CN112436882A - LEO satellite channel modeling method and device based on double Markov models - Google Patents

LEO satellite channel modeling method and device based on double Markov models Download PDF

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CN112436882A
CN112436882A CN202011158158.3A CN202011158158A CN112436882A CN 112436882 A CN112436882 A CN 112436882A CN 202011158158 A CN202011158158 A CN 202011158158A CN 112436882 A CN112436882 A CN 112436882A
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scale fading
multipath
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CN112436882B (en
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邓中亮
刘雯
郑奭轩
林文亮
王珂
刘浩
于晓艺
刘洋
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Beijing University of Posts and Telecommunications
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Abstract

According to the LEO satellite channel modeling method and device based on the double Markov models, the influence of shadow fading on the LOS path under large-scale fading can be reflected through the first-order Markov multi-state channel model; and when the first-order Markov multi-state channel model is in each large-scale fading state, extracting small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path under large-scale fading, and reflecting a multi-path dynamic extinction process under small-scale fading, so that the dynamic time-varying requirement of the LEO satellite channel model on a time axis is met, and the dynamic property of the LEO satellite channel characteristic is met; and the channel parameters required by the complete rice channel model are established by determining the channel parameters to obtain the rice channel model of the LEO satellite, so that the rice channel model of the LEO satellite is more consistent with the actual scene of the low-orbit satellite channel.

Description

LEO satellite channel modeling method and device based on double Markov models
Technical Field
The invention relates to the technical field of low-orbit satellite communication, in particular to an LEO satellite channel modeling method and device based on a double-Markov model.
Background
In communication, channel modeling is an abstract way to model channels and classify them according to their mathematical characteristics of input/output signals and the mathematical characteristics of the relationships between input/output signals. However, a model established for a channel, such as a Land Mobile Satellite (LMS) channel model, is only for a narrowband high-orbit Satellite channel. The narrow-band high-Orbit satellite channel is, for example, a Geosynchronous Orbit (GEO) satellite.
For a Low Earth Orbit (LEO) satellite (which may be referred to as a Low Earth Orbit satellite) channel, in the LEO satellite channel, due to the characteristics of high operation speed of the Low Earth Orbit satellite, severe relative motion change between the LEO satellite and the UE, and the like, a transmission signal is affected by the characteristics of the LEO channel, so that the LEO satellite channel exhibits characteristics different from those of the GEO satellite channel. Therefore, the LMS channel model is not suitable for the low-earth orbit satellite channel, and how to establish a model more suitable for the actual scene of the low-earth orbit satellite channel becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for modeling an LEO satellite channel based on a double-Markov model, which are used for solving the technical problem of how to establish a model more conforming to the actual scene of a low-orbit satellite channel in the prior art. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for modeling a LEO satellite channel based on a dual Markov model, including:
acquiring actual measurement data of a signal received by a low earth orbit LEO satellite during a transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
establishing a first-order Markov multi-state channel model of large-scale fading by using the LOS path, wherein the first-order Markov multi-state channel model comprises the following steps: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
extracting small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path under large-scale fading when a first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
taking the total number of the multipath at the current moment as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
establishing a second-order Markov multi-state channel model of small-scale fading by using the plurality of small-scale fading states and the second transition probability;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out piecewise fitting on unknown channel parameters, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, and determining the channel parameters;
and adding the channel parameters into a rice channel model of unknown channel parameters to obtain the rice channel model of the LEO satellite.
Further, the establishing a first-order Markov multi-state channel model of large-scale fading by using the LOS path includes:
dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals;
obtaining first transition probabilities under the plurality of large-scale fading states;
and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability.
Further, the first-order Markov multi-state channel model is:
Figure BDA0002743418280000031
wherein S isiIs S1，S2，...，SNN is the number of finite states, the number of different intervals corresponding to the average power of the signal is the same, pii+1Is in a state SiTo state Si+1Probability of transition, pii-1Is in a state SiTo state Si-1Probability of transition, piiIs in a state SiTo state SiThe probability of the transition is determined by the probability of the transition,
Figure BDA0002743418280000032
is in a state SiThe duration of the time period of the first,
Figure BDA0002743418280000033
is in a state Si-1The duration of the time period of the first,
Figure BDA0002743418280000034
is in a state Si+1Duration of (S)i+1Is SiThe latter state of (S)i-1Is SiThe last state of (a);
the second-order Markov multi-state channel model of the small-scale fading is as follows:
pnn+2＝p(2，0)
pnn+1＝p(1，0)+p(2，1)
pnn＝p(0，0)+p(1，1)+p(2，2)
pnn-1＝p(0，1)+p(1，2)
pnn-2＝p(0，2)
wherein x isiIs the current state, xn+2Is xiThe last two states of (a), (b), (c), (dn+1Is xiThe latter state of (a), xn-1Is xiThe last state of (a); x is the number ofn-2Is xiThe first two states of (1), pnn+2Is a state xnTo state xn+2Transition probability of, pnn+1Is a state xnTo state xn+1、pnnIs a state xnTo state xn、pnn-1Is a state xnTo state xn-1And pnn-2Is a state xnTo state xn-2The transition probability of (2); p (j, k) is the current state xiGenerating the probability of j paths and k paths to be lost, j being the path generated at the current time, k being the path lost at the current time, P (j, k) ═ PB)j·(PD)kJ is more than or equal to 0, k is less than or equal to 2, and j and k are integers, PBTo generate a probability of multipath, PDTo eliminate the probability of a multipath, n is the number of multipaths in the current state.
