阅读说明：本技术 一种无线信道测量中基于神经网络的伪径消除方法 (Pseudo path eliminating method based on neural network in wireless channel measurement ) 是由 张若南 徐浩晨 蒋毅 唐晓 王大伟 于 2019-09-13 设计创作，主要内容包括：本发明属于信道测量技术领域,公开了一种无线信道测量中基于神经网络的伪径消除方法,利用信道测量采集的时延功率谱构建测量数据矩阵；寻找存在伪径的可疑时延窗,剔除这些时延窗的数据；利用不存在伪径的时延窗的数据构建神经网络；向神经网络输入全部时延窗标号,训练得到新的时延功率谱数据矩阵；判断新的数据矩阵是否已经消除伪径,若消除完成则输出重构的时延功率谱,否则继续寻找伪径并消除；对所述基于神经网络的伪径消除技术进行验证测试。本发明提供的信道测量中基于神经网络的伪径消除方法可以有效消除时延功率谱中的伪径,还原出真实的时延功率谱,从而既减轻了信道测量过程中的工作量,又同时保证了测量结果的可靠性。(The invention belongs to the technical field of channel measurement, and discloses a pseudo path eliminating method based on a neural network in wireless channel measurement, which constructs a measurement data matrix by using a time delay power spectrum acquired by channel measurement; searching suspicious time delay windows with pseudo paths, and rejecting data of the time delay windows; constructing a neural network by using data of a delay window without a pseudo path; inputting all time delay window labels to a neural network, and training to obtain a new time delay power spectrum data matrix; judging whether the pseudo path of the new data matrix is eliminated, if the elimination is completed, outputting a reconstructed time delay power spectrum, and if not, continuously searching for the pseudo path and eliminating the pseudo path; and carrying out verification test on the pseudo path elimination technology based on the neural network. The pseudo path eliminating method based on the neural network in the channel measurement can effectively eliminate the pseudo path in the time delay power spectrum and restore the real time delay power spectrum, thereby reducing the workload in the channel measurement process and simultaneously ensuring the reliability of the measurement result.)

1. A false path elimination method based on a neural network in channel measurement is characterized by comprising the following steps:

firstly, constructing a measurement data matrix by using a time delay power spectrum acquired by channel measurement;

searching a suspicious time delay window with a pseudo path, and rejecting data of the suspicious time delay window with the pseudo path;

thirdly, constructing a neural network by using the data of the delay window without the pseudo path;

inputting all time delay window labels to the neural network, and training to obtain a new time delay power spectrum data matrix;

judging whether the pseudo path of the new time delay power spectrum data matrix is eliminated;

and step six, finishing and outputting the data.

2. The method for eliminating the pseudo path based on the neural network in the channel measurement according to claim 1, wherein in the first step, the method for constructing the data measurement matrix comprises the following steps:

1) defining N groups of measurement data obtained in a measurement scene to obtain N groups of time delay power spectrums; each group of data has M time delay windows which represent time delay from 1 to M;

2) in the channel measurement technology, a transmitting end and a receiving end agree on a group of pseudorandom sequences with specific length, a transmitting end transmits an electric signal after the sequence is modulated, a receiving end obtains channel impulse response by utilizing the correlation operation of a received signal and the PN sequence stored locally after receiving the signal, and a PDP is calculated according to the channel impulse response;

3) if the length of the PN sequence is M, the PDP has M time delay windows, and the time delay windows take a code chip as a unit; constructing a matrix MP₀A matrix of M N; each column of the matrix represents the normalized power values of M time delay windows in a group of measurement data, and N rows of the matrixRepresenting that N groups of measurement data are obtained in the scene, subscript 0 represents that the matrix is an initial data matrix, a new data matrix is obtained after each pseudo path removing operation, and the subscript is increased by 1;

by MP_iRepresenting the data matrix, MP, before the ith false path removal operation_i+1The size of the data matrix newly obtained each time is not changed, which represents the data matrix after the ith false path removing operation.

