CN115314346A - Parameter estimation two-dimensional search method, chip, equipment and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a parameter estimation two-dimensional searching method, a chip, equipment and a storage medium. Wherein the method comprises the following steps: acquiring a first parameter set of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane; determining a first parameter group meeting a set parameter group selection standard from a first parameter group set, and acquiring a first position of the first parameter group corresponding to a candidate parameter search plane; acquiring a second parameter set of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane according to the first position; and determining a second parameter group of the parameter group selection standard from the second parameter group set, and acquiring the parameter value corresponding to the second parameter group as the finally selected parameter value. According to the scheme of the embodiment of the disclosure, through a second-order searching process combining coarse searching and fine searching, on the premise that the performance is the same as or better than that of one-time two-dimensional searching, the calculation complexity can be effectively reduced, and the calculation time consumption and power consumption are reduced.
Description
Technical Field
The present disclosure relates to, but not limited to, the field of wireless communication technologies, and in particular, to a parameter estimation two-dimensional search method, a chip, a device, and a storage medium.
Background
In the channel estimation process, the delay spread and the doppler spread of the channel are used as parameters reflecting the characteristics of the channel, and are very important parameters to be estimated in the channel estimation and parameter estimation processes.
In some implementations, the method for estimating the delay spread or the doppler spread is: channel correlation matrix R is counted based on least square method (LS) channel estimation result hh And polling all Delay spread or Doppler spread values in a predefined set of spreads using a Mean Square Error (MSE) formula to find a Mean Square Error (MSE) value based on the current channel correlation matrix R hh As the result of the spread estimation, the spread value with the minimum MSE.
In other implementation schemes, in order to improve the accuracy and robustness of estimation, a two-dimensional search scheme is also generally adopted, that is, a spread (or Doppler spread) set and a shift (or Doppler shift) set are defined simultaneously, and the MSE is calculated by traversing the combination of the spread and the shift to find the spread and shift combination which minimizes the MSE.
It can be seen that parameter estimation is an important aspect of channel estimation, how to reduce the computational complexity, reduce the computation time and power consumption, and ensure that the performance of parameter estimation is the direction in which the parameter estimation scheme is continuously explored and improved.
Disclosure of Invention
The embodiment of the disclosure provides a parameter estimation two-dimensional search method, a chip, a device and a storage medium, and the method can effectively reduce the computation complexity and reduce the computation time and power consumption through a second-order search process combining rough search and fine search on the premise of having the same or better performance with one-time two-dimensional search.
The embodiment of the disclosure provides a parameter estimation two-dimensional searching method, which includes:
acquiring each group of parameter values of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane, and recording the parameter values as a first parameter group set;
determining a first parameter group meeting a set parameter group selection standard from the first parameter group set, and acquiring a first position of the first parameter group corresponding to the candidate parameter search plane;
according to the first position, acquiring each group of parameter values of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane, and marking as a second parameter group set;
determining a second parameter group meeting the parameter group selection standard from the second parameter group set, and acquiring a parameter value corresponding to the second parameter group as a finally selected parameter value;
wherein the candidate parameter search plane corresponds to X Y locations, X corresponding to X selectable values of a first parameter, and Y corresponding to Y selectable values of a second parameter; the parameter set value corresponding to each position comprises: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
The disclosed embodiment also provides a communication chip, including a processor configured to:
acquiring each group of parameter values of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane, and recording the parameter values as a first parameter group set;
determining a first parameter group meeting a set parameter group selection standard from the first parameter group set, and acquiring a first position of the first parameter group corresponding to the candidate parameter search plane;
according to the first position, obtaining each group of parameter values of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane, and recording the parameter values as a second parameter group set;
determining a second parameter group meeting the parameter group selection standard from the second parameter group set, and acquiring a parameter value corresponding to the second parameter group as a finally selected parameter value;
wherein the candidate parameter search plane corresponds to X Y locations, X corresponding to X selectable values of a first parameter, and Y corresponding to Y selectable values of a second parameter; each position corresponds to a parameter set value comprising: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
An embodiment of the present disclosure further provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method for two-dimensional search for parameter estimation according to any of the embodiments of the present disclosure.
The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the parameter estimation two-dimensional search method according to any of the embodiments of the present disclosure.
Other aspects will be apparent upon reading and understanding the attached drawings and detailed description.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the technical solutions in the present application, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flowchart of a two-dimensional search method for parameter estimation according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a two-dimensional MMSE search in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a second order search process according to an embodiment of the disclosure;
FIG. 4 is a schematic diagram of an enhanced second-order search process according to an embodiment of the present disclosure;
FIG. 5 is a diagram of several search patterns for complexity and performance comparisons in examples of the present disclosure;
FIG. 6 is a performance comparison diagram of different patterns of the MCS0ETU70 scenario in an example of the present disclosure;
FIG. 7 is a performance comparison diagram of different patterns of a MCS7TDLC300-100 scene in an example of the present disclosure;
fig. 8 is a performance comparison diagram of different patterns of an MCS15EVA30 scene in an example of the disclosure;
FIG. 9 is a performance comparison diagram of different patterns of MCS25TDLB100-100 scenarios in an example of the present disclosure.
