CN107682119B - MIMO space-time code identification method based on grouping extreme value model - Google Patents

MIMO space-time code identification method based on grouping extreme value model Download PDF

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CN107682119B
CN107682119B CN201710879629.1A CN201710879629A CN107682119B CN 107682119 B CN107682119 B CN 107682119B CN 201710879629 A CN201710879629 A CN 201710879629A CN 107682119 B CN107682119 B CN 107682119B
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CN107682119A (en
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胡国兵
姜志鹏
陈正宇
杨莉
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Jinling Institute of Technology
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    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/02—Arrangements for detecting or preventing errors in the information received by diversity reception
    • H04L1/06—Arrangements for detecting or preventing errors in the information received by diversity reception using space diversity
    • H04L1/0618—Space-time coding
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L1/00—Arrangements for detecting or preventing errors in the information received
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Abstract

The invention provides a MIMO space-time code identification method based on a grouping extreme value model, aiming at the identification problems of two space-time codes of SM and STBC in an MIMO transmission system. Firstly, performing time delay correlation on signals of any two different receiving antennas, then calculating correlation spectrum module values of the signals, applying a grouping extreme value model, properly grouping correlation spectrum module value sequences, taking each group of maximum values to obtain a grouping extreme value sequence, normalizing the grouping extreme value sequence, and finding out the maximum value of the normalized grouping extreme value sequence as an identification characteristic quantity; and setting a corresponding threshold, if the identification characteristic quantity is greater than the threshold value, identifying as the STBC code pattern, otherwise, identifying as the SM code pattern. Simulation results show that the space-time codes of two types in the MIMO can be effectively identified under the condition of no signal prior information.