Further, the step of performing piecewise fitting on the unknown channel parameters according to the plurality of large-scale fading states divided by the first-order Markov multi-state channel model, counting the characteristics in the same time period in each segment, and determining the channel parameters includes:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out sectional fitting envelope distribution on the envelope distribution, counting the envelope distribution in a time period corresponding to the large-scale fading states in each section, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting delay distribution factor and delay expansion on the multipath delay, counting the multipath delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multipath delay so as to determine the multipath delay in the same time period from the uniform distribution;
and determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
Further, the rayleigh distribution of the envelope distribution is:
Figure BDA0002743418280000041
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure BDA0002743418280000042
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
said determining a total distribution of received signal amplitudes using a rice factor, comprising:
using the following formula for the rice factor,
Figure BDA0002743418280000051
wherein, k (t) is the rice factor at the current time, k (t) 0 indicates that there is no LOS path, the multi-path envelope distribution is converted from the rice distribution to the rayleigh distribution, if k (t) 0 indicates that there is an LOS path, the envelope of the multi-path signal obeys the rice distribution,
Figure BDA0002743418280000056
Figure BDA0002743418280000052
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
Further, the obtaining a rice channel model of the LEO satellite by adding the channel parameter to a rice channel model of an unknown channel parameter includes:
Figure BDA0002743418280000053
wherein h islos(t) and hnlos(t，τi) Time domain impulse responses of LOS path and non-line-of-sight NLOS path, respectively, and | hnlos(t，τi) I is considered to obey Rayleigh distribution, | h (t, τ)i) I is considered to obey the Rice distribution, αlosIs the amplitude of the LOS path and,
Figure BDA0002743418280000054
doppler shift for LOS path, N (t) is the number of multipaths at the current time, αiFor the amplitude of the ith multipath, δ (-) is the Dirichlet function, τiFor the time delay of the ith multipath,
Figure BDA0002743418280000055
the Doppler shift of the ith multipath is shown, and t is the current time.
In a second aspect, an embodiment of the present invention provides a dual Markov model-based LEO satellite channel modeling apparatus, including:
the acquisition module is used for acquiring the actually measured data of the received signals of the low earth orbit LEO satellite during the transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
a first establishing module, configured to establish a first-order Markov multi-state channel model for large-scale fading using the LOS path, where the first-order Markov multi-state channel model includes: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
the extraction module is used for extracting small-scale fading actual measurement data corresponding to the line-of-sight (LOS) path under the large-scale fading when the first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
the first processing module is used for taking the total number of the multipath at the current moment as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
the second establishing module is used for establishing a second-order Markov multi-state channel model of the small-scale fading by utilizing the plurality of small-scale fading states and the second transition probability;
the second processing module is used for performing piecewise fitting on unknown channel parameters according to a plurality of large-scale fading states divided by the first-order Markov multi-state channel model, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, and determining the channel parameters;
and the third processing module is used for adding the channel parameters into a rice channel model of unknown channel parameters to obtain the rice channel model of the LEO satellite.
Further, the first establishing module is configured to:
dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals;
obtaining first transition probabilities under the plurality of large-scale fading states;
and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability.
Further, the second processing module is configured to:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out sectional fitting envelope distribution on the envelope distribution, counting the envelope distribution in a time period corresponding to the large-scale fading states in each section, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting delay distribution factor and delay expansion on the multipath delay, counting the multipath delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multipath delay so as to determine the multipath delay in the same time period from the uniform distribution;
and determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
Further, the rayleigh distribution of the envelope distribution is:
Figure BDA0002743418280000071
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure BDA0002743418280000072
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
the second processing module is configured to determine the total distribution of the received signal amplitudes using a rice factor, and includes:
using the following formula for the rice factor,
Figure BDA0002743418280000081
wherein, k (t) is the rice factor at the current time, k (t) 0 indicates that there is no LOS path, the multi-path envelope distribution is converted from the rice distribution to the rayleigh distribution, if k (t) 0 indicates that there is an LOS path, the envelope of the multi-path signal obeys the rice distribution,
Figure BDA0002743418280000083
Figure BDA0002743418280000082
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
a processor for implementing the steps of the method of any one of the first aspect when executing a program stored in the memory.
The embodiment of the invention has the following beneficial effects:
according to the LEO satellite channel modeling method and device based on the double Markov models, the influence of shadow fading on the LOS path under large-scale fading can be reflected through the first-order Markov multi-state channel model; and under each large-scale fading state of the first-order Markov multi-state channel model, extracting small-scale fading measured data corresponding to a line-of-sight (LOS) path under large-scale fading, reflecting a multi-path dynamic extinction process under small-scale fading, completing description of large-range change of path LOSs and high dynamic property of small-scale fading by using a two-order Markov process, and determining transition probability, so that the requirement of dynamic time variation of an LEO satellite channel model on a time axis is met, the dynamic property of the LEO satellite channel characteristic is met, and the randomness of the LEO satellite channel characteristic is met by using the first-order Markov multi-state channel model and the second-order Markov multi-state channel model; and according to a plurality of large-scale fading states divided by the first-order Markov multi-state channel model, performing piecewise fitting on unknown channel parameters, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, determining the channel parameters, establishing the channel parameters required by the complete Leise channel model, and obtaining the Leise channel model of the LEO satellite, so that the Leise channel model of the LEO satellite is more consistent with the actual scene of the low-orbit satellite channel.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a LEO satellite channel modeling method based on a dual Markov model according to an embodiment of the present invention;
fig. 2(a) is a schematic diagram illustrating a variation curve of a path loss of a LEO satellite channel according to an embodiment of the present invention;
FIG. 2(b) is a diagram illustrating a variation curve of a Doppler shift of a LEO satellite channel according to an embodiment of the present invention;
fig. 2(c) is a schematic diagram illustrating a variation curve of an elevation angle of a LEO satellite channel communication according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of LEO broadband satellite channel fading according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a channel modeling flow based on a two-step Markov process according to an embodiment of the present invention;
FIG. 5 is a schematic representation of a second-order Markov process that may be used in the multipath dynamic birth and death process according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a LEO satellite channel modeling apparatus based on a dual Markov model according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In recent years, Low-Earth Orbit (LEO) satellite internet has become a focus of much attention. Compared with Geosynchronous Orbit (GEO) satellites, the LEO satellite network can simultaneously meet the requirements of small transmission delay, low transmission loss, high data rate and the like; compared with the ground mobile communication, the LEO satellite can be used as an extension of the ground communication, and the requirement of the global wide area communication network coverage is met.