The implementation, functional features and advantages of the object of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that all the directional indicators (such as upper, lower, left, right, front, and rear … …) in the embodiments of the present disclosure are only used to explain the relative position relationship between the components, the motion situation, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, descriptions in this disclosure as to "first", "second", etc. are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicit to the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
In the present disclosure, unless expressly stated or limited otherwise, the terms "connected," "secured," and the like are to be construed broadly, e.g., "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meaning of the above terms in the context of this application will be understood by those of ordinary skill in the art as a matter of context.
In addition, technical solutions between the various embodiments of the present disclosure may be combined with each other, but must be based on the realization of the capability of a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of the present application.
Before describing the embodiments in detail, the abbreviations of the related terms referred to in the present disclosure are as follows:
in some realizable two-dimensional search schemes, when channel Delay spread or Doppler spread parameter estimation is performed, all possible values in a predefined spread (or Doppler spread) set and shift (or Doppler shift) set need to be polled to find a correlation matrix R based on a current channel hh The minimum mean square error MSE, spread and shift values. Therefore, for the predefined spread set and shift set, the estimation accuracy is higher when the spread or shift value in the set is more (the granularity is smaller), but the amount of calculation for performing the two-dimensional traversal search for parameter estimation at this time is larger, and thus the time consumption and the power consumption are also larger.
The embodiment of the disclosure provides a parameter estimation two-dimensional search scheme, and the performance of the two-dimensional search scheme is the same as or better than that of a relatively complex one-time completion two-dimensional search scheme through a second-order search process combining coarse search and fine search. The method can effectively ensure the parameter estimation performance while reducing the calculation complexity and the calculation time consumption and power consumption.
It should be noted that the two-dimensional parameter estimation search scheme provided in the embodiments of the present disclosure is applicable to a two-dimensional parameter search plane, where the two-dimensional parameter search plane includes X × Y positions, that is, Y rows × X columns, X corresponds to X selectable values of a first parameter, Y corresponds to Y selectable values of a second parameter, and each position corresponds to a parameter set (including the first parameter and the second parameter). It will be appreciated that the entire parameter search plane comprises a total of X Y candidate parameter sets.
It should be further noted that, in some exemplary embodiments, the X first parameters in the parameter search plane may correspond to all X parameter values in the range selectable by the first parameters; alternatively, the X parameter values extracted from the selectable range of the first parameter may be used. For example, the first parameter may be selected from integers ranging from 0 to 100, and if X is 10, 10 integers are extracted from 1 to 100; 0, 10, 20, 30, … …,90 can be extracted; alternatively, 1,7,15, 25, 37, 51, 63, 73, 91, 97; or, other extraction rules. The configuration results of the X selectable values of the first parameter in the parameter search plane are specifically determined according to the parameter characteristics and the scheme of the parameter application, meet the application requirements, and are not limited to a specific configuration form. Specific parameter configuration schemes are not discussed in detail in this application. The relevant aspects of the Y second parameters are similar to the above aspects of the first parameters and will not be described in detail.
The embodiment of the present disclosure provides a parameter estimation two-dimensional search method, as shown in fig. 1, including:
wherein the candidate parameter search plane corresponds to X Y locations, X corresponding to X selectable values of a first parameter, and Y corresponding to Y selectable values of a second parameter; each position corresponds to a parameter set value comprising: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
In some exemplary embodiments, the parameter values finally selected in step 140 are also referred to as final estimation results for the first parameter and the second parameter.
In some exemplary embodiments, determining 120 a first parameter set from the first set of parameter sets that satisfies the set parameter set selection criteria comprises:
respectively calculating first estimation results corresponding to each group of parameter values in the first parameter group set according to a set parameter estimation algorithm;
from a plurality of the first estimation results, a first parameter set satisfying the parameter set selection criterion is determined.
In some exemplary embodiments, determining a second parameter set from the second set of parameter sets that satisfies the parameter set selection criteria in step 140 includes:
respectively calculating second estimation results corresponding to each group of parameter values in the second parameter set according to the parameter estimation algorithm;
determining a second parameter set satisfying the parameter set selection criterion from a plurality of the second estimation results.
It is understood that the first estimation result and the second estimation result in steps 120 and 140 correspond to estimation results in different steps, and are calculated by using the same parameter estimation algorithm.
In some exemplary embodiments, the first parameter pattern includes: a plurality of locations selected from the X Y locations at a first column spacing and a first row spacing.
In some exemplary embodiments, the second parameter pattern includes: n x m positions; n and m are integers more than 1, n < X, and m < Y.
In some exemplary embodiments, the second parametric pattern corresponds to n columns and m rows of positions.
In some exemplary embodiments, the first position is a center position of the second parameter pattern.
In some exemplary embodiments, the first position is a center position of the second parameter pattern; the second parameter pattern does not include positions in other first parameter patterns other than the first position.
In some exemplary embodiments, the second parameter pattern is centered on the row where the first position is located, expanded leftward by x1 columns, and/or expanded downward by x2 columns, expanded upward by y1 rows, and/or expanded downward by y2 rows; wherein x1 and x2 are both less than or equal to the first column interval; y1, y2 are both less than or equal to the first row spacing; x1+ x2+1= n, y1+ y2+1= m.
In some exemplary embodiments, the first parameter is delay spread (delay spread), and the second parameter is delay shift (delay shift); alternatively, the first parameter is delay shift (delay shift), and the first parameter is delay spread (delay spread).
