CN114567360B - Channel parameter estimation method for intelligent super-surface wireless communication - Google Patents
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Abstract
The invention discloses a channel parameter estimation method for an intelligent super-surface auxiliary wireless communication scene, which specifically comprises the following steps: s1, initializing channel parameters; s2, utilizing different transmission modes of the intelligent super surface to track and judge the multipath in the channel; s3, estimating basic parameters such as time delay, arrival angle, departure angle, doppler frequency offset, complex amplitude and the like of the multipath in the channel based on a space iteration expectation maximization algorithm, and estimating additional parameters such as incidence angle, reflection angle and the like of the multipath at the intelligent super-surface end based on a likelihood function; s4, distributed updating iteration of the estimated parameters. Compared with the prior art, the channel parameter estimation method provided by the invention has the advantages of effectively identifying the multipath under the action of the intelligent super surface in the environment, accurately estimating the important channel parameters in the intelligent super surface auxiliary wireless communication scene, and the like.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a channel parameter estimation algorithm for an intelligent super-surface auxiliary wireless communication scene.
Background
As a key technology for sixth generation mobile communication, the reconfigurable intelligent super surface (Reconfigurable Intelligent Surface, RIS) can "intelligently" regulate the propagation environment of wireless communication, and by adjusting the reflection coefficient of the intelligent super surface, the reflected beam can be focused to any desired direction. In order to more accurately evaluate intelligent subsurface-assisted wireless communication systems, it is necessary to study their wireless channel characteristics and channel parameters. Channel parameters in a wireless channel depend on estimation from channel measurement experiments, so accurate channel parameter estimation is a precondition for achieving correct analysis of intelligent subsurface-assisted wireless channels.
Different algorithms have been proposed in the prior art for channel parameter estimation. A space iteration expectation maximization algorithm is proposed by the prior study to estimate parameters such as time delay, arrival angle, departure angle, doppler frequency offset, complex amplitude and the like of multipath components in a channel, but parameters such as an incident angle and a reflection angle of a RIS end cannot be estimated; there have also been studies that have proposed using different RIS reflection coefficient settings to estimate the angle of incidence and angle of reflection at the RIS end, but the disadvantage is that multipath reflected via other scatterers is ignored in the estimation process and does not reflect the true RIS wireless channel well.
Disclosure of Invention
The invention aims to solve the problems that the multipath in the environment cannot be distinguished, all important parameters of the multipath cannot be completely estimated and the like in the prior art, and provides a channel parameter estimation method for an intelligent super-surface auxiliary communication scene.
In a typical RIS auxiliary channel propagation environment, according to different propagation paths, part of multipath is regulated by RIS and reflected by RIS during propagation, and the other part of multipath is reflected by other scatterers during propagation. In this case, the latter has channel parameters including basic parameters such as delay, angle of arrival, angle of departure, doppler frequency, complex amplitude, and the former includes two additional parameters of incidence angle and reflection angle of the RIS terminal in addition to the basic parameters. In order to realize accurate estimation of all the parameters, the invention provides a channel parameter estimation method for RIS auxiliary communication scenes, which comprises the following steps:
s1, initializing channel parameters;
s2, utilizing different RIS transmission modes to track and judge multipath in the channel;
s3, estimating basic parameters such as time delay, arrival angle, departure angle, doppler frequency offset, complex amplitude and the like of a multipath in a channel based on a space iteration expectation maximization algorithm;
s4, estimating additional parameters such as an incidence angle, a reflection angle and the like of the multipath at the RIS end based on a likelihood function;
s5, distributed updating iteration of the estimated parameters.
The channel parameter initialization in the step S1 adopts interference elimination initialization;
the propagation mode of the RIS refers to a set of regulation configurations of the adjustable phase of the RIS unit array.
The step S2 specifically includes:
step S201, tracking the multipath observed in different transmission modes; the multipath parameter distance metrics used are specifically as follows:
wherein DP r l,k；l′,k′ In particular a parametric distance measure of the multipath l observed in two different transmission modes k and k' from the multipath l l,k ,v l,k Respectively representing the time delay, angle of arrival, angle of departure, doppler frequency, τ of the multipath l observed in transmission mode k l′,k′ ,/>v l′,k′ Is defined as the same; r= (r) 1 r 2 r 3 r 4 ) T Is a threshold value of four parameters set in advance.