3. The method for eliminating the pseudo path based on the neural network in the channel measurement as claimed in claim 1, wherein in the second step, the method for determining the suspected delay window in which the pseudo path may exist is as follows:

1) forward differencing each column of the matrix to obtain a new matrix MPd of size (M-1) xN_iWherein the matrix MPd_iRepresenting the intensity of the variation between adjacent delay windows in the PDP;

2) for matrix MPd_iPerforming K-means clustering on each row of data, wherein the number of classes is 1; obtaining a vector c with the length of (M-1) formed by cluster center values of the clusters gathered by each row of data after clustering; each element c therein_mM ∈ (1, 2, … M-1) represents a unique cluster center for each class;

₃) Computing vectors

4) for vector L_iThe elements in the vector are ordered from small to large to obtain a new vector Ls_i＝[L_a1，L_a2，…L_a(M-1)]^TThe subscript ak represents the label of the delay window;

5) for Ls_iObtaining the difference of the previous terms to obtain a vector Ls_i′＝[L_b1，L_b2，…L_b(M-2)]^TSubscript thereof represents bkThe labels of the delay windows are shown;

6) find vector Ls_iMaximum value of

τ_i＝[(bk_max+1)，(bk_max+2)，…，a(M-1)]^T；

calculation matrix MPd_iN data per line and cluster center c_mThe absolute values of the differences are summed, and the formula is calculated:

wherein L is_mIs a vector

4. The method for eliminating the pseudo path based on the neural network in the channel measurement according to claim 1, wherein in the third step, the method for constructing the neural network by using the data of the delay window without the pseudo path comprises:

the sample for obtaining the neural network parameter set is a time delay window vector tau_iThe power strength value of the corresponding test data; from the vector tau_iIn the training set, 70% of the training sets are randomly selected as input, and if the number of the delay windows is A, the corresponding time is largePower strength value matrix MPt of A × N time delay window_iAnd as the output of the training set, the other 30% of the time delay window labels and the corresponding power intensity value matrixes thereof are used as verification set data to obtain the constructed neural network.

5. The method according to claim 1, wherein in the fourth step, the constructed neural network is used as a tool for reconstructing the power spectrum of the delay time, all M delay window labels are sent to the neural network as network inputs, and the mxn matrix output by the network is a data matrix after one pseudo path removal operation, which is denoted as MP_i+1。

6. The method for eliminating the pseudo path based on the neural network in the channel measurement according to claim 1, wherein the judging method in the fifth step comprises: a threshold value epsilon when

7. A channel measurement system applying the false path elimination method based on the neural network in the channel measurement according to any one of claims 1 to 6.

Technical Field

The invention belongs to the technical field of channel measurement, and particularly relates to a pseudo path elimination method based on a neural network in wireless channel measurement.

Background

Currently, the closest prior art: in channel measurement, obtaining a Delay Power spectrum (PDP) is an important link, and time and intensity of a multipath component, which is formed by scattering, reflection and diffraction of a radio signal in a wireless channel, reaching a receiving end can be obtained through the Delay Power spectrum, so that important channel time domain parameters such as Delay Spread (DS) and Coherent Bandwidth (CB) can be obtained. Currently, PDPs are acquired using a Channel measurement system (Channel Sounder) that includes a transmitting end and a receiving end. The signal passing through the channel measurement system is inevitably affected by the system performance. One significant impact is the non-linear distortion of the signal due to the excessive amplification of the transmit side power Amplifier and the receive side Low Noise Amplifier (LNA). This non-linear distortion response in the PDP is in the time domain and can produce "glitches", i.e. false paths, in a certain delay window. The presence of these spurious paths interferes with the DS and CB computed values, resulting in the time domain characteristics of the channel being incorrectly estimated.