In some exemplary embodiments, the first parameter is Doppler spread (Doppler spread) and the second parameter is Doppler shift (Doppler shift); alternatively, the first parameter is Doppler shift (Doppler shift) and the first parameter is Doppler spread (Doppler spread).
In some exemplary embodiments, the parameter estimation algorithm is a mean square error algorithm based on a channel correlation matrix.
Correspondingly, in the case that the parameter estimation algorithm is a mean square error algorithm based on a channel correlation matrix, the first estimation result is a mean square error corresponding to a set of parameter values based on the channel correlation matrix, and the parameter set selection criterion is to select a parameter set with a minimum mean square error.
And if the parameter estimation algorithm is a mean square error algorithm based on a channel correlation matrix, the second estimation result is a mean square error corresponding to a group of parameter values based on the channel correlation matrix, and the parameter group selection criterion is to select a parameter group with the minimum mean square error.
In some exemplary embodiments, the parameter estimation algorithm is a likelihood function estimation algorithm based on a channel correlation matrix.
Accordingly, in the case where the parameter estimation algorithm is a channel correlation matrix-based likelihood function estimation algorithm, the first estimation result is a likelihood function value corresponding to a set of parameter values based on the channel correlation matrix, and the parameter set selection criterion is to select a parameter set having a maximum likelihood function value.
In the case that the parameter estimation algorithm is a likelihood function estimation algorithm based on a channel correlation matrix, the second estimation result is a likelihood function value corresponding to a set of parameter values based on the channel correlation matrix, and the parameter set selection criterion is to select a parameter set having a maximum likelihood function value.
In some exemplary embodiments, when performing channel Delay spread or Doppler spread parameter estimation, all possible values in a predefined spread (or Doppler spread) set and shift (or Delay shift) set need to be polled to find a correlation matrix R based on a current channel hh The minimum mean square error MSE, spread and shift values. Channel correlation matrix R is typically counted based on Least Squares (LS) channel estimation results hh And using a Mean Square Error (MSE) formula to find a correlation matrix R based on the current channel hh The spread and shift values with the minimum MSE are taken as the current estimation result of the parameters.
The mean square error is calculated as follows:
channel estimation H for pilot points of N points ls,N ，
H ls,N ＝[h ls,1 ,…,h ls,N ]＝H N +N N
And channel estimation h of pilot frequency point of (N + 1) th point ls,N+1 Then, then
From H ls,N Computing a channel estimate for a (N + 1) th point
W is a vector of dimension 1 xN. H ls,N Is an Nx 1-dimensional vector.
At this time, the MSE of the channel estimation at the (N + 1) th point is:
MSE＝E{(WH ls,N -h N+1 )(WH ls,N -h N+1 ) H }
MSE＝E{(W(H N +N N )-h N+1 )(W(H N +N N )-h N+1 ) H }
(W-1) is a vector of dimension 1x (N + 1).
Since h and n are uncorrelated, then
Wherein,
R hh is the autocorrelation (no noise) of (N + 1) pilot points.
When MSE in formula (1) is minimum, the wiener solution is:
wherein,
for the (N + 1) th pilot point h N+1 And N pilot points H ls,N Is a 1 xN-dimensional vector.
W MMSE Is a vector of 1xN dimension.
Due to the failure to obtain h N+1 By h use of ls,N+1 Substitute for h N+1 At this time, MSE' is:
since h and n are uncorrelated, then
Comparing the formula (1) with the formula (3), the formula (3) is more than the formula (1) by a constant term
The constant term does not change the minimum MSE solution of the equation, so that the minimum MSE W values corresponding to the equations (1) and (3) are the same.
And (3) calculating the MSE (W) value corresponding to the preset gear W by the formula (2), wherein the gear corresponding to the minimum MSE is the optimal estimation value of the formula (1).
Since H is not obtained N And h N+1 By using h ls,N+1 Substitute for h N+1 And use of H ls,N Instead of (H) N +N N ) At this time, MSE "is:
MSE″＝E{(WH ls,N -h ls,N+1 )(WH ls,N -h ls,N+1 ) H }
since h and n are uncorrelated, then
MSE″＝(W-1)R hls,hls (W-1) H (4)
Wherein,
R hls,hls is formed by H ls,N+1 The autocorrelation (noise) of the (N + 1) pilot points calculated.
When MSE in equation (4) is minimum, the wiener solution is:
wherein,
for the (N + 1) th pilot point h N+1 And N pilot points H ls,N Is a 1 xN-dimensional vector.
W MMSE Is a vector of 1xN dimension.
Therefore, when based on the 1xN wiener coefficient W MMSE When the MSE (equation (4)) of the equation (5) is minimum, the estimated spread and shift values are the optimal values estimated by the current parameters (channel delay or Doppler). Namely that
Wherein,
R hls,hls is the autocorrelation matrix calculated using the LS channel estimation results.
W (spread,shift) Is a wiener coefficient of minimum mean square error calculated based on a certain set of spread and shift values and the signal-to-noise ratio (SNR) of the current channel environment.
Tr {. Is the trace-seeking operation.
It can be seen that, in the formula of MSE calculation, each set of spread and shift values corresponds to a wiener coefficient W (spread,shift) And thus corresponds to an MSE value.
