CN116132237A

CN116132237A - OFDM signal detection and identification method based on periodic convolution and fractal

Info

Publication number: CN116132237A
Application number: CN202211702003.0A
Authority: CN
Inventors: 熊刚; 何健辉; 夏明赟; 张辉; 崔旭; 彭勃; 高峰; 任祥维; 谢瑞云
Original assignee: CETC 30 Research Institute
Current assignee: CETC 30 Research Institute
Priority date: 2022-12-29
Filing date: 2022-12-29
Publication date: 2023-05-16
Anticipated expiration: 2042-12-29
Also published as: CN116132237B

Abstract

The invention provides an OFDM signal detection and identification method based on periodic convolution and fractal, which comprises the following steps: step 1, acquiring a signal to be detected, completing whether an OFDM signal exists in the signal to be detected based on a sliding period convolution characteristic, if so, entering a step 2, otherwise, ending detection; and 2, calculating a fractal measure index of the signal to be detected, and distinguishing the OFDM signal from the single carrier signal in the signal to be detected through the fractal measure index. The OFDM signal detection and recognition method provided by the invention has the advantages of good anti-noise performance, no need of too much priori information, insensitivity to OFDM cyclic prefix conditions, strong robustness, suitability for complex electromagnetic environment, relatively small operation amount and higher engineering application value.

Description

OFDM signal detection and identification method based on periodic convolution and fractal

Technical Field

The invention relates to the field of signal detection and identification, in particular to an OFDM signal detection and identification method based on periodic convolution and fractal.

Background

Orthogonal Frequency Division Multiplexing (OFDM) technology has become a research hotspot for new-generation wireless communication due to its high spectrum utilization rate and good multipath fading resistance, and has been widely used in mobile communication, unmanned aerial vehicle communication links, digital broadcasting systems, and the like. The blind detection of the communication signal is to detect the signal on the premise of lacking priori knowledge, and the method is used as a basis for the subsequent demodulation processing. However, the related studies are still not mature in many aspects, especially for OFDM signals, and further studies are required. In the past, some scholars put forward to detect and identify an OFDM signal by a method based on a correlation spectrum, and become a relatively common processing thought at the time, the algorithm can be directly processed at an intermediate frequency without pre-carrier synchronization, and the disadvantage is that the method is relatively easy to be influenced by noise when the spectrum of a periodogram method in the algorithm is estimated, and the method depends on the correlation property of a cyclic prefix to a great extent. For OFDM signal detection, some detection methods based on matched filters are also used, but the requirements on signal-to-noise ratio are high, and some a priori information of the main user signal must also be used.

At present, through patent retrieval, a solution for detecting and identifying an OFDM signal based on the thought of periodic convolution and fractal processing is not found, so the patent application has originality. Similar methods of the presently retrieved and filed patent application are: "an OFDM channel estimation and signal detection method based on a data-driven neural network" (application number: CN202011449045.9, applicant: qilu university of industry, inventor: li Jun; han Yongli; xintong light). The invention provides an OFDM channel estimation and signal detection method based on a data driving neural network, wherein the system trains the neural network according to a received denoising signal and a pre-acquired original real binary signal as training data, and inputs the received signal into the network and outputs the received signal as an estimated original signal. The method requires a large number of priori signal data sample construction for training and construction of a learning network, is relatively complex in operation, and does not relate to a processing result under the condition of lack of priori information; "method for primary synchronization signal detection in dynamic spectrum sharing" (application number: CN202110085629.0, applicant: university of Western An electronics technology, inventor: li Xiaohui; wang Xianwen; dan Mingli; liu Shuaishuai). The method is mainly applied to detection of OFDM signals of a 5G system in cooperative communication, and a user side performs down-sampling on the signals and performs first-order differential processing on the down-sampled signals; and performing sliding detection on the first-order differential signal, performing cross-correlation on the local time domain sequence and the signal in the adjacent interval of the coarse synchronization point to obtain the fine synchronization point and the integral multiple frequency offset, and being not suitable for OFDM signal detection analysis in the absence of prior information.

Disclosure of Invention

Aiming at the problems existing in the prior art, the detection and identification method of the OFDM signal based on the periodic convolution characteristic and fractal processing is provided, is suitable for the rapid processing of a low signal-to-noise environment, does not need to acquire an OFDM preamble, synchronous information and the like in advance, and still has better robustness under the condition of shorter OFDM cyclic prefix.

The technical scheme adopted by the invention is as follows: an OFDM signal detection and identification method, comprising:

step 1, acquiring a signal to be detected, completing whether an OFDM signal exists in the signal to be detected based on a sliding period convolution characteristic, if so, entering a step 2, otherwise, ending detection;

and 2, calculating a fractal measure index of the signal to be detected, and distinguishing the OFDM signal from the single carrier signal in the signal to be detected through the fractal measure index.

