CN109327859B - Frequency spectrum compression transmission method for railway GSM-R air interface monitoring system - Google Patents

Frequency spectrum compression transmission method for railway GSM-R air interface monitoring system Download PDF

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CN109327859B
CN109327859B CN201811431368.8A CN201811431368A CN109327859B CN 109327859 B CN109327859 B CN 109327859B CN 201811431368 A CN201811431368 A CN 201811431368A CN 109327859 B CN109327859 B CN 109327859B
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frequency spectrum
data
arma model
spectrum
air interface
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CN109327859A (en
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陈翔
李�一
谢绍航
杜伟
李忠发
潘燕峰
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Guangzhou Guangyuan Electronic Technology Co ltd
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Sun Yat Sen University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0205—Traffic management, e.g. flow control or congestion control at the air interface
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Network arrangements, protocols or services independent of the application payload and not provided for in the other groups of this subclass
    • H04L69/04—Protocols for data compression, e.g. ROHC
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information

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  • Signal Processing (AREA)
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Abstract

The invention discloses a frequency spectrum compression transmission method for a railway GSM-R air interface monitoring system, which adopts an autoregressive moving average model (ARMA model) to approximately represent the frequency spectrum occupation process of the railway GSM-R air interface, thereby realizing an effective compression method of front-end frequency spectrum acquisition data, and belonging to the field of wireless communication. Compared with a method for directly transmitting original frequency spectrum data, the method has the advantages that the original frequency spectrum data are compressed by the ARMA model, transmission of redundant data is sufficiently and greatly reduced, the frequency spectrum data transmission efficiency is greatly improved, meanwhile, the processing difficulty of a background server on the frequency spectrum data is reduced, the processing efficiency is greatly improved, and convenience is brought to analysis and application of a subsequent data server on the frequency spectrum data.

