CN114268526A - BPSK and QPSK signal modulation identification method based on degree characteristics of graph - Google Patents
BPSK and QPSK signal modulation identification method based on degree characteristics of graph Download PDFInfo
- Publication number
- CN114268526A CN114268526A CN202111576119.XA CN202111576119A CN114268526A CN 114268526 A CN114268526 A CN 114268526A CN 202111576119 A CN202111576119 A CN 202111576119A CN 114268526 A CN114268526 A CN 114268526A
- Authority
- CN
- China
- Prior art keywords
- signal
- bpsk
- graph
- qpsk
- spectrum
- 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.)
- Granted
Links
Images
Classifications
-
- 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
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention provides a BPSK and QPSK signal modulation identification method based on the degree characteristics of a graph, aiming at the identification problem of two modulation signals of BPSK and QPSK. Firstly, a square spectrum of a signal to be identified is obtained, and then a rectangular window is added to obtain a truncated square spectrum. And then, carrying out image domain conversion on the truncated square spectrum, taking the sum of the degrees of each vertex of the image as identification statistic, setting a proper threshold, and comparing the identification statistic with the threshold to realize the modulation identification of the BPSK and QPSK signals. Simulation results show that the invention can more effectively identify BPSK and QPSK modulation signals under the condition of low signal-to-noise ratio, and the algorithm complexity is lower because the number of input signal points of graph conversion is less.
Description
Technical Field
The invention belongs to the field of signal identification and processing, and particularly relates to a BPSK and QPSK signal modulation identification method based on degree characteristics of a graph.
Background
The modulation mode identification of radar and communication signals is widely applied to the fields of military and civil use. In the military field such as electronic warfare, signal modulation mode identification is an important prerequisite for acquiring performance parameters of enemy radar; in the civil field, on the premise that the wireless communication channel resource is limited, the receiving end realizes the modulation mode identification of the signal, so that the channel can be saved to a greater extent. BPSK and QPSK signals are the two most commonly used phase-coded modulation signals in radar signals.
In recent years, a new graph-based signal processing algorithm has been widely used in signal processing and the like. In particular, in 2017, Kun Yan et al proposed a processing algorithm (Yan K, Wu H C, Xiao H, et al. novel Robust Band-Limited Signal Detection application Graphs [ J ]. IEEE Communications Letters, 2017.) for converting time series signals into graph topology, the main idea of the algorithm is to change the noise into a complete graph and convert the noisy signals into an incomplete graph to detect the complete connectivity of the graph to detect the presence or absence of signals. If applied to signal modulation recognition in this framework, it is necessary to change one of the time domain or other transform domain forms of a BPSK or QPSK signal to a noisy form and the other to a non-noisy form. One of the possible ideas is: after the BPSK signal and the QPSK signal are subjected to square operation, a plurality of larger values are removed, respective square correction spectrums can be obtained and are respectively converted into a complete graph and a non-complete graph, and the modulation identification problem can be converted into a complete graph detection problem. However, since the larger spectral line contains most of the information of the signal component, this processing will lose this important information, resulting in poor performance at lower signal-to-noise ratio. Furthermore, how many such large spectral lines are removed is itself a difficult technical problem to compromise.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a BPSK and QPSK signal modulation identification method based on the degree characteristics of a graph. The method carries out graph conversion on the truncated square spectrum, selects a specific characteristic quantity and a threshold, completes the identification of BPSK and QPSK modulation signals, has low calculation complexity of the algorithm, and has higher identification accuracy when the signal to noise ratio is low.
In order to achieve the purpose, the invention adopts the following technical scheme:
BPSK and QPSK signal modulation identification method based on degree characteristics of graph is characterized by comprising the following steps:
step 1: calculating a square spectrum of a signal to be identified;
step 2: taking the peak spectral line of the square spectrum as a central spectral line, and adding a rectangular window to obtain a truncated square spectrum;
and step 3: carrying out graph conversion on the truncated square spectrum, and extracting the sum of all vertex degrees of the graph as identification characteristic quantity;
and 4, step 4: setting a corresponding threshold;
and 5: and comparing the identification characteristic quantity with a set threshold, and further identifying the BPSK signal and the QPSK signal.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, in step 1, let the signal to be identified be x (n), perform a square operation and DFT conversion on the signal, square the spectrum module to obtain a square spectrum, which is denoted as y (k) ═ DFT [ x (x) ]2(n)]|)2K is 0, 1,., N-1, where N is the number of sample points of the signal.