Further, in the step 1, the sliding period convolution characteristic value of the signal to be detected is calculated and matched with the known characteristic value of the OFDM signal, if the matching is successful, the OFDM signal is indicated to exist, otherwise, the OFDM signal is indicated to not exist.

Further, the calculation method of the sliding period convolution characteristic value comprises the following steps:

wherein ,

the sliding period convolution characteristic value of the signal to be detected is represented, N is the sliding window length for windowing the input signal, ω represents the period convolution frequency, and τ represents the delay factor.

Further, the specific substeps of the step 2 are as follows:

step 2.1, obtaining a discrete sequence of a signal to be detected, and constructing a new time sequence based on the discrete sequence;

step 2.2, calculating fractal measure index characteristics of the constructed time sequence;

step 2.3, dividing fractal measure index features of the time sequence into l arrays, and calculating the average value after division;

and 2.4, calculating the normalized fractal measurement index value by adopting a least square optimal fitting method based on the mean value.

Further, in the step 2.1, the time sequence is:

where m represents the initial point in time of the sequence, m=1, 2,3, i, l represents the spacing between sample points,

representation (N) _t Integer part of m/l, N _t Representing the total length of the time series.

Further, in the step 2.2, the method for calculating the fractal measure index feature is as follows:

wherein ,

and a normalization factor representing the fractal measure index.

Further, in the step 2.3, the method for calculating the average value is as follows:

wherein the value range of l is the set {1,2, …, l _max -from 1 to l _max Repeatedly calculating the above formula until l=l _max Thus, the traversal of the number sequence numbers of the sample intervals is completed, and the fractal measure index of the whole sample sequence is calculated.

Further, in the step 2.4, L (L) therebyl is used ^-FD Representing fractal measure index, FD is ln (L (L)) pair

And (3) calculating a normalized fractal measurement index value FD by adopting a least square optimal fitting method according to the slope relation of ln (1/l).

Compared with the prior art, the beneficial effects of adopting the technical scheme are as follows: the OFDM signal detection and recognition method provided by the invention has the advantages of good anti-noise performance, no need of too much priori information, insensitivity to OFDM cyclic prefix conditions, strong robustness, suitability for complex electromagnetic environment, relatively small operation amount and higher engineering application value.

Drawings

Fig. 1 is a schematic diagram of conventional OFDM signal transmission.

Fig. 2 is a flowchart of OFDM signal detection and identification according to the present invention.

Fig. 3 is a graph showing the detection and recognition performance of OFDM signals with different sliding window lengths according to an embodiment of the present invention.

Fig. 4 is a schematic block diagram of an FPGA implementation of the detection processing based on the periodic convolution feature in an embodiment of the present invention.

Fig. 5 is a diagram showing a discrimination between an OFDM signal based on fractal processing and a Single Carrier (SC) signal according to an embodiment of the present invention.

Fig. 6 is a graph showing the comparison of performance curves of the OFDM signal recognition method and the conventional method according to the present invention.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar modules or modules having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the present application include all alternatives, modifications, and equivalents as may be included within the spirit and scope of the appended claims.

The detection and identification of the OFDM communication signals are realized, and the method has important significance in the fields of frequency spectrum monitoring, cognitive radio and the like. OFDM signals are widely used in mobile communication, digital broadcasting systems, unmanned aerial vehicle communication systems, and the like. How to correctly detect and identify the OFDM signal has become a hot spot problem. And carrying out analysis through signal detection and identification, distinguishing target signals from noise, and taking the target signals and the noise as the premise of subsequent analysis. However, since in many practical applications the received signal is weak at the receiver; moreover, the electromagnetic environment is relatively complex due to lack of priori information or almost no priori knowledge condition, so that the difficulty of signal detection and identification is high, and many past methods fail.

Before introducing the OFDM signal detection and identification method proposed in this embodiment, description is first given to an OFDM signal:

as shown in fig. 1, which is an OFDM signal transmission schematic diagram, serial data streams input at an OFDM signal transmitting end are converted into parallel data through serial-parallel conversion, then data on each subcarrier is respectively subjected to constellation mapping modulation and then IFFT conversion, and finally cyclic prefix is added to form an OFDM transmitting end signal. At the signal receiving end, the signals after down-conversion and other treatments are subjected to cyclic prefix removal, synchronous demodulation, parallel-serial conversion and the like, and finally bit stream data is output.

In the OFDM communication model, N is contained _s The OFDM signal of a subcarrier can be expressed as:

wherein ,N_s Represents the number of sub-carriers, D _i Data information symbol i=0, 1,2 … N representing each subchannel _s -1, T denotes the length of the OFDM symbol, f _k Represents carrier frequency of kth subcarrier, rect (T) represents rectangular function, and rect (T) =1, |t|is less than or equal to T/2, k represents number of subcarrier carrier frequency, T _M Representing the symbol time period, M represents the number of symbol periods.

Let t be _M =0, and ignoring the rectangular function, further available is:

/>

wherein ,

it can be seen that the components I (t) and Q (t) of their signal expression have a form similar to that of a multipath signal, and that OFDM signals are fractal in nature because multipath signals generally have some fractal properties, such as self-similarity, time-variability, etc.

Based on this, as shown in fig. 2, the application proposes an OFDM signal detection and identification method based on sliding period convolution and fractal, including:

The presence of the OFDM signal is detected in step 1, and the OFDM signal is further finely identified in step 2, so that the OFDM signal can be distinguished from other single carrier type signals, and the processing process basically does not depend on any priori information. Two steps are further described herein, in particular:

in step 1, the characteristic value calculation of the signal to be measured is completed mainly by adopting a mode based on the periodic convolution characteristic, and the calculation expression is as follows:

wherein ,

representing periodic convolution characteristics, T ₀ Represents the sampling period of the signal, ω represents the periodic convolution frequency, τ represents the delay factor, and K represents the number of intervals of the sampling period of the signal.

Discretizing the above formula to obtain:

for an OFDM sliding periodic convolution function, when ω is 0 and τ is equal to the useful signal length of OFDM,

will have a peak. Wherein (1)>

For sliding period Fourier transform, m represents the number of signal sampling points, N _w Is the sliding window length.

In this stepWindowing an input signal by adopting a windowing function, and performing fast Fourier transform on each windowed truncated sequence; to avoid spectral aliasing and leakage, sliding window length N _w The value range of (2) should satisfy: n (N) _w And ≡4l, wherein L represents the overlap in each sliding fourier transform.

Further, sliding window length N _w The efficiency of calculating the eigenvalues is determined, so the length needs to be planned, in particular:

let the number of subcarriers of the OFDM signal be n=256, for a sliding window length N _w And analyzing the value to obtain detection and identification performance curves of different sliding window length conditions, as shown in figure 3. It can be seen that, when the number of subcarriers is the same, the detection recognition rate of the OFDM signal can be improved by properly increasing the number of sample points, but the algorithm operation efficiency is reduced at this time; when N is _w The performance of the two is relatively close when the signal-to-noise ratio is more than 0dB, and the difference is not significant, compared with the case of the value of 100 and the case of the value of 500. And through a plurality of practical tests, in the embodiment, the typical value of the sliding window length is taken as N _w ＝100。

And after calculating the sliding period convolution characteristic value of the signal to be detected, matching the sliding period convolution characteristic value with the known characteristic value of the OFDM signal, if the matching is successful, indicating that the OFDM signal exists, and further identifying the signal, otherwise, indicating that the signal does not exist, and ending the detection of the current signal.

In a preferred embodiment, the feature extraction of step 1 may be implemented by using an FPGA, as shown in fig. 4, including:

(1) The DDR storage signal is adopted and preprocessed, the DDR storage signal comprises four RAM modules, A, B, C, D respectively represent the internal addresses of the four RAMs, I_Data of a signal to be detected is input to the A, B module, Q_Data of the signal to be detected is input to the C, D module, wherein Data in the B, D module are subjected to delay processing, and the four modules are subjected to staggered multiplication in the calculation process to obtain four outputs AB, CD, AD and BC.

(2) Sum 1=ab+cd, sum 2=ad+bc are calculated and accumulated to obtain accumulated values Accum1 and Accum2, respectively.

(3) Finally, integration is performed to increase the cumulative signal ratio, which is also achieved by accumulation, sum_all_accum (i) =all_accum (1) (i) +. And matching the calculated characteristic value (namely peak value) with the known data to quickly obtain a detection result.

After detecting that an OFDM signal exists in a signal to be detected, the embodiment adopts fractal measure, wherein the fractal measure is a statistical characteristic parameter representing signal complexity, and nonlinear signal phase space non-uniformity can be quantitatively described, so that the OFDM signal and a Single Carrier (Single Carrier) signal are finely distinguished and identified; however, the conventional OFDM recognition ideas such as correlation spectrum recognition algorithm are all calculated based on symbol correlation characteristics generated by Cyclic Prefix (CP), and when the cyclic prefix is shortened, the performance of the algorithm is reduced or even fails. In the fractal algorithm processing process, the sequence interval of signal sample data is reasonably selected according to experience and used as an adjusting parameter for extracting and calculating fractal measure characteristics. Specific:

step 2.1, obtaining a discrete sequence y (1), y (2), y (i), y (N) of signal data _t ) Where i=1, 2,3 _t ，N _t Representing the total length of the time series. A new time sequence is constructed from sequence y, defined as follows:

where m=1, 2,3, l, m represents the initial point in time of the sequence, l represents the number of intervals between sample points, and

representative (N) _t -integer part of m)/l.