Currently, the scheme that can be adopted in channel measurement is to adjust the configuration of the transceiver system so that the test signal matches the linear working area of the system, thereby eliminating the glitch. However, this method is cumbersome to operate, because in actual measurement, the receiving end needs to collect signals and store data on site, in order to verify the rationality of the test data, it needs to process data on site to obtain PDP, if there is a pseudo path, the receiving end needs to adjust the amplification factor of LNA or replace LNA, even if LNA is removed according to actual conditions, sometimes the transmitting end needs to reduce the transmitting power value, and each change of the system needs to re-collect signals, store data, and process on site, and in actual test, the distance between the receiving and transmitting ends is often long, and links such as two-party communication negotiation and the like also consume a certain time. At present, because the channel measurement activity is high in cost, different devices are adopted and are not popularized yet, engineering problems encountered in the measurement process are often solved by experience, and if a system is proper, a pseudo path problem is not generated every time, so that the problem is not listed as a problem needing deep research in the field, and as a result, no scheme for solving the problem from a data processing level exists at present. Accordingly, a pseudo path elimination technique is needed to effectively eliminate the pseudo path in the PDP and restore the real PDP.

In summary, the problems of the prior art are as follows: the method for adjusting the system configuration adopted in the existing channel measurement is complex in operation, a transmitting end and a receiving end are required to be matched, and proper transmitting power is determined through continuous attempts in a test field, so that the pseudo path is eliminated, and the test efficiency is reduced.

The difficulty of solving the technical problems is as follows:

the method is characterized in that the pseudo paths existing in the PDP are eliminated from the data processing level, one difficulty is how to accurately identify the pseudo paths, and the other difficulty is how to eliminate the pseudo paths and simultaneously reserve real multi-path components, so that the true PDP is restored to the maximum extent.

The significance of solving the technical problems is as follows:

the problem of pseudo path is solved from a data processing layer, so that the workload in the channel measurement process can be reduced, the system configuration of a transmitting and receiving end does not need to be adjusted repeatedly during actual test, and the test efficiency is improved.

Disclosure of Invention

Aiming at the problem of the pseudo path in the PDP, the invention provides a pseudo path elimination method based on a neural network in wireless channel measurement.

The invention is realized in this way, a false path eliminating method based on neural network in channel measurement, including the following steps:

step one, constructing a measurement data matrix by using a time delay power spectrum acquired by channel measurement.

And step two, finding suspicious time delay windows with pseudo paths, and rejecting data of the time delay windows. The signal power strength values in the time delay windows can not participate in the construction of the neural network, and the reliability of the constructed neural network is ensured.

And step three, constructing a neural network by using the data of the time delay window without the pseudo path. The parameter set of the network is gradually improved in the process of continuous optimization, and finally the correct PDP for eliminating the pseudo path can be output.

And step four, inputting all time delay window labels to the neural network, and training to obtain a new PDP data matrix. And the data of the matrix is used for judging whether the pseudo path is eliminated or not, and if the judgment result is that the pseudo path is eliminated, the data matrix is a reconstructed PDP matrix which is finally output.

And step five, judging whether the new data matrix eliminates the pseudo path.

And step six, finishing and outputting the data.

Further, the method for constructing the data measurement matrix in the first step is as follows:

1) defining to obtain N groups of measurement data in a measurement scene, namely obtaining N groups of time delay power spectrums; each set of data has M delay windows, representing delays from 1 to M.

2) In the Channel measurement technique, a transmitting end agrees a group of Pseudo random (PN) sequences with specific length, a transmitting end transmits an electrical signal after the sequence is modulated, and a receiving end obtains a Channel Impulse Response (CIR) by using the correlation operation between a received signal and the locally stored PN sequence after receiving the signal, and calculates a PDP accordingly.