Further, when the four parameter portions of the parameter distance metric do not exceed 1, it is determined that two multipath l and multipath l' observed in different transmission modes belong to the same path in the channel environment.
Step S202, judging whether a given multipath I is subjected to RIS action in the propagation process; the discriminating factor usedIn particular asThe following is shown:
wherein,in particular the complex amplitude parameter of multipath, E { · } represents the desired operation.
Further, the discrimination factor does not exceed a preset threshold r 5 And if the path does not pass through the RIS effect in the propagation process, judging that the path passes through the intelligent reflection effect of the RIS in the propagation process.
The step S3 specifically includes:
s301, calculating a log-likelihood function lambda of the basic parameter based on observation data of the received signal k (θ l,k ；x l )：
Wherein s is k (t；θ l,k ) Representing the vector of the received signal,representing the basic parameter set to be estimated for the first path in the kth transmission mode, the elements in the parameter set in turn representing the departure angle, arrival angle, delay, doppler frequency, complex amplitude of the overall link for the path, x l,k (t) represents observation data in the kth transmission mode, re {.cndot. } is the real part operation.
S302, obtaining an objective function for likelihood estimation of the basic parameters based on the likelihood function.
S303, maximizing the objective function can obtain an estimated value of the basic parameter in the kth transmission mode.
S304, since the basic parameters are not changed due to the change of the transmission modes, the final estimated value of the basic parameters can be regarded as the expected value of the estimated value in different transmission modes.
The step S4 is directed only to multipath that has undergone RIS action during propagation, and specifically includes:
s401, based on the complex amplitude estimated in different transmission modesLog likelihood function for calculating additional parameters>
Wherein W is l Is a matrix containing complex amplitude information of Tx-RIS and RIS-Rx links, and is calculated byMatrix->And->Complex amplitudes of Tx-RIS and RIS-Rx links, respectively representing the path/observed in the kth transmission mode, +.>Representing the response of RIS, incidence angle to RIS end +.>Angle of reflection->Related to;
s402, performing bias guide on the log-likelihood function and makingObtaining an objective function for likelihood estimation of the additional parameter;
s403, maximizing the objective function can obtain an estimated value of the additional parameter.
The specific method in step S5 is to divide the parameter set into corresponding parameter subsets, sequentially perform E step and M step continuous updating iterative process in the space iteration expectation maximization algorithm until the channel parameter iteration converges, and the last iterative result is the estimated value output by the channel parameter estimation method, wherein E step is to obtain the observation data x in the kth transmission mode l,k The process of (t), the method is as follows:
wherein,a set of parameters, y, representing the first path in the kth transmission mode estimated in the last iteration k (t) represents the received signal obtained in the kth transmission mode, < >>Representing the sum of the other L-1 path signals. The M step is a process of searching for parameter values, and solving for parameter values that maximize the objective function in step S302 and step S402.
Compared with the prior art, the invention has the following beneficial effects:
the invention is based on a space iteration expected maximum algorithm, considers the reality situation that the paths under the RIS effect and the paths used by other scatterers exist in the RIS wireless channel at the same time, and completes the multipath tracking and discrimination based on the multipath information observed under different RIS transmission modes. Besides accurately estimating the basic parameters of the multipath, the method also realizes accurate estimation of important parameters including the incidence angle and the reflection angle of the RIS end, which has important significance for researching RIS auxiliary wireless channel characteristics and improving the accuracy of RIS auxiliary wireless channel modeling.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic diagram of a propagation environment of an RIS auxiliary channel according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a time-division multiplexing time frame structure after RIS transmission mode switching is introduced in the embodiment of the present invention;
FIG. 4 is a diagram showing the performance of the mean square estimation error at different signal-to-noise ratios and different numbers of phase shift modulation matrices according to the embodiment of the present invention;
FIG. 5 is a diagram showing the performance of the mean square estimation error at different signal-to-noise ratios and different RIS sizes in the example of the present invention;
fig. 6 is a schematic diagram showing the performance of the mean square estimation error under different signal-to-noise ratios and different RIS phase designs in the example of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Examples
As shown in fig. 2, in a typical RIS-assisted channel propagation environment, according to propagation paths, some multipaths are subjected to the regulation and reflection of RIS during propagation, and another part of multipaths are only subjected to the reflection of other scatterers during propagation. In this context, the latter has parameters including basic parameters such as delay, angle of arrival, departure, doppler frequency, complex amplitude, and the former includes two additional parameters of angle of incidence and angle of reflection at the RIS terminal in addition to the basic parameters. In order to realize accurate estimation of all the parameters, the invention provides a channel parameter estimation method for RIS auxiliary communication scene, as shown in fig. 1, which specifically comprises the following steps:
s1, initializing channel parameters;
s2, tracking and judging multipath components in the channel by using different RIS transmission modes;
s3, estimating basic parameters such as time delay, arrival angle, departure angle, doppler frequency offset, complex amplitude and the like of multiple paths in a channel based on space iteration expectation maximization algorithm
S4, estimating additional parameters such as an incidence angle, a reflection angle and the like of the multipath at the RIS end based on a likelihood function;
s5, distributed updating iteration of the estimated parameters.
The channel parameter initialization in the step S1 adopts interference elimination initialization;
the propagation mode of the RIS refers to a set of regulation configurations of the adjustable phase of the RIS unit array. The time division multiplexing time frame structure after introducing the different transmission modes of the RIS switch is shown in fig. 3.
The step S2 specifically includes:
step S201, tracking the multipath observed in different transmission modes; the multipath parameter distance metrics used are specifically as follows:
wherein DP r l,k；l′,k′ In particular a parametric distance measure of the multipath l observed in two different transmission modes k and k' from the multipath l l,k ,v l,k Respectively representing the time delay, angle of arrival, angle of departure, doppler frequency, τ of the multipath l observed in transmission mode k l′,k′ ,/>v l′,k′ Is defined as the same; r= (r) 1 r 2 r 3 r 4 ) T Is a threshold value of four parameters set in advance.
Further, when the four parameter portions of the parameter distance metric do not exceed 1, it is determined that two multipath l and multipath l' observed in different transmission modes belong to the same path in the channel environment.
Step S202, judging whether a given multipath I is subjected to RIS action in the propagation process; the discriminating factor usedThe method is specifically as follows:
wherein,in particular the complex amplitude parameter of multipath, E { · } represents the desired operation.
Further, the discrimination factor does not exceed a preset threshold r 5 And if the path does not pass through the RIS effect in the propagation process, judging that the path passes through the intelligent reflection effect of the RIS in the propagation process.
The step S3 specifically includes:
s301, calculating a log-likelihood function lambda of the basic parameter based on observation data of the received signal k (θ l,k ；x l )：
Wherein s is k (t；θ l,k ) Representing the vector of the received signal,representing the basic parameter set to be estimated for the first path in the kth transmission mode, the elements in the parameter set in turn representing the departure angle, arrival angle, delay, doppler frequency, complex amplitude of the overall link for the path, x l,k (t) represents the observed data in the kth transmission mode, re {. Cndot. } is the real partAnd (3) operating.
S302, obtaining an objective function for likelihood estimation of the basic parameters based on the likelihood function.
S303, maximizing the objective function can obtain an estimated value of the basic parameter in the kth transmission mode.
S304, as the basic parameters are not changed due to the change of the transmission modes, the final estimated value of the basic parameters can be regarded as the expected value of the estimated result under different transmission modes.
The step S4 is directed to multipath that is subjected to RIS action during propagation, and specifically includes:
s401, based on the complex amplitude estimated in different transmission modesLog likelihood function for calculating additional parameters>
Wherein W is l Is a matrix containing complex amplitude information of Tx-RIS and RIS-Rx links, and is calculated byMatrix->And->Complex amplitudes of Tx-RIS and RIS-Rx links, respectively representing the path/observed in the kth transmission mode, +.>Representing the response of RIS, incidence angle to RIS end +.>Angle of reflection->Related to;
s402, solving bias guide and order of likelihood functionThe objective function used for likelihood estimation of the additional parameters is obtained, specifically:
s403, maximizing the objective function to obtain the estimated value of the additional parameter
The specific method of the step S5 is to divide the parameter set into corresponding parameter subsets, and sequentially perform the continuous updating iterative process of the step E and the step M in the space iteration expectation maximization algorithm until the channel parameter iteration converges. The last iteration result is the estimation value output by the channel parameter estimation method, wherein, the step E is to obtain the observation data x in the kth transmission mode l,k The process of (t), the method is as follows:
wherein,a set of parameters, y, representing the first path in the kth transmission mode estimated in the last iteration k (t) represents the received signal obtained in the kth transmission mode, < >>Representing the sum of the other L-1 path signals. The M step is a process of searching for parameter values, and solving for parameter values that maximize the objective function in step S302 and step S402.
For RIS auxiliary wireless channel parameter estimation, analog simulation is carried out to evaluate the performance of the channel parameter estimation method provided by the invention. The mean square estimation error (root-mean square estimation error, RMSE) is used as a standard for evaluating the performance of the method, and the influence of the number of RIS transmission modes, the RIS specification size and the RIS phase quantization mode on the performance of the method is examined. The simulation results are all average values of 500 Monte Carlo test results. The simulation parameter settings are shown in table 1.
Table 1 simulation parameter settings
| Parameters (parameters) | Value taking |
| Number of transmitting end antennas | 32 |
| Number of receiver antennas | 64 |
| Carrier frequency | 5.4GHz |
| Bandwidth of a communication device | 320MHz |
| Sum of multipath | 20 |
| Total number of multipaths through RIS action | 5 |
| Time delay | Random, reference 3GPP standard protocol generation |
| Angle of arrival, angle of departure, angle of reflection, angle of incidence | Random, reference 3GPP standard protocol generation |
| Doppler frequency offset | Random, reference 3GPP standard protocol generation |
| Complex amplitude | Random, reference 3GPP standard protocol generation |
The performance of the mean square estimation error under different signal-to-noise ratios and different numbers of transmission modes is shown in fig. 1, when the signal-to-noise ratio is low, the increase of the number of the transmission modes can improve the performance of the method provided by the invention, and when the signal-to-noise ratio is high, the channel parameter estimation method provided by the invention even under fewer transmission modes can also show higher performance. The performance of the mean square estimation error at different signal-to-noise ratios and different RIS specification sizes is shown in fig. 5, it can be seen that a larger size RIS array would result in lower RMSE with unchanged RIS array cell spacing, because the estimation performance of the additional parameters would increase with increasing RIS array aperture. The performance of the mean square estimation error under different signal-to-noise ratios and different RIS phase design modes is shown in fig. 6, when the signal-to-noise ratio is lower than 15dB, the RIS phase design mode can influence the accuracy of the method provided by the invention on parameter estimation, the application of the method under the continuous phase design mode that RIS is ideal can bring higher accuracy, the 2-bit quantized phase design mode is the 2-bit quantized phase design mode, and the 1-bit quantized phase design mode is the 1-bit quantized phase design mode; when the signal-to-noise ratio is higher than 15dB, the phase design mode of RIS does not influence the parameter estimation accuracy of the method provided by the invention obviously, and the parameter estimation by using the method has higher accuracy.
It can be seen from this example that the method provided by the present invention can be well applied to parameter estimation of the RIS auxiliary channel, and has the following advantages compared with the prior art: firstly, the method is suitable for RIS auxiliary wireless communication scenes; secondly, estimation of the RIS end incident angle and reflection angle parameters is realized; thirdly, the accuracy of the obtained parameter estimation value is extremely high.
