CN111628804A - PLC signal filtering method and system based on Gilbert optimization - Google Patents

PLC signal filtering method and system based on Gilbert optimization Download PDF

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CN111628804A
CN111628804A CN202010519964.2A CN202010519964A CN111628804A CN 111628804 A CN111628804 A CN 111628804A CN 202010519964 A CN202010519964 A CN 202010519964A CN 111628804 A CN111628804 A CN 111628804A
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signal sequence
sequence
noise
gilbert
value
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翟明岳
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Guangdong University of Petrochemical Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/04—Control of transmission; Equalising
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines

Abstract

The embodiment of the invention discloses a PLC signal filtering method and a system optimized by Gilbert, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; 102, solving a Gaussian random projection matrix F; step 103, solving a Gilbert freedom degree P; step 104 finds a noise-filtered signal sequence Snew。

Description

PLC signal filtering method and system based on Gilbert optimization
Technical Field
The invention relates to the field of communication, in particular to a PLC signal filtering method and system.
Background
Compared with various wired communication technologies, the power line communication has the advantages of no need of rewiring, easiness in networking and the like, and has wide application prospect. The power line communication technology is divided into Narrowband over power line (NPL) and Broadband over power line (BPL); the narrow-band power line communication refers to a power line carrier communication technology with the bandwidth limited between 3k and 500 kHz; the power line communication technology includes a prescribed bandwidth (3148.5kHz) of european CENELEC, a prescribed bandwidth (9 to 490kHz) of the Federal Communications Commission (FCC) in the united states, a prescribed bandwidth (9 to 450kHz) of the Association of Radio Industries and Businesses (ARIB) in japan, and a prescribed bandwidth (3 to 500kHz) in china. The narrow-band power line communication technology mainly adopts a single carrier modulation technology, such as a PSK technology, a DSSS technology, a Chirp technology and the like, and the communication speed is less than 1 Mbits/s; the broadband power line communication technology refers to a power line carrier communication technology with a bandwidth limited between 1.6 and 30MHz and a communication rate generally above 1Mbps, and adopts various spread spectrum communication technologies with OFDM as a core.
Although power line communication systems are widely used and the technology is relatively mature, a large number of branches and electrical devices in the power line communication system generate a large amount of noise in the power line channel; random impulse noise has high randomness and high noise intensity, and seriously damages a power line communication system, so that the technology for inhibiting the random impulse noise is always the key point for the research of scholars at home and abroad; and the noise model does not fit into a gaussian distribution. Therefore, the traditional communication system designed aiming at the gaussian noise is not suitable for a power line carrier communication system any more, and a corresponding noise suppression technology must be researched to improve the signal-to-noise ratio of the power line communication system, reduce the bit error rate and ensure the quality of the power line communication system.
In practical applications, some simple non-linear techniques are often applied to eliminate power line channel noise, such as Clip-ping, Blanking and Clipping/Blanking techniques, but these research methods all have to work well under a certain signal-to-noise ratio condition, and only consider the elimination of impulse noise, in a power line communication system, some commercial power line transmitters are characterized by low transmission power, and in some special cases, the transmission power may be even lower than 18w, so that in some special cases, signals are submerged in a large amount of noise, resulting in a low signal-to-noise ratio condition of the power line communication system.
Disclosure of Invention
With the application and popularization of nonlinear electrical appliances, background noise in a medium and low voltage power transmission and distribution network presents obvious non-stationarity and non-Gaussian characteristics, a common low-pass filter is difficult to achieve an ideal filtering effect in a non-stationarity and non-Gaussian noise environment, the non-stationarity and non-Gaussian noise is difficult to filter, and the performance of a PLC communication system is seriously influenced. .
The invention aims to provide a PLC signal filtering method and system based on Gilbert optimization. The method has good noise filtering performance and is simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC signal filtering method using Gilbert optimization, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure BDA0002531663280000021
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
step 103 of solving for the Gilbert degree of freedom P, specifically
Figure BDA0002531663280000022
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure BDA0002531663280000023
Figure BDA0002531663280000024
Is a lower rounding operation;
step 104 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure BDA0002531663280000025
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
A PLC signal filtering system with Gilbert optimization, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure BDA0002531663280000026
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
the module 203 finds the Gilbert degree of freedom P, specifically
Figure BDA0002531663280000027
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure BDA0002531663280000028
Figure BDA0002531663280000029