In order to improve the accuracy and robustness of estimation, a two-dimensional search scheme is usually adopted, that is, a spread set and a shift set are defined simultaneously, and all spread values and shift values in the predefined spread set and the shift set are polled to calculate the MSE. Among all the calculated MSE values, the correlation matrix R based on the current channel is found hh The spread value and the shift value with the minimum MSE. In the MSE search process, assuming that there are X (also denoted as numread) spread values in the spread set and Y (also denoted as numShift) shift values in the shift set, the MSE computation amount required in the two-dimensional search is (numread — numShift) times of MSE computations. For example, when numpredad =10,numshift =12, the entire two-dimensional MSE search needs to be completed by calculating 120 MSE values (e.g., the pattern (Normal) shown in fig. 2). Typically, one MSE computation requires about 150 cycles (instruction cycles) and 120 MSE computations require about 1.8 cycles.
It should be noted that, in the embodiment of the present application, as in the pattern shown in fig. 2, 0,1 only identifies one position, and does not represent a specific parameter value.
In the candidate parameter search plane shown in fig. 2, the first parameter is extended spread/Doppler spread, and includes X =10 optional parameter values; the second parameter is shift (delay shift/Doppler shift), and includes Y =12 optional parameter values, and includes 10 × 12 parameter groups in the entire search plane, where each parameter group includes: a first parameter spread and a second parameter shift. Some realizable two-dimensional search schemes perform corresponding calculations for all 120 parameter sets in the candidate parameter search plane corresponding pattern in fig. 2, and select a set of parameter values corresponding to the minimum mean square error as the finally selected parameter values.
It should be noted that, in the scheme of the embodiment of the present disclosure, finding a final parameter value in a candidate parameter search plane includes 2 searches, which is also referred to as second-order search; in step 120, a process of determining a first position according to the first parameter pattern is also referred to as a first coarse search, and in step 140, a process of determining a finally selected parameter value according to the second parameter pattern is also referred to as a second fine search.
In some exemplary embodiments provided by the present disclosure, the first parameter pattern is a pattern shown as a mark 0 in fig. 3 (a), (b) or (c), and includes n × m positions selected from 10 × 12 positions. For example, in fig. 3 (a), the first column interval is 2, the first row interval is 2, the first parameter pattern includes 3 × 4=12 positions, and in step 120, the MSE values corresponding to the 12 positions are calculated, and the minimum MSE value among the current 12 MSE values is determined as the first position (for example, the position x =6, y =6 in fig. 3 (a)); n =5,m =5, the second parameter pattern is 5*5 positions including the first position (corresponding to the column in which the first position is located is expanded leftwards by 2 columns, rightwards by 2 columns, upwards by 2 rows, and downwards by 2 rows to determine the second parameter pattern), and accordingly, in step 140, MSE values corresponding to 5*5-1=24 positions are calculated except for the first position, and the spread and shift values corresponding to the MSE value with the minimum MSE value corresponding to the 25 positions are the final estimation result of the parameter estimation. It can be seen that the first position is determined by the first coarse search, the finally selected parameter value is determined by the second fine search, and 12+24=36 MSE values need to be calculated by the two searches.
For example, in fig. 3 (b), the first column interval is 1, the first row interval is 2, the first parameter pattern includes 5 × 4=20 positions, the first coarse search is performed, the MSE values corresponding to the 20 positions are calculated, and the minimum MSE value of the current 20 MSE values is determined as the first position (for example, the position x =6, y =6 in fig. 3 (b)); n =3,m =5, the second parameter pattern is 3*5 positions including the first position (which is equivalent to extending 1 column to the left, 1 column to the right, 2 rows to the up, and 2 rows to the down as the center of the row in which the first position is located), and accordingly, the second fine search is performed, except for the first position, MSE values corresponding to 3*5-1=14 positions are calculated, and the spread and shift values corresponding to the MSE value with the minimum MSE value corresponding to the 15 positions are the final estimation result of the parameter estimation. It can be seen that the first position is determined by the first coarse search, the finally selected parameter value is determined by the second fine search, and 20+14=34 MSE values need to be calculated by the two searches.
For example, in fig. 3 (c), the first column interval is 1, the first row interval is 1, the first parameter pattern includes 5 × 6=30 positions, the first coarse search is performed, the MSE values corresponding to the 30 positions are calculated, and the minimum MSE value of the current 30 MSE values is determined as the first position (for example, the position x =6, y =6 in fig. 3 (c)); n =3,m =3, the second parameter pattern is 3*3 positions including the first position (which is equivalent to extending 1 column to the left, 1 column to the right, 1 row to the up, 1 row to the down, and determining as the second parameter pattern), correspondingly, the second fine search calculates the MSE values corresponding to 3*3-1=8 positions except the first position, and the spread and shift values corresponding to the MSE value with the minimum MSE value corresponding to the 9 positions are the final estimation result of the parameter estimation. It can be seen that the first position is determined by the first coarse search, the finally selected parameter value is determined by the second fine search, and 30+8=38 MSE values need to be calculated by the two searches.
In some exemplary embodiments, the obtaining, according to the first position, each set of parameter values of a plurality of positions corresponding to the second parameter pattern from the candidate parameter search plane includes:
acquiring a column position x corresponding to the first position; namely, the x-th column of the first position in the candidate parameter search plane, also called as x-axis;
according to the X, n and X, when determining that a search plane column expansion condition is met, expanding the candidate parameter search plane;
acquiring each group of parameter values of a plurality of positions corresponding to the second parameter pattern from the expanded candidate parameter search plane according to the first position;
and/or the presence of a gas in the gas,
acquiring a line position y corresponding to the first position; i.e. the y-th row of the first position in the candidate parameter search plane, also referred to as ordinate y;
according to the Y, the m and the Y, when the condition of expanding the row of the search plane is determined to be met, expanding the candidate parameter search plane;
and acquiring each group of parameter values of a plurality of positions corresponding to the second parameter pattern from the expanded candidate parameter search plane according to the first position.