Claims (3)
1. The channel parameter estimation method for the intelligent super-surface auxiliary wireless communication scene is characterized by comprising the following steps of:
s1, initializing channel parameters;
s2, utilizing different RIS transmission modes to track and judge multipath in the channel;
s3, estimating time delay, arrival angle, departure angle, doppler frequency offset and complex amplitude of the multipath in the channel based on a space iteration expectation maximization algorithm;
s4, estimating the incidence angle and the reflection angle of the multipath at the RIS end based on a likelihood function;
s5, estimating distributed updating iteration of parameters;
the step S2 specifically comprises the following steps:
step S201, tracking the multipath observed in different transmission modes; the multipath parameter distance metrics used are specifically as follows:
wherein DP r l,k；l',k' In particular the parametric distance measure of the multipath l and multipath l 'observed in two different transmission modes k and k',respectively represent the transmission mode k inferior viewDelay, angle of arrival, angle of departure, doppler frequency, +.>Is defined as the same; r= (r) 1 r 2 r 3 r 4 ) T Is a threshold value of four parameters set in advance;
step S202, judging whether a given multipath I is subjected to RIS action in the propagation process; the discriminating factor usedThe method is specifically as follows:
wherein,in particular, the complex amplitude parameter of multipath, E { · } represents the expected operation;
the step S3 specifically comprises the following steps:
s301, calculating a log-likelihood function lambda of the basic parameter based on observation data of the received signal k (θ l,k ；x l )：
Wherein s is k (t；θ l,k ) Representing the vector of the received signal,represents the basic parameter set to be estimated for the first path in the kth transmission mode, and the elements in the parameter set represent the departure angle, arrival angle, delay, doppler frequency, complex amplitude of the whole link of the multipath in turn, and x l,k (t) represents the transmission mode in the kthObservation data below, re {. Cndot. }, is the operation for the real part;
s302, obtaining an objective function for likelihood estimation of basic parameters based on a log likelihood function;
s303, maximizing an objective function to obtain an estimated value of a basic parameter in a kth transmission mode;
s304, as the basic parameters are not changed due to the change of the transmission modes, the final estimated value of the basic parameters can be considered as the expected value of the estimated value in different transmission modes;
step S4 is directed only to multipaths that have undergone RIS action during propagation, and specifically includes:
s401, based on the complex amplitude estimated in different transmission modesLog likelihood function for calculating additional parameters
Wherein W is l Is a matrix containing complex amplitude information of Tx-RIS and RIS-Rx links, and is calculated byMatrix->And->Complex amplitudes of Tx-RIS and RIS-Rx links, respectively representing the path/observed in the kth transmission mode, +.>Representing the response of RIS, incidence angle to RIS end +.>Angle of reflection->Related to;
s402, solving and guiding the log likelihood functionObtaining an objective function for likelihood estimation of the additional parameter;
s403, maximizing an objective function to obtain an estimated value of the additional parameter;
the specific method of the step S5 is that the parameter set is divided into corresponding parameter subsets, and the expected E step and the objective function maximizing M step in the space iteration expected maximizing algorithm are sequentially carried out to continuously update the iterative process until the channel parameter iteration converges; the last iteration result is the estimation value output by the channel parameter estimation method, wherein, the step E is to obtain the observation data x in the kth transmission mode l,k The process of (t), the method is as follows:
wherein,a set of parameters, y, representing the first path in the kth transmission mode estimated in the last iteration k (t) represents the received signal obtained in the kth transmission mode, < >>Represents the sum of other L-1 path signals; m step is searching for parameter values, solving for the purpose in step S302 and step S402And (3) maximizing the parameter value of the standard function.
2. The method according to claim 1, wherein when the four parameter portions of the parameter distance metric in step S201 do not exceed 1, it is determined that two multipath l and multipath l' observed in different transmission modes belong to the same path in the channel environment.
3. The method for channel parameter estimation in an intelligent subsurface assisted wireless communication scenario as claimed in claim 1, wherein said discrimination factor in step S202 does not exceed a preset threshold r 5 And if the path does not pass through the RIS effect in the propagation process, judging that the path passes through the intelligent reflection effect of the RIS in the propagation process.
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| CN112910807A (en) * | 2021-02-04 | 2021-06-04 | 华中科技大学 | Intelligent super-surface channel estimation method and system based on space random sampling |
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| CN113472705A (en) * | 2021-06-16 | 2021-10-01 | 复旦大学 | Estimation and prediction method of RIS auxiliary channel based on ZC sequence |
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