CN117278367A - Distributed compressed sensing sparse time-varying channel estimation method - Google Patents
Distributed compressed sensing sparse time-varying channel estimation method Download PDFInfo
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- H—ELECTRICITY
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- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0204—Channel estimation of multiple channels
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/024—Channel estimation channel estimation algorithms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
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Abstract
The invention relates to a distributed compressed sensing sparse time-varying channel estimation method, and belongs to the technical field of wireless communication. The method comprises the following steps: based on the structural characteristics of the high-dimensional channel matrix, constructing a system model and a time-varying channel evolution model based on Markov random switching topology; and compressing the sparse random input signal into a low-dimensional vector through a compressed sensing matrix, and constructing a compression-estimation-decompression distributed self-adaptive algorithm. The method uses a distributed technology to fuse different data source information, solves the problem that the existing channel estimation method is based on estimating single or homogeneous data sources, overcomes the difficulties of insufficient excitation and the like based on original high-dimensional sparse regression vectors, does not need to require regression vectors to meet strict statistical assumptions such as independence and the like, and has the characteristics of strong instantaneity, high precision and the like.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a distributed compressed sensing sparse time-varying channel estimation method.
Background
The Multiple Input Multiple Output (MIMO) technology obviously improves the data transmission rate of the system and increases the channel reliability on the premise of not expanding the bandwidth by using a plurality of antennas at a transmitting end and a receiving end. For example, in the current popular 5 th generation (5G) wireless system, the mimo technology is one of core technologies of the 5G wireless system, and the massive multi-antenna system achieves higher capacity and higher reliability by configuring a large number of antennas at the base station side, which is advantageous in that it relies on a channel estimation technology to obtain accurate channel state information. However, since the signal propagation path between the transmitter and the receiver is complex and variable, the wireless channel has a strong randomness with respect to the wired channel, and it is difficult to analyze the wireless channel. The signal is affected by multipath delay and fading during propagation, and in order to realize correct detection at the receiving end, the channel estimation technology is critical in a communication system. Good channel state information is a precondition for fully exploiting the potential advantages of large-scale MIMO technology. Therefore, it is important to study channel estimation methods with high performance. For example, chinese patent: CN116319196B, CN115776424B and CN114500184B.
However, with the continuous development and the continuous increase of the dimension in the digital age, the dimension of the channel matrix to be estimated also increases sharply, so that the high-dimensional channel estimation in a limited coherence time faces serious challenges, and the channel estimation has poor real-time performance, low precision and poor independence.
Disclosure of Invention
In view of the above problems, the invention provides a distributed compressed sensing sparse time-varying channel estimation method, which solves the problems of poor estimation instantaneity, low precision and poor independence when the channel estimation method in the prior art finishes high-dimensional channel estimation in a limited coherence time.
The invention provides a distributed compressed sensing sparse time-varying channel estimation method, which comprises the following specific steps:
step 1, establishing a system model based on a Markov switching directed topological graph;
step 2, establishing an evolution process model of a sparse channel transmission matrix to be estimated by each device in the system model;
step 3, establishing an observation model of each device based on an evolution process model of a sparse channel transmission matrix to be estimated by each device;
step 4, giving a compressed sensing matrix, initial sparse input signals of each device, initial high-dimensional matrix estimated values and initial positive information matrixes; obtaining an initial weighted average estimated value based on the compressed sensing matrix and the initial high-dimensional matrix estimated values of each device; obtaining initial distribution compressed sensing sparse estimation based on initial sparse input signals, initial high-dimensional matrix estimation values, initial positive information matrix and initial weighted average estimation values of all devices;
step 5, ordert=0, wherein,twhen=0, the initial time is;
step 6, based on the firsttObtaining a reconstructed restored high-dimensional channel transmission matrix estimated value by time distributed compressed sensing sparse estimation;
step 7, judgingtWhether or not to be smaller thanTThe method comprises the steps of carrying out a first treatment on the surface of the If it ist＜TFor a pair oftAdding 1 to the value of (2) and returning to step 6 ift≥TAnd ending the channel estimation, and using the reconstructed restored high-dimensional channel transmission matrix estimation value as a final channel estimation value.
