CN107248964B - Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals - Google Patents
Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals Download PDFInfo
- Publication number
- CN107248964B CN107248964B CN201710353940.2A CN201710353940A CN107248964B CN 107248964 B CN107248964 B CN 107248964B CN 201710353940 A CN201710353940 A CN 201710353940A CN 107248964 B CN107248964 B CN 107248964B
- Authority
- CN
- China
- Prior art keywords
- signal
- energy operator
- frequency
- spectrum
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0262—Arrangements for detecting the data rate of an incoming signal
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Power Engineering (AREA)
- Monitoring And Testing Of Transmission In General (AREA)
Abstract
The invention belongs to the technical field of time-frequency overlapping signals, and discloses a code rate estimation method for an underlay frequency spectrum sharing time-frequency overlapping signal, which comprises the following steps: calculating an energy operator by the received time-frequency overlapping MPSK signal; taking an absolute value of the energy operator of the obtained signal and performing FFT to obtain an energy operator spectrum; the energy operator spectrum is used for solving an energy operator again; and extracting the peak value of the energy operator spectrum according to the number of the overlapped signal components to obtain the code element rate of each component signal. The method has good estimation performance in the environment with lower signal-to-noise ratio; and when the number of the overlapped signals is 2 or 3, estimating a simulation result of the code rate under different SNR. For any two signal overlap or three signal overlap, the estimation performance of the symbol rate is better when the signal-to-noise ratio is greater than 0 dB.
Description
Technical Field
The invention belongs to the technical field of time-frequency overlapping signal technology and blind signal processing, and particularly relates to a code rate estimation method for an underlay frequency spectrum sharing time-frequency overlapping signal.
Background
Under the underlay frequency spectrum sharing mode, the primary user and a plurality of secondary users share the frequency spectrum, and the frequency spectrum utilization rate is improved. The primary user and the secondary user are completely overlapped in time domain, partially overlapped in frequency domain, and signals are independent. In underlay cognitive radio, a secondary user and a primary user can share a frequency spectrum on the premise of not exceeding interference temperature, the interference temperature is influenced by parameters such as bandwidth, and the symbol rate is directly related to the bandwidth, so that the estimation of the symbol rate is needed for measuring the interference temperature. Most of the existing methods for estimating the code rate of time-frequency overlapping signals utilize the cyclostationarity characteristics of the signals, such as cyclic cumulant, cyclic spectrum and the like. Guo Li et al groups the four-order cycle accumulated magnitudes at different cycle frequencies into a set of pulses and performs spectral analysis on the pulses. And finally, calculating the symbol rate of the component signals in the time-frequency overlapping signals through extracting the spectrum period. The method can estimate the symbol rates of BPSK, QPSK, and 16 QAM; the disadvantage is that the fourth-order cycle cumulant is taken as a statistical characteristic quantity, and a large number of data points are needed for calculation. (Guo Li Kuang Dynasty, Li Rong, etc.. Single-channel time-frequency overlapped double-signal code rate estimation method [ J ] electronic information countermeasure technology, 2009,24(1): 1-4). Slowly smelling to solve the cyclic spectrum of the time-frequency overlapping signal, estimating the carrier frequency of the component signal according to the cyclic spectrum, then estimating the code element rate of each overlapping component according to the characteristic, wherein the section contains the spectral line information related to the code rate. The problem of pairing of carrier frequencies and code rates among different signals is solved, and joint estimation of the carrier frequencies and the code rates can be carried out. The calculation complexity of the cyclic spectrum estimation is large, and the code rate estimation precision is directly influenced by the carrier frequency estimation precision. The method [ J ] data acquisition and processing, 2014,29(3):456 and 464 ] Zhang Yangyang improves a frequency domain smoothing algorithm of the cyclic spectrum, and the improved algorithm can carry out rapid estimation on the cyclic spectrum and extract the symbol rate of the MPSK. Compared with the traditional cyclic spectrum estimation algorithm, the method has the advantages that the complexity is reduced, but the calculation amount is still large. (Zhang Yangyu Penhua. single channel mixed signal parameter estimation fast algorithm for improving cyclic spectrum by correlation of bit spectrum [ J ]. Signal processing, 2016,32(4):404- & 416). The LiuK et al do two wavelet transforms on single-channel overlapping signals, then solve the FFT on the wavelet transform coefficients, and extract the symbol rate from the spectrum. Although the method does not need to know or estimate carrier frequency information in advance, the method can only be used for the special case that the carrier frequencies and the code rates of two overlapped signals are the same. Meanwhile, wavelet transformation is extremely sensitive to noise, and a good estimation effect can be obtained when the SNR is more than 6 dB. (Liu K, ZuoZY, Huang Q. Asymbolate evaluation method for linear modulation of signals [ C ]. IetInternational communication transfer Wireless Mobile and digital modulation. IEEEXplore,2009:233-
In summary, the problems of the prior art are as follows: the existing method for estimating the code rate of the time-frequency overlapping signals has the problems of higher calculation complexity, only being suitable for the special condition that the carrier frequencies and the code rates of the two overlapping signals are the same, and poorer estimation performance under the condition of low signal-to-noise ratio.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for estimating the code rate of an underlay frequency spectrum sharing time-frequency overlapping signal.