In an LEO satellite channel, due to the characteristics of short satellite transit time, constantly regular change of elevation angle, violent relative motion change between an LEO satellite and a UE and the like, transmission signals are affected by dynamically changed shadow effect, time-varying large doppler frequency shift, large dynamic range power attenuation, high dynamic multipath fading and the like, so that the LEO satellite channel has the characteristics different from a GEO satellite channel and a ground mobile channel.
Based on the above, the inventor finds that an accurate channel model is an important premise for communication system design and performance test, and therefore, the embodiment of the invention provides a method and a device for modeling a LEO satellite channel based on a double Markov model aiming at the characteristics of multi-scene continuous change, time-varying doppler, large-range power loss change, high dynamic multipath fading and the like in the LEO satellite communication channel.
The following continues to describe in detail the LEO satellite channel modeling method based on the dual Markov model according to the embodiment of the present invention.
According to the LEO satellite channel modeling method based on the double Markov models, provided by the embodiment of the invention, the LEO satellite Internet can provide a plurality of services including but not limited to global mobile broadband communication, aviation and navigation monitoring, spectrum monitoring, navigation enhancement, Internet of things, airborne broadband and the like.
As shown in fig. 1, a method for modeling a LEO satellite channel based on a dual Markov model according to an embodiment of the present invention may include the following steps:
step 110, acquiring actually measured data of a signal received by a low earth orbit LEO satellite during a transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading.
The LOS path, the instantaneous multipath number, the envelope distribution of multipath signals, the multipath time delay and other contents under large-scale fading are all represented in a data form. For example, line-of-sight LOS path data, instantaneous multi-path number data, envelope distribution data of multi-path signals, multi-path time delay data and the like under large-scale fading, the two words of data are omitted in the embodiment of the invention for simplifying description.
The actual measurement data refers to data actually measured during the transit period or data obtained by investigation. This data conforms to the actual communication scenario of the LEO satellite. The measured data meet the requirement of dynamic time variation of a Leise channel model of the LEO satellite on a time axis.
In an actual communication scene of an LEO satellite, because the satellite moves at a high speed to the ground during the transit, the distance between the satellite and the UE and the communication elevation angle between the satellite and the UE are regularly changed, and the path loss and shadow attenuation of a transmission signal show large-scale dynamic change; meanwhile, because the relative motion changes at the receiving end and the transmitting end are severe, the channel fading is accompanied by high-speed time-varying large Doppler frequency shift; in addition, due to the presence of scatterers, such as walls, trees, vehicles, the ground, etc., in the environment near the ground UE, the transmitted signal is subject to scattering and multipath effects near the ground. Therefore, as the satellite continues to move, the channel fading also changes dynamically, and as shown in fig. 2(a), fig. 2(b) and fig. 2(c), the simulation parameters are: the orbit height is 800km, the frequency is 18.75GHz, and the UE position is located at the intersatellite point.
Here, the variation of power, doppler shift, and elevation angle caused by the satellite motion is predictable, as shown in fig. 3 (fig. 3 is a schematic content: during the period of communication between the LEO satellite and the ground mobile terminal, the LEO satellite itself moves to the ground, causing regular variation of communication elevation angle, and the channel fading conditions are different in different communication elevation angles, including doppler effect, path loss, shadow fading, multipath fading, etc., among them,
Figure BDA0002743418280000111
is the angular velocity, alpha, of the satellite relative to the center of the earth1，α2，α3Respectively, are different angles of elevation for the communications,
Figure BDA0002743418280000112
Figure BDA0002743418280000113
respectively at an elevation angle alpha1，α2，α3The location of the satellite, the UE is a terrestrial user terminal). According to the existing LEO satellite channel measurement data, the small-scale fading caused by the ground receiving environment is known, and under different elevation angles, the multipath fading characteristic parameters are different, including multipath number, power, time delay Doppler power spectrum, envelope distribution and the like.
Step 120, using the LOS path to establish a first-order Markov multi-state channel model of large-scale fading, wherein the first-order Markov multi-state channel model includes: the method comprises the steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are obtained, the large-scale fading states are different intervals of signal average power, the different intervals are different intervals of LOS (local area system) path calculation average power, and the average power is divided from zero to positive infinity to obtain N intervals.
Wherein the first transition probability is a transition probability of transitioning between a plurality of large-scale fading states.
And, the maximum endpoint may be a preset division point, and the division point may be determined according to a result of a specific measured quantity.
Step 130, extracting small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path under the large-scale fading when the first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: the multipath generation rate, the multipath extinction rate and the total number of the multipath at the current moment.
The small-scale fading actual measurement data refers to data actually measured during the transit period or data obtained through research. This data conforms to the actual communication scenario of the LEO satellite. The small-scale fading measured data meets the requirement of dynamic time variation of a Leise channel model of the LEO satellite on a time axis.
In the same way as in step 110, the contents of the multipath generation rate, the multipath extinction rate, the total number of the multipath at the current time, and the like are all represented in a data form. For example, data such as multipath generation rate data, multipath extinction rate data, and total number data of multipath at the current time are omitted in the embodiment of the present invention to simplify the description.
Step 140, taking the total number of the multipath at the current moment as a plurality of small-scale fading states; and acquiring second transition probabilities under the plurality of small-scale fading states.
Wherein the second transition probability is a transition probability of a transition between a plurality of small-scale fading states.
And 150, establishing a second-order Markov multi-state channel model of the small-scale fading by using the plurality of small-scale fading states and the second transition probability.
