CN113726463B - Broadband wireless channel modeling method based on finite state Markov - Google Patents
Broadband wireless channel modeling method based on finite state Markov Download PDFInfo
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Abstract
The invention provides a broadband wireless channel modeling method based on finite state Markov, which is used for jointly modeling a main path and two adjacent main paths by eliminating the influence of a receiving-transmitting distance in a received signal, and determining the signal-to-noise ratio range of different states of each tap by adopting an Lloyd-Max quantizer so as to further realize the modeling of the broadband wireless channel of the Internet of vehicles. The method provided by the invention overcomes the defect that the channel state in the prior Markov channel model is seriously dependent on the distance, so that the channel is not stable in each state range; meanwhile, the narrowband model is improved to a broadband system, and accuracy of modeling of the wireless channel of the Internet of vehicles is improved.
Description
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 internet of vehicles communication technology has important significance in the aspects of improving traffic safety, reducing congestion, improving traffic efficiency and the like, and the accurate cognitive wireless channel provides theoretical support for early design, link simulation and later network optimization of the internet of vehicles communication system. The current modeling method for the wireless channel of the Internet of vehicles mainly takes a theoretical model as a mainstream, and typical wireless channel modeling theoretical models such as a ray tracing method, a geometric-based random channel model and the like. The theoretical method can provide a closed expression form of the Internet of vehicles channel, but the method is a simulation of the actual wireless propagation environment, and cannot truly restore the actual propagation condition of the Internet of vehicles radio waves.
The finite state Markov chain channel model approximates the attenuation of the wireless channel using a discrete time Markov chain based on the actual measured channel data. The model implements modeling of all possible sets of 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 the one hand, the channel state is severely restricted by the distance between the receiving and transmitting ends, namely the channel state still contains the influence of the distance between the receiving and transmitting ends; 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 broadband wireless channel modeling method based on finite state Markov, which is used for solving the problems existing in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A method of finite state markov based modeling a broadband wireless channel, comprising:
s1, performing first-order linear fitting on the received power and the distance between the receiving end and the transmitting end based on channel impulse response acquired in a scene of the Internet of vehicles, and eliminating the influence of the distance between the receiving end and the transmitting end;
s2, constructing a main path and a tap adjacent to the main path in the channel impulse response of the step S1 into a plurality of signal-to-noise ratio sets, dividing each signal-to-noise ratio set into intervals, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set;
s3, based on the primary path and the initial state of the tap adjacent to the primary path, the channel state probability and the channel transition probability of each signal-to-noise ratio set are combined with the influence of the receiving and transmitting distance on the receiving power, and the broadband wireless channel data of the Internet of vehicles are obtained through a Markov model in a first-order N state.
Preferably, step S1 comprises:
s11 through type
Eliminating the large-scale fading influence in the received power, and further eliminating the influence of the receiving-transmitting distance; wherein P is t dB Representing the transmit power, in dBm,the received power at a transmission/reception interval d is expressed in dBm,/and d>The unit of the received power is dBm, the parameter n is the path loss fitting index, f 0 Representing carrier frequency d 0 Represents the reference position distance, FSPL (f 0 ,d 0 ) Indicated at reference position d 0 In dB, expressed as
FSPL(f 0 ,d 0 )＝20log 10 (4πd 0 c/f 0 ) (2)；
Wherein c represents the speed of light;
s12 through type
Elimination ofThe influence of the transmitting end spacing, and calculating the signal-to-noise ratio of each multipath tap; in the formula, h raw (d, τ) represents the CIR at the different locations originally acquired, τ represents the Shi Yanwei independent variable and, symbol ||·| the representation takes the absolute value of the value,the received power, which is linear scale and eliminates the influence of large scale fading, can be expressed as +.>N 0 Is the noise power.