3) If the length of the PN sequence is M, the PDP has M time delay windows, and the time delay windows take chips as units. Constructing a matrix MP₀A matrix of M N; each column of the matrix represents a normalized power value (unit is dB) of M time delay windows in a group of measured data, N rows of the matrix represent that N groups of measured data are obtained under the scene, subscript 0 represents that the matrix is an initial data matrix, a new data matrix is obtained after each pseudo-path removing operation, and the subscript is increased by 1;

typically, with MP_iRepresenting the data matrix, MP, before the ith false path removal operation_i+1The size of the data matrix newly obtained each time is not changed, which represents the data matrix after the ith false path removing operation.

Further, the method for determining the suspected delay window in which the pseudo path may exist in the step two is as follows:

1) forward differencing each column of the matrix to obtain a new matrix MPd of size (M-1) xN_iWherein the matrix MPd_iIndicating how strongly the variation between adjacent delay windows in the PDP is.

2) For matrix MPd_iPerforming K-means clustering on each row of data, wherein the number of classes is 1; obtaining a vector c with the length of (M-1) formed by cluster center values of the clusters gathered by each row of data after clustering; each element c therein_mAnd M e (1, 2, … M-1) represents a unique cluster center for each class.

3) Computing vectorsL1 norm to obtain vector L_i＝[L₁，L₂，…L_M-1]^T. Wherein E_nRepresenting a unit matrix of size NxN, c_nRepresenting the nth element in the vector c.

4) For vector L_iThe elements in the vector are ordered from small to large to obtain a new vector Ls_i＝[L_a1，L_a2，…L_a(M-1)]^TThe subscript ak represents the index of the delay window.

5) For Ls_iObtaining the difference of the previous terms to obtain a vector Ls_i′＝[L_b1，L_b2，…L_b(M-2)]^TThe subscript bk denotes the index of the delay window.

6) Find vector Ls_iMaximum value of L_bk_maxThen the delay window for which the pseudo path may exist is [ a1, a2, … bkT ]_max]The delay window without the pseudo path is:

τ_i＝[(bk_max+1)，(bk_max+2)，…，a(M-1)]^T(1-1)。

further, the calculation matrix MPd_iN data per line and cluster center c_mThe absolute values of the differences are summed, and the calculation formula is shown as the formula (1-2):

wherein L is_MIs a vector

The L1 norm of (a),

the representation matrix is formed by MPd_iThe nth row of data is in accordance with its at matrix MPd_iThe vector composed of the arrangement order in (1), diag (E)_n) Is a full 1 column vector with length n; vector [ L₁，L₂，…L_M-1]T represents how strongly the N measurement data varies between adjacent delay windows.

Further, in the third step, a neural network is constructed by using the data of the delay window without the pseudo path:

the sample for obtaining the neural network parameter set is a time delay window vector tau_iThe power strength value of the test data corresponding thereto. From the vector tau_iIn the training set, 70% is randomly selected as input of the training set, and if the number of the delay windows is A, the corresponding power strength value matrix MPt of the delay window with the size of A multiplied by N_iAnd as the output of the training set, the other 30% of the time delay window labels and the corresponding power intensity value matrixes thereof are used as verification set data, and the constructed neural network is obtained on the basis of the data.

Further, in the fourth step, the constructed neural network is used as a tool for reconstructing the delay power spectrum, all M delay window labels are used as network inputs and are sent to the neural network, and the mxn matrix output by the network is a data matrix after one pseudo path elimination operation and is recorded as MP_i+1。

Further, the judging method in the fifth step is as follows:

setting a threshold value epsilon when

Stopping the elimination of the false path and outputting data after the elimination of the false path is judged to be finished; otherwise, judging that a false path still exists and returning to the step two; current MP_i+1For eliminating data moments after false pathAnd (5) arraying.

In summary, the advantages and positive effects of the invention are: in the PDP obtained under a certain measuring scene, the pseudo path always exists in a fixed time delay window, the strength values are almost the same, and the pseudo path does not change according to the relative position change of a transmitting end and a receiving end in the testing scene, so that the invention further provides a pseudo path eliminating method based on a neural network in channel measurement, which is used for eliminating all the pseudo paths in the PDP obtained by one-time channel measurement.

