CN107733569A - Load multi-beam sampled data compression method on a kind of star - Google Patents
Load multi-beam sampled data compression method on a kind of star Download PDFInfo
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- CN107733569A CN107733569A CN201710888627.9A CN201710888627A CN107733569A CN 107733569 A CN107733569 A CN 107733569A CN 201710888627 A CN201710888627 A CN 201710888627A CN 107733569 A CN107733569 A CN 107733569A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18578—Satellite systems for providing broadband data service to individual earth stations
- H04B7/1858—Arrangements for data transmission on the physical system, i.e. for data bit transmission between network components
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
- H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0041—Arrangements at the transmitter end
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0045—Arrangements at the receiver end
Abstract
The invention provides load multi-beam sampled data compression method on a kind of star, and in particular to a kind of quantization bit counting method for being used to compress multichannel Larger Dynamic range signal on star.Methods described has following feature：(1) on star, 12 bit quantizations is carried out to multi-beam aliasing signal first and extract the maximum per road signal amplitude；Secondly realize that amplitude dynamic range is compressed using the quantization amplitude maximum structure matrix of a linear transformation of acquisition；6 bit second quantizations are carried out to the multiple signals after compression again and pass through Ka band transmissions.(2) in earth station, the dictionary matrix under ideal signal is obtained first with K SVD methods；Then user's initial signal is recovered using the satellite data received and sparse restructing algorithm.The advantage of the invention is that：Forward that cost is low, and compression effectiveness is good and strong applicability on star.
Description
Technical field
The invention provides load multi-beam sampled data compression method on a kind of star, and in particular to one kind is used to compress star
The quantization bit counting method of upper multichannel Larger Dynamic range signal.
Background technology
In recent years, with the fast development of high performance satellite communication system, load multichannel Larger Dynamic range signal on star
The market demand of quantizing bit number compression method is more and more stronger.Preceding 2 satellites of U.S. army's MUOS systems were at 2014,2015
Launch mission is completed, has been put into operation.To being traditional transparent transmission before load on star, reversely using DavidK with
Quantization bit counting methods of the Randall in a kind of reduction multichannel coherent signal proposed in 2006.This method is first to multidiameter delay
Signal carries out the A/D conversions of 12 bits, recycles complex scrambling code sequence to carry out decorrelative transformation, the signal after decorrelation is carried out
Hadamard linear transformations, so that the multiple signals after conversion have equal mean power.To with same average power
Multiple signals, according to the performance requirement of system, select suitable quantizing bit number to carry out second quantization to it.The master of this method
It is that information has damage to want shortcoming, forwards complexity high on star.
In fact, Crowther et al. just proposed to become using Hadamard early in 1967 brings reduction amount bit number
Method.The linear transformation that this method possesses in itself using the strong correlation characteristic between multichannel input signal and Hadamard matrixes
Characteristic, realize the compression to A/D input signal dynamic ranges.It is easy that this method implements comparison, but existing subject matter
Be each road input signal compression ratio it is inconsistent, it is necessary to dynamically distributes quantizing bit number, add the actual overhead of system.
On the basis of Crowther et al. achievements in research, Frangoulis and Turner further proposed in 1977
Hadamard-Haar quantization bit compression methods.Successively after Hadamard and Haar linear transformations, not only multi-channel A/
The dynamic range of D input signals is compressed, and signal power is only concentrated on sub-fraction Haar coefficients, and this is for reduction amount
It is once significant exploration to change bit number and transmission rate.However, due to the application of Haar transform and part important coefficient, the party
The information trauma of method also further increases.
Babarada in 2011 etc. proposes adaptive gain (AGC) control method.This method is increased before A/D is sampled
Adaptation control circuit, without being the dynamic of compressible A/D input signals by the correlation between multiple signals and decorrelation operation
State scope, reduce quantizing bit number.The major defect of this method is to be in course of adjustment clipping phenomena than more serious, destroys letter
Number linear relationship and complexity height be unfavorable for forwarding on star.