Optionally, the system model based on the markov switching directed topology map in step 1The expression of (2) is:
；
wherein,represent the firsttTime-of-day markov random handoff procedure,t=0,1,2,…,T，Tthe termination time of the channel estimation; />Representing a set of devices in a system，/>，i=1,2,…,n/>，nIs the total number of devices; />Is indicated at +.>Relation set of transfer information between time devices, < +.>；/>Is indicated at +.>Time->Personal device->Is->Personal device->Is (are) neighbor devices>，/>Indicate->Personal device->In->A set of neighbor devices at a time; />Is indicated at +.>Weight for information transfer between devices at the moment, < >>；/>Is indicated at +.>Time->Personal device->To (1)>Personal device->Information weight of the information is transferred.
Optionally, in step 2, the expression of the evolution process model of the sparse channel transmission matrix to be estimated by each device is:
wherein,the updating estimation sparse channel transmission matrix of each device at the time t+1 is represented; />Indicating that the respective device is at->Estimating a sparse channel transmission matrix at the moment; />Indicating that the channel transmission matrix is at +.>The amount of change in time; />Is a positive constant.
Optionally, the expression of the observation model of each device in step 3 is:
wherein,indicate->Personal device->In->An output signal at a time; />Indicate->Personal device->In->Sparse input signal of moment; />Indicating that the signal is at->Noise in the transmission process of time; />Indicating that the respective device is at->The estimate of time of day is a transpose of the sparse channel transmission matrix.
Optionally, the compressed sensing matrix M is used in step 4 for the first pairPersonal device->Initial high-dimensional matrix estimate +.>Compressing to obtain initial weighted average estimated value +.>The expression is:
；
wherein,for compressing the perceptual matrix.
Optionally, in step 6, based on the firsttThe specific steps of obtaining the reconstructed restored high-dimensional channel transmission matrix estimated value by the time distributed compressed sensing sparse estimation are as follows:
step 61, obtain the firstPersonal device->In->Sparse input signal +.>Low-dimensional non-sparse signal->Weighted average estimate->And output signal +.>；
Step 62, based on the firstPersonal device->In->Low-dimensional non-sparse signal of time instant->Positive information matrix->Weighted average estimate->And output signal +.>Obtain->Personal device->Transition information matrix->And transition channel transmission matrix low-dimensional estimation value +.>；
Step 63, the firstPersonal device->And->Personal device->Information interaction is carried out, and an updated positive definite information matrix is obtained according to neighbor weights>And updating weighted average estimate +.>The method comprises the steps of carrying out a first treatment on the surface of the The interactive information is the low-dimensional estimated values of the transition information matrix and the transition channel transmission matrix of each device obtained in the step 62;
step 64, updating weighted average estimation parameters using the value of the reduction error norm as the objective function pairPerforming high-dimensional reduction to obtain the reconstructed +.>Personal device->Is a reduced high-dimensional channel transmission matrix estimation value +.>。
Optionally, in step 61, a compressed sensing matrix is usedMFor the firstPersonal device->In->Sparse input signal of momentCompressing to obtain compressed low-dimensional non-sparse signal +.>The expression is:
wherein,is->Personal device->In->A low-dimensional non-sparse signal compressed by a compressed sensing matrix at any moment;Mis a compressed sensing matrix; />Is->Personal device->In->Sparse input signal at time instant.
Alternatively, the observation mode of step 3 is used in step 61Acquisition of the firstPersonal device->In->Output signal of time of day。
Optionally, step 62 is based on the firstPersonal device->In->Low-dimensional non-sparse signal of time instant->Positive information matrixWeighted average estimate->And output signal +.>Obtain->Personal device->Transition information matrix->And transition channel transmission matrix low-dimensional estimation value +.>The expression of (2) is:
wherein,representing an adaptive rate; />Representing the transpose of the compressed low-dimensional non-sparse signal.
Optionally, in step 63Personal device->And->Personal device->Information interaction is carried out, and an updated positive definite information matrix is obtained according to neighbor weights>And updating weighted average estimate +.>The expression is:
wherein,indicate->Personal device->Is defined in the form of an updated positive information matrix->Is the inverse of (2); />Represent the firstpPersonal devicev p Transition information matrix->Is the inverse of (2); />Indicate->Personal device->Low-dimensional estimates of the transition channel transmission matrix of (c).