The invention is realized in this way, a method for estimating the code rate of an underlay frequency spectrum sharing time-frequency overlapping signal code, the method for estimating the code rate of the underlay frequency spectrum sharing time-frequency overlapping signal code comprises the following steps:
step one, calculating an energy operator by using the received time-frequency overlapping MPSK signal;
taking an absolute value of the obtained energy operator of the signal and performing FFT to obtain an energy operator spectrum;
thirdly, solving an energy operator again by the energy operator spectrum;
and step four, extracting the peak value of the energy operator spectrum according to the number of the overlapped signal components to obtain the code element rate of each component signal.
Further, the implementation of taking an absolute value of the received time-frequency overlapping MPSK signal energy operator and performing FFT to obtain an energy operator spectrum in the second step is performed as follows:
the signal model of time-frequency overlapping MPSK is represented as:
si(t)＝Aiexp[j(wcit+φi(t))]；
wherein N is the number of signal components of the time-frequency overlapping signal, N (t) is additive white Gaussian noise, si(t) is the signal component of the time-frequency superposed signal, where AiRepresenting the amplitude of a signal component, anN-th symbol representing a signal component, p (T) a shaping filter function, TbiSymbol period, w, representing a signal componentciRepresenting the carrier frequency of the signal component.
Further, in the second step, an absolute value is obtained by an energy operator of the time-frequency overlapped MPSK signal:
z(n)＝|x(n)2-x(n-1)x(n+1)|；
wherein, z (n) represents the absolute value of the energy operator of the received time-frequency overlapping MPSK signal, and x (n) represents the nth sampling point of the received time-frequency overlapping MPSK signal.
And (f) performing FFT on z (n) to obtain an energy operator spectrum S (f) of the time-frequency overlapping signal:
S(f)＝FFT(z(n))。
further, the solving of the energy operator spectrum for the second time in the third step specifically includes:
V(f)＝S(f)2-S(f-1)S(f+1)；
wherein f represents the frequency corresponding to the spectral line in the energy operator spectrum s (f), and v (f) represents the quantum of the energy operator spectrum, which can eliminate noise and facilitate spectral line extraction.
The invention also aims to provide a code rate estimation method applying the underlay frequency spectrum sharing mode time-frequency overlapping signals in the cognitive radio.
The invention has the advantages and positive effects that: the method has good estimation performance in a low signal-to-noise ratio environment; fig. 2 and fig. 3 show the simulation results of code rate estimation under different SNRs when the number of the overlapped signals is 2 or 3, respectively; for any two signal overlap or three signal overlap, the estimation performance of the symbol rate is better when the signal-to-noise ratio is greater than 0 dB.
Drawings
Fig. 1 is a flowchart of a method for estimating code rate of an underlay spectrum sharing time-frequency overlapping signal according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of Normalized Mean Square Error (NMSE) of code rate estimation of time-frequency overlapping dual signals under different signal-to-noise ratios according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of Normalized Mean Square Error (NMSE) of code rate estimation of three time-frequency overlapped signals under different signal-to-noise ratios according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the method for estimating the code rate of an underlay spectrum sharing time-frequency overlapping signal provided by the embodiment of the present invention includes the following steps:
s101: calculating an energy operator by the received time-frequency overlapping MPSK signal;
s102: taking an absolute value of the energy operator of the obtained signal and performing FFT to obtain an energy operator spectrum;
s103: the energy operator spectrum is used for solving an energy operator again;
s104: and extracting the peak value of the energy operator spectrum according to the number of the overlapped signal components to obtain the code element rate of each component signal.