Further, in step 2, for the square spectrum y (k), the peak spectral line position k ═ k of the square spectrum is searched outmaxWhere k denotes the line position, kmaxRepresenting peak spectral line position, setting rectangular window length as 2d, and k being equal to kmaxWindowing with the peak spectral line as the center to obtain:
the number of points of 0 in Y '(k) is deleted to obtain a truncated square spectrum B (m) ═ Y' (k), where m is 0. ltoreq. m.ltoreq.2d.
Further, when the number N of sample points of the signal is 1024, d is taken as 50.
Further, step 3 specifically includes the following steps:
step 3.1: normalizing the truncated square spectrum B (m) to obtain a normalized spectrum:
setting a quantization series q, and uniformly quantizing the normalized frequency spectrum, namely when i/q is less than B' (m) < i +1/q, i is more than or equal to 0 and less than or equal to q-1, the quantized frequency spectrum is U (m) ═ i + 1;
converting u (m) to the graph domain, constituting graph G (V, E), where the set of vertices V of the graph represents a mapping of quantization levels {1, 21，v2，...vq}; set of edges E ═ E of graphα，β|να∈V，νβ∈V}，eα，βRepresents an edge between two vertices of the graph; the specific way of constructing the diagram G (V, E) is: for each quantized sample U (m), traversing the level relation between the quantized sample U (m) and U (m +1) one by one when v existsαTo vβWhen the level of (2) jumps, the two vertices are connected, e α，β1 is ═ 1; otherwise, there is no connection between the two vertices, eα，β＝0；
Step 3.2: calculating a degree matrix D of G (V, E), and extracting a degree vector D (D) of a diagram formed by diagonal elements of the degree matrix D1，d2，...，dj，...，dq) Wherein d isjThe sum (d) of the degrees of computation, which is the sum of the numbers of edges connected to the jth vertex, is used as the identification feature amount.
Further, in step 4, a recognition threshold λ of BPSK and QPSK signals is setevtWhen the number of sample points N of the signal is 1024 and the number of graph vertices is 10, lambda isevtAnd 10 is taken.
Further, in step 5, if the identification characteristic quantity is smaller than the set threshold, the signal is a BPSK modulation signal; otherwise, it is a QPSK modulated signal.
The invention has the beneficial effects that: the invention properly windows the square spectrum of the observation signal, then carries out graph conversion, and completes the identification of the BPSK signal and the QPSK signal according to the degree matrix of the graph and the determined identification characteristic quantity, wherein, the square spectrum is directly utilized, the correction process of removing the large value is not needed, and the information of the signal component can be better reserved. Compared with the traditional correction spectrum-based identification algorithm, the method has the advantages that the power spectrums squared by the two types of signals are directly subjected to image domain transformation, the main information of the signals is effectively stored, the BPSK and QPSK signals can be effectively identified under the condition of low signal-to-noise ratio, the influence of parameter change is small, certain robustness is realized, and the algorithm efficiency is high. In addition, the invention is different from the existing recognition processing algorithm based on complete graph detection, analyzes the difference of signals and defines graph domain characteristics from the angle of a random graph theory, further expands the application field of the existing graph domain processing method, and enriches the processing means of the method.
Drawings
FIG. 1 is a flow chart of the identification method of the present invention.
Fig. 2 shows the mean of the sum of the degrees of the truncated squared spectrum generation plots for BPSK and QPSK signals at different signal-to-noise ratios.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
Fig. 1 is a flowchart of a BPSK and QPSK signal modulation identification method based on the degree characteristics of the graph. Firstly, a square spectrum of a signal to be identified is obtained, and then the square spectrum is windowed to obtain a truncated square spectrum. Then, the BPSK signal is converted into a map domain, the sum of the maps is used as identification statistic, a proper threshold is set, and the identification statistic is compared with the threshold to realize the modulation identification of the BPSK signal and the QPSK signal. Simulation results show that the method can identify BPSK and QPSK modulation signals under the condition of no signal prior information. The BPSK and QPSK signal modulation identification method based on the degree characteristics of the graph specifically comprises the following steps:
the number of points of 0 in Y '(k) is deleted to obtain a truncated square spectrum B (m) ═ Y' (k), where m is 0. ltoreq. m.ltoreq.2d.
Step 3, carrying out graph conversion on the truncated square spectrum, and extracting the sum of all vertex degrees of the graph as an identification characteristic quantity, wherein the method specifically comprises the following steps:
step 3.1, graph conversion: normalizing the truncated square spectrum B (m) to obtain a normalized spectrum:
then, setting a quantization series q, and uniformly quantizing the quantization series q, namely when i/q < B (m) < i +1/q, and 0 ≦ i ≦ q-1, the quantization frequency spectrum is U (m) ≦ i + 1;
finally, u (m) is converted to the graph domain, forming graph G (V, E), where the set of vertices V of the graph is a mapping of quantization levels {1, 21，ν2，...vq}; set of edges E ═ E of graphα，β|vα∈V，νβIs belonged to V }. The specific way of constructing the diagram G (V, E) is: for each quantized sample U (m), traversing the level relation between the quantized sample U (m) and U (m +1) one by one when v existsαTo vβWhen the level of (2) is changed, two vertices are connected, i.e. e α，β1 is ═ 1; otherwise, there is no connection between the two vertices, i.e. eα，β＝0；
Step 3.2, extracting and identifying characteristic quantity: calculating a degree matrix D of G (V, E), and extracting a degree vector D (D) of a diagram formed by diagonal elements of the degree matrix D1，d2，...，dj，...，dq) Wherein d isjThe sum (d) of the degrees of computation, which is the sum of the numbers of edges connected to the jth vertex, is used as the identification feature amount.
Step 5, comparing the identification characteristic quantity with a threshold, and identifying BPSK and QPSK signals. When sum (d) < lambdaevtThen, BPSK modulation signal is obtained; otherwise, the signal is QPSK modulated.
Table 1 shows the identification performance of BPSK/QPSK signals under different signal-to-noise ratios, and the simulation conditions are as follows: the signal-to-noise ratio is [ -6, -4, -2, 0, 2, 4, 6, 8], the sampling frequency is 100MHz, the carrier frequency is 20.76MHz, the symbol width is 640ns, the number of sample points is 1024, the initial phase is pi/4, the length of a rectangular window is 100, the number of vertexes of the graph is 10, and the simulation is carried out 1000 times under each condition. As can be seen from Table 1, when the SNR is greater than-4 dB, the average recognition accuracy can reach more than 95%.
TABLE 1 BPSK/QPSK signal identification performance under different SNR conditions
There is no connected component of a certain scale (the total number of vertices is greater than 10) in a graph generated by graph conversion of the truncated square spectrum extracted from the BPSK signal, and there is a connected component of a certain scale in a graph generated by graph conversion of the truncated square spectrum extracted from the QPSK signal. The magnitude of the sum of the degrees from which the graph is generated may characterize whether a connected component of a certain size is present in the graph. Fig. 2 is the average value of the sum of the degrees of the truncated square spectrum generation graphs of the BPSK signal and the QPSK signal under different signal-to-noise ratios, and accordingly, the BPSK/QPSK signal can be used for realizing modulation identification.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (7)
1. BPSK and QPSK signal modulation identification method based on degree characteristics of graph is characterized by comprising the following steps:
step 1: calculating a square spectrum of a signal to be identified;
step 2: taking the peak spectral line of the square spectrum as a central spectral line, and adding a rectangular window to obtain a truncated square spectrum;
and step 3: carrying out graph conversion on the truncated square spectrum, and extracting the sum of all vertex degrees of the graph as identification characteristic quantity;
and 4, step 4: setting a corresponding threshold;
and 5: and comparing the identification characteristic quantity with a set threshold, and further identifying the BPSK signal and the QPSK signal.
2. The BPSK and QPSK signal modulation identification method according to claim 1, wherein: in step 1, let the signal to be identified be x (n), perform square operation and DFT conversion on the signal, take the square of the spectrum mode to obtain a square spectrum, which is denoted as y (k) ═ DFT [ x | (x) }2(n)]|)2K is 0, 1,., N-1, where N is the number of sample points of the signal.
3. The BPSK and QPSK signal modulation identification method according to claim 1, wherein: in step 2, the square spectrum y (k) is searched for the peak spectral line position k ═ k of the square spectrummaxWhere k denotes the line position, kmaxRepresenting peak spectral line position, setting rectangular window length as 2d, and k being equal to kmaxWindowing with the peak spectral line as the center to obtain:
the number of points of 0 in Y' (k) is deleted to obtain a truncated square spectrum B (m) ═ Y (k), where m is 0. ltoreq. m.ltoreq.2d.
4. The BPSK and QPSK signal modulation identification method according to claim 3, wherein: when the number of sample points N of the signal is 1024, d is taken as 50.