The invention can effectively eliminate the pseudo path in the PDP and restore the real PDP, thereby not only reducing the workload in the channel measurement process, but also ensuring the reliability of the measurement result.

Drawings

Fig. 1 is a flowchart of a method for eliminating a pseudo path based on a neural network in channel measurement according to an embodiment of the present invention.

Fig. 2 is a flowchart of an implementation of a pseudo path elimination method based on a neural network in channel measurement according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a PDP with 12 sets of data before the pseudo path is removed according to an embodiment of the present invention.

FIG. 4 is a schematic diagram of two specific groups of PDPs without pseudo paths removed according to an embodiment of the present invention;

in the figure: (a) the transmitting end is located at position 1; (b) the transmitting end is located at position 5.

FIG. 5 is a schematic diagram illustrating comparison between two sets of PDPs before and after removing the pseudo path by using the technical process of the present invention according to an embodiment of the present invention;

in the figure: (a) the transmitting end is located at position 3; (b) the transmitting end is located at position 4.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Aiming at the problems in the prior art, the invention provides a pseudo path elimination method based on a neural network in channel measurement, and the invention is described in detail below with reference to the accompanying drawings.

The false path eliminating method based on the neural network in the channel measurement is realized according to the following characteristics of the false path: in the study of PDPs obtained in a certain measurement scenario, it was found that the pseudo path always exists in a fixed delay window and the intensity value is almost the same, which does not vary depending on the relative position change of the transceiving end in the test scenario.

As shown in fig. 1, the method for eliminating the pseudo path based on the neural network in the channel measurement provided by the embodiment of the present invention includes the following steps:

s101: and constructing a measurement data matrix by using the time delay power spectrum acquired by channel measurement.

S102: finding suspicious time delay windows with possible pseudo paths and rejecting data of the time delay windows.

S103: and constructing a neural network by using the data of the delay window without the pseudo path.

S104: and inputting all time delay window labels to the neural network, and training to obtain a new time delay power spectrum data matrix.

S105: and judging whether the pseudo path of the new time delay power spectrum data matrix is eliminated.

S106: ending and outputting the data.

The technical solution of the present invention is further described with reference to the following specific examples.

As shown in fig. 2, the method for eliminating the pseudo path based on the neural network in the channel measurement provided by the embodiment of the present invention includes the following steps:

in the first step, a measurement data matrix is constructed.

N groups of measurement data obtained by channel measurement in a certain scene are assumed. For example, in making uplink measurements, the transmitting end is the user equipment and the receiving end is the base station. The base station is fixed at a certain position in the measurement scenario. The transmitting end is located at different N positions in the scene, so that N groups of measurement data can be obtained, and the N groups of measurement data are used for reflecting the channel characteristics in the tested environment. The PDP can be obtained from each set of data. In particular toIn one method of operation, a same Pseudo random (PN) sequence is first agreed at the transmitting end, and the PN sequence is cyclically transmitted at the transmitting end. The sequence forms a signal containing a plurality of multipath components through a wireless channel to reach a receiving end, and the receiving end carries out correlation operation on the received signal and the PN sequence. The channel impulse response containing a plurality of multipath components is solved, and a PDP is calculated according to the channel impulse response, and if the length of the PN sequence is M, namely the PN sequence contains M chips, the PDP correspondingly contains M delay windows, and the labels of the delay windows are from 1 to M. Thus, N groups of data can be organized into a matrix of size MxN, which is MP_iThe index i of the initial data matrix is 0, and each time the pseudo path elimination operation is performed, the value of the index i is added with 1, so as to distinguish the data matrix of each operation.

And secondly, searching a time delay window in which a pseudo path possibly exists.