Compared with the prior art, the invention has at least the following beneficial effects:
(1) The method of the invention fuses the information of the distributed different data sources, solves the problem that the existing channel estimation methods are all based on the estimation of single or homogeneous data sources, and improves the actually required identification precision.
(2) The method solves the problem of channel time variation caused by frequency selective fading which is easy to cause by multipath delay expansion and time selective fading which is caused by Doppler frequency shift by utilizing the structural characteristics (such as low rank property and sparsity) of the high-dimensional channel matrix, and improves the precision of high-dimensional channel estimation.
(3) The method provided by the invention considers the sparsity of the regression vector and other characteristics, adopts the compressed sensing theory to solve the problems that the original high-dimensional sparse regression vector is easy to generate insufficient excitation and the like in the existing method, and the regression vector needs to meet severe statistical assumptions such as independence and the like, so that the estimation performance and the spectrum utilization rate are improved.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention.
Fig. 1 is a flow chart of a distributed compressed sensing sparse time-varying channel estimation method of the present invention.
Fig. 2 is a schematic diagram of information exchange at different time points for 5 devices according to the present invention.
Fig. 3 is a graph comparing estimation errors of the distributed compressed sensing sparse time-varying channel estimation method (compressed FFLS algorithm) of the present invention with those of the prior art and the non-cooperative compressed sensing algorithm.
Fig. 4 is a graph comparing estimation errors of the distributed compressed sensing sparse time-varying channel estimation method (compressed FFLS algorithm) with other algorithms according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other. In addition, the invention may be practiced otherwise than as specifically described and thus the scope of the invention is not limited by the specific embodiments disclosed herein.
1-4, a distributed compressed sensing sparse time-varying channel estimation method is provided, comprising the following specific steps:
step 1, establishing a system model based on a Markov switching directed topological graphThe expression is:
；
wherein,represent the firsttTime-of-day markov random handoff procedure,t=0,1,2,…,T，Tthe termination time of the channel estimation; />Representing the set of devices in the system, +.>，i=1,2,…,n/>，nIs the total number of devices; />Is indicated at +.>Relation set of transfer information between time devices, < +.>The method comprises the steps of carrying out a first treatment on the surface of the First->Personal device->Is->Personal device->Is (are) neighbor devices>，/>Indicate->Personal device->In->A set of neighbor devices at a time; />Is indicated at +.>Weight for information transfer between devices at the moment, < >>；Is indicated at +.>Time->Personal device->To (1)>Personal device->Information weight of the information is transferred.
The invention adds the time-sharing Markov random switching process at the t momentThe influence of the channel estimation result is more consistent with the actual engineering, and the accuracy is higher.
Optionally, a system modelnIdentical in each attributeA device; each device includes a signal transmitter and a signal receiver. The devices in the system model are antennas for transmitting and receiving signals, etc.
Step 2, establishing an evolution process model of a sparse channel transmission matrix to be estimated by each device, wherein the expression is as follows:
wherein,indicating the respective device->Updating and estimating a sparse channel transmission matrix at the moment; />Indicating that the respective device is at->Estimated sparse channel transmission matrix of time of day, +.>；/>Indicating that the channel transmission matrix is at +.>The amount of change in time; />Is a positive constant.
Further, the channel transmission matrix is at the firstThe change of the time->The dimension of which is the same as the channel transmission matrix; positive constant->For representing the variation of the channel transmission matrix, the channel transmission matrix is not changed too fast, and the +.>。
Step 3, based on the evolution process model of the sparse channel transmission matrix which needs to be estimated by each device, establishing the first stepPersonal device->In->The observation model at the moment has the expression:
wherein,indicate->Personal device->Receiver of (2) at%>Output signal of time,/->；/>Indicate->Personal device->The transmitter of (2) is at%>Sparse input signal at time,/>；/>Indicating that the signal is at->Noise during the transmission of time, +.>；/>Indicating that the respective device is at->Estimating transposition of a sparse channel transmission matrix at moment; />Represents a real number; />Indicating the number of antennas set by the receiver of each device; />Indicating the number of antennas set by the transmitters of the respective devices;
it will be appreciated that, as shown in FIG. 2, a first is providedPersonal device->The transmitter in (a) is provided with->Root antenna, receiver equipped with +.>The root antenna has->The signal transmission channels (i.e., channels).
Further, the estimated sparse channel transmission matrix contains varying signals during signal transmission.