In some exemplary embodiments, the obtaining, from the candidate parameter search plane according to the first position, each set of parameter values of a plurality of positions corresponding to the second parameter pattern includes:
acquiring a column position x corresponding to the first position; namely, the x-th column of the first position in the candidate parameter search plane, also called as x-axis;
acquiring a line position y corresponding to the first position; namely the y-th row of the first position in the candidate parameter search plane, also called the ordinate y;
determining that the column expansion condition of the search plane is met according to the X, n and X, and expanding the candidate parameter search plane when the row expansion condition of the search plane is met according to the Y, m and Y;
and acquiring each group of parameter values of a plurality of positions corresponding to the second parameter pattern from the expanded candidate parameter search plane according to the first position.
It can be understood that when the search plane column expansion condition is satisfied, the column expansion of the candidate parameter search plane is correspondingly performed; when the row expansion condition of the search plane is met, correspondingly performing row expansion of the candidate parameter search plane; and when the column expansion condition and the row expansion condition of the search plane are met, correspondingly performing column expansion and row expansion of the candidate parameter search plane.
In some exemplary embodiments, the row position corresponding to the upper left corner position of the candidate parameter search plane is 1, the column position is 1, and the position coordinate is described as (1,1). Accordingly, when the candidate parameter search plane includes X × Y positions, the row position corresponding to the lower right corner position of the candidate parameter search plane is Y, the column position is X, and the position coordinate is described as (X, Y).
In some exemplary embodiments, n = x1+ x2, and m = y1+ y2, x1, x2, y1, y2 are each integers greater than 0;
determining that the expansion condition of the search plane column is met at X + X2> X or X-X1< 1;
at Y + Y2> Y or Y-Y1<1, it is determined that the search plane row expansion condition is satisfied.
It can be understood that the second parameter pattern is expanded leftwards or rightwards by taking the column of the first position as the center, and when the column range of the original candidate parameter search plane is exceeded, the column expansion condition of the search plane is determined to be met, and the original candidate parameter search plane is subjected to column expansion, including leftward expansion or rightward expansion. And expanding the second parameter pattern upwards or downwards by using the behavior center where the first position is located, determining that the line expansion condition of the search plane is met when the line range of the original candidate parameter search plane is exceeded, and performing line expansion, including upwards expansion or downwards expansion, on the original candidate parameter search plane.
In some exemplary embodiments, expanding the candidate parametric search plane in the case of X + X2> X comprises: and expanding X + X2-X columns of position points on the right side of the number of the candidate parameter search planes.
In some exemplary embodiments, expanding the candidate parametric search plane in the case of x-x1<1 comprises: on the left side of the number of candidate parameter search planes, | x-x1-1| column location points are expanded.
In some exemplary embodiments, in the case of Y + Y2> Y, expanding the candidate parametric search plane comprises: and expanding Y + Y2-Y row position points at the lower side of the number of the candidate parameter search planes.
In some exemplary embodiments, in case y-y1<1, expanding the candidate parametric search plane comprises: and expanding the | y-y1-1| row position points on the upper side of the number of the candidate parameter search planes.
The expanded candidate parameter search plane is also called an enhanced candidate parameter search plane or an enhanced candidate parameter pattern.
It will be appreciated that when the first position is close to the edge of the candidate parameter search plane, the second parameter pattern may be determined by extending the rows and/or columns according to the aforementioned rule with the row and column at the center of the first position, and may exceed the range of the original candidate parameter search plane. In some exemplary embodiments, the intersection of the expanded range and the original candidate parameter search plane is used as a second parameter pattern; optionally, after the original candidate parameter search plane is expanded, the row and/or column is expanded with the row and column at the center of the first position, and a second parameter pattern is determined.
It should be noted that, in the case that the n × m region preliminarily determined with the first position as the center exceeds the original candidate parameter search plane, the original candidate parameter search plane may be expanded, so that the expanded candidate parameter search plane may include the n × m region preliminarily determined with the first position as the center. Wherein, according to the specific position of the first position in the original candidate parameter search plane, the expanding comprises: row expansion, column expansion, or both row and column expansion.
For example, as shown in fig. 4 (a), X × Y =10 × 12, when the first position is (9, 12), the column position X =9 of the first position, the row position Y =12, n × m =5 × 5= (2 +1+ 2) (2 +1+ 2), 9+2=11 = X, then expand to the right by 11-10=1 column, 12+2=14 = Y, then expand downward by 14-12=2 rows, and the expanded candidate parameter search plane is shown in fig. 4 (a).
For another example, as shown in fig. 4 (B), the first position (10, 12), the column position x =10 of the first position, the row position y =12, n × m =3 × 5= (1 + 1) = (2 +1+ 2), 10+1=11> x, then extend to the right by 11-10=1 column, 12+2=14> y, then extend downward by 14-12=2 rows, and the candidate parameter search plane after extension is as shown in fig. 4 (B).
For another example, as shown in fig. 4 (C), the first position (10, 12), the column position x =10 of the first position, the row position y =12, n × m =3 × 3= (1 + 1), 10+1=11> x, then extend to the right by 11-10=1 column, 12+1=13> y, then extend downward by 13-12=1 row, and the candidate parameter search plane after extension is as shown in fig. 4 (C).
When the candidate parameter search plane is expanded, the parameter value corresponding to the expanded row or column is the set parameter value. For example, the original candidate parameter search plane includes: for example, the set parameter values may be preset static parameter values different from existing lines in the original candidate parameter search plane, or may be dynamically determined according to the number of lines to be expanded. And the first parameter value at each position in each new extended row is the same as the first parameter value at the column position (namely the abscissa x) corresponding to each position. And a set first parameter value corresponding to each column in the newly expanded columns, wherein a second parameter value at each position in each newly added column is the same as a second parameter value at a row position (namely, a vertical coordinate y) corresponding to each position.
It will be appreciated that the expanded candidate parameter search plane corresponds to X 'X Y' positions, where X 'corresponds to X' selectable values of the first parameter, and Y 'corresponds to Y' selectable values of the second parameter, where X 'is greater than or equal to X and Y' is greater than or equal to Y.
Taking fig. 4 (a) as an example, all positions in the newly added 11 th column correspond to a set first parameter value spread, and the second parameter values shift at the positions in the column are the same as the second parameter values shift at the positions of each row in the 10 th (or 9, 8 …) column. All positions in the newly added 13 th row correspond to a set second parameter value shift, the first parameter value spread at each position in the row is the same as the first parameter value spread at each column position of the 1 st (or 2, 3 …) row, and all positions in the newly added 14 th row correspond to another set second parameter value shift, and the first parameter value spread at each position in the row is the same as the first parameter value spread at each column position of the 1 st (or 2, 3 …) row. It is understood that the expanded candidate parameter search plane corresponds to 11 × 14 positions, 11 corresponds to 11 selectable values of the first parameter spread, and 14 corresponds to 14 selectable values of the second parameter shift.
It can be seen that, for the candidate parameter pattern (a), the candidate parameter pattern (b), and the candidate parameter pattern (c) in fig. 3, at the time of the second fine search, the current configuration values of X (numpred) and Y (numShift) and the configuration condition of the first parameter pattern may cause that when the first position is close to the edge position of the original candidate search plane, the second fine search may not reach the calculated amounts of 24 MSE values, 14 MSE values, and 8 MSE values. However, since the number of cycles realized by the product is determined by the maximum calculation amount, for the pattern (a), the pattern (B), and the pattern (C), the enhanced candidate parameter pattern (a), the pattern (B), and the pattern (C) may be used to supplement the spread value of the edge position in the spread set and the shift value of the edge position in the shift set, so as to improve the search precision and thus the accuracy of the parameter estimation while keeping the number of cycles unchanged. The enhanced candidate parameter pattern is shown in fig. 4. It can be seen that, in the enhanced candidate parametric pattern (a), the enhanced candidate parametric pattern (B), and the enhanced candidate parametric pattern (C), the computation amount of the MSE calculation performed by the second fine search is the same no matter whether the first position obtained by the first coarse search is at the center position or the edge position of the entire original candidate parametric pattern (two-dimensional grid), that is, no cycle is wasted.
Compared with the method for performing one-time full search by adopting a candidate parameter search plane in a related scheme, according to the parameter estimation two-dimensional search scheme provided by the embodiment of the disclosure, based on the configured candidate parameter search plane or the expanded candidate parameter search plane, a two-order search mode combining the first coarse search and the second fine search is adopted, so that the same or better performance is realized, but the calculation complexity can be obviously reduced, the calculation time and the power consumption are reduced, and finally the parameter estimation performance can be effectively ensured.
In some exemplary embodiments, in the processes of performing second-order coarse search and fine search, the size of the candidate parameter search plane is expanded in combination with the search result of the first coarse search, so that the search precision is improved and the accuracy of parameter estimation is improved under the condition that the cycle number is kept unchanged.
It should be noted that in the embodiments of the present disclosure, spread and shift are mostly used as details of implementation of the first parameter and the second parameter. However, the two-dimensional search for parameter estimation provided by the embodiment of the present disclosure is not limited to be applied to these two parameters. The scheme provided by the embodiment of the disclosure can be adopted for comparing all estimators of the two-dimensional search plane by adopting a set parameter estimation algorithm and finally determining various parameter estimates of the parameter estimation result. For example, the adopted parameter estimation algorithm is a mean square error algorithm or a likelihood function estimation algorithm, and the like, and is not limited to a specific parameter estimation algorithm; different parameter estimation algorithms are adopted, and the corresponding parameter group selection standard is set correspondingly.
Examples of the invention
According to the parameter estimation two-dimensional search scheme provided by the embodiment of the disclosure, it can be seen that, under different first parameter patterns and second parameter patterns, the total calculated amount (the sum of the coarse search calculated amount + the fine search calculated amount) is slightly different, but is obviously better than the two-dimensional search scheme of one search. For each optimized search pattern, the final search performance is different due to the different proportions of the coarse search and the fine search in the total calculation amount.
This example presents a comparison of computational complexity and performance for several embodiments of the first parameter pattern + the second parameter pattern. As shown in fig. 5, the pattern (Normal) and the pattern (C) are candidate parameter patterns that have been described above, the pattern (AP) is a simplified version of the pattern (a), and the shift value of the last row is deleted relative to the pattern (a), so that the two-dimensional search planes of the pattern (AP) and the pattern (C) are guaranteed to have the same size, and the performance difference effects of different coarse search calculation amounts and fine search calculation amounts can be compared more fairly.
The pattern (D) is an enhanced version of the pattern (C), and the shift values of the last two lines are increased relative to the pattern (C), so that the accuracy of parameter estimation and the external field robustness are obviously improved compared with the pattern (C) while the pattern (D) is ensured to be obviously reduced in computation complexity compared with the pattern (Normal).
The difference between the calculated amounts for the 4 patterns is shown in the following table
| Pattern Normal | Pattern AP | Pattern C | Pattern D | |
| |
120 | 143 | 143 | 165 |
| First coarse |
120 | 12 | 30 | 35 |
| Second fine search calculated |
0 | 24 | 8 | 8 |
| Gross plus fine |
120 | 36 | 38 | 43 |
| Computation savings over Normal patternMeasurement of | 84 | 82 | 77 | |
| Ratio for saving calculation amount relative to Normal pattern | 70.00％ | 68.33％ | 64.17％ | |
| Number of cycles expected to be used | 18000 | 5400 | 5700 | 6450 |
| Cycle number savings relative to Normal pattern | 12600 | 12300 | 11550 |
It can be seen that the amount of computation saved for pattern (AP), pattern (C), and pattern (D) is 70%, 68%, and 64%, respectively, with respect to pattern (Normal). Namely, the maximum calculation amount of the three patterns of the pattern (AP), the pattern (C) and the pattern (D) is only about one third of the pattern (Normal), and about 1 ten thousand cycles can be saved.
Further, fig. 6-9 show the performance difference of pattern (Normal), pattern (AP), pattern (C) and pattern (D) under different SNR and different channel environments. From the performance curves it can be seen that:
comparing the performance of pattern (AP) with that of pattern (C), the performance of pattern (C) is approximately equal to pattern (AP) under the test environment shown in fig. 9; under the test environment shown in FIG. 8, pattern (C) performs slightly better than pattern (AP); in the test environment shown in fig. 6 and 7, the performance of the pattern (C) is better than that of the pattern (AP). Thus, in summary, the performance of pattern (C) is better than or equal to pattern (AP).
Comparing the performance of the pattern (C) and the pattern (D), wherein the performance of the pattern (D) is approximately equal to the performance of the pattern (C) under the test environment shown by 9; under the test environment shown in FIG. 8, pattern (D) performs slightly better than pattern (C); in the test environment shown in fig. 6 and 7, the performance of pattern (D) is better than that of pattern (C). Therefore, in summary, the performance of pattern (D) is better than or equal to pattern (C).
In summary, it can be seen that the amount of computation of pattern (D), which is only about one third of that of pattern (Normal), can save about 1 ten thousand cycles of time, and perform better than or equal to that of pattern (Normal).
It should be noted that fig. 6 to fig. 9 are graphs drawn by experimental simulation data, and in the case of close performance, the performance curves of several patterns are not overlapped or distinguished obviously. In the embodiment of the present disclosure, fig. 6 to 9 are only exemplary functions, and represent the size relationship of the relevant data, and do not represent specific numerical values.
The disclosed embodiment also provides a communication chip, including a processor configured to:
acquiring each group of parameter values of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane, and recording the parameter values as a first parameter group set;
determining a first parameter group meeting a set parameter group selection standard from the first parameter group set, and acquiring a first position of the first parameter group corresponding to the candidate parameter search plane;
according to the first position, obtaining each group of parameter values of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane, and recording the parameter values as a second parameter group set;
determining a second parameter group meeting the parameter group selection standard from the second parameter group set, and acquiring a parameter value corresponding to the second parameter group as a finally selected parameter value;
wherein the candidate parameter search plane corresponds to X Y locations, X corresponding to X selectable values of a first parameter, and Y corresponding to Y selectable values of a second parameter; each position corresponds to a parameter set value comprising: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
An embodiment of the present disclosure further provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method for two-dimensional search for parameter estimation according to any of the embodiments of the present disclosure.
The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the parameter estimation two-dimensional search method according to any of the embodiments of the present disclosure.
It can be seen that, the parameter estimation two-dimensional search method provided by the embodiment of the disclosure can significantly reduce the computation complexity and reduce the computation time and power consumption on the premise of ensuring the search performance based on the set candidate parameter search plane. In some exemplary embodiments, in the process of performing the second-order coarse search and the fine search, the search result of the coarse search and the second parameter pattern are combined to perform size expansion of the search plane, so that the search precision can be improved under the condition of keeping the cycle number unchanged, and then the accuracy of parameter estimation is improved. Meanwhile, the size of the candidate parameter search plane is properly expanded based on the original candidate parameter search plane, so that the precision and the performance of parameter estimation are obviously improved while the calculation complexity of a specific pattern (Normal) is obviously reduced.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications and equivalents of the subject matter of the present application, which are made by the following claims and their accompanying drawings, or which are directly or indirectly applicable to other related arts, are intended to be included within the scope of the present application.
Claims (13)
1. A two-dimensional search method for parameter estimation is characterized by comprising the following steps:
acquiring each group of parameter values of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane, and recording the parameter values as a first parameter group set;
determining a first parameter group meeting a set parameter group selection standard from the first parameter group set, and acquiring a first position of the first parameter group corresponding to the candidate parameter search plane;
according to the first position, obtaining each group of parameter values of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane, and recording the parameter values as a second parameter group set;
determining a second parameter group meeting the parameter group selection standard from the second parameter group set, and acquiring a parameter value corresponding to the second parameter group as a finally selected parameter value;
wherein the candidate parameter search plane corresponds to X Y positions, where X corresponds to X selectable values of a first parameter and Y corresponds to Y selectable values of a second parameter; each position corresponds to a parameter set value comprising: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
2. The method of claim 1,
the determining a first parameter group satisfying a set parameter group selection criterion from the first parameter group set includes:
respectively calculating first estimation results corresponding to each group of parameter values in the first parameter group set according to a set parameter estimation algorithm;
determining a first parameter set satisfying the parameter set selection criterion from a plurality of the first estimation results;
said determining a second parameter set from said second set of parameter sets that satisfies said parameter set selection criteria comprises:
according to the parameter estimation algorithm, respectively calculating second estimation results corresponding to each group of parameter values in the second parameter group set;
determining a second parameter set satisfying the parameter set selection criterion from a plurality of the second estimation results.
3. The method of claim 1 or 2,
the first parameter pattern includes: a plurality of locations selected from the X Y locations at a first column spacing and a first row spacing.
4. The method of claim 1 or 2,
the second parameter pattern includes: n x m positions; n, m are integers greater than 1, n < X, m < Y.
5. The method of claim 1 or 2,
the first parameter is delay spread, and the second parameter is delay shift;
or,
the first parameter is Doppler spread and the second parameter is Doppler shift.
6. The method of claim 2,
the parameter estimation algorithm is a mean square error algorithm based on a channel correlation matrix;
or,
the parameter estimation algorithm is a likelihood function estimation algorithm based on a channel correlation matrix.
7. The method of claim 6,
in case the parameter estimation algorithm is a channel correlation matrix based mean square error algorithm,
the first estimation result is a mean square error corresponding to a group of parameter values based on a channel correlation matrix, and the parameter group selection criterion is to select a parameter group with a minimum mean square error;
and/or the presence of a gas in the gas,
the second estimation result is a mean square error corresponding to a set of parameter values based on the channel correlation matrix, and the parameter set selection criterion is to select a parameter set with a minimum mean square error.
8. The method of claim 4,
the obtaining, according to the first position, each set of parameter values of multiple positions corresponding to the second parameter pattern from the candidate parameter search plane includes:
acquiring a column position x corresponding to the first position;
according to the X, n and X, when the condition of expanding the search plane column is determined to be met, expanding the candidate parameter search plane;
acquiring each group of parameter values of a plurality of positions corresponding to the second parameter pattern from the expanded candidate parameter search plane according to the first position;
and/or the presence of a gas in the gas,
acquiring a line position y corresponding to the first position;
according to the Y, the m and the Y, when the condition of expanding the row of the search plane is determined to be met, expanding the candidate parameter search plane;
and acquiring each group of parameter values of a plurality of positions corresponding to the second parameter pattern from the expanded candidate parameter search plane according to the first position.
9. The method of claim 8,
n = x1+ x2, m = y1+ y2, x1, x2, y1, y2 are all integers greater than 0;
determining that the expansion condition of the search plane column is met at X + X2> X or X-X1< 1;
at Y + Y2> Y or Y-Y1<1, it is determined that the search plane row expansion condition is satisfied.
10. The method of claim 9,
in the case of X + X2> X, expanding the candidate parametric search plane, including: expanding X + X2-X columns of position points on the right side of the number of the candidate parameter search planes;
or,
in the case of x-x1<1, expanding the candidate parametric search plane, including: expanding | x-x1-1| column position points on the left side of the number of the candidate parameter search planes;
or,
in the case of Y + Y2> Y, expanding the candidate parametric search plane, including: expanding Y + Y2-Y row position points on the lower side of the number of the candidate parameter search planes;
or,
in the case of y-y1<1, expanding the candidate parametric search plane, including: and expanding the | y-y1-1| row position points on the upper side of the number of the candidate parameter search planes.
11. A communication chip comprising a processor configured to:
acquiring each group of parameter values of a plurality of positions corresponding to the first parameter pattern from the candidate parameter search plane, and recording the parameter values as a first parameter group set;
determining a first parameter group meeting a set parameter group selection standard from the first parameter group set, and acquiring a first position of the first parameter group corresponding to the candidate parameter search plane;
according to the first position, obtaining each group of parameter values of a plurality of positions corresponding to a second parameter pattern from the candidate parameter search plane, and recording the parameter values as a second parameter group set;
determining a second parameter group meeting the parameter group selection standard from the second parameter group set, and acquiring a parameter value corresponding to the second parameter group as a finally selected parameter value;
wherein the candidate parameter search plane corresponds to X Y locations, X corresponding to X selectable values of a first parameter, and Y corresponding to Y selectable values of a second parameter; each position corresponds to a parameter set value comprising: a first parameter value and a second parameter value; x and Y are integers more than 1;
the plurality of positions corresponding to the second parameter pattern include the first position.
12. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the parameter estimation two-dimensional search method of any one of claims 1-10.
13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for two-dimensional search for parameter estimation according to any one of claims 1 to 10.
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