CN108933703B - Environment self-adaptive perception wireless communication channel estimation method based on error modeling - Google Patents

Environment self-adaptive perception wireless communication channel estimation method based on error modeling Download PDF

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CN108933703B
CN108933703B CN201810919208.1A CN201810919208A CN108933703B CN 108933703 B CN108933703 B CN 108933703B CN 201810919208 A CN201810919208 A CN 201810919208A CN 108933703 B CN108933703 B CN 108933703B
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CN108933703A (en
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薛江
孟德宇
赵谦
张海培
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Xian Jiaotong University
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Abstract

The invention relates to an algorithm for improving the estimation accuracy of channel state information by accurately estimating noise in a communication environment, which comprises the following steps: a receiving end receives a pilot signal and an original pilot signal which are transmitted by a transmitting end and pass through a channel, and parameterized distributed modeling is carried out on noise in a communication environment based on randomness of noise in the communication environment; embedding regularization noise information coding into the model to realize self-adaptive modeling of a real noise environment; combining the noise parametric model and the regularization noise information coding to obtain a regularization model, and estimating noise parameters and a channel state by using an EM (effective noise) algorithm; the channel state information of the temporally and spatially close regions is saved by the base station. The method is based on the error modeling principle, and improves the accuracy of wireless communication channel estimation by self-adaptively depicting the real noise in the communication environment.

Description

Environment self-adaptive perception wireless communication channel estimation method based on error modeling
Technical Field
The invention belongs to the field of communication and the technical field of machine learning, and particularly relates to an environment self-adaptive perception wireless communication channel estimation method based on error modeling.
Background
In the process of wireless signal transmission, due to the influence of various environmental factors in the transmission process, received signals can have attenuation, distortion, delay, phase shift and the like in different degrees, which makes channel estimation extremely difficult, and therefore, accurate estimation of channel state information is always a primary target pursued in the field of wireless communication.
The basic premise of accurately estimating the channel state information is to accurately model noise in a communication environment, most of currently applied wireless communication theories are based on gaussian noise, however, the ideal assumption is almost impossible to exist in a real communication environment, for example, a classical least square method and a minimum mean square error estimation method in channel estimation are optimal algorithms under the assumption of gaussian noise, but are not optimal solutions under the non-gaussian noise condition.
In order to better fit noise, the noise distribution is modeled according to the universal approximability of the mixed Gaussian distribution, so that the accuracy of channel estimation can be improved, but the noise cannot be well adapted to real noise, on one hand, the mixed Gaussian distribution cannot be accurately fitted to some complex distributions in actual use, such as Laplace distribution, and can be perfectly fitted only when the number of mixed Gaussian components is infinite, but the mixed Gaussian components cannot be perfectly fitted in actual use, so that the search for a more universal distribution is necessary; on the other hand, it also lacks a theoretically reasonable way to correctly select the number of gaussian mixture components based on the actual noise situation of the signal.
In view of the deficiencies of the prior art, it is necessary to provide a channel estimation algorithm capable of adaptively and accurately fitting noise in a communication environment.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an environment adaptive sensing wireless communication channel estimation method based on error modeling, which improves the channel state information estimation accuracy by accurately estimating the noise in the communication environment.
In order to achieve the above object, the core elements of the present invention include: based on the randomness of the noise of the communication environment, the noise in the communication environment is subjected to parametric distribution modeling, and the specific modeling is mixed exponential power distribution, because the noise has better universality to the noise of the general environment, the noise can be accurately fitted, and regularized noise information coding is embedded, so that the self-adaptive fitting to the real noise is realized. The method comprises the following specific steps:
1) a receiving end receives a pilot signal and an original pilot signal which are transmitted by a transmitting end and pass through a channel;
2) carrying out parametric distribution modeling on noise in the communication environment based on the randomness of the noise in the communication environment;
3) embedding regularization noise information coding into the established model to realize self-adaptive modeling of real noise;
4) combining the noise parametric model in the step 2) and the regularized noise information coding in the step 3) to obtain a maximum posterior model, and estimating noise parameters and a channel state by using an EM (effective noise) algorithm;
5) the base station is used for saving the channel state information of the time and space similar area so as to be used by the new user for communication.