Step 4, giving a compressed sensing matrix, initial sparse input signals of each device, initial high-dimensional matrix estimated values and initial positive information matrixes; obtaining an initial weighted average estimated value based on the compressed sensing matrix and the initial high-dimensional matrix estimated values of each device; obtaining initial distribution compressed sensing sparse estimation based on initial sparse input signals, initial high-dimensional matrix estimation values, initial positive information matrix and initial weighted average estimation values of all devices;
preferably, the initial high-dimensional matrix estimateIs->High-dimensional matrix estimates of dimensions.
Using compressed sensing matricesMFor the firstPersonal device->Initial high-dimensional matrix estimate +.>Compressing to obtain initial weighted average estimated value +.>The expression is:
；
wherein,for compressed sensing matrix +.>A dimension matrix for reducing complexity in the estimation process,dfor compressing the sensing matrixMIs a number of rows of (a).
First, thePersonal device->Is +.>Initial high-dimensional matrix estimate ++>An initial positive information matrix->And initial weighted average estimate +.>An initial distributed compressed sensing sparse estimate is constructed.
Further, the perceptual matrix is compressedSatisfy->The order limit equidistant property RIP has the expression:
wherein,minimum constant for limiting the equidistant properties RIP to satisfy the s-order, +.>；/>Indicating at most +.>Zero ∈>And (5) a dimension vector.
The invention makes the compressed sensing matrixSatisfy->And (3) taking the restricted equidistant property RIP of the order, taking each element of the compressed sensing matrix M to meet normal distribution or binomial distribution, and compressing the estimated sparse channel transmission matrix so as to obtain a compressed transmission matrix containing effective channel information.
Step 5, ordert=0, wherein,twhen=0, the initial time is;
step 6, based on the firsttObtaining a reconstructed restored high-dimensional matrix estimated value by time distributed compressed sensing sparse estimation;
step 61, obtain the firstPersonal device->In->Sparse input signal +.>Low-dimensional non-sparse signal->Weighted average estimate->And output signal +.>。
In particular, compressed sensing matrices are usedMFor the firstPersonal device->In->Sparse input signal +.>Compressing to obtain compressed low-dimensional non-sparse signal +.>The expression is:
wherein,is->Personal device->The transmitter of (2) is at%>A low-dimensional non-sparse signal compressed by a compressed sensing matrix at any moment;Mis a compressed sensing matrix; />Is->Personal device->The transmitter of (2) is at%>Sparse input signal of moment;
preferably, the firstPersonal device->The transmitter of (2) is at%>The low-dimensional non-sparse signal compressed by the compressed sensing matrix at moment isdLow-dimensional non-sparse signals of dimensions.
Acquisition of the first Using the observation model of step 3Personal device->Receiver of (2) at%>Output signal +.>。
Step 62, based on the firstPersonal device->The transmitter of (2) is at%>Time of dayLow-dimensional non-sparse signal->Positive information matrix->Weighted average estimate->And->Personal device->Receiver of (2) at%>Output signal +.>Obtain->Personal deviceTransition information matrix->And transition channel transmission matrix low-dimensional estimation value +.>The expression is:
wherein,representing an adaptive rate; />Representing the transpose of the compressed low-dimensional non-sparse signal.
It will be appreciated that the number of components,adaptive Rate->The larger the signal, the smaller the influence of the past output signal and input signal on the channel estimation value at the current time, and the larger the influence of the output signal and input signal at the current time on the channel estimation value at the current time.
Step 63, the firstPersonal device->And->Personal device->Information interaction is carried out, and an updated positive definite information matrix is obtained according to neighbor weights>And updating weighted average estimate +.>The interactive information is a transition information matrix and a transition channel transmission matrix low-dimensional estimated value of each device obtained in step 62, and the expression is:
wherein,indicate->Personal device->Is defined in the form of an updated positive information matrix->Is the inverse of (2); />Represent the firstpPersonal devicev p Transition information matrix->Is the inverse of (2); />Indicate->Personal device->Low-dimensional estimates of the transition channel transmission matrix of (c).