Description

Frequency spectrum compression transmission method for railway GSM-R air interface monitoring system
Technical Field
The invention relates to the field of wireless communication, in particular to a front-end processor frequency spectrum compression transmission method for a GSM-R air interface monitoring system, which is an effective compression method for realizing front-end frequency spectrum acquisition data by adopting an autoregressive moving average model (ARMA model) to approximately represent the frequency spectrum occupation process of an air interface of a railway GSM-R system.
Background
GSM-R is one of the important technical guarantee means for the safe operation of high-speed rails in China, and a third-party system for GSM-R air interface monitoring application is still blank in the field of special products for railways in China. As shown in fig. 1, a topology diagram of a GSM-R air interface integrated monitoring system based on a general processor software radio technology is shown, in which a GSM-R front-end processor (i.e., an acquisition module shown in the figure) is installed at a GSM-R base station end of a high-speed rail, transmits various acquired data to a GSM-R air interface detection server end through a GSM-R network for processing, and provides the processed data for a worker to view and use through a local area network inside the high-speed rail.
However, the GSM-R air interface monitoring system faces such a difficult problem in the process of transmitting spectrum data, and the amount of spectrum data to be transmitted is very large, but the time occupied by effective information is very short. In the research on the efficient transmission of the spectrum data of the system, it is found that an uplink channel power spectrum model can be regarded as a random process, a transient spectrum peak appears whenever a high-speed rail train approaches a base station, but the spectrum peak disappears gradually when the train is far away, generally, the time interval of two trains passing through the same base station is longer, so that a large amount of invalid information appears in the uplink spectrum data, the effectiveness of data transmission is seriously influenced, and meanwhile, great difficulty is brought to the data processing process applied to an upper layer. However, the characteristics of the model are similar to those of the ARMA model, and effective spectrum data information in the ARMA model can be accurately represented by properly selecting the order of the ARMA model, and the receiving end can realize recovery and reconstruction, so that the efficient transmission of the spectrum data is realized.
Disclosure of Invention
The invention aims to solve the problem of effective data transmission of overlarge spectrum data volume and extremely short effective spectrum information occupation time in the current railway GSM-R air interface monitoring system, and provides a spectrum compression transmission method for the railway GSM-R air interface monitoring system.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a method of compressed spectrum transmission for a railway GSM-R air interface monitoring system, the method comprising the steps of:
s1, randomly selecting spectrum data of any M days collected by front-end spectrum collection equipment (GSM-R front-end processor for short) of N railway GSM-R air interface monitoring systems, analyzing, and drawing corresponding spectrum occupation autocorrelation functions and partial autocorrelation functions;
s2, analyzing the regularity of the autocorrelation function and the partial autocorrelation function of the frequency spectrum in the S1, and determining the orders p and q of the ARMA model preliminarily according to the regularity;
s3, optimizing the order (namely the values of p and q) of the ARMA model according to the Akaike information criterion (namely the AIC criterion) to ensure that the AIC is minimum;
s4, modeling by using the optimized ARMA model order (namely the optimized values of p and q) to compress the frequency spectrum of the GSM-R front-end processor, and transmitting the parameterized and compressed ARMA model order and the corresponding coefficient to a central machine room data server through a railway special network;
and S5, on the data server of the central machine room, adopting the received ARMA model order and the corresponding coefficient to realize the data recovery and reconstruction of the frequency spectrum data under the occupation condition of the corresponding frequency band.
Preferably, the ARMA model expression in step S2 is:
y(n)+a1y(n-1)+...+apy(n-p)＝w(n)+b1w(n-1)+...+bqw (n-q), wherein w (n) is zero mean and variance
Figure GDA0002965307780000031
Is white noise, y (n) is a random sequence to be studied, a1,...,apIs an autoregressive parameter, b1,…,bqIs a moving average parameter. Alternatively, the system function can be expressed as:
Figure GDA0002965307780000032
preferably, the AIC criterion in step S3 is:
Figure GDA0002965307780000033
wherein
Figure GDA0002965307780000034
For residual square, k ═ p + q +1 is the number of all estimated parameters, and T is the sample size.
Preferably, the data recovery and reconstruction process in step S5 is as follows:
s51, obtaining a system function according to the received ARMA model order and the corresponding coefficient as follows:
Figure GDA0002965307780000035
and S52, recovering the required spectrum data according to the system function in S51 and the characteristics of w (n).
Compared with the prior art, the invention has the beneficial effects that:
compared with a method for directly transmitting original frequency spectrum data, the method for compressing and transmitting the frequency spectrum of the front-end processor for the GSM-R air interface monitoring system compresses the original frequency spectrum data by using the ARMA model, fully and greatly reduces the transmission of redundant data, greatly improves the efficiency of frequency spectrum data transmission, reduces the difficulty of processing the frequency spectrum data by a background server, greatly improves the processing efficiency, and provides convenience for the analysis and application of subsequent data servers to the frequency spectrum data.
Drawings
FIG. 1 is a diagram of a GSM-R air interface integrated monitoring system topology in current use;
FIG. 2 is a flowchart of the steps of the GSM-R front-end processor spectrum compression transmission proposed by the present invention;
fig. 3(a) is a graph of an autocorrelation function plotted using the six-day spectral data of the base station 1;
FIG. 3(b) is a graph of a partial autocorrelation function plotted using the six-day spectral data of base station 1;
fig. 4(a) is a graph of an autocorrelation function plotted using the six-day spectrum data of the base station 2;
FIG. 4(b) is a graph of a partial autocorrelation function plotted using the six-day spectral data of the base station 2;
FIG. 5 is a comparison plot plotted using source spectral data and compressed spectral data;
fig. 6 is a residual map of source spectral data and compressed spectral data.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples of the embodiments
The present embodiment will describe in detail a spectrum compression transmission method of a front-end processor for a GSM-R air interface monitoring system according to the present invention with reference to fig. 2 to fig. 6. The processing flow of the spectrum compression transmission method in the invention is shown in fig. 2, and mainly comprises the following steps:
s1, randomly selecting spectrum data of any 6 days acquired by front-end spectrum acquisition equipment (hereinafter referred to as GSM-R front-end processor) of 2 railway GSM-R air interface monitoring systems, analyzing the spectrum data, and drawing corresponding spectrum occupancy autocorrelation function and partial autocorrelation function graphs, which are respectively shown in fig. 3(a), fig. 3(b), fig. 4(a) and fig. 4(b), wherein the graphs can be obtained by observing the images, six days of partial autocorrelation functions of the same base station show strong truncation characteristics, the values are generally close to 0 after 3 orders, and the partial autocorrelation graphs on different days are very similar. While the six-day autocorrelation function also shows a strong similarity in general. The autocorrelation function and the partial autocorrelation of different days of the same base station show strong similarity and regularity, which indirectly shows that the power spectrum occupation situation is regular.
S2, analyzing the regularity of the autocorrelation function and the partial autocorrelation function of the frequency spectrum in the S1, and accordingly primarily determining the order p of the ARMA model to be 2 and the order q to be 4;
s3, according to Akaike information criterion (i.e., AIC criterion):
Figure GDA0002965307780000051
(wherein
Figure GDA0002965307780000052
For residual square, k ═ p + q +1 is the number of all estimated parameters, and T is the sample size. ) Optimizing the order of the ARMA model to minimize AIC, thereby obtaining the optimized ARMA model with the order p being 1 and q being 4;
s4, modeling by using the optimized ARMA model order (namely p is 1 and q is 4) to compress the GSM-R front-end processor frequency spectrum, and transmitting the parameterized and compressed ARMA model order and the corresponding coefficient to the central machine room data server through the railway special network;
and S5, on the data server of the central machine room, adopting the received ARMA model order and the corresponding coefficient to realize the data recovery and reconstruction of the frequency spectrum data under the occupation condition of the corresponding frequency band. The restored spectrum data is compared with the source spectrum data, a comparison graph and a residual graph are drawn as shown in fig. 5 and fig. 6 respectively, the difference between the model data and the actual data can be obtained through the comparison graph and the residual graph, and the spectrum compression transmission method has a good effect.
The technical effects of the present invention will be summarized and explained in conjunction with the technical solutions of the present invention.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (1)

1. A method for compressed spectrum transmission for a railway GSM-R air interface monitoring system, the method comprising the steps of:
s1, randomly selecting spectrum data of any M days collected by front-end spectrum collection equipment of N railway GSM-R air interface monitoring systems, analyzing, and drawing corresponding spectrum occupation autocorrelation functions and partial autocorrelation functions;
s2, analyzing the regularity of the autocorrelation function and the partial autocorrelation function of the frequency spectrum in the step S1, and determining the orders p and q of an autoregressive moving average model ARMA according to the order, wherein the ARMA model expression in the step S2 is as follows:
y(n)+a1y(n-1)+...+apy(n-p)＝w(n)+b1w(n-1)+...+bqw(n-q)；
wherein w (n) is zero mean and variance
Figure FDA0002983399950000011
Is white noise, y (n) is a random sequence to be studied, a1,...,apIs an autoregressive parameter, b1,…,bqIs a moving average parameter; the system function of the ARMA model is further expressed as:
Figure FDA0002983399950000012
s3, optimizing the order of the ARMA model according to Akaike information criterion, i.e. AIC criterion, so as to minimize AIC, wherein the order of the ARMA model corresponds to the values of p and q, respectively, and the AIC criterion in step S3 is:
Figure FDA0002983399950000013
wherein
Figure FDA0002983399950000014
The square of residual error, where k is p + q +1 is the number of all estimation parameters, and T is the sample capacity;
s4, compressing the frequency spectrum acquired by the front-end frequency spectrum acquisition equipment by using the optimized ARMA model order modeling, and transmitting the parameterized and compressed ARMA model order, the corresponding autoregressive parameter and the moving average parameter to a central machine room data server through a railway special network;
s5, on the data server of the central machine room, the data recovery reconstruction of the frequency spectrum data in the corresponding frequency band occupation situation is realized by adopting the received ARMA model order, the corresponding autoregressive parameter and the moving average parameter, wherein the data recovery reconstruction process in the step S5 is as follows:
s51, obtaining a system function according to the received ARMA model order and the corresponding coefficient as follows:
Figure FDA0002983399950000021
wherein
Figure FDA0002983399950000022
Is a parameter of auto-regression,
Figure FDA0002983399950000023
is a moving average parameter;
s52, according to the system function in the step S51
Figure FDA0002983399950000024
And w (n) is zero mean with variance of white noise
Figure FDA0002983399950000025
The desired spectral data is recovered.
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