5. The BPSK and QPSK signal modulation identification method according to claim 3, wherein: the step 3 specifically comprises the following steps:
step 3.1: normalizing the truncated square spectrum B (m) to obtain a normalized spectrum:
setting a quantization series q, and uniformly quantizing the normalized frequency spectrum, namely when i/q is less than B' (m) < i +1/q, i is more than or equal to 0 and less than or equal to q-1, the quantized frequency spectrum is U (m) ═ i + 1;
converting u (m) to the graph domain, constituting graph G (V, E), where the set of vertices V of the graph represents a mapping of quantization levels {1, 21，v2，...vq}; set of edges E ═ E of graphα，β|vα∈V，νβ∈V}，eα，βRepresents an edge between two vertices of the graph; the specific way of constructing the diagram G (V, E) is: for each quantized sample U (m), traversing the level relation between the quantized sample U (m) and U (m +1) one by one when v existsαTo vβWhen the level of (2) jumps, the two vertices are connected, eα，β1 is ═ 1; otherwise, there is no connection between the two vertices, eα，β＝0；
Step 3.2: calculating a degree matrix D of G (V, E), and extracting a degree vector D (D) of a diagram formed by diagonal elements of the degree matrix D1，d2，...，dj，...，dq) Wherein d isjThe sum (d) of the degrees of computation, which is the sum of the numbers of edges connected to the jth vertex, is used as the identification feature amount.
6. The BPSK and QPSK signal modulation identification method according to claim 2, wherein: in step 4, set the identification threshold λ of BPSK and QPSK signalsevtWhen the number of sample points N of the signal is 1024 and the number of graph vertices is 10, lambda isevtAnd 10 is taken.
7. The BPSK and QPSK signal modulation identification method according to claim 1, wherein: in step 5, if the identification characteristic quantity is smaller than a set threshold, the signal is a BPSK modulation signal; otherwise, it is a QPSK modulated signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111576119.XA CN114268526B (en) | 2021-12-21 | 2021-12-21 | BPSK and QPSK signal modulation identification method based on degree characteristics of graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111576119.XA CN114268526B (en) | 2021-12-21 | 2021-12-21 | BPSK and QPSK signal modulation identification method based on degree characteristics of graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114268526A true CN114268526A (en) | 2022-04-01 |
| CN114268526B CN114268526B (en) | 2023-05-26 |
Family
ID=80828515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111576119.XA Active CN114268526B (en) | 2021-12-21 | 2021-12-21 | BPSK and QPSK signal modulation identification method based on degree characteristics of graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114268526B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115225438A (en) * | 2022-07-07 | 2022-10-21 | 金陵科技学院 | BPSK and QPSK signal modulation identification method and system based on piecewise linear compression quantization |
| CN115225440A (en) * | 2022-07-08 | 2022-10-21 | 金陵科技学院 | CR signal modulation identification method and system based on graph maximum degree characteristic |
| CN115277322A (en) * | 2022-07-13 | 2022-11-01 | 金陵科技学院 | CR signal modulation identification method and system based on graph and continuous entropy characteristics |
| CN117289236A (en) * | 2023-11-27 | 2023-12-26 | 成都立思方信息技术有限公司 | Short-time radar signal intra-pulse modulation type identification method, device, equipment and medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070283216A1 (en) * | 2006-05-20 | 2007-12-06 | Samsung Electronics Co., Ltd. | Apparatus and method for transmitting/receiving signal in a communication system |
| CN107360110A (en) * | 2017-06-09 | 2017-11-17 | 佛山科学技术学院 | Radio communication high-precision signal identifies and the method for baud rate parameter Estimation |
| CN108881084A (en) * | 2017-09-15 | 2018-11-23 | 金陵科技学院 | A kind of BPSK/QPSK signal recognition method based on GP distribution |
| CN110244271A (en) * | 2019-05-17 | 2019-09-17 | 中国人民解放军战略支援部队信息工程大学 | Radar emitter sorting recognition methods and device based on multiple simultaneous compressed transform |
| CN111935046A (en) * | 2020-10-12 | 2020-11-13 | 湖南国科锐承电子科技有限公司 | Low-complexity frequency shift keying signal symbol rate estimation method |
| CN112422465A (en) * | 2019-10-09 | 2021-02-26 | 上海矢元电子有限公司 | Signal modulation identification equipment |
| CN112787964A (en) * | 2021-02-18 | 2021-05-11 | 金陵科技学院 | BPSK and QPSK signal modulation identification method based on range median domain features |
-
2021
- 2021-12-21 CN CN202111576119.XA patent/CN114268526B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070283216A1 (en) * | 2006-05-20 | 2007-12-06 | Samsung Electronics Co., Ltd. | Apparatus and method for transmitting/receiving signal in a communication system |
| CN107360110A (en) * | 2017-06-09 | 2017-11-17 | 佛山科学技术学院 | Radio communication high-precision signal identifies and the method for baud rate parameter Estimation |