Step 64, using the reduction error normValue of->Weighting average estimates for updating objective function to low dimensionPerforming high-dimensional reduction to obtain the reconstructed +.>Personal device->Is a reduced high-dimensional matrix estimate of +.>The expression is:
wherein,Hthe value of the result of the optimization is represented,Hat the position ofMiddle value (L.) of (L)>Indicating all->Compressed sense matrix of dimension->Compressed value and estimation parameter of objective function for low dimension +.>A set of matrices having a norm less than or equal to C;；/>representing an error range for restoring the high-dimensional matrix estimation value;
preferably, the firstPersonal device->Is a reduced high-dimensional matrix estimate of +.>Is->High-dimensional matrix estimates of dimensions.
Step 7, judgingtWhether or not to be smaller thanTThe method comprises the steps of carrying out a first treatment on the surface of the If it ist＜TFor a pair oftThe value of (1) is added to 1 and the step S6 is returned to, ift≥TEnding the channel estimation and using the reconstructed firstPersonal device->Is a reduced high-dimensional matrix estimate of +.>As the final channel estimate.
The invention is based on the compressed sensing technology, compresses the original high-dimensional sparse signal by utilizing a compressed sensing matrix, constructs a distributed self-adaptive filtering algorithm in a compressed low-dimensional space, and constructs the original high-dimensional estimation by utilizing a reconstruction principle; and different data source information is fused, the difficulty of insufficient excitation based on original high-dimensional sparse random signals is overcome, harsh statistical assumptions such as independence of random signals are not needed, the signal processing effect equivalent to that of full sampling is realized by less sampling, the traditional Nyquist sampling frequency limitation is broken through, and the method has the advantages of high instantaneity, high precision and the like.
In order to illustrate the effectiveness of the method provided by the invention, the effectiveness verification is carried out on the technical scheme of the invention, and the specific implementation steps are as follows:
construction of the firstPersonal device->At->Error of time->And obtaining a recursive expression of the error equation:
；
wherein,indicate->Personal device->An updated positive information matrix of the transmitter of (a); />Indicate->Personal device->In->A low-dimensional non-sparse signal of time; />Indicate->Personal device->At->Estimating error of the compressed channel transmission matrix at the moment; />Representing the size of the channel matrix variation to be estimated; />Indicating that the channel transmission matrix is at +.>The amount of change in time; />Indicate->Personal device->The transmitter of (2) is at%>Means for sparse input signal at time; />Representation->A dimension identity matrix; />Representing compressed sensing matrix +.>Is a transpose of (2); />Indicating that the signal is at->Transpose at->Noise in the transmission process of time;
combining the parameters into dimension expansionThe matrix of multiples, expressed as:
the error of the estimated value of the compressed channel transmission matrix by each device at the time t is included;indicate->Personal device->At->Error in time;
all information of input signals of all devices is contained; />Indicate->Personal device->The transmitter of (2) is at%>A positive information matrix of time;
representing the adjacency matrix +.>And d-dimensional identity matrix->Do->Accumulating;
the method comprises the steps that information updated by each device after neighbor information exchange is included; />Representation->Time->Personal device->Is a transition information matrix of (a);
the input signals compressed by the devices at the time t are included; />Indicate->Personal device->In->A low-dimensional non-sparse signal of time;
the measurement errors of all devices at the time t are included; />Indicating that the signal is at->The personal device is at->Measuring error of time;
extending the dimension of the channel transmission matrix change amount to be consistent with other symbols; get just +.>A matrix containing all device information is correlated.
Constructing a recursive error equation of all device information, wherein the expression is as follows:
wherein,is adaptive rate->Is the size of the channel matrix variation to be estimated.
Error of estimated value of compressed channel transmission matrix by each device at time t+1; />All information representing the input signals of the devices at time t+1; />The method comprises the steps that information updated by each device at time t+1 after exchanging with neighbor information is included;representing->Taking a transpose; />The input signals compressed by the devices at the time t are included; />Is to input signal->Taking a transpose; />Error of estimation value of compressed channel transmission matrix by each device at t moment is included; />The measurement error of each device at the time t is included; />Representing the amount of channel transmission matrix change. From the recursive error formula above, it is necessary toThe cumulative multiplication of the terms goes to zero and the two latter terms are bounded and controllable. In the compression space, all +.>Low-dimensional non-sparse signal of transmitter of personal device after compressed sensing matrix compression +.>At time->To the momentAll the information sets the following excitation conditions: there is a positive integer +.>And normal number->So thatWherein->Is a column of non-decreasing +.>-algebra; />Representing a minimum eigenvalue of the corresponding matrix; />Indicate->Personal device->The transmitter of (2) is at%>A low-dimensional non-sparse signal compressed by a compressed sensing matrix at any moment; />Representing conditional probability +_>Is a positive constant.