(1) For MP_iThe forward difference of each group of PDP data is obtained to obtain the variation degree of the power intensity value between adjacent time delay windows, and the matrix after the forward difference is obtained is MPd_i。

(2) For matrix MPd_iAnd performing K-means clustering on each row of data, wherein the number of classes is 1, and obtaining a vector c formed by cluster center values of the classes clustered by each row of data after clustering, wherein the length of the vector is (M-1).

(3) Computing vectors

L1 norm to obtain vector L_i＝[L₁，L₂，…L_M-1]^T. Wherein E_nA unit matrix of size N × N is shown. c. C_nRepresenting the nth element in the vector c.

(4) For vector L_iThe elements in the vector are ordered from small to large to obtain a new vector Ls_i＝[L_a1，L_a2，…L_a(M-1)]^T. The subscript ak of which represents the index of the delay window.

(5) For Ls_iObtaining the difference of the previous terms to obtain a vector Ls_i′＝[L_b1，L_b2，…L_b(M-2)]^T. The subscript bk of which represents the index of the delay window.

(6) Find vector Ls_iMaximum value ofThen the delay window for which the pseudo path may exist is a1, a2, … bkT_max]The window of time delay without pseudo path is tau_i＝[(bk_max+1)，(bk_max+2)，…，a(M-1)]^T(1-1)。

Further, the calculation matrix MPd_iN data per line and cluster center c_mThe absolute values of the differences are summed, and the calculation formula is shown as the formula (1-2):

wherein L is_MIs a vector

The L1 norm of (a),

the representation matrix is formed by MPd_iThe nth row of data is in accordance with its at matrix MPd_iThe vector composed of the arrangement order in (1), diag (E)_n) Is a full 1 column vector with length n; vector [ L₁，L₂，…L_M-1]^TRepresenting the severity of the variation of the N measurement data between adjacent delay windows.

And thirdly, constructing a neural network by using the data of the delay window without the pseudo path.

From the vector tau_iAnd in addition, 70% of labels of the time delay windows and the corresponding power intensity value matrixes of the time delay windows are used as verification set data, and the constructed neural network is obtained on the basis of the data.

And fourthly, inputting all time delay window labels to the neural network and obtaining output values.

Using the neural network constructed in the third step as a tool for reconstructing the time delay power spectrum, using all M time delay window labels as network inputs to be sent to the neural network, wherein an MxN matrix output by the network is a data matrix after one pseudo path elimination operation and is recorded as MP_i+1。

And fifthly, judging whether the data eliminate the false path, if so, performing S106, otherwise, returning to S102. The method of determination is to set a fixed threshold epsilon. When in use

Judging that the elimination of the false path is finished, otherwise, judging that the false path still exists.

And sixthly, outputting data. Outputting the MPi obtained in S104₊₁As a PDP matrix after eliminating the pseudo path.

The technical effects of the present invention will be described in detail with reference to the tests below.

Figure 3 shows the results of a certain test. The test was developed in a rural macrocellular environment. The receiving end is placed on the roof of a four-storey office building, the height of the building is about 20 meters, the transmitting end is positioned on a road which is about 200 meters away from the receiving end, the transmitting end is positioned at 12 different positions on the road in total in the test, each position collects a group of data, and 12 groups of data are collected in total, and 12 groups of data are shown in figure 3. It can be seen from the figure that within some delay window of 12 groups of data, there are always pseudo paths with power values of approximately the same magnitude. Further, the existence of the pseudo path can be clearly observed from fig. 4. All 12 sets of collected data were substituted into the algorithm, setting the threshold parameter epsilon value to 10. The reconstructed PDP is obtained through the technical process of the invention. Fig. 5 shows the result of PDP reconstruction. It can be seen from the figure that the apparent pseudo-path has been completely eliminated. Its PDP exhibits an exponential decay trend, which is consistent with the statistical modeling of PDPs in the 3GPP TR 36.873 standard. Also, as can be seen in fig. 5, the true independent multipath components are not eliminated.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.