The step 2) carries out parametric distribution modeling on the noise in the communication environment as follows:
Y＝HX+E (1)
wherein the base station receives a signal Y and the noise E is NrX L matrix, NrThe number of antennas at the receiving end, L is the number of pilot signals used; original pilot signal X is NtX L matrix, NtThe number of antennas at the transmitting end; the channel matrix H being Nr×NtOf the received signal matrix Y, each element Y of the received signal matrix YijComprises the following steps:
Figure GDA0002400415360000021
wherein i is 1,2, …, Nr；j＝1,2,…,L，
Figure GDA0002400415360000022
Is the ith row, X of the channel matrix HjIs the j-th column, e, of the original pilot signal matrix XijFor each element of noise E, in order to simulate the randomness of the noise of the communication environment, assume EijObeying a mixed exponential power distribution consisting of K exponential power distributions, of the form:
Figure GDA0002400415360000023
Figure GDA0002400415360000024
wherein the k-th mixed exponential power distribution is in the form of
Figure 100002_DEST_PATH_IMAGE002
Wherein p iskShape parameter, ηkScale parameter, μkThe position parameter, Γ (·) is a Gamma function; z is a radical ofij＝{zij1,zij2,…,zijk…,zijKIs eijCorresponding hidden variables, wherein
Figure GDA0002400415360000033
Figure GDA0002400415360000034
zijkDenotes eijA kth mixed component belonging to a mixed exponential power distribution; pikRepresents the weight parameter corresponding to the kth mixed distribution and satisfies pik≥0,
Figure GDA0002400415360000035
π＝{π1,π2,…,πk…,πkThe K weight parameters are set, and the complete likelihood function is:
Figure GDA0002400415360000036
where Θ is { H, pi, μ, η }, and μ is { μ ═ μ1,μ2,…,μk,…,μKThe K position parameters are set, η ═ η1,η2,…,ηk…ηKIs the set of K scale parameters,
Figure GDA0002400415360000037
then the log-likelihood function is:
Figure GDA0002400415360000038
the step 3) is used for embedding the regularization noise information into the model and coding, so that the self-adaptive modeling of the real noise is realized, and the form is as follows:
Figure GDA0002400415360000039
where N is the total number of elements in matrix Y, ε is a very small positive number, ε is 10-6Or
Figure GDA00024004153600000310
λ is the adjustment coefficient, λ>0，DkIs the number of the kth component free variable, Dk3, corresponding to pik,μk,ηk。
The maximum posterior model obtained in the step 4) is as follows:
Figure GDA00024004153600000311
wherein
Figure GDA00024004153600000312
The specific process of estimating the noise parameters and the channel state by using the EM algorithm in the step 4) comprises the following steps:
4.1) giving the membership degree gamma of the step E in the EM algorithmijkThe update formula of (2):
Figure GDA0002400415360000041
4.2) the iteration format and termination condition of the M steps in the EM algorithm are given:
the iteration format is:
Figure GDA0002400415360000042
Figure GDA0002400415360000043
Figure GDA0002400415360000044
Figure GDA0002400415360000045
wherein
Figure GDA0002400415360000046
The iteration termination condition is as follows:
Figure GDA0002400415360000047
the superscript (t) in the above formula represents the tth iteration,
Figure GDA0002400415360000048
in a formula (9)
4.3) solving the formulas (12) and (14) by adopting a quasi-Newton method;
4.4) setting an initial value of iteration;
4.5) carrying out the iterative operations of (11) to (14) until a termination condition is met;
4.6) saving channel State and noise parameter information in the base station, i.e. Theta at algorithm termination(t+1)And updating the channel state and noise parameter information of the area so as to facilitate the communication of the new user.
Step 4.4) sets an initial value of iteration, and initially sets a larger KstartGenerally set up to K start6, and setting a mixed exponential parameter
Figure GDA0002400415360000049
Wherein p isi∈[0.1,3]Some of pi during the iteration of the algorithmkIf the noise becomes zero, deleting the kth mixed component to realize self-adaptation to different environmental noises;
initial channel state information H0The first method is that if the pilot frequency contains position information, the first method is directly determined according to the position information; secondly, if the pilot frequency does not contain position information, estimating parameters such as multipath path number, multipath arrival angle, multipath time delay and the like of the received signal according to the existing algorithm, and determining the position of the transmitting terminal by comparing the parameters with the parameters of each area;
initial mean value μ in [ min (Y-H)0X)，max(Y-H0X)]Uniformly finding K points;
the variance σ and the weight π are initialized randomly.
Said step 4.4) sets an initial value setting for the iteration, wherein the adjustment factor λ is according to the modified BIC criterion:
Figure GDA0002400415360000051
an appropriate one is then selected by the following equation
Figure GDA0002400415360000052
Figure GDA0002400415360000053
Wherein
Figure GDA0002400415360000054
Is given by the maximization of equation (8) under a lambda.