The invention is directed toPersonal device->In->Time-of-day compressed low-dimensional non-sparse input signal +.>Excitation conditions are set, and effectiveness of channel estimation is guaranteed.
In combination with the given compressed excitation conditions, the following conclusions can be drawn:
is provided withAnd->Is difference column, wherein ∈>Representing the channel transmission matrix as the variable; />The method comprises the steps that noise interference existing in the transmission process of each device at the moment t is included;
the representation is composed oftAll of the input signals, channel transmission matrix change amounts, noise error generation of each device before and after the timeσAlgebraic generation; />Representing the compressed input signals of the devices at the s moment;
representing the change amount of the channel transmission matrix at the s moment; />The noise of each device in the signal transmission process at the s moment is represented;
compressed sensing matrixThe corresponding compressed sensing constant satisfies->Wherein->Representation such that the compressed sense matrix M satisfies +.>The minimum constant of the order limiting equidistant property RIP; />Representation such that the compressed sense matrix M satisfies +.>The minimum constant of the order limiting equidistant property RIP; if for->And the excitation condition given before is satisfied, the error is estimated +.>Controllable, the concrete expression is:
wherein,representing the estimated error +.>Is->Norms (F/F)>Representing mathematical expectations, characterizing estimation errorsIs limited by the nature of (2); />Is adaptive to the rate；/>Sign->Indicating that over time, the value of this term will tend to 0; sign->Indicating the presence of a constant +.>So that the estimation error will be less than or equal to +.>。
It can be seen that whenVery close to 0, the effect of the error due to compression is almost 0. As time->The influence of initial estimation errors gradually becomes zero, the estimation errors are kept within a constant boundary, and the algorithm is stable. And is adaptive to the rate->A compromise is needed that neither too large nor too small would affect the accuracy of the estimation, which is consistent with the importance of the parameter in the algorithm to affect the estimate update.
Under the same condition, the restored original high-dimensional parameter estimation value is further consideredAnd the real channel matrix parameters->P-norm of compressed estimation error is noted +.>Wherein->Is an adaptive rate; />Representing the minimum constant of the limited equidistant property RIP that makes the compressed sensing matrix M satisfy the order 4 s.
The boundary for the estimation error under the method is:i.e.)>Wherein C is the error range of the compressed channel transmission matrix estimation value;
。
the present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The distributed compressed sensing sparse time-varying channel estimation method is characterized by comprising the following specific steps of:
step 1, establishing a system model based on a Markov switching directed topological graph;
step 2, establishing an evolution process model of a sparse channel transmission matrix to be estimated of each device in the system model;
step 3, establishing an observation model of each device based on an evolution process model of a sparse channel transmission matrix to be estimated by each device;
step 4, giving a compressed sensing matrix, initial sparse input signals of each device, initial high-dimensional matrix estimated values and initial positive information matrixes; obtaining an initial weighted average estimated value based on the compressed sensing matrix and the initial high-dimensional matrix estimated values of each device; obtaining initial distribution compressed sensing sparse estimation based on initial sparse input signals, initial high-dimensional matrix estimation values, initial positive information matrix and initial weighted average estimation values of all devices;
step 5, ordert=0, wherein,twhen=0, the initial time is;
step 6, based on the firsttObtaining a reconstructed restored high-dimensional channel transmission matrix estimated value by time distributed compressed sensing sparse estimation;
step 7, judgingtWhether or not to be smaller thanTThe method comprises the steps of carrying out a first treatment on the surface of the If it ist＜TFor a pair oftAdding 1 to the value of (2) and returning to step 6 ift≥TAnd ending the channel estimation, and using the reconstructed restored high-dimensional channel transmission matrix estimation value as a final channel estimation value.