Is a lower rounding operation;
module 204 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure BDA00025316632800000210
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
with the application and popularization of nonlinear electrical appliances, background noise in a medium and low voltage power transmission and distribution network presents obvious non-stationarity and non-Gaussian characteristics, a common low-pass filter is difficult to achieve an ideal filtering effect in a non-stationarity and non-Gaussian noise environment, the non-stationarity and non-Gaussian noise is difficult to filter, and the performance of a PLC communication system is seriously influenced. .
The invention aims to provide a PLC signal filtering method and system based on Gilbert optimization. The method has good noise filtering performance and is simple in calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a PLC signal filtering method using Gilbert optimization
Fig. 1 is a flow chart illustrating a PLC signal filtering method using Gilbert optimization according to the present invention. As shown in fig. 1, the PLC signal filtering method using Gilbert optimization specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure BDA0002531663280000031
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
step 103 of solving for the Gilbert degree of freedom P, specifically
Figure BDA0002531663280000032
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure BDA0002531663280000033
Figure BDA0002531663280000034
Is a lower rounding operation;
step 104 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure BDA0002531663280000035
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
FIG. 2 structural intent of a PLC signal filtering system optimized with Gilbert
Fig. 2 is a schematic diagram of a PLC signal filtering system optimized by Gilbert according to the present invention. As shown in fig. 2, the PLC signal filtering system optimized by Gilbert includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure BDA0002531663280000041
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
the module 203 finds the Gilbert degree of freedom P, specifically
Figure BDA0002531663280000042
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure BDA0002531663280000043
Figure BDA0002531663280000044
Is a lower rounding operation;
module 204 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure BDA0002531663280000045
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302, solving a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure BDA0002531663280000046
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
step 303 of solving for the Gilbert degree of freedom P, specifically
Figure BDA0002531663280000047
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure BDA0002531663280000048
Figure BDA0002531663280000049
Is a lower rounding operation;
step 304 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure BDA00025316632800000410
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. The PLC signal filtering method using Gilbert optimization is characterized by comprising the following steps of:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure FDA0002531663270000011
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
step 103 of solving for the Gilbert degree of freedom P, specifically
Figure FDA0002531663270000012
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure FDA0002531663270000013
Figure FDA0002531663270000014
Is a lower rounding operation;
step 104 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure FDA0002531663270000015
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
2. The PLC signal filtering system optimized by using Gilbert is characterized by comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a gaussian random projection matrix F, specifically: the ith row and the jth column element F of the Gaussian random projection matrix FijIs calculated by the formula
Figure FDA0002531663270000016
Wherein, XijIs independent and identically distributed Gaussian random variable XijHas a mean value of m0Mean square error of σ0；m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; i is a row sequence number, and the value range of the row sequence number i is 1,2, ·, N; j is a column serial number, and the value range of the column serial number j is 1,2, ·, N; n is the length of the signal sequence S;
the module 203 finds the Gilbert degree of freedom P, specifically
Figure FDA0002531663270000017
Wherein snr is the signal-to-noise ratio of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the normalized correlation matrix B; lambda [ alpha ]maxThe maximum eigenvalue of the normalized correlation matrix B; the calculation formula of the normalized correlation matrix B is
Figure FDA0002531663270000018
Figure FDA0002531663270000019
Is a lower rounding operation;
module 204 finds a noise-filtered signal sequence SnewThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to
Figure FDA00025316632700000110
Taking the minimum value of the intermediate parameter vector x as the noise-filtered signal sequence SnewThe value of (c).
CN202010519964.2A 2020-06-10 2020-06-10 PLC signal filtering method and system based on Gilbert optimization Withdrawn CN111628804A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112270282A (en) * 2020-11-03 2021-01-26 华北电力大学 Power signal filtering method and system by utilizing matrix spectral mode
CN112383326A (en) * 2020-11-03 2021-02-19 华北电力大学 PLC signal filtering method and system using spectral mode threshold

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112270282A (en) * 2020-11-03 2021-01-26 华北电力大学 Power signal filtering method and system by utilizing matrix spectral mode
CN112383326A (en) * 2020-11-03 2021-02-19 华北电力大学 PLC signal filtering method and system using spectral mode threshold
CN112270282B (en) * 2020-11-03 2021-12-10 华北电力大学 Power signal filtering method and system by utilizing matrix spectral mode
CN112383326B (en) * 2020-11-03 2021-12-31 华北电力大学 PLC signal filtering method and system using spectral mode threshold

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