Preferably, step S2 includes:
s21, taking a tap with the largest signal-to-noise ratio in each frame snapshot as a tap of a main path based on the channel impulse response of the step S1;
s22 through type
Obtaining the time delay coordinate tau of the main-diameter tap LOS ；
S23, acquiring two taps adjacent to the main path based on the taps of the main path, and passing through
τ 1 ＝(τ LOS +1/B) (5) and τ 2 ＝(τ LOS +2/B) (6)
Obtaining time delay coordinates of the two taps adjacent to the main path; wherein B is bandwidth;
s24 by Nakagami-m function
Acquiring signal-to-noise ratio set distribution of a tap of a main path and a tap adjacent to the main path; in the method, in the process of the invention,for the desired signal-to-noise ratio of each tap, Γ (·) represents the gamma function, each tapThe parameter m corresponding to the head set is obtained by fitting;
s25, dividing the signal-to-noise ratio of each tap into N continuous non-overlapping ranges, and setting the nth signal-to-noise ratio range as [ Γ ] n-1 ,Γ n ) A signal to noise ratio falling within this range is considered to be state s n ；
S26 through type
Performing quantization operation on the range of the signal-to-noise ratio of each tap; through type
Calculating the mean square error of the range of the quantized signal-to-noise ratio of each tap; in the method, in the process of the invention,for the quantization level of the nth quantization interval, f (γ) represents a distribution function of the signal-to-noise ratio, D k Representing the mean square error obtained by the kth iterative computation;
s27, repeatedly executing the substep S26 until the difference value of the mean square errors of two adjacent iterations is smaller than a preset error delta, and obtaining the range [ Γ ] of the signal to noise ratios of all the divided taps n-1 ,Γ n ),n＝1,2,…,N-1；
S28 through type
Calculating to obtain channel state probability; where num {.cndot } represents state s n Number of occurrences, gamma t Representing the SNR obtained by the t-th sampling, if gamma t ∈[Γ n-1 ,Γ n ) Then consider its state as s n ；
S29 through type
Calculating a channel transition probability, wherein s n Sum s j Representing two different states, p n,j Representing the state s n Transition to state s j Is a probability of (2).
Preferably, step S3 includes:
s31 generating state sequences { [ S ] of the main path and the two sub paths at different positions by a Markov model of a first-order N state based on an initial state of a tap of the main path, an initial state of a tap adjacent to the main path, a channel state probability and a 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 ]T=1, 2, … }, and combining the influence factors of the transceiving space on the receiving power, through type
And obtaining the sequence of the broadband wireless channel data of the Internet of vehicles.
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 receiving and transmitting distance in the received signal is eliminated, the main path and two adjacent main paths are modeled together, the Lloyd-Max quantizer is adopted to determine the signal-to-noise ratio range of different states of each tap, and further 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 prior Markov channel model is seriously dependent on the distance, so that the channel is not stable in each state range; meanwhile, the narrowband model is improved to a broadband system, and 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 required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a process flow diagram of a finite state Markov-based wideband wireless channel modeling method provided by the present invention;
FIG. 2 is a diagram illustrating a conventional state interval division;
FIG. 3 is a schematic diagram of state interval division for eliminating distance factors in a finite state Markov-based broadband 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 broadband wireless channel modeling method of an internet of vehicles based on finite state markov in the broadband wireless channel modeling method based on finite state markov provided by the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for 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 expressly stated otherwise, as understood by those skilled in the art. 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. The term "and/or" as used herein 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 purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The invention provides a finite state Markov-based broadband wireless channel modeling method for the Internet of vehicles, which aims to solve the problem that the existing finite state Markov chain channel modeling method cannot be suitable for broadband Internet of vehicles channel modeling because the state range division is too rough.
Referring to fig. 1, the present invention provides a method for modeling a broadband wireless channel based on finite state markov, comprising:
s1, performing first-order linear fitting on the received power and the distance between the receiving end and the transmitting end based on channel impulse response (channel impulse response, CIR) acquired in a scene of the Internet of vehicles, and eliminating the influence of the distance between the receiving end and the transmitting end;
s2, constructing a main path and a tap adjacent to the main path in the channel impulse response of the step S1 into a plurality of signal-to-noise ratio sets, dividing each signal-to-noise ratio set into intervals, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set;
s3, based on the primary path and the initial state of the tap adjacent to the primary path, the channel state probability and the channel transition probability of each signal-to-noise ratio set are combined with the influence factor of the receiving and transmitting distance on the receiving power, and the broadband wireless channel data of the Internet of vehicles are obtained through a Markov model in a first-order N state.