2. The distributed compressed sensing sparse time-varying channel estimation method of claim 1, wherein the markov switching directed topology based system model of step 1The expression of (2) is:
；
wherein,represent the firsttTime-of-day markov random handoff procedure,t=0,1,2,…,T，Tthe termination time of the channel estimation; />Representing the set of devices in the system, +.>，i=1,2,…, n/>，nIs the total number of devices; />Is indicated at +.>Relation set of transfer information between time devices, < +.>The method comprises the steps of carrying out a first treatment on the surface of the First->Personal device->Is->Personal device->Is (are) neighbor devices>，/>Indicate->Personal device->In->A set of neighbor devices at a time; />Is indicated at +.>Weight for information transfer between devices at the moment, < >>；Is indicated at +.>Time->Personal device->To (1)>Personal device->Information weight of the information is transferred.
3. The distributed compressed sensing sparse time-varying channel estimation method of claim 2, wherein the expression of the evolution process model of the sparse channel transmission matrix to be estimated by each device in step 2 is:
wherein,indicate->Each timeUpdating and estimating a sparse channel transmission matrix by the device; />Indicate->Estimating a sparse channel transmission matrix of each device at the moment; />Indicating that the channel transmission matrix is at +.>The amount of change in time; />Is a positive constant.
4. The distributed compressed sensing sparse time-varying channel estimation method of claim 3, wherein the expression of the observation model of each device in step 3 is:
wherein,indicate->Personal device->In->An output signal at a time; />Indicate->Personal device->In->Sparse input signal of moment; />Indicating that the signal is at->Noise in the transmission process of time; />Indicating that the respective device is at->The estimate of time of day is a transpose of the sparse channel transmission matrix.
5. The method of distributed compressed sensing sparse time-varying channel estimation of claim 4, wherein step 4 uses compressed sensing matrix M pair onePersonal device->Initial high-dimensional matrix estimate +.>Compressing to obtain initial weighted average estimated value +.>The expression is:
；
wherein,for compressing the perceptual matrix.
6. The distributed compressed sensing sparse time-varying channel estimation method of claim 1, wherein step 6 is based on the firsttThe specific steps of obtaining the reconstructed restored high-dimensional channel transmission matrix estimated value by the time distributed compressed sensing sparse estimation are as follows:
step 61, obtain the firstPersonal device->In->Sparse input signal +.>Low-dimensional non-sparse signal->Weighted average estimate->And output signal +.>；
Step 62, based on the firstPersonal device->In->Low-dimensional non-sparse signal of time instant->Positive information matrix->Weighted average estimate->And output signal +.>Obtain->Personal device->Transition information matrix->And transition channel transmission matrix low-dimensional estimation value +.>；
Step 63, the firstPersonal device->And->Personal device->Information interaction is carried out, and an updated positive definite information matrix is obtained according to neighbor weights>And updating weighted average estimate +.>The method comprises the steps of carrying out a first treatment on the surface of the The interactive information is the low-dimensional estimated values of the transition information matrix and the transition channel transmission matrix of each device obtained in the step 62;
step 64, updating weighted average estimation parameters using the value of the reduction error norm as the objective function pairPerforming high-dimensional reduction to obtain the reconstructed +.>Personal device->Is a reduced high-dimensional channel transmission matrix estimation value +.>。
7. The method of distributed compressed sensing sparse time-varying channel estimation of claim 6, wherein in step 61, a compressed sensing matrix is usedMFor the firstPersonal device->In->Sparse input signal +.>Compressing to obtain compressed low-dimensional non-sparse signal +.>The expression is:
wherein,is->Personal device->In->A low-dimensional non-sparse signal compressed by a compressed sensing matrix at any moment;Mis a compressed sensing matrix; />Is->Personal device->In->Sparse input signal at time instant.
8. The method of distributed compressed sensing sparse time-varying channel estimation of claim 6, wherein step 61 uses the observation model of step 3 to obtain the thPersonal device->In->Output signal +.>。
9. The method of distributed compressed sensing sparse time-varying channel estimation of claim 6, wherein step 62 is based on the firstPersonal device->In->Low-dimensional non-sparse signal of time instant->Positive information matrix->Weighted average estimate->And output signal +.>Obtain->Personal device->Transition information of (a)Matrix->And transition channel transmission matrix low-dimensional estimation valueThe expression of (2) is:
wherein,representing an adaptive rate; />Representing the transpose of the compressed low-dimensional non-sparse signal.