Description

MIMO space-time code identification method based on grouping extreme value model
Technical Field
The invention belongs to the field of signal identification and processing, and particularly relates to a MIMO space-time code identification method based on a grouping extreme value model.
Background
The signal identification is a classic subject in military and civil fields such as communication reconnaissance and cognitive radio, and is also an indispensable technical link in reconfigurable communication, and the task generally comprises links such as the number estimation of transmitting antennas, the identification of space-time codes, the identification of modulation modes and the like. Under non-cooperative conditions, space-time code identification is usually a prerequisite and basis for a modulation scheme and subsequent decoding links. The existing methods can be mainly divided into likelihood ratio identification and feature identification, the likelihood ratio identification has the best performance, needs prior information of signals and channels, is easily influenced by model mismatch, and has high complexity, and the feature identification method mainly comprises a cyclostationary frequency detection method, a four-order moment peak feature method and the like, and the methods have slightly low complexity but poor performance at low noise ratio.
The invention is based on the grouping extreme value model in the Extreme Value Theory (EVT), takes the correlation spectrums of any two receiving antennas as the basis, selects the specific characteristic quantity and the threshold, completes the identification of the SM code pattern and the STBC code pattern, has low calculation complexity of the algorithm, and has better performance when the signal to noise ratio is low. In particular, the method can overcome the defect of algorithm failure caused by spectral line splitting in the cyclostationary frequency detection.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a MIMO space-time code identification method based on a grouping extreme value model.
In order to achieve the purpose, the invention adopts the following technical scheme:
1) calculating the time delay correlation spectrum module value of the observation signal among different receiving antennas;
2) appropriately grouping the related spectrum module values, and taking the maximum value of each group to construct a group maximum value sequence;
3) obtaining a normalized grouping maximum sequence based on the extreme value theory EVT;
4) extracting the maximum value of the normalized grouping maximum value sequence as an identification characteristic quantity;
5) setting a threshold for identifying the MIMO space-time code;
6) the identifying characteristic quantity is compared with a threshold, and two codes of SM and STBC in the MIMO signal are identified.
In order to optimize the technical scheme, the specific measures adopted further comprise:
in step 1):
in the MIMO transmission environment, L transmitting antennas and P receiving antennas are set, L is greater than 1, P is greater than 1, and the receiving signal vector of the receiving antenna is as follows: r (N) ═ h (N) s (N) + w (N), N ═ 0.., N-1, where h (N) is a rayleigh fading type channel matrix, w (N) is additive white gaussian noise, and the variance is white gaussian noise
Figure BDA0001418741480000021
s (N) is a signal vector, N is a signal sample length;
let the received signal of the ith antenna be ri(n) the received signal of the jth antenna is rj(n), if the delay is τ, the correlation spectrum module value is: u (k) ═ DFT [ ri(n)rj(n-τ)]I, k ≠ 0., N-1, where i ≠ j, ginsengThe signals operating with the correlation spectrum must come from different receiving antennas, τ > 1.
In step 2):
grouping the correlation spectrum modulus values U (K), uniformly dividing the correlation spectrum modulus values into K groups, and taking a maximum value gamma for each grouplK-1, forming K grouping maxima into a sequence of grouping maxima { γ ═ 0l}，l＝0，..，K-1。
In step 3):
grouping a sequence of maxima { gamma } based on the EVT methodlNormalizing to obtain a normalized grouping maximum value sequence U' (l) [ [ gamma ] ]l-aK]/bKK-1, where the normalization coefficients are 0
Figure BDA0001418741480000022
σzIs the standard deviation of the relevant spectral sequences.
In step 4):
the maximum value of the normalized grouping maximum value sequence U' (l) is selected as an identification characteristic quantity which is marked as Revt＝max[U′(l)]。
In step 5):
setting a threshold th for MIMO space-time code recognitionevt，thevt＝-1n[-ln(1-Pfa)]In the formula, PfaIs the false alarm probability.
In step 6):
when R isevt≥thevtIf so, the code type is STBC; otherwise, the code pattern is SM.
The invention has the beneficial effects that: the identification of the SM code pattern and the STBC code pattern is completed by extracting the maximum value of the normalized grouping extreme value of the correlation spectrum as the basis, only the receiving signals of any two receiving antennas are needed, the prior information of the signals is not needed, the method still has good performance at the time of low signal-to-noise ratio, and the method is easy to realize.
Drawings
FIG. 1 is a flow chart of the identification method of the present invention.
Fig. 2 is a statistical histogram of the maxima of the correlation spectrum normalized grouped extremum sequence under different code patterns.
FIG. 3 is a graph comparing the performance of the present invention and the cyclostationary frequency detection method under the same simulation conditions.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