Step 160, performing piecewise fitting on unknown channel parameters according to a plurality of large-scale fading states divided by the first-order Markov multi-state channel model, counting the characteristics of a time period corresponding to the plurality of large-scale fading states in each segment, and determining the channel parameters;
step 170, adding the channel parameters into a rice channel model of unknown channel parameters to obtain a rice channel model of the LEO satellite.
The LOS path component in the LEO satellite communication channel accounts for an absolute main part, and simultaneously, a small number of multi-paths are accompanied, and according to measured data, the number of the multi-paths of the LEO satellite communication channel is usually small and generally does not exceed 6. Thus, embodiments of the present invention describe the LEO satellite channel using the rice channel model and make improvements on this model.
In an embodiment of the present invention, the step 170 includes:
Figure BDA0002743418280000131
wherein hl isos(t) and hnlos(t，τi) Time domain impulse responses of LOS path and non-line-of-sight NLOS path, respectively, and | hnlos(t，τi) I is considered to obey Rayleigh distribution, | h (t, τ)i) I is considered to obey the Rice distribution, αlosIs the amplitude of the LOS path and,
Figure BDA0002743418280000132
doppler shift for LOS path, N (t) is the number of multipaths at the current time, αiFor the amplitude of the ith multipath, δ (-) is the Dirichlet function, τiFor the time delay of the ith multipath,
Figure BDA0002743418280000133
is the Doppler shift of the ith multipath, t is the current time, fdFor Doppler shift, where τ is τi。
In the embodiment of the invention, the influence of shadow fading on the LOS path under large-scale fading can be reflected by a first-order Markov multi-state channel model; and under each large-scale fading state of the first-order Markov multi-state channel model, extracting small-scale fading measured data corresponding to a line-of-sight (LOS) path under large-scale fading, reflecting a multi-path dynamic extinction process under small-scale fading, completing description of large-range change of path LOSs and high dynamic property of small-scale fading by using a two-order Markov process, and determining transition probability, so that the requirement of dynamic time variation of an LEO satellite channel model on a time axis is met, the dynamic property of the LEO satellite channel characteristic is met, and the randomness of the LEO satellite channel characteristic is met by using the first-order Markov multi-state channel model and the second-order Markov multi-state channel model; and according to a plurality of large-scale fading states divided by the first-order Markov multi-state channel model, performing piecewise fitting on unknown channel parameters, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, determining the channel parameters, establishing the channel parameters required by the complete Leise channel model, and obtaining the Leise channel model of the LEO satellite, so that the Leise channel model of the LEO satellite is more consistent with the actual scene of the low-orbit satellite channel, can accurately describe the dynamic variation characteristic of the LEO broadband channel fading, and has strong dynamic representation capability and flexibility.
In addition, compared with a non-geometric stochastic model proposed by the university of heroit-Watt in england, the description of the physical parameters of the channel is also based on probabilistic statistical characteristics, a markov chain is adopted to represent the birth and death characteristics of a dynamic cluster, the time-varying non-stationary characteristics of the channel are described, and communication scenes such as vehicle-to-vehicle communication, high-speed rail communication and the like are supported. Compared with the Markov chain due to an ideal hypothesis, the statistical characteristic of the channel parameter in the related art is due to the ideal, and generally the ideal statistics of the hypothesis is not changed along with the scene change in the time evolution process.
The process of establishing the first order Markov multi-state channel model and the related contents of the second order Markov multi-state channel model are described in detail below:
for LEO satellite channels, aiming at wide-range channel path loss changes and obvious fast fading characteristics caused by high-speed motion of satellites relative to ground UE and regular changes of elevation angles, the embodiment of the invention provides and adopts a method for describing on a time axis based on a two-order Markov process, wherein the first-order Markov process is used for describing dynamic path loss changes, and the second-order Markov process is used for describing a multipath dynamic birth and death process in small-scale fading. There is a corresponding gating relationship between the first-order state and the second-order state, that is, the dynamics and characteristic parameters of multipath fading are affected by the degree of path loss.
For the acquired measurement data of the signals received by the satellite during the transit period, the embodiment of the invention divides a plurality of large-scale fading states S by using the average power of the signals in different intervals1，S2，...，SNRetention time of each state
Figure BDA0002743418280000141
May also be acquired. At the same time, at each SiDuring the period, the corresponding small-scale fading parameters can be extracted, for example, by using Poisson distribution to SiThe fitting of the actual measurement results in the state period can obtain SiMultipath generation rate during states
Figure BDA0002743418280000142
Rate of multipath fading
Figure BDA0002743418280000143
Therefore, the transition probability of the second-order small-scale fading multi-state is obtained, and a Markov multi-state channel model of the second-order small-scale fading is established. In the same way, SiOther multipath parameters during the state may also be obtained in this manner. This is the relationship of the corresponding strobes of the first order state and the second order state.
Based on the corresponding gated relationships described above, a first order Markov process is used to describe large dynamic variations in path loss. Because the fading of this type is large-scale fading composed of free space loss and shadowing effect, the large-scale fading degree can be represented by the average power and the signal-to-noise ratio of the received signal and the state division can be performed, here, the embodiment of the present invention uses the average power of the received signal to divide the states, and the specific implementation manner is as follows:
step 120 in embodiments of the present invention may include, but is not limited to: dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals; obtaining first transition probabilities under the plurality of large-scale fading states; and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability, wherein N is the total number of intervals.