The step 5) of storing the channel state information of the areas with close time and space by using the base station comprises the following steps:
the base station divides the service area into a plurality of sub-service areas according to the communication scene of the service area, and respectively stores the channel state information within a certain time as an initialization channel matrix H of the next channel estimation0。
Compared with the prior art, the invention has the beneficial effects that:
1) the noise is modeled into mixed exponential power distribution, and the mixed exponential power distribution is a parameterized distribution type with good fitting performance, and Laplace distribution, Gaussian distribution, mixed Gaussian distribution and the like are all special situations of the mixed exponential power distribution, so that complex noise in a communication environment can be accurately depicted, and the accuracy of wireless communication channel estimation in a real environment is greatly improved.
2) The invention embeds maximum posterior coding into the noise model, so that the noise model can adaptively fit the real noise in different communication environments, and the redundancy of parameter information is reduced.
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Fig. 1 is a flow chart of an algorithm for improving the accuracy of channel state information estimation by accurately estimating noise in a communication environment.
FIG. 2 is a graph comparing the accuracy of channel estimation using least squares and the method under different simulated noise environments, i.e., different signal-to-noise ratios (SNRs), where
Figure GDA0002400415360000061
The accuracy of the channel estimation is measured. In the figure, the abscissa is SNR, the ordinate is NMSE, the hollow dots are lines connected by NMSE obtained by a least square method, and the star points are lines connected by NMSE obtained by the method.
Fig. 3 is a comparison graph of a noise probability density curve and a true noise probability density curve obtained by an algorithm for accurately estimating noise in a communication environment to improve the accuracy of channel state information estimation under simulated noise conditions, where the noise is, in an example, SNR-8.5 dB, the shape parameter set is {0.5,1,2}, the scale parameter set η is {0.9,2,8}, the position parameter set μ is {0,2.5, -2.5}, the weight parameter set pi is {0.1,0.2,0.7 }.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
Example (b): the invention adopts a communication system with Multiple Input and Multiple Output (MIMO), and adopts a widely used Rayleigh fading channel and BPSK modulation signals as computer simulation experiment objects of the invention. Simulations produced mixed exponential power noise. The specific value is the number N of the antennas at the receiving end r8, the number of used pilot signals L is 500, and the number of transmitting-end antennas N is N t2; the simulation yields a mixture component number K of 3 for the mixture exponential power noise. Referring to fig. 1, the specific steps include:
1) a receiving end receives a pilot signal Y and an original pilot signal X which are transmitted by a transmitting end and pass through a channel;
2) carrying out parametric distribution modeling on noise in the communication environment based on the randomness of the noise in the communication environment; expressed in the following form:
Y＝HX+E (1)
wherein the base station receives a signal Y and the noise E is NrX L matrix, NrThe number of antennas at the receiving end, L is the number of pilot signals used; original pilot signal X is NtX L matrix, NtThe number of antennas at the transmitting end; the channel matrix H being Nr×NtOf the matrix of (a). Each element Y of the received signal matrix Yij(i＝1,2,…,Nr(ii) a j — 1,2, …, L) can be written as:
Figure GDA0002400415360000071
wherein
Figure GDA0002400415360000072
Is the ith row, X of the channel matrix HjIs the j-th column, e, of the pilot signal matrix XijFor each element of the noise E. To simulate the randomness of the noise in the communication environment, assume eijObeying a mixed exponential power distribution of the form:
Figure GDA0002400415360000073
Figure GDA0002400415360000074
wherein
Figure GDA0002400415360000075
Is a shape parameter of pkScale parameter ηkPosition parameter is mukIs a Gamma function. z is a radical ofij＝[zij1,zij2,…,zijK]Is eijCorresponding hidden variables, wherein
Figure GDA0002400415360000076
π＝[π1,π2,…,πK]Is a weight parameter and satisfies pik≥0,
Figure GDA0002400415360000077
The complete likelihood function can be written as:
Figure GDA0002400415360000078
where, Θ is { H, pi, μ, η }, and μ is [ μ ═ μ1,μ2,…,μK],η＝[η1,η2,…ηK]，
Figure GDA0002400415360000079
Then the log-likelihood function is:
Figure GDA00024004153600000710
3) embedding regularization noise information coding into the model to realize self-adaptive modeling of real noise; expressed in the following form:
Figure GDA00024004153600000711
wherein N is the total number of Y elements, and epsilon is 10-6Lambda adjustment factor (lambda)>0)，DkIs the number of the kth component free variable, which corresponds to D in this modelk3 (corresponding to pi)k,μk,ηk)。
4) Combining the noise parameterization model in the step 2) and the regularization noise information coding in the step 3) to obtain a maximum posterior model, wherein the expression form is as follows:
Figure GDA00024004153600000712
wherein
Figure GDA00024004153600000713
Then, estimating the noise parameters and the channel state by using an EM algorithm, wherein the process is as follows (the superscript (t) in the following formula represents the t-th iteration):
4.1) giving the membership degree gamma of the step E in the EM algorithmijkThe update formula of (2):
Figure GDA0002400415360000081
4.2) the iteration format and termination condition of the M steps in the EM algorithm are given:
the iteration format is:
Figure GDA0002400415360000082
Figure GDA0002400415360000083
Figure GDA0002400415360000084
Figure GDA0002400415360000085
wherein
Figure GDA0002400415360000086
The iteration termination condition is as follows:
Figure GDA0002400415360000087
4.3) solving equations (12) and (14) by a quasi-Newton method
4.4) setting the initial value of the iteration: initial setting K start6 and sets the mixed exponent power parameter p to {0.2,0.5,1,2,2,2.5}, some pi during iteration of the algorithmkWill become zero, the k-th mixed component is deleted, trueThe method is adaptive to different environmental noises.