In step S102, the implementation of taking an absolute value of the received time-frequency overlapping MPSK signal energy operator and performing FFT to obtain an energy operator spectrum is performed as follows:
the signal model of time-frequency overlapping MPSK is represented as:
si(t)＝Aiexp[j(wcit+φi(t))]；
wherein N is the number of signal components of the time-frequency overlapping signal, N (t) is additive white Gaussian noise, si(t) is the signal component of the time-frequency superposed signal, where AiRepresenting the amplitude of a signal component, anN-th symbol representing a signal component, p (T) a shaping filter function, TbiSymbol period, w, representing a signal componentciRepresenting the carrier frequency of the signal component.
Taking an absolute value of an energy operator of the time-frequency overlapped MPSK signal:
z(n)＝|x(n)2-x(n-1)x(n+1)|；
wherein, z (n) represents the absolute value of the energy operator of the received time-frequency overlapping MPSK signal, and x (n) represents the nth sampling point of the received time-frequency overlapping MPSK signal.
And (f) performing FFT on z (n) to obtain an energy operator spectrum S (f) of the time-frequency overlapping signal:
S(f)＝FFT(z(n))；
in step S103, the implementation of the energy operator for solving the energy operator spectrum again is performed as follows:
V(f)＝S(f)2-S(f-1)S(f+1)；
wherein f represents the frequency corresponding to the spectral line in the energy operator spectrum S (f), and V (f) represents the quantum of the energy operator spectrum.
And step S104, performing spectral line extraction on the processed energy operator spectrum. And respectively extracting the first N peak values according to the number N of the signal sources, wherein the frequency corresponding to the spectrum peaks is the code rate of each component signal.
The application effect of the present invention will be described in detail with reference to simulation experiments.
The experiment adopts time-frequency aliasing MPSK signals, and 2000 MonteCarlo experiments are carried out. The evaluation criterion of the detection is normalized mean square error
And respectively carrying out simulation verification on the condition that two or three signals are contained in the overlapped signals. The modulation types of the signal components include BPSK, QPSK, and 8 PSK. And selecting the conditions of pairwise combination or three combination of signals with different modulation modes for simulation. Wherein: sampling frequency fs12000Hz, and the power ratio of each component is gamma1:γ21:1 or γ1:γ2:γ31:1:1, spectrum overlap ratio ξ 50%, f is set for modulation parameters of each component of time-frequency overlapped dual signalc1＝2000Hz,fc2＝3000Hz，rc1＝400Hz,rc21000 Hz; for a time-frequency overlapping signal of three components, the parameter is set as rc1＝400Hz,rc2＝1000Hz、rc3600 Hz. Fig. 2 and fig. 3 show simulation results of code rate estimation under different SNRs when the number of overlapped signals is 2 or 3, respectively. For any two signal overlap or three signal overlap, the estimation performance of the symbol rate is better when the signal-to-noise ratio is greater than 0 dB.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (1)
1. An underlay frequency spectrum sharing time-frequency overlapping signal code rate estimation method is characterized by comprising the following steps of:
step one, calculating an energy operator by using the received time-frequency overlapping MPSK signal;
taking an absolute value of the obtained energy operator of the signal and performing FFT to obtain an energy operator spectrum;
thirdly, solving an energy operator again by the energy operator spectrum;
extracting the peak value of the processed energy operator spectrum according to the number of the overlapped signal components to obtain the code element rate of each component signal;
the second step of taking an absolute value of the energy operator of the time-frequency overlapped MPSK signal and performing FFT to obtain an energy operator spectrum specifically comprises:
z(n)＝|x(n)2-x(n-1)x(n+1)|；
wherein z (n) represents the absolute value of the energy operator of the received time-frequency overlapping MPSK signal, and x (n) represents the nth sampling point of the received time-frequency overlapping MPSK signal;
and (f) performing FFT on z (n) to obtain an energy operator spectrum S (f) of the time-frequency overlapping signal:
S(f)＝FFT(z(n))；
the solving of the energy operator spectrum for the second time in the third step specifically includes:
V(f)＝S(f)2-S(f-1)S(f+1)；