Since large scale fading is slow on the time axis, a first order Markov process is used to represent the fading process, the current state is determined only with respect to the last state, and the probability of state transition between non-adjacent states is 0, as shown in fig. 4. Thus, the first order Markov multi-state channel model is:
Figure BDA0002743418280000151
wherein S isiIs S1，S2，...，SNN is the number of finite states, the number of different intervals corresponding to the average power of the signal is the same, pii+1Is in a state SiTo state Si+1Probability of transition, pii-1Is in a state SiTo state Si-1Probability of transition, piiIs in a state SiTo state SiThe probability of the transition is determined by the probability of the transition,
Figure BDA0002743418280000152
is in a state SiThe duration of the time period of the first,
Figure BDA0002743418280000153
is in a state Si-1The duration of the time period of the first,
Figure BDA0002743418280000154
is in a state Si+1Can be obtained by statistical calculation from the channel measurement data, Si+1Is SiThe latter state of (S)i-1Is SiThe last state of (a);
as shown in fig. 4, there is a corresponding gating relationship between the first order Markov process and the second order Markov process. Since the distance and the elevation angle between the satellite and the ground UE are constantly and regularly changed during the transit period, the distance change may cause the change of path loss, and the elevation angle change may cause the change of small-scale fading degree generated near the UE, which may be expressed as that the second-order small-scale fading process may be restricted by the first-order large-scale fading state, that is, the first-order state at a certain time corresponds to a second-order sub-process, the multipath fading parameters and the state transition probability in the sub-process are determined by the first-order state, and the generation of the fading parameters and the calculation of the state transition probability are explained below.
The second-order Markov process is used to describe the multipath dynamic birth and death process in small-scale fading. The channel has the characteristic of obvious fast fading due to continuous change of a communication scene caused by elevation angle change and large Doppler frequency shift, and the characteristic can be modeled and represented by a Markov process based on multipath dynamic extinction. On a time axis, each state is composed of the number of multipath, and the multipath has three states of path generation, path maintenance and path extinction in each state, for a satellite channel, because the number of multipath is small, even if the channel dynamic is high, severe jump of the number of multipath between adjacent states generally does not occur, and here, the embodiment of the invention reasonably assumes that the number of path generation and path extinction does not exceed 2, so the multipath dynamic life-off process can be represented by a second-order Markov process, as shown in FIG. 5. The second-order Markov multi-state channel model of the small-scale fading is as follows:
pnn+2＝p(2，0)
pnn+1＝p(1，0)+p(2，1)
pnn＝p(0，0)+p(1，1)+p(2，2)
pnn-1＝p(0，1)+p(1，2)
pnn-2＝p(0，2)
wherein x isiIs the current state, xn+2Is xiThe last two states of (a), (b), (c), (dn+1Is xiThe latter state of (a), xn-1Is xiThe last state of (a); x is the number ofn-2Is xiThe first two states of (1), pnn+2Is a state xnTo state xn+2Transition probability of, pnn+1Is a state xnTo state xn+1、pnnIs a state xnTo state xn、pnn-1Is a state xnTo state xn-1And pnn-2Is a state xnTo state xn-2The transition probability of (2); p (j, k) is the current state xiGenerating the probability of j paths and k paths to be lost, j being the path generated at the current time, k being the path lost at the current time, P (j, k) ═ PB)j·(PD)kJ is more than or equal to 0, k is less than or equal to 2, and j and k are integers, PBTo generate a probability of multipath, PDIn order to eliminate the probability of a multipath, the generation of the path and the elimination process are statistically independent, so that the transition probability of a second-order Markov process can be determined; n is the number of multipaths in the current state, and p (2, 0) is the current state xiGenerating 2 paths and probability of 0 paths being lost, p (1, 0) being the current state xiGenerating 1 path and probability of 0 path loss, p (2, 1) being current state xiThe probability of generating 2 paths and 1 path of death, p (0, 0) the current state xiGenerating 0 paths and probability of 0 paths being lost, p (1, 1) being the current state xiGenerating 2 paths and probability of 1 path loss, p (2, 2) current state xiGenerating 2 paths and probability of 2 paths being lost, p (0, 1) being the current state xiGenerating a summary of 0 paths and 1 path of extinctionRate, p (1, 2) current state xiGenerating the probability of 1 path and 2 paths of death, and p (0, 2) is the current state xiGenerating the probability of 0 path and 2 paths for death; wherein the content of the first and second substances,
Figure BDA0002743418280000171
in the above formula, PBIn order to generate a new path probability in the short observation time delta t in the path generation process of the Poisson distribution, B is the abbreviation of Birth and is used for representing the generation of the new path,
Figure BDA0002743418280000172
λBin order to express the arrival rate of a new path when the path new process is expressed by Poisson distribution, lambda is the average arrival rate, and D is a factor related to the propagation environment. Thus, the probability distribution for path generation is:
Figure BDA0002743418280000173
the above formula shows the distribution so that the distribution can be used to fit, for example,
Figure BDA0002743418280000176
Figure BDA0002743418280000177
this is just an expression, and the parameters inside are not known. PB[Δt]Is to show the distribution FB[t＜Δt]If there is distribution, the measured data is used to fit the parameters in the distribution to obtain the specific probability PB。
Wherein the content of the first and second substances,
Figure BDA0002743418280000174
in the above formula, PDThe probability of the radial death is shown as,
Figure BDA0002743418280000175
μDsince the probability distribution of the path extinction process is assumed to represent the speed of path extinction when the probability distribution of the path extinction process is similar to the probability distribution of the path birth process, μ is the speed of the path average extinction, the probability distribution of the path extinction is:
Figure BDA0002743418280000178
the generation of channel parameters relies on statistical feature extraction of the measured data and then is calculated from the density function to which the respective channel parameters are subjected. The specific channel parameters include multipath time delay, instantaneous multipath number, multipath power, envelope distribution, Rice factor K, etc. Here, the embodiments of the present invention give general statistical properties that these channel parameters exhibit in the existing research. In order to determine the respective channel parameters, in a possible implementation, the step 170 further includes:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
wherein, the dividing into a plurality of large scale fading states: we use the average power P of the received signal to divide the states as follows: for the measured received signal, the average power P is calculated and its range is divided into N finite states, where 0 < P1＜P2＜…＜PN-1The [ infinity ] is a state division point, and the corresponding N states are S1＝[0，P1]，S2＝[P1，P2]…，SN＝[PN-1，+∞]The segmentation points may be self-defined based on actual measurements.