10. The method of distributed compressed sensing sparse time-varying channel estimation of claim 6, wherein step 63 isPersonal device->And->Personal device->Information interaction is carried out, and an updated positive definite information matrix is obtained according to neighbor weights>And updating weighted average estimate +.>The expression is:
wherein,indicate->Personal device->Is defined in the form of an updated positive information matrix->Is the inverse of (2); />Represent the firstpPersonal devicev p Transition information matrix->Is the inverse of (2); />Indicate->Personal device->Is to update positive definite moment of informationArray by the method of->Personal device->The inverse inversion of the updated positive definite information matrix; />Is indicated at +.>Time->Personal device->To (1)>Personal device->Information weight of the transmitted information; />Indicate->Personal device->Low-dimensional estimates of the transition channel transmission matrix of (c).
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103780521A (en) * | 2014-02-28 | 2014-05-07 | 重庆邮电大学 | Sparsity self-adaptive OFDM system channel estimation method |
| US20140126617A1 (en) * | 2012-11-05 | 2014-05-08 | Electronics And Telecommunications Research Institute | Signal compression apparatus and method for dynamic compressive sensing using sparsity information of input signal |
| CN106789766A (en) * | 2016-11-28 | 2017-05-31 | 杭州电子科技大学 | Sparse OFDM channel estimation method based on homotopy method |
| CN108322409A (en) * | 2018-01-25 | 2018-07-24 | 杭州电子科技大学 | Sparse OFDM channel estimation method based on generalized orthogonal matching pursuit algorithm |
| CN109274614A (en) * | 2018-10-26 | 2019-01-25 | 上海无线电设备研究所 | A kind of robust channel estimation algorithm suitable for time-varying condition of sparse channel |
| CN110519189A (en) * | 2019-08-30 | 2019-11-29 | 东南大学 | Compressed sensing based millimeter wave channel estimation methods under highly mobile scene |
| CN112202481A (en) * | 2020-06-24 | 2021-01-08 | 北京邮电大学 | Compressed sensing channel estimation algorithm based on adaptive sensing matrix and implementation device |
| US20210377079A1 (en) * | 2020-05-26 | 2021-12-02 | Wuhan University | Time-frequency block-sparse channel estimation method based on compressed sensing |
| CN116016051A (en) * | 2022-12-28 | 2023-04-25 | 哈尔滨工程大学 | Channel fitting and estimating method of FBMC-OQAM system based on base expansion model |
-
2023
- 2023-11-23 CN CN202311566816.6A patent/CN117278367B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140126617A1 (en) * | 2012-11-05 | 2014-05-08 | Electronics And Telecommunications Research Institute | Signal compression apparatus and method for dynamic compressive sensing using sparsity information of input signal |
| CN103780521A (en) * | 2014-02-28 | 2014-05-07 | 重庆邮电大学 | Sparsity self-adaptive OFDM system channel estimation method |
| CN106789766A (en) * | 2016-11-28 | 2017-05-31 | 杭州电子科技大学 | Sparse OFDM channel estimation method based on homotopy method |
| CN108322409A (en) * | 2018-01-25 | 2018-07-24 | 杭州电子科技大学 | Sparse OFDM channel estimation method based on generalized orthogonal matching pursuit algorithm |
| CN109274614A (en) * | 2018-10-26 | 2019-01-25 | 上海无线电设备研究所 | A kind of robust channel estimation algorithm suitable for time-varying condition of sparse channel |
| CN110519189A (en) * | 2019-08-30 | 2019-11-29 | 东南大学 | Compressed sensing based millimeter wave channel estimation methods under highly mobile scene |
| US20210377079A1 (en) * | 2020-05-26 | 2021-12-02 | Wuhan University | Time-frequency block-sparse channel estimation method based on compressed sensing |
| CN112202481A (en) * | 2020-06-24 | 2021-01-08 | 北京邮电大学 | Compressed sensing channel estimation algorithm based on adaptive sensing matrix and implementation device |
| CN116016051A (en) * | 2022-12-28 | 2023-04-25 | 哈尔滨工程大学 | Channel fitting and estimating method of FBMC-OQAM system based on base expansion model |
Non-Patent Citations (2)
| Title |
|---|
| 战非: "基于压缩感知OMP 算法对稀疏信号重构的研究", 电子设计工程, vol. 25, no. 1 * |
| 齐萌，等: "基于压缩感知的低压电力线载波通信信道估计", 计算机测量与控制, vol. 24, no. 9 * |
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