Initial channel state information H0The determination is based on the previously stored information of temporally and spatially proximate regions, and there are two methods for determining temporal and spatial proximity of regions. The first is to determine directly from the location information if it is contained in the pilot; the second is that if the pilot frequency does not contain position information, the parameters of multipath number, multipath arrival angle, multipath time delay and the like of the received signal are estimated according to the existing algorithm, and the position of the transmitting terminal is determined by comparing the parameters with the parameters of each area.
Initial value μ at [ min (Y-H)0X)，max(Y-H0X)]Find K points evenly. The variance σ and the weight π are initialized randomly.
The adjustment factor λ is according to the modified BIC criterion:
Figure GDA0002400415360000091
an appropriate one is then selected by the following equation
Figure GDA0002400415360000092
Figure GDA0002400415360000093
Wherein
Figure GDA0002400415360000094
Is obtained by maximizing (8) given a lambda.
5) According to the result obtained in step 4)
Figure GDA0002400415360000095
Figure GDA0002400415360000096
And H(t+1)As a final output and from this the NMSE diagram of fig. 2) and the probability distribution curve of fig. 3) are obtained. As can be seen from FIG. 2, the method is more reliable than the least square methodThe accuracy of the channel estimation is greatly improved. As can be seen from FIG. 3, the probability density curve of the noise estimated by the method substantially matches the probability density curve of the true distribution.
In summary, the present invention relates to an algorithm for improving accuracy of channel state information estimation by accurately estimating noise in a communication environment, comprising: a receiving end receives a pilot signal and an original pilot signal which are transmitted by a transmitting end and pass through a channel, and parameterized distributed modeling is carried out on noise in a communication environment based on randomness of noise in the communication environment; embedding regularization noise information coding into the model to realize self-adaptive modeling of a real noise environment; combining the noise parametric model and the regularization noise information coding to obtain a regularization model, and estimating noise parameters and a channel state by using an EM (effective noise) algorithm; the channel state information of the temporally and spatially close regions is saved by the base station. The method is based on the error modeling principle, and improves the accuracy of wireless communication channel estimation by self-adaptively depicting the real noise in the communication environment.

Claims (8)

1. The environment self-adaptive perception wireless communication channel estimation method based on error modeling is characterized by comprising the following steps of:
1) a receiving end receives a pilot signal and an original pilot signal which are transmitted by a transmitting end and pass through a channel;
2) based on the randomness of the noise of the communication environment, carrying out parametric distribution modeling on the noise in the communication environment as follows:
Y＝HX+E (1)
wherein the base station receives a signal Y and the noise E is NrX L matrix, NrThe number of antennas at the receiving end, L is the number of pilot signals used; original pilot signal X is NtX L matrix, NtThe number of antennas at the transmitting end; the channel matrix H being Nr×NtOf the received signal matrix Y, each element Y of the received signal matrix YijComprises the following steps:
Figure FDA0002400415350000011
wherein i 1,2r；j＝1，2，...，L，
Figure FDA0002400415350000012
Is the ith row, X of the channel matrix HjIs the j-th column, e, of the original pilot signal matrix XijFor each element of noise E, in order to simulate the randomness of the noise of the communication environment, assume EijObeying a mixed exponential power distribution consisting of K exponential power distributions, of the form:
Figure FDA0002400415350000013
Figure FDA0002400415350000014
wherein the k-th mixed exponential power distribution is in the form of
Figure DEST_PATH_IMAGE002
Wherein p iskAs a shape parameter, ηkIs a scale parameter, mukFor the position parameter, Γ (·) is a Gamma function; z is a radical ofij＝{zij1，zij2，...，zijk...，zijKIs eijCorresponding hidden variables, wherein zijk∈{0，1}，
Figure 1
，zijkDenotes eijA kth mixed component belonging to a mixed exponential power distribution; pikRepresents the weight parameter corresponding to the kth mixed distribution and satisfies pik≥0，
Figure FDA0002400415350000018
π＝{π1，π2，...，πk...，πKThe K weight parameters are set, and the complete likelihood function is:
Figure FDA0002400415350000019
where Θ is { H, pi, μ, η }, and μ is { μ ═ μ1，μ2，...，μk，...，μKThe K position parameters are set, η ═ η1，η2，...，ηk...ηKIs the set of K scale parameters,
Figure FDA0002400415350000021
then the log-likelihood function is:
Figure FDA0002400415350000022
3) embedding regularization noise information coding into the established model to realize self-adaptive modeling of real noise;
4) combining the noise parametric model in the step 2) and the regularized noise information coding in the step 3) to obtain a maximum posterior model, and estimating noise parameters and a channel state by using an EM (effective noise) algorithm;
5) the base station is used for saving the channel state information of the time and space similar area so as to be used by the new user for communication.