These data time periods may not be contiguous for the data processed in each state. Benefit toUsing the data, extracting small scale fading parameters, using Poisson distribution to SiThe fitting of the actual measurement results in the state period can obtain SiMultipath generation rate during states
Figure BDA0002743418280000181
Rate of multipath fading
Figure BDA0002743418280000182
Therefore, the transition probability of the inner layer small-scale fading multi-state is obtained, and a Markov multi-state channel model of the inner layer small-scale fading is established. The piecewise fitting method in the embodiment of the invention can obtain important parameters in the distribution by using a Minimum Mean Square Error (MMSE for short) or least Square (LS for short) and other general methods.
Because LEO satellite channel scene changes continuously, the variance of multipath envelope Rayleigh distribution under different scenes
Figure BDA0002743418280000183
Performing piecewise fitting envelope distribution on the envelope distribution according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, counting the envelope distribution in a time period corresponding to the large-scale fading states in each segment, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
and according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing segment fitting on the multi-path time delay to obtain a time delay distribution factor and time delay extension, counting the multi-path time delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multi-path time delay so as to determine the multi-path time delay in the same time period from the uniform distribution.
And determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
The calculation process of the power of each diameter is as follows. Using the number of multipaths of the signal and the amplitude of each multipath in the envelope profile of the multipath signal
Figure BDA0002743418280000193
Calculating power per diameter
Figure BDA0002743418280000194
The embodiment of the invention also comprises the following steps: according to multipath delay tauiWill be
Figure BDA0002743418280000195
And sequencing from large to small, and distributing to each multipath time delay so as to sequentially reduce the multipath power according to the sequence of the multipath arrival time. This is true because the power is generally greater the earlier the path is reached.
Wherein a rayleigh distribution of the envelope distribution is:
Figure BDA0002743418280000191
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure BDA0002743418280000196
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
said determining a total distribution of received signal amplitudes using a rice factor, comprising:
using the following formula for the rice factor,
Figure BDA0002743418280000192
wherein, k (t) is the rice factor at the current time, k (t) 0 indicates that there is no LOS path, the multi-path envelope distribution is converted from the rice distribution to the rayleigh distribution, if k (t) 0 indicates that there is an LOS path, the envelope of the multi-path signal obeys the rice distribution,
Figure BDA0002743418280000197
Figure BDA0002743418280000198
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
In the embodiment of the invention, the Rice channel model is improved, and a Markov process based on a two-order nested structure is adopted to describe the large-range change of the path loss and the high dynamics of small-scale fading in the continuous change of the LEO broadband satellite communication scene. Therefore, the dynamic change of the LEO2 satellite channel in the time evolution can be accurately described, so that the flexibility and the dynamic characterization capability of the traditional satellite channel are improved, and a new thought and suggestion are provided for the research of an LEO satellite communication channel model. And aiming at the large-range change of path loss and the high dynamic property of small-scale fading caused by the continuous change of an LEO broadband satellite channel scene, a two-order nested Markov process algorithm is used for describing the change in the large-range change of the path loss and the high dynamic property of small-scale fading in the time evolution direction, so that an accurate LEO satellite dynamic channel model is established.
The description continues to be provided below for a LEO satellite channel modeling apparatus based on a dual Markov model according to an embodiment of the present invention.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a LEO satellite channel modeling apparatus based on a dual Markov process according to an embodiment of the present invention. The LEO satellite channel modeling device based on the double Markov models provided by the embodiment of the invention can comprise the following modules:
the acquisition module 21 is configured to acquire measured data of a signal received by a low earth orbit LEO satellite during a transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
a first establishing module 22, configured to establish a first-order Markov multi-state channel model for large-scale fading using the LOS path, where the first-order Markov multi-state channel model includes: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
the extraction module 23 is configured to extract small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path in the large-scale fading condition when the first-order Markov multi-state channel model is in each large-scale fading condition; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
the first processing module 24 is configured to use the total number of multipaths at the current time as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
a second establishing module 25, configured to establish a second-order Markov multi-state channel model of small-scale fading by using the plurality of small-scale fading states and the second transition probability;
the second processing module 26 is configured to perform piecewise fitting on the unknown channel parameters according to the multiple large-scale fading states divided by the first-order Markov multi-state channel model, count characteristics of a time period corresponding to the multiple large-scale fading states in each segment, and determine the channel parameters;
a third processing module 27, configured to obtain a rice channel model of the LEO satellite by adding the channel parameter to a rice channel model of an unknown channel parameter.
In a possible implementation manner, the first establishing module is configured to:
dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals;
obtaining first transition probabilities under the plurality of large-scale fading states;
and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability.
In a possible implementation manner, the second processing module is configured to:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out sectional fitting envelope distribution on the envelope distribution, counting the envelope distribution in a time period corresponding to the large-scale fading states in each section, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting delay distribution factor and delay expansion on the multipath delay, counting the multipath delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multipath delay so as to determine the multipath delay in the same time period from the uniform distribution;
and determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
In one possible implementation, the rayleigh distribution of the envelope distribution is:
Figure BDA0002743418280000221
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure BDA0002743418280000223
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
the second processing module is configured to determine the total distribution of the received signal amplitudes using a rice factor, and includes:
using the following formula for the rice factor,
Figure BDA0002743418280000222
where, k (t) is the rice factor at the current time, k (t) ≠ 0 denotes that there is no LOS path, the multipath envelope distribution is converted from the rice distribution to the rayleigh distribution, and if k (t) ≠ 0 denotes that there is LOS path, the packet of the multipath signalThe collaterals obey the rice distribution,
Figure BDA0002743418280000224
Figure BDA0002743418280000225
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
The following continues to describe the electronic device provided by the embodiment of the present invention.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. The embodiment of the present invention further provides an electronic device, which includes a processor 31, a communication interface 32, a memory 33 and a communication bus 34, wherein the processor 31, the communication interface 32 and the memory 33 complete mutual communication through the communication bus 34,
a memory 33 for storing a computer program;
the processor 31, configured to implement the steps of the above-mentioned LEO satellite channel modeling method based on the dual Markov model when executing the program stored in the memory 33, may implement the following steps in one possible implementation manner of the present invention:
acquiring actual measurement data of a signal received by a low earth orbit LEO satellite during a transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
establishing a first-order Markov multi-state channel model of large-scale fading by using the LOS path, wherein the first-order Markov multi-state channel model comprises the following steps: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
extracting small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path under large-scale fading when a first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
taking the total number of the multipath at the current moment as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
establishing a second-order Markov multi-state channel model of small-scale fading by using the plurality of small-scale fading states and the second transition probability;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out piecewise fitting on unknown channel parameters, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, and determining the channel parameters;
and adding the channel parameters into a rice channel model of unknown channel parameters to obtain the rice channel model of the LEO satellite.
The communication bus mentioned in the electronic device may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a RAM (Random Access Memory) or an NVM (Non-Volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The embodiment of the invention provides a computer-readable storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of the LEO satellite channel modeling method based on the double Markov model are realized.
Embodiments of the present invention provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the steps of the above-described method for dual Markov model-based modeling of a LEO satellite channel.
Embodiments of the present invention provide a computer program which, when run on a computer, causes the computer to perform the steps of the above-described method for modeling a LEO satellite channel based on a dual Markov model.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device/electronic apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to some descriptions of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. A LEO satellite channel modeling method based on a double Markov model is characterized by comprising the following steps:
acquiring actual measurement data of a signal received by a low earth orbit LEO satellite during a transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
establishing a first-order Markov multi-state channel model of large-scale fading by using the LOS path, wherein the first-order Markov multi-state channel model comprises the following steps: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
extracting small-scale fading actual measurement data corresponding to a line-of-sight (LOS) path under large-scale fading when a first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
taking the total number of the multipath at the current moment as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
establishing a second-order Markov multi-state channel model of small-scale fading by using the plurality of small-scale fading states and the second transition probability;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out piecewise fitting on unknown channel parameters, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, and determining the channel parameters;
and adding the channel parameters into a rice channel model of unknown channel parameters to obtain the rice channel model of the LEO satellite.
2. The method of claim 1, wherein said building a first order Markov multi-state channel model for large scale fading using said LOS path comprises:
dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals;
obtaining first transition probabilities under the plurality of large-scale fading states;
and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability.
3. The method of claim 1 or 2, wherein the first order Markov multi-state channel model is:
Figure FDA0002743418270000021
wherein S isiIs S1，S2，...，SNN is the number of finite states, the number of different intervals corresponding to the average power of the signal is the same, pii+1Is in a state SiTo state Si+1Probability of transition，pii-1Is in a state SiTo state Si-1Probability of transition, piiIs in a state SiTo state SiThe probability of the transition is determined by the probability of the transition,
Figure FDA0002743418270000022
is in a state SiThe duration of the time period of the first,
Figure FDA0002743418270000023
is in a state Si-1The duration of the time period of the first,
Figure FDA0002743418270000024
is in a state Si+1Duration of (S)i+1Is SiThe latter state of (S)i-1Is SiThe last state of (a);
the second-order Markov multi-state channel model of the small-scale fading is as follows:
pnn+2＝p(2，0)
pnn+1＝p(1，0)+p(2，1)
pnn＝p(0，0)+p(1，1)+p(2，2)
pnn-1＝p(0，1)+p(1，2)
pnn-2＝p(0，2)
wherein x isiIs the current state, xn+2Is xiThe last two states of (a), (b), (c), (dn+1Is xiThe latter state of (a), xn-1Is xiThe last state of (a); x is the number ofn-2Is xiThe first two states of (1), pnn+2Is a state xnTo state xn+2Transition probability of, pnn+1Is a state xnTo state xn+1、pnnIs a state xnTo state xn、pnn-1Is a state xnTo state xn-1And pnn-2Is a state xnTo state xn-2The transition probability of (2); p (j, k) is the current state xiGenerating the probability of j paths and k paths to be lost, j being the path generated at the current time, k being the path lost at the current time, P (j, k) ═ PB)j·(PD)kJ is more than or equal to 0, k is less than or equal to 2, and j and k are integers, PBTo generate a probability of multipath, PDTo eliminate the probability of a multipath, n is the number of multipaths in the current state.
4. The method of claim 1, wherein the step of performing a piecewise fitting on the unknown channel parameters according to the plurality of large-scale fading states partitioned by the first-order Markov multi-state channel model, counting the features in the same time period in each segment, and determining the channel parameters comprises:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out sectional fitting envelope distribution on the envelope distribution, counting the envelope distribution in a time period corresponding to the large-scale fading states in each section, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting delay distribution factor and delay expansion on the multipath delay, counting the multipath delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multipath delay so as to determine the multipath delay in the same time period from the uniform distribution;
and determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
5. The method of claim 3, wherein a Rayleigh distribution of the envelope distribution is:
Figure FDA0002743418270000041
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure FDA0002743418270000042
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
said determining a total distribution of received signal amplitudes using a rice factor, comprising:
using the following formula for the rice factor,
Figure FDA0002743418270000043
wherein, k (t) is the rice factor at the current time, k (t) 0 indicates that there is no LOS path, the multi-path envelope distribution is converted from the rice distribution to the rayleigh distribution, if k (t) 0 indicates that there is an LOS path, the envelope of the multi-path signal obeys the rice distribution,
Figure FDA0002743418270000044
Figure FDA0002743418270000046
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
6. The method of claim 1, wherein obtaining a rice channel model for a LEO satellite by adding the channel parameters to a rice channel model for unknown channel parameters comprises:
Figure FDA0002743418270000045
wherein h islos(t) and hnlos(t，τi) Time domain impulse responses of LOS path and non-line-of-sight NLOS path, respectively, and | hnlos(t，τi) I is considered to obey Rayleigh distribution, | h (t, τ)i) I is considered to obey the Rice distribution, αlosIs the amplitude of the LOS path and,
Figure FDA0002743418270000051
doppler shift for LOS path, N (t) is the number of multipaths at the current time, αiFor the amplitude of the ith multipath, δ (-) is the Dirichlet function, τiFor the time delay of the ith multipath,
Figure FDA0002743418270000052
the Doppler shift of the ith multipath is shown, and t is the current time.
7. A LEO satellite channel modeling device based on a double Markov model is characterized by comprising the following components:
the acquisition module is used for acquiring the actually measured data of the received signals of the low earth orbit LEO satellite during the transit period; wherein the measured data comprises: line-of-sight (LOS) path, instantaneous multi-path number, envelope distribution of multi-path signals and multi-path time delay under large-scale fading;
a first establishing module, configured to establish a first-order Markov multi-state channel model for large-scale fading using the LOS path, where the first-order Markov multi-state channel model includes: the method comprises the following steps that a plurality of large-scale fading states, holding time under each large-scale fading state and first transition probabilities under the large-scale fading states are respectively different intervals of signal average power, the different intervals are obtained by calculating average power of LOS (local area network) paths and dividing the average power from zero to positive infinity, and N is the total number of the intervals;
the extraction module is used for extracting small-scale fading actual measurement data corresponding to the line-of-sight (LOS) path under the large-scale fading when the first-order Markov multi-state channel model is in each large-scale fading state; wherein the small-scale fading measured data comprises: generating a multipath, eliminating a multipath and the total number of the multipath at the current moment;
the first processing module is used for taking the total number of the multipath at the current moment as a plurality of small-scale fading states; acquiring second transition probabilities under the plurality of small-scale fading states;
the second establishing module is used for establishing a second-order Markov multi-state channel model of the small-scale fading by utilizing the plurality of small-scale fading states and the second transition probability;
the second processing module is used for performing piecewise fitting on unknown channel parameters according to a plurality of large-scale fading states divided by the first-order Markov multi-state channel model, counting the characteristics of a time period corresponding to the large-scale fading states in each segment, and determining the channel parameters;
and the third processing module is used for adding the channel parameters into a rice channel model of unknown channel parameters to obtain the rice channel model of the LEO satellite.
8. The apparatus of claim 7, wherein the first establishing module is to:
dividing a plurality of large-scale fading states and the retention time of each large-scale fading state by adopting the average power of signals in different intervals determined by the LOS path; the large-scale fading states are different intervals of the average power of the signal, the different intervals are obtained by calculating the average power of the LOS path and dividing the average power from zero to positive infinity to obtain N intervals;
obtaining first transition probabilities under the plurality of large-scale fading states;
and establishing a first-order Markov multi-state channel model of the large-scale fading by using the plurality of large-scale fading states, the holding time of each large-scale fading state and the first transition probability.
9. The apparatus of claim 7, wherein the second processing module is to:
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting on the instantaneous multipath number to obtain a multipath generation rate, a multipath fading rate and an environmental factor, counting the instantaneous multipath number in a time period corresponding to the large-scale fading states in each segment, and determining Poisson distribution of the instantaneous multipath number so as to determine the instantaneous multipath number in each time period from the Poisson distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, carrying out sectional fitting envelope distribution on the envelope distribution, counting the envelope distribution in a time period corresponding to the large-scale fading states in each section, and determining Rayleigh distribution of the envelope distribution so as to determine the envelope distribution in each time period from the Rayleigh distribution;
according to a plurality of large-scale fading states divided by a first-order Markov multi-state channel model, performing piecewise fitting delay distribution factor and delay expansion on the multipath delay, counting the multipath delay in a time period corresponding to the large-scale fading states in each segment, and determining the uniform distribution of the multipath delay so as to determine the multipath delay in the same time period from the uniform distribution;
and determining the total distribution of the amplitude of the received signal by using a rice factor, wherein the rice factor is the ratio of LOS path power and multipath power at the current moment, the LOS path power is the square of the amplitude of each LOS path, and the multipath power is calculated by using the number of signal multipaths and the amplitude of each multipath in the envelope distribution of the multipath signal.
10. The apparatus of claim 9, wherein a rayleigh distribution of the envelope distribution is:
Figure FDA0002743418270000071
wherein, f (x)t) As a function of the probability density of the multipath signal amplitude, xtIn order to be the amplitude of the multi-path signal,
Figure FDA0002743418270000072
t is the current time to represent the variance;
the uniform distribution of the multipath time delay is as follows:
τi＝-rtσtln(Xi)
wherein r istIs a delay spread factor, σtFor time delay spread, XiIs the uniform distribution among (0, 1), i is the serial number of the diameter;
the second processing module is configured to determine the total distribution of the received signal amplitudes using a rice factor, and includes:
using the following formula for the rice factor,
Figure FDA0002743418270000073
wherein, k (t) is the rice factor at the current time, k (t) 0 indicates that there is no LOS path, the multi-path envelope distribution is converted from the rice distribution to the rayleigh distribution, if k (t) 0 indicates that there is an LOS path, the envelope of the multi-path signal obeys the rice distribution,
Figure FDA0002743418270000074
Figure FDA0002743418270000075
power of the ith multipath, αiIs the amplitude of the ith multipath, αlosThe magnitude of the LOS path.
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