Step 2.2, calculating fractal measure index characteristics, wherein the method comprises the following steps:

wherein ,

and a normalization factor representing the fractal measure index.

Step 2.3 defining L (L) as L _m (l) The average value of the number of the groups of the L is:

wherein the value range of l is the set {1,2, …, l _max -from 1 to l _max Repeatedly calculating the above formula until l=l _max Thus, the traversal of the number sequence numbers of the sample point intervals is completed, and the fractal measure index of the whole sample sequence is calculated.

Step 2.4, L (L) ≡l ^-FD And (3) representing fractal measure index, wherein FD is the slope relation of ln (L (L)) to ln (1/L), and calculating normalized fractal measure index value FD by adopting a least square optimal fitting method.

In this embodiment, the preferred value for l is 64.

As shown in fig. 5, normalized fractal characteristic curves of OFDM signals and various types of modulation signals such as BPSK, QPSK, 8PSK, 16QAM, 64QAM and the like of Single Carrier (SC) are shown when the signal to noise ratio is in a range from-20 dB to 20 dB. According to fig. 4, when the signal-to-noise ratio is above 5dB, the normalized fractal measure index value of the single-carrier modulation signal has a difference from that of the OFDM signal, and as the signal-to-noise ratio increases, the classification characteristic of the OFDM signal and the degree of distinguishing between the single carriers become larger, so that correct distinguishing and identification can be effectively realized.

Performance simulation test under the condition that an OFDM signal is a short cyclic prefix, namely selecting an actual OFDM signal (such as a typical wireless digital communication system signal) as an object, wherein the Cyclic Prefix (CP) length ratio is 1/32, the center frequency of the receiver is set to be 60MHz, the sampling rate is 200MHz, and the number of experiments is 1000. And compares the new method with the previous spectral correlation based algorithm as shown in fig. 6. As can be seen from the graph, when the signal-to-noise ratio is above 0dB, the accuracy of the new method can reach above 90%; and when the signal-to-noise ratio is above 2dB, the accuracy can reach above 99.5%, which is superior to the traditional correlation spectrum recognition algorithm.

The method has wide applicability, has high detection and identification performance on the OFDM signal, still shows robustness under the condition of short cyclic prefix, optimizes operation, improves processing efficiency, shows good engineering practicability, and is a method with excellent performance.

It should be noted that, in the description of the embodiments of the present invention, unless explicitly specified and limited otherwise, the terms "disposed," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; may be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention will be understood in detail by those skilled in the art; the accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims

1. The OFDM signal detection and identification method based on the periodic convolution and fractal is characterized by comprising the following steps of:

2. The method for detecting and identifying the OFDM signal based on the periodic convolution and the fractal according to claim 1, wherein in the step 1, the sliding periodic convolution characteristic value of the signal to be detected is calculated and matched with the known characteristic value of the OFDM signal, if the matching is successful, the existence of the OFDM signal is indicated, and otherwise, the existence of the OFDM signal is indicated.

3. The method for detecting and identifying the OFDM signal based on the periodic convolution and the fractal according to claim 2, wherein the method for calculating the sliding periodic convolution eigenvalue is as follows:

wherein ,

4. The method for detecting and identifying OFDM signals based on periodic convolution and fractal according to claim 1 or 2, wherein the specific substeps of step 2 are as follows:

step 2.3, dividing fractal measure index features of the time sequence into k arrays, and calculating the average value after division;

5. The method for detecting and identifying OFDM signals based on periodic convolution and fractal according to claim 4, wherein in the step 2.1, the time sequence is:

where m represents the initial time point of the sequence, m=1, 2,3,..,

represents an integer part of (N-m)/k, N represents the total length of the time series.

6. The method for detecting and identifying an OFDM signal based on cyclic convolution and fractal according to claim 5, wherein in the step 2.2, the method for calculating the fractal measure index feature is as follows:

wherein ,

and a normalization factor representing the fractal measure index.

7. The method for detecting and identifying the OFDM signal based on the periodic convolution and the fractal of claim 6, wherein in the step 2.3, the method for calculating the mean value is as follows:

wherein, the value of k is from 1 to k _max Repeating the calculation until k=k _max 。

8. The method for detecting and identifying OFDM signals based on periodic convolution and fractal according to claim 7, wherein in said step 2.4, L (k) ≡k is used ^-FD And (3) representing fractal measure index, wherein FD is the slope relation of ln (L (k)) to ln (1/k), and calculating normalized fractal measure index value FD by adopting a least square optimal fitting method.