CN109257313B - Signal demodulation method based on compressed sensing technology - Google Patents

Signal demodulation method based on compressed sensing technology Download PDF

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CN109257313B
CN109257313B CN201811096408.8A CN201811096408A CN109257313B CN 109257313 B CN109257313 B CN 109257313B CN 201811096408 A CN201811096408 A CN 201811096408A CN 109257313 B CN109257313 B CN 109257313B
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马涛
张典典
陶致远
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Shaanxi Yixing Yuanneng Technology Co ltd
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/10—Frequency-modulated carrier systems, i.e. using frequency-shift keying
    • H04L27/14—Demodulator circuits; Receiver circuits
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/10—Frequency-modulated carrier systems, i.e. using frequency-shift keying
    • H04L27/14—Demodulator circuits; Receiver circuits
    • H04L27/144—Demodulator circuits; Receiver circuits with demodulation using spectral properties of the received signal, e.g. by using frequency selective- or frequency sensitive elements
    • H04L27/148—Demodulator circuits; Receiver circuits with demodulation using spectral properties of the received signal, e.g. by using frequency selective- or frequency sensitive elements using filters, including PLL-type filters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate

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Abstract

The invention relates to a signal demodulation method based on a compressed sensing technology, which comprises the following steps: preprocessing an original signal to obtain a signal to be sampled; sampling the signal to be sampled at unequal intervals to obtain a sampling signal; constructing a perceptual matrix for demodulating the sampled signals; and demodulating the frequency position of the original signal according to the sampling signal and the perception matrix. The signal demodulation method can recover the low-rate sampled signal by a non-equal-interval compressed sensing sampling technology and by constructing a sensing matrix, so that the average sampling rate is greatly reduced, and the signal demodulation method does not use mixing processing, so that the power consumption is remarkably reduced.

Description

Signal demodulation method based on compressed sensing technology
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a signal demodulation method based on a compressed sensing technology.
Background
In the conventional research method, the acquisition and processing of signals in wireless communication are divided into four steps: sampling, compression, transmission and decompression. However if the signal itself is compressible, it is possible to combine sampling and compression into the same process. In 2006, Candes proves that a signal can be accurately reconstructed from partial Fourier transform coefficients of the signal, and the signal is taken as a theoretical basis of compressed sensing, so that the compressed sensing technology becomes a brand-new signal acquisition method. According to the compressed sensing theory, if a signal is in a certain domain
Figure GDA0002384862480000012
Can be sparsely represented (e.g., linearly synthesized with K basis vectors), and can be represented with a sample point value slightly larger than K.
At present, the receiver based on the compressed sensing technology has a compressed sensing receiver with high-speed mixing, and the receiver has multiple types: random modulation receiver, random modulation pre-integration receiver, random coiling machine receiver, etc. A random modulation receiver multiplies a radio frequency signal by a high rate (greater than the nyquist sampling rate) pseudorandom sequence, filters the multiplied signal with a low pass filter, and then sample demodulates at a low rate. As long as the filtering bandwidth and sampling rate are greater than
Figure GDA0002384862480000011
The sparse signal can be recovered, wherein K is the maximum frequency point number of the radio frequency signal, and W is the Nyquist sampling speedAnd (4) rate. The random modulation pre-integration receiver mixes a radio frequency signal with a group of pseudo-random sequences, then uses an integrator to integrate the signals after multi-channel mixing, and finally carries out low-rate sampling demodulation on the group of integration results. A random convolutional receiver convolves a radio frequency signal with a random wideband signal and then samples and demodulates the signal at low rates and unequal intervals.
In summary, the high-speed mixing compressed sensing receiver recovers the original signal by low-rate sampling, and reduces the sampling rate, but because a high-rate pseudo-random signal generator is used, the power consumption is high, and therefore, a compressed sensing signal demodulation method capable of reducing the power consumption is required to be provided.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a signal demodulation method based on compressed sensing technology. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a signal demodulation method based on a compressed sensing technology, which comprises the following steps:
s1: preprocessing an original signal to obtain a signal to be sampled;
s2: sampling the signal to be sampled at unequal intervals to obtain a sampling signal;
s3: constructing a perceptual matrix for demodulating the sampled signals;
s4: and demodulating the frequency position of the original signal according to the sampling signal and the perception matrix.
In an embodiment of the present invention, the S1 includes:
and respectively filtering and amplifying the original signal received by the receiver through a band-pass filter and a low-noise amplifier to obtain the signal to be sampled.
In an embodiment of the present invention, the expression of the signal to be sampled is:
Sr(t)＝cos(2πfct+φ1)cos(2πf0nt)，
wherein f is0In order to modulate the frequency of the base carrier,fcis the frequency of the carrier signal, fc＝Mf0M is a positive integer, n is the frequency position of the original signal, phi1Is the phase.
In an embodiment of the present invention, the S2 includes:
for the signal to be sampled at a time interval T0Carrying out non-equal interval sampling for K times to obtain K sampling point values, wherein the expression of the sampling point values is as follows:
Figure GDA0002384862480000031
wherein, T0＝1/f0G (K) is additive white gaussian noise, N is a parameter, K is 1, 2, 3 … K, and M is M mod N.
In an embodiment of the present invention, the S3 includes:
constructing a sine and cosine basis perception matrix composed of cosine vectors and sine vectors, wherein the expression of the perception matrix is as follows: phi is ═ phi1φ2… φL]Wherein, in the step (A),
Figure GDA0002384862480000034
Figure GDA0002384862480000032
in an embodiment of the present invention, the S3 includes: constructing a Fourier-based sensing matrix, wherein the expression of the Fourier-based sensing matrix is as follows: phi is ═ phi1φ2… φL]Wherein, in the step (A),
Figure GDA0002384862480000035
Figure GDA0002384862480000033
in an embodiment of the present invention, the S4 includes:
s41: calculating a least square estimation value t according to the sampling signal and the perception matrix;
s42: selecting two maximum values t in the least square estimation value t1And t2；
S43: obtaining the maximum value t1And t2Corresponding frequency positions p and q, then p ═ m + n, q ═ m-n, where p is>q；
S44: and obtaining the frequency position n of the original signal according to p-m + n and q-m-n.
In an embodiment of the present invention, before the S41, the method further includes:
representing the K sample point values as a vector, i.e., x ═ x1,x2,…,xK)TWherein, in the step (A),
Figure GDA0002384862480000041
in one embodiment of the present invention, the expression of the least squares estimate t is:
Figure GDA0002384862480000042
compared with the prior art, the invention has the beneficial effects that:
1. the signal demodulation method based on the compressive sensing technology recovers the sparse signal by the sampling value through the unequal-interval compressive sensing sampling technology and the construction of the sensing matrix, and can recover the radio frequency signal sampled at a low rate, so that the average sampling rate of a receiver is greatly reduced.
2. The signal demodulation method based on the compressed sensing technology does not perform mixing processing, so that the power consumption is obviously reduced.
Drawings
Fig. 1 is a flowchart of a signal demodulation method based on a compressed sensing technology according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a non-equidistant sampling method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a signal receiver based on a compressed sensing technology according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1 to fig. 3, fig. 1 is a flowchart of a signal demodulation method based on a compressive sensing technique according to an embodiment of the present invention. The signal demodulation method of the embodiment includes:
s1: preprocessing an original signal to obtain a signal to be sampled;
s2: sampling the signal to be sampled at unequal intervals to obtain a sampling signal;
s3: constructing a perceptual matrix for demodulating the sampled signals;
s4: and demodulating the frequency position of the original signal according to the sampling signal and the perception matrix.
Further, the S1 includes:
and respectively filtering and amplifying the original signal received by the receiver through a band-pass filter and a low-noise amplifier to obtain the signal to be sampled.
Specifically, the expression of the signal to be sampled is:
Sr(t)＝cos(2πfct+φ1)cos(2πf0nt)，
wherein f is0For modulating the frequency of the fundamental carrier, fcIs the frequency of the carrier signal, fc＝Mf0M is a positive integer, n is the frequency position of the original signal, phi1Is the phase. That is, the frequency f of the carrier signalcFor the frequency f of the modulated base carrier0M times of.
Further, in this embodiment, the original signal is composed of an intermediate frequency signal cos (2 π f)0nt) multiplied by a carrier signal cos (2 pi f)ct+φ1) Is formed of wherein f0And n is the frequency of the intermediate frequency signal. In the present embodiment, the frequency position of the intermediate frequency signal is essentially to be acquired finally.
By modulating the intermediate frequency signal to other frequencies, the noise in transmission can be reduced; the frequency division multiplexing is realized, namely, the same channel between the same frequencies transmits multi-channel signals without aliasing; and can propagate a greater distance, facilitating reception.
Further, referring to fig. 2, fig. 2 is a schematic diagram of a non-equidistant sampling method according to an embodiment of the present invention. The S2 includes:
for the signal to be sampled at a time interval T0Carrying out non-equal interval sampling for K times to obtain K sampling point values, wherein T0＝1/f0The expression of the K sampling point values is as follows:
Figure GDA0002384862480000061
g (K) is additive white gaussian noise, N is a parameter, K is 1, 2, 3 … K, and M is M mod N. mod represents a remainder operation, i.e., M is equal to the remainder of M divided by N.
Specifically, N is a parameter related to N, and N is limited by the frequency location N of the original signal. In the present embodiment, the specific relationship between N and N is: l is more than or equal to N + N,
Figure GDA0002384862480000062
but is not limited thereto.
Figure GDA0002384862480000063
Meaning a down-rounding operation, i.e. L takes the largest integer value smaller than (N-1)/2.
Further, in the above expression, { μ 1, μ 2, …, μ K } is a set of cyclic difference sets with the parameters (N, K, λ). If we need to sample N samples at the nyquist sampling rate, we can recover the original signal from the cyclic difference set by sampling K points only. That is, N sampling points are originally needed to be sampled at { μ 1, μ 2, …, μ K }, and in this case, K sampling points are needed to be sampled at { μ 1, μ 2, …, μ K }, so that the original signal can be recovered, and K is less than N.
From the aboveAs can be seen, the sampled signal contains two frequency point values, i.e., (m + n) f0And (m-n) f0。
Then, by adopting the compressed sensing theory, the two frequency points (m + n) f can be estimated through the K sampling points0And (m-n) f0And then the frequency position n of the original signal can be demodulated.
Specifically, the S3 includes:
constructing a sine and cosine basis sensing matrix composed of cosine vectors and sine vectors, wherein the expression of the sensing matrix is as follows: phi is ═ phi1φ2… φL]Wherein, in the step (A),
Figure GDA0002384862480000064
Figure GDA0002384862480000071
it should be noted that, since the signal of this embodiment is in cosine form, the sensing matrix of this embodiment uses sine and cosine basis sensing matrix, however, in other embodiments, other forms of sensing matrix may also be used.
Alternatively, the sensing matrix may be further configured as a fourier-based sensing matrix, where the expression of the fourier-based sensing matrix is: phi is ═ phi1φ2… φL]Wherein, in the step (A),
Figure GDA0002384862480000076
Figure GDA0002384862480000072
further, the S4 includes:
s41: obtaining a least square estimation value t according to the sampling signal and the perception matrix;
specifically, the K sampling point values obtained by non-equidistant sampling in step S2 are first expressed in a vector form, i.e., x is (x ═ x)1,x2,…,xK)TWherein, in the step (A),
Figure GDA0002384862480000073
Figure GDA0002384862480000074
the expression of the least square estimation value t is as follows:
Figure GDA0002384862480000075
wherein v is 1, 2, 3 … L.
S42: selecting two maximum values t in the least square estimation value t1And t2；
S43: obtaining the maximum value t1And t2Corresponding frequency positions p and q, then p ═ m + n, q ═ m-n, where p is>q；
According to the expression formula analysis of the sampling signal, t is a larger value at the m + n or m-n frequency position, and the other positions are smaller values. Therefore, let two maximum values t of the least squares estimate t1And t2The corresponding frequency positions are p and q, and then p is m + n and q is m-n.
S44: and obtaining the frequency position n of the original signal according to p-m + n and q-m-n.
Specifically, m is 1/2(p + q), n is 1/2(p-q), and the value of n is calculated, i.e., the frequency position of the original signal, i.e., the demodulation of the original signal is completed.
The signal demodulation method based on the compressed sensing technology recovers the sparse signal by the sampling value through the unequal-interval compressed sensing sampling technology and the sensing matrix, and can recover the radio frequency signal sampled at the low rate, so that the average sampling rate of a receiver is greatly reduced. In addition, the signal demodulation method does not perform mixing processing, and thus reduces power consumption.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a signal receiver based on a compressed sensing technique according to an embodiment of the present invention. The signal receiver comprises a band-pass filter 1, a low noise amplifier 2, a sample-and-hold unit 3, a sampling unit 4 and a micro-control unit 5. The band-pass filter 1 is connected to the low noise amplifier 2, and the band-pass filter 1 is used for inputting the received radio frequency signal and filtering the unwanted signal. The low noise amplifier 2 is connected to the sample-and-hold circuit 3, and the low noise amplifier 2 is used for performing signal amplification processing on the filtered radio frequency signal. The sample-and-hold unit 3 is connected to the sampling unit, and the sample-and-hold unit 3 is used for capturing a signal voltage value at a specific time point and holding the signal voltage value. The sampling unit 4 is used for sampling the signal to be sampled after amplifying the signal, and the sampling unit 4 includes an enable terminal EN, the enable terminal EN is connected with the sample hold circuit 3 and the low noise amplifier 2 respectively, and is used for making the low noise amplifier 2 and the sample hold circuit 3 work only when sampling, and in a closed state at other times, so that the power consumption of the whole receiver only depends on the average sampling rate, and the whole power consumption is reduced. In the present embodiment, the sampling unit 4 is an ADC analog-to-digital converter.
Further, the micro control unit 5 is connected to the low noise amplifier 2, the sample-and-hold unit 3, and the sampling unit 4, respectively, for controlling the low noise amplifier 2, the sample-and-hold unit 3, and the sampling unit 4 to perform corresponding operations, respectively. In this embodiment, the micro control unit 5 is a single chip microcomputer. Further, the signal demodulation processing in the above-described signal demodulation method is also performed in the micro control unit 5.
The embodiment constructs a signal receiver with transmission capability and low power consumption based on a compressed sensing technology, the signal receiver has a simple structure, a local oscillator is not needed to generate a local oscillation signal, a low noise amplifier and a sample hold circuit are only operated during sampling, and are closed at other times, so that the power consumption of the receiver is only related to an average sampling rate. According to the theory of compressed sensing, if signals are very sparse, a sensing matrix with a large compression ratio can be adopted, so that a sampling unit samples at a low rate, and then the frequency position of the received original radio frequency signal is restored, and the power consumption of the signal receiver can reach a low level.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (7)

1. A method for demodulating a signal based on a compressed sensing technique, the method comprising:
s1: preprocessing an original signal to obtain a signal to be sampled;
s2: sampling the signal to be sampled at unequal intervals to obtain a sampling signal;
s3: constructing a perceptual matrix for demodulating the sampled signals;
s4: demodulating the frequency position of the original signal according to the sampling signal and the perception matrix;
wherein, the expression of the signal to be sampled is:
Sr(t)＝cos(2πfct+φ1)cos(2πf0nt)，
wherein f is0For modulating the frequency of the base carrier, fcIs the frequency of the carrier signal, fc＝Mf0M is a positive integer, n is the frequency position of the original signal, phi1In order to be the phase position,
the S2 includes:
for the signal to be sampled at a time interval T0Carrying out non-equal interval sampling for K times to obtain K sampling point values, wherein the expression of the sampling point values is as follows:
Figure FDA0002384862470000011
wherein, T0＝1/f0G (k) is additive white Gaussian noise, N is T0The total number of frequency positions in time, K is 1, 2, 3 … K, M is M mod N, μkIs the position of the kth sample point, [ phi ]2Is a phase。
2. The signal demodulation method according to claim 1, wherein said S1 includes:
and respectively filtering and amplifying the original signal received by the receiver through a band-pass filter and a low-noise amplifier to obtain the signal to be sampled.
3. The signal demodulation method according to claim 1, wherein said S3 includes:
constructing a sine and cosine basis perception matrix composed of cosine vectors and sine vectors, wherein the expression of the perception matrix is as follows: phi is ═ phi1φ2…φL]Wherein, in the step (A),
Figure FDA0002384862470000012
Figure FDA0002384862470000021
4. the signal demodulation method according to claim 1, wherein said S3 includes: constructing a Fourier-based sensing matrix, wherein the expression of the Fourier-based sensing matrix is as follows: phi is ═ phi1φ2…φL]Wherein, in the step (A),
Figure FDA0002384862470000024
Figure FDA0002384862470000022
v is 1, 2, 3 … L, i denotes an imaginary unit.
5. The signal demodulation method according to claim 3, wherein said S4 includes:
s41: calculating a least square estimation value t according to the sampling signal and the perception matrix;
s42: selecting two maximum values t in the least square estimation value t1And t2；
S43: obtaining the maximum value t1And t2Corresponding frequency positions p and q, then p ═ m + n, q ═ m-n, where p is>q；
S44: and obtaining the frequency position n of the original signal according to p-m + n and q-m-n.
6. The signal demodulation method according to claim 5, further comprising, before said S41:
representing the K sample point values as a vector, i.e., x ═ x1,x2,…,xK)TWherein, in the step (A),
Figure FDA0002384862470000023
7. the signal demodulation method according to claim 6, wherein said least squares estimate t is expressed by:
Figure FDA0002384862470000031
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102253117A (en) * 2011-03-31 2011-11-23 浙江大学 Acoustic signal collection method based on compressed sensing
EP2660618A1 (en) * 2012-05-04 2013-11-06 Esaote S.p.A. Biomedical image reconstruction method
CN103983850A (en) * 2014-05-13 2014-08-13 天津大学 Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing
CN107064919A (en) * 2017-04-25 2017-08-18 西安电子科技大学 The ultra-broadband signal method for parameter estimation being combined based on photoelectricity

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CN105207966A (en) * 2015-08-10 2015-12-30 中国民航大学 Compressed sensing PIE (Pulse Interference Elimination) system based on space-frequency coding
CN106789809A (en) * 2016-12-02 2017-05-31 天津大学 A kind of non-orthogonal multi-carrier transmission method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102253117A (en) * 2011-03-31 2011-11-23 浙江大学 Acoustic signal collection method based on compressed sensing
EP2660618A1 (en) * 2012-05-04 2013-11-06 Esaote S.p.A. Biomedical image reconstruction method
CN103983850A (en) * 2014-05-13 2014-08-13 天津大学 Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing
CN107064919A (en) * 2017-04-25 2017-08-18 西安电子科技大学 The ultra-broadband signal method for parameter estimation being combined based on photoelectricity

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