wherein f represents the frequency corresponding to the spectral line in the energy operator spectrum S (f), and V (f) represents the energy operator of the energy operator spectrum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710353940.2A CN107248964B (en) | 2017-05-18 | 2017-05-18 | Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710353940.2A CN107248964B (en) | 2017-05-18 | 2017-05-18 | Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107248964A CN107248964A (en) | 2017-10-13 |
| CN107248964B true CN107248964B (en) | 2020-06-09 |
Family
ID=60017008
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710353940.2A Active CN107248964B (en) | 2017-05-18 | 2017-05-18 | Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107248964B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107612933A (en) * | 2017-10-20 | 2018-01-19 | 广东岭南职业技术学院 | A kind of novel computer internet worm system of defense |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267421A (en) * | 2008-04-21 | 2008-09-17 | 上海大学 | An OFDM time shift channel measuring method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8849605B2 (en) * | 2011-08-23 | 2014-09-30 | Qualcomm Incorporated | Method and apparatus for sensor based pedestrian motion detection in hand-held devices |
| CN103116064A (en) * | 2013-02-06 | 2013-05-22 | 湖南大学 | Method and device for detecting voltage fluctuation and flicker based on energy operator and spectrum correction |
| CN103607361B (en) * | 2013-06-05 | 2017-03-08 | 西安电子科技大学 | The parameter prediction method of time-frequency overlapped signal under Alpha Stable distritation noise |
| CN106130940B (en) * | 2016-06-22 | 2019-07-26 | 中国人民解放军信息工程大学 | Satellite-signal fine feature estimation method based on the progressive CZT of precision |
-
2017
- 2017-05-18 CN CN201710353940.2A patent/CN107248964B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267421A (en) * | 2008-04-21 | 2008-09-17 | 上海大学 | An OFDM time shift channel measuring method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107248964A (en) | 2017-10-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sun et al. | Wideband spectrum sensing for cognitive radio networks: a survey | |
| Lundén et al. | Robust nonparametric cyclic correlation-based spectrum sensing for cognitive radio | |
| CN102546061B (en) | Self-adaptive time-frequency hole detection method based on wavelet transformation | |
| CN108880604B (en) | Multi-component frequency modulation interference suppression method and device in spread spectrum communication system | |
| Carvajal et al. | EM-based maximum-likelihood channel estimation in multicarrier systems with phase distortion | |
| CN111935046B (en) | Low-complexity frequency shift keying signal symbol rate estimation method | |
| Shi et al. | Blind OFDM systems parameters estimation for software defined radio | |
| CN113037663A (en) | Improved code element rate estimation algorithm suitable for non-constant envelope signal | |
| Sisi et al. | Carrier frequency and symbol rate estimation based on cyclic spectrum | |
| Baxley et al. | MAP metric for blind phase sequence detection in selected mapping | |
| CN107248964B (en) | Method for estimating code rate of underlay frequency spectrum sharing time-frequency overlapping signals | |
| Gorcin et al. | An OFDM signal identification method for wireless communications systems | |
| Cai et al. | Further results on multicarrier MFSK based underwater acoustic communications | |
| CN104270328B (en) | A kind of signal to noise ratio real-time estimation method | |
| Liu et al. | Fractional frequency offset estimation for OFDM systems in non-cooperative communication | |
| CN115378776A (en) | MFSK modulation identification method based on cyclic spectrum parameters | |
| CN114584232A (en) | Wireless communication sub-band signal-to-noise ratio measuring method based on channel detection reference signal | |
| Mishra et al. | A novel ramp-based pulse shaping filter for reducing out of band emission in 5g GFDM system | |
| TWI396415B (en) | Channel length estimation method and estimator of orthogonal frequency division multiplexing system | |
| Björsell et al. | A waveform digitizer-based automatic modulation classifier for a flexible spectrum management | |
| Pambudi et al. | Statistical properties proposed for blind classification OFDM modulation scheme | |
| Naiqi et al. | Research of Adaptive Resolution Spectrum Sensing Method Based on Discrete Wavelet Packet Transform | |
| Wang et al. | An Improved Channel Estimation Algorithm Based on WD‐DDA in OFDM System | |
| An et al. | Multimodality-aided multicarrier waveform recognition in low SNR regimes based on denoised cyclic autocorrelation transformation | |
| Szczepaniak et al. | Radio transmission masking on the example of FSK modulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |