CN111756408B - PLC signal reconstruction method and system using model prediction - Google Patents

PLC signal reconstruction method and system using model prediction Download PDF

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CN111756408B
CN111756408B CN202010593744.4A CN202010593744A CN111756408B CN 111756408 B CN111756408 B CN 111756408B CN 202010593744 A CN202010593744 A CN 202010593744A CN 111756408 B CN111756408 B CN 111756408B
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CN111756408A (en
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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
    • H04B3/54—Systems for transmission via power distribution lines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/46—Monitoring; Testing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H04B3/544—Setting up communications; Call and signalling arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/147—Network analysis or design for predicting network behaviour

Abstract

The embodiment of the invention discloses a PLC signal reconstruction method and a system by utilizing model prediction, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining a delay length L; step 103 finds an approximation sequence Sappro(ii) a Step 104 of obtaining a model matrix Wopt(ii) a Step 105 finds the reconstructed signal sequence Snew。

Description

PLC signal reconstruction method and system using model prediction
Technical Field
The invention relates to the field of electric power, in particular to a PLC signal reconstruction method and a 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 to 3k 500 kHz; the power line communication technology includes a prescribed bandwidth (3148.5kHz) of european CENELEC, a prescribed bandwidth (9490kHz) of the Federal Communications Commission (FCC) in the united states, a prescribed bandwidth (9450kHz) of the Association of Radio Industries and Businesses (ARIB) in japan, and a prescribed bandwidth (3500kHz) 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 1.630MHz 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
As described above, 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, so that the phenomenon of data loss in a power line communication system is more serious, the communication quality is obviously reduced, and the performance of a PLC communication system is seriously affected.
The invention aims to provide a PLC signal reconstruction method and a system by utilizing model prediction. The method has better signal reconstruction performance and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC signal reconstruction method using model prediction, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay length L, specifically: the calculation formula of the delay length L is
Figure BDA0002556764540000021
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure BDA0002556764540000022
meaning rounding modulo 2N;
step 103 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure BDA0002556764540000023
Figure BDA0002556764540000024
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the N-mode differential sequence delta S is 0; the ith element of the N-mode differential sequence delta S is
Figure BDA0002556764540000025
siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S; i is an element serial number, and the value range of the element serial number i is i-2, 3, ·, N;
step 104 of obtaining a model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure BDA0002556764540000026
Step 105 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS]. Wherein mu is an adjusting coefficient, and the calculation formula of the adjusting coefficient mu is
Figure BDA0002556764540000027
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
A PLC signal reconstruction system using model prediction, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a delay length L, which specifically is: the calculation formula of the delay length L is
Figure BDA0002556764540000028
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure BDA0002556764540000029
meaning rounding modulo 2N;
module 203 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure BDA00025567645400000210
Figure BDA00025567645400000211
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the N-mode differential sequence delta S is 0; the ith element of the N-mode differential sequence delta S is
Figure BDA00025567645400000212
siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S; i is an element serial number, and the value range of the element serial number i is i-2, 3, ·, N;
module 204 finds the model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure BDA00025567645400000213
The module 205 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS]. Wherein mu is an adjusting coefficient, and a calculation formula of the adjusting coefficient muIs composed of
Figure BDA0002556764540000031
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as described above, 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, so that the phenomenon of data loss in a power line communication system is more serious, the communication quality is obviously reduced, and the performance of a PLC communication system is seriously affected.
The invention aims to provide a PLC signal reconstruction method and a system by utilizing model prediction. The method has better signal reconstruction performance and simpler calculation.
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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 flow chart illustrating a PLC signal reconstruction method using model prediction
Fig. 1 is a schematic flow chart of a PLC signal reconstruction method using model prediction according to the present invention. As shown in fig. 1, the method for reconstructing a PLC signal using model prediction specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay length L, specifically: the calculation formula of the delay length L is
Figure BDA0002556764540000032
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure BDA0002556764540000033
meaning rounding modulo 2N;
step 103 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure BDA0002556764540000034
Figure BDA0002556764540000035
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the N-mode differential sequence delta S is 0; the ith element of the N-mode differential sequence delta S is
Figure BDA0002556764540000041
siIs the ith element of the signal sequence S; si-1Is the signalThe i-1 th element of the sequence S; i is an element serial number, and the value range of the element serial number i is i-2, 3, ·, N;
step 104 of obtaining a model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure BDA0002556764540000042
Step 105 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS]. Wherein mu is an adjusting coefficient, and the calculation formula of the adjusting coefficient mu is
Figure BDA0002556764540000043
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
FIG. 2 structural intention of a PLC signal reconstruction system using model prediction
Fig. 2 is a schematic structural diagram of a PLC signal reconstruction system using model prediction according to the present invention. As shown in fig. 2, the PLC signal reconstruction system using model prediction includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a delay length L, which specifically is: the calculation formula of the delay length L is
Figure BDA0002556764540000044
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure BDA0002556764540000045
meaning rounding modulo 2N;
module 203 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure BDA0002556764540000046
Figure BDA0002556764540000047
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the N-mode differential sequence delta S is 0; the ith element of the N-mode differential sequence delta S is
Figure BDA0002556764540000048
siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S; i is an element serial number, and the value range of the element serial number i is i-2, 3, ·, N;
module 204 finds the model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure BDA0002556764540000049
The module 205 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS]. Wherein mu is an adjusting coefficient, and the calculation formula of the adjusting coefficient mu is
Figure BDA0002556764540000051
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
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, obtaining the delay length L, specifically: the calculation formula of the delay length L is
Figure BDA0002556764540000052
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure BDA0002556764540000053
meaning rounding modulo 2N;
step 303 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure BDA0002556764540000054
Figure BDA0002556764540000055
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the N-mode differential sequence delta S is 0; the ith element of the N-mode differential sequence delta S is
Figure BDA0002556764540000056
siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S; i is an element serial number, and the value range of the element serial number i is i-2, 3, ·, N;
step 304 finds the model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure BDA0002556764540000057
Step 305 finds a reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS]. Wherein mu is an adjusting coefficient, and the calculation formula of the adjusting coefficient mu is
Figure BDA0002556764540000058
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
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 (1)