| CN108881084A (en) * | 2017-09-15 | 2018-11-23 | 金陵科技学院 | A kind of BPSK/QPSK signal recognition method based on GP distribution |
| CN110244271A (en) * | 2019-05-17 | 2019-09-17 | 中国人民解放军战略支援部队信息工程大学 | Radar emitter sorting recognition methods and device based on multiple simultaneous compressed transform |
| CN112422465A (en) * | 2019-10-09 | 2021-02-26 | 上海矢元电子有限公司 | Signal modulation identification equipment |
| CN111935046A (en) * | 2020-10-12 | 2020-11-13 | 湖南国科锐承电子科技有限公司 | Low-complexity frequency shift keying signal symbol rate estimation method |
| CN112787964A (en) * | 2021-02-18 | 2021-05-11 | 金陵科技学院 | BPSK and QPSK signal modulation identification method based on range median domain features |
Non-Patent Citations (2)
| Title |
|---|
| XIANGYU FAN; PENG BAI; XIAOLONG LIANG; JIAQIANG ZHANG; BIN LIU: ""Detection Algorithm of BPSK Signal of Parameter-Adjusted Bistable Stochastic Resonance Model Based on Scale Change"" * |
| 赵建印: ""变参数扩跳频通信信号抗截获性能研究"" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115225438A (en) * | 2022-07-07 | 2022-10-21 | 金陵科技学院 | BPSK and QPSK signal modulation identification method and system based on piecewise linear compression quantization |
| CN115225440A (en) * | 2022-07-08 | 2022-10-21 | 金陵科技学院 | CR signal modulation identification method and system based on graph maximum degree characteristic |
| CN115277322A (en) * | 2022-07-13 | 2022-11-01 | 金陵科技学院 | CR signal modulation identification method and system based on graph and continuous entropy characteristics |
| CN115277322B (en) * | 2022-07-13 | 2023-07-28 | 金陵科技学院 | CR signal modulation identification method and system based on graph and continuous entropy characteristics |
| CN117289236A (en) * | 2023-11-27 | 2023-12-26 | 成都立思方信息技术有限公司 | Short-time radar signal intra-pulse modulation type identification method, device, equipment and medium |
| CN117289236B (en) * | 2023-11-27 | 2024-02-09 | 成都立思方信息技术有限公司 | Short-time radar signal intra-pulse modulation type identification method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114268526B (en) | 2023-05-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114268526B (en) | BPSK and QPSK signal modulation identification method based on degree characteristics of graph | |
| CN112787964B (en) | BPSK and QPSK signal modulation identification method based on range median domain features | |
| CN104468001B (en) | Signal identification method and system based on radio signal frequency spectrum feature template | |
| CN107147599B (en) | Automatic map domain feature construction method for communication signal modulation recognition | |
| CN113014524B (en) | Digital signal modulation identification method based on deep learning | |
| CN112364729A (en) | Modulation identification method based on characteristic parameters and BP neural network | |
| CN111428817A (en) | Defense method for resisting attack by radio signal identification | |
| CN113055107B (en) | Interference strategy generation method for radio station with unknown communication mode | |
| CN112749633B (en) | Separate and reconstructed individual radiation source identification method | |
| CN106357575A (en) | Multi-parameter jointly-estimated interference type identification method | |
| CN105429719A (en) | Strong interference signal detection method based on power spectrum and multiple dimensioned wavelet transformation analysis | |
| CN113098638B (en) | Weak signal detection method based on grouped range diagram | |
| CN108881084B (en) | BPSK/QPSK signal identification method based on GP distribution | |
| CN112737992A (en) | Underwater sound signal modulation mode self-adaptive in-class identification method | |
| CN112183300B (en) | AIS radiation source identification method and system based on multi-level sparse representation | |
| CN109525528B (en) | Image domain signal identification method facing MQAM modulation signal | |
| CN108494711B (en) | Communication signal map domain feature extraction method based on KL divergence | |
| CN105375992A (en) | Frequency spectrum cavity detection method based on gradient operator and mathematical morphology | |
| CN114268393B (en) | Cognitive radio spectrum sensing method based on number characteristics of connected components | |
| CN115378776A (en) | MFSK modulation identification method based on cyclic spectrum parameters | |
| CN116506273A (en) | Novel MPSK modulation signal identification and classification method | |
| CN115664905A (en) | Wi-Fi equipment identification system and method based on multi-domain physical layer fingerprint characteristics | |
| CN107395540B (en) | Modulation signal identification method based on LMD approximate entropy, high accumulation product quantity and SVM | |
| CN115225438B (en) | BPSK (binary phase shift keying) and QPSK (quadrature phase shift keying) signal modulation identification method and system based on piecewise linear compression quantization | |
| CN114584441A (en) | Digital signal modulation identification method based on deep learning |
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 |