In addition, the reduction Peak-to-Average Power Ratio method generally used in multi-carrier OFDM systems, such as direct margining amplitude technique plus peak value window limit
Width method, Choose for user method and partial sequence transmission method etc. can also be effectively compressed the dynamic range of input signal, but because it is against mistake
Journey is difficult to and generally requires extra side information to recover initial data mostly, therefore such method does not apply to transparent turn
Hair, band efficiency be not also high.
The content of the invention
The technical problems to be solved by the invention are the sampled datas for multi-beam satellite reverse link, and research is applied to
The dynamic range compression method of spread-spectrum signal and non-spread-spectrum signal, captures that existing method information trauma is big, compression ratio is low and needs
The serial problems such as decorrelation operation.
The technical solution adopted by the present invention is：
The invention provides load multi-beam sampled data compression method on a kind of star, comprise the following steps：
On star in repeating process：
(1) multi-beam aliasing signal on star is quantified to obtain quantized signal；
(2) maximum of the quantized signal per row element is extracted, is negated linear become is built after being handled with diagonalization successively
Change matrix A；
(3) realize that amplitude dynamic range is compressed by way of matrix multiple；I.e. by quantized signal and the matrix of a linear transformation
Multiplication obtains condensation matrix；
(4) to condensation matrix carry out DA conversions, then it is quantified after pass through Ka band transmissions；
During ground station reception：
(5) dictionary matrix Φ is obtained using dictionary learning method in the case where ideal is without noise cancellation signal；
(6) initially believed into user using forwarding data recovery on star of the sparse restructing algorithm by reception under dictionary matrix Φ
Number.
Wherein, step (5) comprises the following steps：
[501] piecemeal processing is carried out without noise cancellation signal to ideal；
[502] sparse coding is carried out without noise cancellation signal to the ideal after piecemeal, builds initial dictionary；
[503] renewal is iterated to initial dictionary by dictionary learning method, obtains dictionary matrix.
Wherein, iteration is terminated when iteration precision is less than given threshold or iterations meets C >=WQ in step [503],
Wherein Q is the preferable number that piecemeal is carried out without noise cancellation signal, and W is to be less than more than 1Integer.
Wherein, step (6) is specially：
[601] utilized under dictionary matrix Φ and data recovery is forwarded on star of the sparse restructing algorithm by reception into multichannel piecemeal
Data；
[602] piecemeal recovery is carried out to multichannel block data, reverts to user's initial signal.
Wherein, in the step (1) multi-beam aliasing signal show in form be spread-spectrum signal or non-spread-spectrum signal；
It is phase modulated signal, frequency modulated signal or am signals in modulation system.
Wherein, in the step (4) ideal without noise cancellation signal in modulation system it is identical with multi-beam aliasing signal.
Wherein, dictionary learning method is K-SVD, optimal dictionary learning method or Fisher discriminates in the step (5)
Dictionary learning method.
Wherein, sparse restructing algorithm is matching pursuit algorithm, convex optimized algorithm or has Fast Convergent in the step (6)
The non-convex algorithm of iteration of characteristic.
The invention has the advantages that：
(1) forwarding is low with forwarding cost without scrambler sequence decorrelation and Hadamard shift steps, complexity on star；
(2) earth station is good using sparse reconstructing method recovery subscriber signal, strong antijamming capability, compression effectiveness；
(3) it is not only suitable for spread-spectrum signal and is applied to non-spread-spectrum signal again, general applicability is good.
Brief description of the drawings
Fig. 1 is the overview flow chart for showing load multi-beam sampled data compression method on the star according to the present invention；
Fig. 2 is the composition procedure chart for showing the matrix of a linear transformation A according to the present invention；
Fig. 3 is to show that the dynamic range on the star according to the present invention before and after multipath spread-spectrum reception signal compressed transform compares
Figure；
Fig. 4 is to show that the dynamic range that the non-spread spectrum receiver Signal Compression conversion of multichannel is front and rear on the star according to the present invention compares
Figure；
Fig. 5 is to show second quantization procedure chart on the star according to the present invention；
Fig. 6 is the flow chart that the K-SVD methods for showing to be used according to the present invention obtain dictionary matrix Φ；
Fig. 7 is to show to utilize the stream for receiving data and sparse restructing algorithm recovery user's initial signal under dictionary matrix Φ
Cheng Tu；
Fig. 8 is to show the simulation experiment result figure according to the present invention；
Fig. 9 is to show the simulation experiment result figure according to the present invention.
Embodiment
Load multi-beam sampled data compression method, comprises the following steps on a kind of star：
Fig. 1 is the overview flow chart of load multi-beam sampled data compression method on the star according to the present invention.Specifically include
Following steps：
On star in repeating process：
[101] AD samplings are carried out to M roads wave beam aliasing signal X and complete 12 bit quantizations obtaining quantized signal Xq.At this
In embodiment, dimension M >=2, the quantization of use can be that uniform quantization can also be non-uniform quantizing, according to system performance requirements
Determine；
[102] maximums of the Xq per row element is extracted, is negated linear transformation square is built after being handled with diagonalization successively
Battle array A；
[103] it is that Y=A × Xq realizes amplitude compression by way of matrix multiple；
[104] DA conversions are carried out to Y, then is handled through 6 bit second quantizations；
[105] Ka wave bands link transmission to earth station is passed through；
During ground station reception：
[106] in earth station, dictionary matrix Φ is obtained without noise cancellation signal and K-SVD methods using ideal；
[107] utilized under dictionary matrix Φ and receive data and sparse restructing algorithm recovery user's initial signal.
The multi-beam aliasing signal X that the present invention mentions can be spread-spectrum signal or non-spread spectrum in form showing
Signal.Can be phase modulated signal or frequency modulated signal or am signals in modulation system.Due to upper
It is signal known to field personnel of the present invention to state signal, therefore is not repeated excessively.
The specific embodiment of the invention is：
(1) multi-beam aliasing signal X on star is quantified to obtain quantized signal Xq；
(2) maximums of the Xq per row element is extracted, is negated build the matrix of a linear transformation after being handled with diagonalization successively
A；
Fig. 2 is the composition procedure chart for showing the matrix of a linear transformation A according to the present invention, is comprised the following steps：
[201] extract maximums of the Xq per row element and form vector M ax=[max1, max2 ... maxM]；
[202] inversion operation is carried out.Foundation in the present embodimentRealize inversion operation,Generation
Table rounds up computing；
[203] diagonalization processing, i.e. A=diag (P) are carried out to data vector P of the inverted.
(3) it is that Y=A × Xq realizes amplitude compression by way of matrix multiple；
Fig. 3 and Fig. 4 is shown respectively according to before multipath spread-spectrum on star of the invention and the conversion of non-spread spectrum receiver Signal Compression
Dynamic range afterwards compares figure.In specific embodiment provided by the invention, wave beam dimension is set as M=32, and there is idol
Wave beam is aliased into the situation of strange wave beam.In Fig. 3, the normalization mean power maximum 301 and normalization of the method provided by the present invention
The difference of the small value 302 of mean power is only 0.238.In Fig. 4, the normalization mean power maximum 303 of the method provided by the present invention
Difference with normalizing the small value 304 of mean power is only 0.162.Therefore, load multi-beam hits on star provided by the invention
Multipath spread-spectrum and the amplitude range of non-spread spectrum receiver signal on star can be effectively compressed according to compression method.In addition, contrasted from figure bent
Line can also be clearly seen, and the data compression performance of method provided by the present invention is substantially better than direct Hadamard conversion compression side
Method.
(4) DA conversions are carried out to Y, then passes through Ka band transmissions after 6 bit quantizations.
Fig. 5 is to show second quantization procedure chart on the star according to the present invention.Comprise the following steps：
[401] realize that DA is changed under reference voltage V to Y.In the present embodiment, the reference voltage V and step of DA conversions
(1) the AD conversion voltage in is identical.
[402] Y after being changed to DA carries out 6 bit second quantizations and obtains Y1.In the present embodiment, 6 bits two of use
Secondary quantization can be that uniform quantization can also be non-uniform quantizing, be determined according to system performance requirements.
[403] sent after Y1 being modulated to Ka wave bands to earth station.
During ground station reception：
(5) dictionary matrix Φ is obtained using K-SVD methods in the case where ideal is without noise cancellation signal；
Fig. 6 is the flow chart that the K-SVD methods for showing to be used according to the present invention obtain dictionary matrix Φ, including is walked as follows
Suddenly：
[501] piecemeal processing is carried out without noise cancellation signal to ideal.In the particular embodiment, the preferable nothing that dimension is N is made an uproar
Signal is uniformly divided into Q parts, and 1<Q<N；
[502] sparse coding is carried out, builds initial dictionary.In the present embodiment, dictionary dimension is Q × WQ, and W is more than 1
It is less thanInteger, the specific values of W determine according to actual conditions；Work as in a kind of QPSK signals dictionary structure of the present invention
In, W=2 is a kind of preferably selection.
[503] dictionary updating is carried out by KSVD methods iteration；
[504] dictionary Φ is exported.In the present embodiment, when precision is less than threshold tau≤10-3Or during iterations C >=WQ, i.e.,
Terminate iteration and export dictionary Φ.
It should be further stated that the dictionary matrix Φ that the present invention mentions is not limited to obtain by K-SVD methods, also
It can be obtained by other dictionary learning methods such as optimal dictionary learning method, Fisher discriminate dictionary learning methods.Due to
Above-mentioned dictionary learning method is method known to field personnel of the present invention, therefore is not repeated excessively.
(6) utilized under dictionary matrix Φ and receive data and sparse restructing algorithm recovery user's initial signal.
Fig. 7 is to show to utilize the stream for receiving data and sparse restructing algorithm recovery user's initial signal under dictionary matrix Φ
Cheng Tu；Its specific implementation process includes：
[601] utilized under dictionary matrix Φ and receive data and sparse restructing algorithm recovery multichannel block data.Specific
Embodiment in, can according to system complexity and required precision using matching pursuit algorithm (such as MP, OMP), convex optimized algorithm (such as
BP, BPDN) or with Fast Convergent characteristic the non-convex algorithm of iteration.
[602] piecemeal recovery is carried out to the multichannel data reconstructed.In the particular embodiment, foundation and step [501]
Opposite process carries out piecemeal recovery to the multichannel data that reconstructs, same or like initial of final output and user's transmitting terminal
Signal.
Illustrate the effect of load multi-beam sampled data method on present invention compression star from the result of emulation experiment below.
Illustrated first from the effect of multipath spread-spectrum signal on compression star：In a particular embodiment of the present invention, set
Experiment condition be：Wave beam dimension is M=32, and QPSK signals are transmitted through Gaussian channel, and signal to noise ratio is by -6dB using 3dB as step
Length transforms to 9dB, and frequency expansion sequence uses spreading factor as 32 OVSF sequences, spreading rate 3.84MHz, wave beam dynamic range
Simulated by random function randn (1, M), close on wave beam and be coupled to the signal of this wave beam and decay 30dB.Fig. 8 is the inventive method
Output signal-to-noise ratio under spread-spectrum signal with input signal-to-noise ratio transformation relation curve map.As seen from the figure, side provided by the invention
Method can provide reliable output while quantizing bit number is effectively compressed.
Secondly illustrated from the effect of the non-spread-spectrum signal of multichannel on compression star：In a particular embodiment of the present invention, if
Fixed experiment condition is：Wave beam dimension is M=32, and QPSK signals are transmitted through Gaussian channel, signal to noise ratio by -6dB using 3dB as
Step-length transforms to 9dB, signal rate 16Kbaud.Wave beam dynamic range is simulated by random function randn (1, M), due to non-expansion
Signal in frequency communication system is that frequency range can divide, therefore does not consider wave beam aliasing.Fig. 9 is respectively the method provided by the present invention in non-expansion
Output signal-to-noise ratio under frequency signal and the transformation relation curve map with input signal-to-noise ratio.Similar with the case of spread spectrum, the present invention carries
The method of confession can not only be effectively compressed quantizing bit number, and can provide preferably output.
Above-mentioned the simulation experiment result has absolutely proved load multi-beam sampled data dynamic range on star provided by the invention
Compression method has good applicability.
Claims (8)
1. load multi-beam sampled data compression method on a kind of star, it is characterised in that comprise the following steps：
On star in repeating process：
(1) multi-beam aliasing signal on star is quantified to obtain quantized signal；
(2) maximum of the quantized signal per row element is extracted, is negated linear transformation square is built after being handled with diagonalization successively
Battle array；
(3) realize that amplitude dynamic range is compressed by way of matrix multiple, i.e., quantized signal is multiplied with the matrix of a linear transformation
Obtain condensation matrix；
(4) to condensation matrix carry out DA conversions, then it is quantified after pass through Ka band transmissions；
During ground station reception：
(5) dictionary matrix Φ is obtained using dictionary learning method in the case where ideal is without noise cancellation signal；
(6) initially believed into user using the data recovery forwarded on star of the sparse restructing algorithm by reception under dictionary matrix Φ
Number.
2. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that step
(5) comprise the following steps：
[501] it is preferable without noise cancellation signal progress piecemeal processing to what is received；
[502] sparse coding is carried out without noise cancellation signal to the ideal after piecemeal, builds initial dictionary；
[503] renewal is iterated to initial dictionary by dictionary learning method, obtains dictionary matrix.
3. load multi-beam sampled data compression method on a kind of star according to claim 2, it is characterised in that step
[503] iteration is terminated when given threshold or iterations meet C >=WQ when iteration precision is less than in, wherein C is iterations, Q
The number of piecemeal is carried out without noise cancellation signal for ideal, W is the integer for being less than Q/2 more than 1.
4. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that step
(6) comprise the following steps：
[601] under dictionary matrix Φ using the data recovery forwarded on star of the sparse restructing algorithm by reception into multichannel block count
According to；
[602] piecemeal recovery is carried out to multichannel block data, reverts to user's initial signal.
5. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that the step
Suddenly in (1) multi-beam aliasing signal show in form be spread-spectrum signal or non-spread-spectrum signal；Adjusted in modulation system for phase
Signal, frequency modulated signal or am signals processed.
6. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that the step
Suddenly in (4) ideal without noise cancellation signal in modulation system it is identical with multi-beam aliasing signal.
7. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that the step
Suddenly dictionary learning method is K-SVD, optimal dictionary learning method or Fisher discriminate dictionary learning methods in (5).
8. load multi-beam sampled data compression method on a kind of star according to claim 1, it is characterised in that the step
Suddenly sparse restructing algorithm is that matching pursuit algorithm, convex optimized algorithm or the non-convex of iteration with Fast Convergent characteristic are calculated in (6)
Method.
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| CN109041002A (en) * | 2018-08-22 | 2018-12-18 | 中国农业科学院农业信息研究所 | A kind of reading intelligent agriculture Internet of Things compression method |
| WO2024032775A1 (en) * | 2022-08-12 | 2024-02-15 | 华为技术有限公司 | Quantization method and apparatus |
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| CN109041002A (en) * | 2018-08-22 | 2018-12-18 | 中国农业科学院农业信息研究所 | A kind of reading intelligent agriculture Internet of Things compression method |
| CN109041002B (en) * | 2018-08-22 | 2020-05-26 | 中国农业科学院农业信息研究所 | Intelligent agricultural Internet of things signal compression method |
| WO2024032775A1 (en) * | 2022-08-12 | 2024-02-15 | 华为技术有限公司 | Quantization method and apparatus |
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