2. The method of claim 1, wherein the method comprises: the step 3) is used for embedding the regularization noise information into the model and coding, so that the self-adaptive modeling of the real noise is realized, and the form is as follows:
Figure FDA0002400415350000023
where N is the total number of elements in the matrix Y,. epsilon.is a very small positive number,. lambda.is the adjustment coefficient,. lambda. > 0, DkIs the number of the kth component free variable, Dk3, corresponding to pik，μk，ηk。
3. The method of claim 2, wherein the method comprises: 10 ∈ ═ 10-6Or
Figure FDA0002400415350000024
4. The method of claim 2, wherein the method comprises: the maximum posterior model obtained in the step 4) is as follows:
Figure 2
wherein
Figure 3
。
5. The method of claim 4, wherein the method comprises: the specific process of estimating the noise parameters and the channel state by using the EM algorithm in the step 4) comprises the following steps:
4.1) giving the membership degree gamma of the step E in the EM algorithmijkThe update formula of (2):
Figure FDA0002400415350000031
4.2) the iteration format and termination condition of the M steps in the EM algorithm are given:
the iteration format is:
Figure FDA0002400415350000032
Figure FDA0002400415350000033
Figure FDA0002400415350000034
Figure FDA0002400415350000035
wherein
Figure FDA0002400415350000036
The iteration termination condition is as follows:
Figure FDA0002400415350000037
the superscript (t) in the above formula represents the tth iteration,
Figure FDA0002400415350000038
in a formula (9)
4.3) solving the formulas (12) and (14) by adopting a quasi-Newton method;
4.4) setting an initial value of iteration;
4.5) carrying out the iterative operations of (11) to (14) until a termination condition is met;
4.6) saving channel State and noise parameter information in the base station, i.e. Theta at algorithm termination(t+1)And updating the channel state and noise parameter information of the area so as to facilitate the communication of the new user.
6. The method of claim 5, wherein the method comprises: step 4.4) sets an initial value of iteration, and initially sets a larger KstartSet up Kstart6, and setting a mixed exponential parameter
Figure FDA0002400415350000039
Wherein p isi∈[0.1，3]Some of pi during the iteration of the algorithmkWill become zero, then delete the k mixed component, realize to different ringsSelf-adaptation of ambient noise;
initial channel state information H0The first method is that if the pilot frequency contains position information, the first method is directly determined according to the position information; secondly, if the pilot frequency does not contain position information, estimating the multipath path number, multipath arrival angle and multipath time delay of the received signal according to the existing algorithm, and determining the position of the transmitting terminal by comparing the multipath path number, the multipath arrival angle and the multipath time delay with the parameters of each area;
initial mean value μ in [ min (Y-H)0X)，max(Y-H0X)]Uniformly finding K points;
the variance σ and weights are not initialized randomly.
7. The method of claim 5 or 6, wherein the method comprises: said step 4.4) sets an initial value setting for the iteration, wherein the adjustment factor λ is according to the modified BIC criterion:
Figure FDA0002400415350000041
an appropriate one is then selected by the following equation
Figure FDA0002400415350000042
Figure FDA0002400415350000043
Wherein
Figure FDA0002400415350000044
Is given by the maximization of equation (8) under a lambda.
8. The method of claim 5, wherein the method comprises: the step 5) of storing the channel state information of the areas with close time and space by using the base station comprises the following steps:
the base station divides the service area into a plurality of sub-service areas according to the communication scene of the service area, and respectively stores the channel state information within a certain time as an initialization channel matrix H of the next channel estimation0。
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