The wireless signal will experience large-scale fading and small-scale fading when reaching the receiving end from the transmitting end, 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), and can be described by logarithmic linear fitting, namely: p (P) t dB -FSPL(f 0 ,d 0 )+10nlog 10 (d/d 0 ) The method comprises the steps of carrying out a first treatment on the surface of the Small scale fading describes the tendency of received signal strength to change rapidly over short distances (several wavelengths) or short periods of time (seconds). And subtracting the log-linear fitting result from the received power to eliminate the influence of the receiving-transmitting distance. In the preferred embodiment provided by the present invention, step S1 specifically includes the following processes:
s11 through type
Eliminating the large-scale fading influence in the received power, and further eliminating the influence of the receiving-transmitting distance; wherein P is t dB Representing the transmit power, in dBm,the received power at a transmission/reception interval d is expressed in dBm,/and d>The unit of the received power is dBm, the parameter n is the path loss fitting index, f 0 Representing carrier frequency d 0 Represents the reference position distance, FSPL (f 0 ,d 0 ) Indicated at reference position d 0 Free space propagation loss at the point, expressed in dB, expressed as
FSPL(f 0 ,d 0 )＝20log 10 (4πd 0 c/f 0 ) (2)；
In the formula, c represents the speed of light. And subtracting the log-linear fitting result from the received power to eliminate the influence of the receiving-transmitting distance.
S12 through type
Eliminating an influence factor of the source end distance from the acquired CIR, and calculating the signal-to-noise ratio of each multipath tap; in the formula, h raw (d, τ) represents the CIR at the different locations originally acquired, τ represents the Shi Yanwei independent variable and, symbol ||·| the representation takes the absolute value of the value,the received power for linear scale canceling the large scale fading influence can be expressed as +.>N 0 Is the noise power.
Further, the step S2 specifically includes the following steps:
s21, taking a tap with the maximum signal-to-noise ratio in each frame snapshot as a tap of the main path based on the channel impulse response of the step S1;
s22 through type
Obtaining the time delay coordinates of the main path tap;
s23, based on the taps of the main paths, acquiring two taps adjacent to the main paths, and passing through
τ 1 ＝(τ LOS +1/B) (5) and τ 2 ＝(τ LOS +2/B) (6)
Obtaining time delay coordinates of the two taps adjacent to the main path; wherein B is bandwidth;
s24 by Nakagami-m function
The signal-to-noise ratio set describing the tap of the main path and the taps of two neighboring main paths ({ γ (d, τ) LOS (d))}、{γ(d,τ 1 (d))}、{γ(d,τ 2 (d) -a) distribution; in the method, in the process of the invention,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 minimum and maximum functions, dividing the signal-to-noise ratio of each tap into N continuous non-overlapping ranges, and setting the N-th signal-to-noise ratio range as [ Γ ] n-1 ,Γ n ) A signal to noise ratio falling within this range is considered to be state s n Wherein the upper and lower limits of the range are randomly generated;
s26 through type
Performing quantization operation on the range of the signal-to-noise ratio of each tap; through type
Calculating the mean square error of the range of the quantized signal-to-noise ratio of each tap; in the method, in the process of the invention,quantization level, f (gamma) table for n-th quantization intervalDistribution function showing signal-to-noise ratio, D k Representing the mean square error obtained by the kth iterative computation;
s27 repeatedly executing the substep S26 until the difference value of the mean square errors of two adjacent iterations is smaller than the preset error delta, namely |D k+1 -D k Obtaining the range [ Γ ] of the signal-to-noise ratio of all taps after division n-1 ,Γ n ),n＝1,2,…,N-1；
S28 through type
Calculating to obtain each state s n Channel state probabilities of (a); where num {.cndot } represents state s n Number of occurrences, gamma t Representing the SNR obtained by the t-th sampling, if gamma t ∈[Γ n-1 ,Γ n ) Then consider its state as s n ；
S29 through type
Calculation s n S to s j Channel transition probability of s where s n Sum s j Representing two different states, p n,j Representing the state s n Transition to state s j Is a probability of (2).