1. A PLC signal reconstruction method using model prediction is characterized by comprising the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, obtaining a delay length L, specifically: the calculation formula of the delay length L is
Figure FDA0002983608590000011
Wherein, SNR is the signal-to-noise ratio of the signal sequence S; n is the length of the signal sequence S;
Figure FDA0002983608590000012
meaning rounding modulo 2N;
step 103 finds an approximation sequence SapproThe method specifically comprises the following steps: the approximation sequence SapproIs calculated by the formula
Figure FDA0002983608590000013
Wherein σ is the mean square error of the signal sequence S; sigmaΔIs the mean square error of the difference sequence deltas; the 1 st element of the differential sequence Δ S is 0; the ith element of the differential sequence Delta S is
Figure FDA0002983608590000014
Figure FDA0002983608590000015
Is the second of the signal sequence S
Figure FDA0002983608590000016
An element; wherein the content of the first and second substances,
Figure FDA0002983608590000017
indicating that L is taken as the lower module to be rounded; si-1Is the i-1 th element of the signal sequence S; i is an element serial number, and the value range of the element serial number i is 2,3, … and N;
step 104 of obtaining a model matrix WoptThe method specifically comprises the following steps: the model matrix WoptIs calculated by the formula Wopt＝D[ΔSTΔS+STS](ii) a Wherein D is a model selection factor matrix, and the expression of the model selection factor matrix D is
Figure FDA0002983608590000018
Step 105 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps: the reconstructed signal sequence SnewIs calculated by the formula Snew＝Wopt[Sappro-μΔS](ii) a Wherein mu is an adjusting coefficient, and the calculation formula of the adjusting coefficient mu is
Figure FDA0002983608590000019
Figure FDA00029836085900000110
max | Δ S | represents the element of the difference sequence Δ S having the largest absolute value; max | S | represents the element of the signal sequence S having the largest absolute value.
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