the receiving power, through the first-order N state Markov model, the vehicle networking broadband wireless channel data is obtained.

When arriving at a receiving end from a transmitting end, a wireless signal will experience large-scale fading and small-scale fading, wherein the large-scale fading describes the slow variation trend of the intensity of the received signal in a long distance (hundreds of meters or even longer), which can be described by log-linear fitting, that is:small scale fading describes the tendency of received signal strength to change rapidly over short distances (a few wavelengths) or short times (in the order of seconds). The influence of the transmitting-receiving distance can be eliminated by subtracting the result of the logarithmic linear fitting from the received power. In a preferred embodiment provided by the present invention, step S1 specifically includes the following processes:

s11 passing formula

Eliminating large-scale fading influence in received power so as to eliminate the influence of transmitting and receiving intervals; in the formula (I), the compound is shown in the specification,representing the transmitted power, in dBm,which represents the received power at a transmit-receive spacing, d, in dBm,representing the received power without the effect of the transmit-receive spacing in dBm, the parameter n representing the path loss fitting index, f₀Representing the carrier frequency, d₀Indicates the reference position distance, FSPL (f)₀,d₀) Is shown at a reference position d₀Free space propagation loss indB, expression is

FSPL(f₀,d₀)＝20log₁₀(4πd₀c/f₀) (2)；

In the formula, c represents the speed of light. The influence of the transmitting-receiving distance can be eliminated by subtracting the result of the logarithmic linear fitting from the received power.

S12 passing formula

Eliminating the influence factor of the distance between the sending ends from the collected CIR, and calculating the signal-to-noise ratio of each multipath tap; in the formula, h^raw(d, tau) represents the originally collected CIR at different positions, tau represents the time delay dimension independent variable, the symbol | | · | | represents the absolute value,the received power for eliminating the large-scale fading influence in a linear scale can be expressed asN₀Is the noise power.

Further, step S2 specifically includes the following steps:

s21, based on the channel impulse response executed in step S1, takes the tap with the largest signal-to-noise ratio in each frame snapshot as the tap of the main path;

s22 passing formula

Obtaining a time delay coordinate of the main diameter tap;

s23 obtaining two taps adjacent to the main diameter based on the taps of the main diameter and passing through the formula

τ₁＝(τ_LOS+1/B) (5) and τ₂＝(τ_LOS+2/B) (6)

Obtaining the time delay coordinates of the two taps adjacent to the main diameter; wherein B is the bandwidth;

s24 expressed by Nakagami-m function

Set of signal-to-noise ratios ({ γ (d, τ) describing the taps of the main path and the taps of two adjacent main paths_LOS(d))}、{γ(d,τ₁(d))}、{γ(d,τ₂(d) ) } distribution; in the formula (I), the compound is shown in the specification,for the expectation of signal-to-noise ratio, Γ (-) represents a gamma function, and the parameter m corresponding to each tap set is obtained by fitting;

s25 determines the signal-to-noise ratio range [ min [ gamma ], max [ gamma ] for each tap set]Min {. cndot.and max {. cndot.represent the minimum and maximum functions, the SNR of each tap is divided into N consecutive non-overlapping ranges, and the nth SNR range is set as [ Γ {_n-1,Γ_n) The signal-to-noise ratio falling within this range is considered as state s_nWherein the upper and lower limits of the range are randomly generated;

s26 passing formula

And

carrying out quantization operation on the range of the signal-to-noise ratio of each tap; passing through type

Calculating the mean square error of the range of the signal-to-noise ratio of each tap after quantization; in the formula (I), the compound is shown in the specification,for the nth quantization regionThe quantization level between, f (gamma) represents the distribution function of the signal-to-noise ratio, D_kRepresenting the mean square error obtained by the k iteration calculation;

s27 repeating the sub-step S26 until the difference of the mean square errors of two adjacent iterations is less than the preset error delta, i.e. | D_k+1-D_kThe signal-to-noise ratio range [ gamma ] of all the divided taps is obtained_n-1,Γ_n),n＝1,2,…,N-1；

S28 passing formula

Calculating to obtain each state s_nThe channel state probability of (a); wherein num {. represents a state s_nNumber of occurrences, γ_tRepresents the SNR obtained by the t-th sampling, if gamma_t∈[Γ_n-1,Γ_n) Then, the status is considered as s_n；

S29 passing formula

Calculating s_nTo s_jChannel transition probability of (1), where s_nAnd s_jRepresenting two different states, p_n,jRepresenting a state s_nTransition to State s_jThe probability of (c).