The wireless channel is slowly varying during the sampling interval Δd, and the SNR is relatively slow, so that the SNR state transition only occurs between two adjacent states, namely: p is p n,j ＝0,if|n-j|>1。
Further, step S3 includes:
s31 generating state sequences { [ S ] of the main path and the two sub paths at different positions by a Markov model of a first order N state based on the initial state of the tap of the main path, the initial state of the tap adjacent to the main 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 ]T=1, 2, … }, combined with the influence factor of the transmit-receive distance d on the received power, by the formula
And obtaining the sequence of the broadband wireless channel data of the Internet of vehicles.
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 dividing a state interval according to a traditional finite state Markov channel modeling method, the SNR of a receiving end contains receiving and transmitting end distance information, 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 of state interval division for eliminating distance factors, and referring to fig. 3, it can be seen that:
the distance factor between the receiving end and the transmitting end is eliminated from the signal of the receiving end, the span range of the SNR is reduced, the SNR range corresponding to each state is also reduced, and the statistical result of the state transition probability is more accurate, so that 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 seen that:
the channel modeling is carried out on the main path tap and the two adjacent main path taps by adopting three finite state Markov models, and as the vehicle moves from one position to the next position, the states of the three taps of the channel correspondingly change, and when the states are changed, the state transition can only be carried out between the main path tap and the adjacent states.
Fig. 5 is a schematic diagram of a method for modeling a broadband wireless channel of an internet of vehicles based on finite state markov, and referring to fig. 5, it can be known that:
(1) and inputting the acquired Channel Impulse Response (CIR) and the distance between the receiving and transmitting, eliminating the influence of the distance between the 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 CIRs at different positions.
(2) Describing the signal-to-noise ratio distribution of three tap sets by using Nakagami-m, determining the maximum/minimum value of each signal-to-noise ratio set, dividing the signal-to-noise ratio set into N sections, and determining the upper and lower bounds and the quantization level of each section by using an Lloyd-Max algorithm.
(3) And counting the state occurrence probability and transition probability of each tap set, giving the initial states of three taps, generating a new state sequence by adopting a first-order Markov model for each tap, and taking the influence of the inter-receiving distance on the power into consideration to obtain the broadband channel tap sequence of the Internet of vehicles.
In summary, the present invention provides a method for modeling a broadband wireless channel based on finite state markov, including: aiming at the acquired impulse response of the wireless channel 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 ends, and the influence of the distance between the receiving ends is eliminated from the received signal; finding out two multipath taps on the main path and the side of each frame of wireless channel snapshot, and constructing signal-to-noise ratios corresponding to the three taps at different positions into three sets; describing the distribution of each set by adopting a Nakagami-m function, and dividing the signal-to-noise ratio of each set into a plurality of continuous non-overlapping channel states according to an Lloyd-Max algorithm; counting occurrence probability and state transition probability of different channel states of each set; giving the initial states of the three taps, generating a new state sequence based on a first-order Markov model, calculating the received power by combining the distance between the receiving and transmitting ends, and generating new broadband wireless channel data of the Internet of vehicles. The method overcomes the defect that the channel state in the prior Markov channel model is seriously dependent on the distance, so that the channel is not stable in each state range; meanwhile, the narrowband model is improved to a broadband system, and accuracy of modeling of the wireless channel of the Internet of vehicles is improved.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (1)
1. A method for modeling a wideband wireless channel based on finite state markov, comprising:
s1, performing first-order linear fitting on the received power and the distance between the receiving end and the transmitting end based on Channel Impulse Response (CIR) acquired in a scene of the Internet of vehicles, and eliminating the influence of the distance between the receiving end and the transmitting end; the method specifically comprises the following steps:
s11 through type
Eliminating the large-scale fading influence in the received power, and further eliminating the influence of the receiving-transmitting distance; wherein P is t dB Representing the transmit power, in dBm,the received power at a transmit-receive spacing d in dBm, the parameter n represents the path loss fitting index, f 0 Representing carrier frequency d 0 Represents the reference position distance, FSPL (f 0 ,d 0 ) Indicated at reference position d 0 In dB, expressed as
FSPL(f 0 ,d 0 )＝20log 10 (4πd 0 c/f 0 ) (2)；
Wherein c represents the speed of light;
s12 through type
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, τ) represents the CIR at the different locations originally acquired, τ represents the Shi Yanwei independent variable and, symbol ||·| the representation takes the absolute value of the value,is a linear rulerThe received power of the degree, which eliminates the influence of large-scale fading, is expressed as +.>Wherein->Representing the received power in dBm, N with the effect of the transmit-receive spacing removed 0 Is the noise power;
s2, constructing a main path and a tap adjacent to the main path in the channel impulse response of the step S1 into a plurality of signal-to-noise ratio sets, dividing each signal-to-noise ratio set into intervals, and counting the channel state probability and the channel transition probability of each signal-to-noise ratio set; the method specifically comprises the following steps:
s21, taking a tap with the maximum signal-to-noise ratio in each frame snapshot as a tap of the main path based on the channel impulse response of the step S1;
s22 through type
Obtaining the time delay coordinate tau of the main path tap LOS ；
S23, based on the taps of the main paths, acquiring two taps adjacent to the main paths, and passing through
τ 1 ＝(τ LOS +1/B) (5) and τ 2 ＝(τ LOS +2/B) (6)
Obtaining time delay coordinates of the two taps adjacent to the main path; wherein B is bandwidth;
s24 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 method, in the process of the invention,for the expectation of the signal-to-noise ratio of each tap, Γ (·) represents a gamma function, and the parameter m corresponding to each tap set is obtained by fitting;
s25, dividing the signal-to-noise ratio of each tap into N continuous non-overlapping ranges, and setting the nth signal-to-noise ratio range as [ Γ ] n-1 ,Γ n ) A signal to noise ratio falling within this range is considered to be state s n ；
S26 through type
And->
Performing quantization operation on the range of the signal-to-noise ratio of each tap; through type
Calculating the mean square error of the range of the quantized signal-to-noise ratio of each tap; in the method, in the process of the invention,for the quantization level of the nth quantization interval, f (γ) represents a distribution function of the signal-to-noise ratio, D k Representing the mean square error obtained by the kth iterative computation;
s27, repeatedly executing the substep S26 until the difference value of the mean square errors of two adjacent iterations is smaller than a preset error delta, and obtaining the range [ Γ ] of the signal to noise ratios of all the divided taps n-1 ,Γ n ),n＝1,2,…,N-1；
S28 through type
Calculating to obtain the channel state probability; where num {.cndot } represents state s n Number of occurrences, gamma t Representing the SNR obtained by the t-th sampling, if gamma t ∈[Γ n-1 ,Γ n ) Then consider its state as s n ；
S29 through type
Calculating the channel transition probability, wherein s n Sum s j Representing two different states, p n,j Representing the state s n Transition to state s j Probability of (2);
s3, based on the initial states of the main path and the tap adjacent to the main path, the channel state probability and the channel transition probability of each signal-to-noise ratio set are combined with the influence of the receiving and transmitting distance on the receiving power, and the broadband wireless channel data of the Internet of vehicles are obtained through a Markov model in a first-order N state; specifically comprises
S31 generating state sequences { [ S ] of the main path and the two sub paths at different positions by a Markov model of a first order N state based on the initial state of the tap of the main path, the initial state of the tap adjacent to the main 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 ]T=1, 2, … }, and combining the influence factors of the transceiving space on the receiving power, through type
And obtaining the sequence of the broadband wireless channel data of the Internet of vehicles.
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