The wireless channel is slowly varying during the sampling interval ad and the SNR varies more slowly, so that the SNR state transition only occurs between two adjacent states, namely: p is a radical of_n,j＝0,if|n-j|>1。

Further, step S3 includes:

s31 generating a state sequence of the primary path and the two secondary paths at different positions by a Markov model of a first order N state based on the initial state of the tap of the primary path, the initial state of the tap adjacent to the primary path, the channel state probability and the channel transition probability_t,1,s_t,2,s_t,3],t＝1,2,…}；

S32 is based on the state sequence { [ S ]_t,1,s_t,2,s_t,3]And t is 1,2, … }, and is combined with the influence factor of the transmitting-receiving distance d on the receiving power by the formula

A sequence of vehicle networking broadband wireless channel data is obtained.

The invention also provides an embodiment for displaying the effect of the method provided by the invention.

Fig. 2 is a schematic diagram of conventional state interval division, and referring to fig. 2, it can be seen that:

when the state interval is divided according to the traditional finite state Markov channel modeling method, the SNR of the receiving end contains the distance information of the receiving end and the transmitting end, so that the SNR span range of the receiving end is larger, the SNR span range corresponding to each state is also larger, and the channel modeling precision is lower.

Fig. 3 is a schematic diagram illustrating division of state intervals for eliminating the distance factor, and referring to fig. 3, it can be seen that:

the distance factor between the transmitting end and the receiving end is eliminated from the receiving end signal, the span range of SNR is reduced, the range of SNR corresponding to each state is reduced, the statistical result of state transition probability is more accurate, and therefore the channel modeling precision can be improved.

Fig. 4 is a schematic diagram of multi-tap joint modeling, and referring to fig. 4, it can be known that:

three finite state Markov models are respectively adopted for carrying out channel modeling on the main path tap and two adjacent main path taps, the states of the three taps of the channel correspondingly change along with the movement of a vehicle from one position to the next position, and the three taps of the channel can only be transferred between the three taps and the adjacent states when the states are changed.

Fig. 5 is a schematic diagram of a finite state markov-based vehicle networking broadband wireless channel modeling method, and referring to fig. 5, it can be known that:

inputting the collected Channel Impulse Response (CIR) and the distance between receiving and transmitting, eliminating the influence of the distance between receiving and transmitting in the received signal by adopting a first-order linear fitting mode, finding out a main path and two adjacent main path taps in each frame of CIR snapshot, and constructing the three sets according to the CIRs at different positions.

Secondly, describing the signal-to-noise ratio distribution of the three tap sets by Nakagami-m, determining the maximum/small value of each signal-to-noise ratio set, dividing the maximum/small value into N intervals, and determining the upper and lower bounds and the quantization level of each interval by using a Lloyd-Max algorithm.

And thirdly, counting the state occurrence probability and the transition probability of each tap set, giving the initial states of the three taps, generating a new state sequence for each tap by adopting a first-order Markov model, and considering the influence of the distance between receiving and transmitting on the power to obtain the broadband channel tap sequence of the Internet of vehicles.

In summary, the present invention provides a finite state markov-based wideband wireless channel modeling method, including: aiming at the acquired wireless channel impulse response of the Internet of vehicles, a first-order linear fitting method is adopted to obtain a first-order expression of the receiving power and the distance between the receiving and transmitting terminals, and the influence of the distance between the receiving and transmitting terminals is eliminated from the received signal; finding out a main path and two adjacent multipath taps in each frame of wireless channel snapshot, and constructing signal-to-noise ratios corresponding to the three taps at different positions into three sets; for each set, a Nakagami-m function is adopted to describe the distribution of the sets, and the signal-to-noise ratio of each set is divided into a plurality of continuous non-overlapping channel states according to a Lloyd-Max algorithm; counting the occurrence probability and the state transition probability of different channel states of each set; giving initial states of the three taps, generating a new state sequence based on a first-order Markov model, calculating receiving power by combining the distance between the receiving and transmitting ends, and generating new data of the vehicle networking broadband wireless channel. The method of the invention makes up the defect that the channel state in the existing Markov channel model depends on the distance seriously, which causes the channel in each state range to be unstable; meanwhile, a narrow-band model is improved to be a wide-band system, and the accuracy of modeling of the wireless channel of the Internet of vehicles is improved.

Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.

From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.