In the identification method, signals of two different receiving antennas are selected firstly, time delay correlation is carried out on the signals, then correlation spectrum module values of the signals are calculated, correlation spectrum module value sequences are properly grouped and maximum values are selected, grouped extreme value sequences are obtained and are normalized based on an EVT method, the maximum values of the normalized grouped extreme value sequences are found out to be used as identification characteristic quantities, then corresponding thresholds are set, if the identification characteristic quantities are larger than the threshold values, the STBC code type is identified, otherwise, the SM code type is identified.
Fig. 1 shows a MIMO space-time code identification method based on a grouping extremum model, which specifically includes the following steps.
First, calculate the module value of the correlation spectrum
Assuming that there are L transmitting antennas and P receiving antennas in the MIMO transmission environment, where L > 1 and P > 1, the received signal vector of the receiving antennas is:
r(n)＝H(n)s(n)+w(n)，n＝0，...，N-1
in the formula: h (n) is a Rayleigh fading type channel matrix, w (n) is an additive white Gaussian noise (variance of
Figure BDA0001418741480000034
) S (N) is a signal vector and N is a signal sample length.
Let the received signal of the ith antenna be ri(n) taking the received signal of the jth antenna as rj(n) the correlation spectrum modulus is:
U(k)＝|DFT[ri(n)rj(n-τ)]|，k＝0，...，N-1
in the formula, i ≠ j is required, namely, signals participating in correlation spectrum calculation must come from different receiving antennas, tau is delay amount, and tau is greater than 1.
Second, construct the grouping maximum value sequence
Will be correlatedGrouping the spectral modulus U (K), uniformly dividing the spectral modulus U (K) into K groups (generally, the number of samples in each group is 5-15), and taking the maximum value gamma of each grouplK-1, forming K grouping maxima into a sequence of grouping maxima { γ ═ 0l}，l＝0，...，K-1。
Three, normalization
Grouping a sequence of maxima { gamma } based on the EVT methodlNormalizing to obtain a normalized grouping maximum value sequence:
U′(l)＝[γl-aK]/bK.，l＝0，...，K-1
wherein the normalization coefficient
Figure BDA0001418741480000031
Wherein sigmazIs the standard deviation of the relevant spectral sequence, in practice, sigmazBy
Figure BDA0001418741480000032
Estimated to obtain in
Figure BDA0001418741480000033
Is the statistical average value of the correlation spectrum U (k) after 3-5 large spectral lines are removed.
Defining identification characteristic quantity
The maximum value of the normalized grouping maximum value sequence U' (l) is selected as an identification characteristic quantity which is marked as Revt＝max[U′(l)]。
Fifthly, setting threshold
Setting a threshold th for MIMO space-time code recognitionevtCalculated from the following formula:
thevt＝-ln[-ln(1-Pfa)]
in the formula, PfaThe false alarm probability is generally between 0.01 and 0.0001.
Six, code pattern recognition
When R isevt≥thevtIf so, the code type is STBC; otherwise, the code pattern is SM.
Table 1 illustrates the average identification accuracy of the method, and assuming that there are 3 transmitting antennas and 4 receiving antennas in the MIMO transmission environment, the 1 st receiving antenna and the 2 nd receiving signal are delay-correlated, and the delay amount is 20 sample points. The channel is a Rayleigh fading type channel matrix, the additive noise is additive white Gaussian noise, the signal is divided into 4 time slots when being transmitted, the number of symbols of each time slot is 1024 points, and the signal adopts a QPSK modulation mode. The step length of the signal-to-noise ratio is set to be 3dB from-3 dB to 12dB, 1000 times of simulation is respectively carried out on two different code patterns when each signal-to-noise ratio is set, and the false alarm probability is 0.0001.
From table 1, the correct recognition probability of the method under the above simulation conditions can be seen: when the signal-to-noise ratio is larger than-3 dB, the recognition accuracy of the two code patterns is more than 91%.
SNR(dB) -6 -3 0 3 6 9 12
Average recognition accuracy 0.655 0.811 0.9075 0.933 0.9505 0.9555 0.9625
TABLE 1 Performance of the identification method of the present invention under different SNR conditions
Fig. 2 shows a statistical histogram of normalized maximum values of the block maximum value sequence of two space-time coding systems, SM and AL (i.e. STBC code when the number of transmit-receive antennas is 2, and transmission time slot is 2) obtained by simulation. The signal-to-noise ratio is set to 0dB, 1000 times of simulation are respectively carried out on two different code patterns, and the conditions of other simulations are the same as those in reference table 1. It can be seen from the figure that under different space-time code conditions, the statistical histograms of the maximum values of the normalized grouping maximum value sequence of the correlation spectrum values have certain differences, which provides a basis for the implementation of the algorithm.
FIG. 3 shows the performance of the method compared to the cyclostationary frequency detection method under the simulation conditions set forth in Table 1. As can be seen from fig. 3, the algorithm proposed by the present invention is slightly better than the cyclostationary frequency detection method.
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 (4)

1. A MIMO space-time code identification method based on a grouping extreme value model specifically comprises the following steps:
1) calculating the time delay correlation spectrum module value of the observation signal among different receiving antennas;
in the MIMO transmission environment, L transmitting antennas and P receiving antennas are set, L is greater than 1, P is greater than 1, and the receiving signal vector of the receiving antenna is as follows: r (N) ═ h (N) s (N) + w (N), N ═ 0.., N-1, where h (N) is a rayleigh fading type channel matrix, w (N) is additive white gaussian noise, and the variance is white gaussian noise
Figure FDA0002428357040000012
s (N) is a signal vector, N is a signal sample length;
let the received signal of the ith antenna be ri(n) the received signal of the jth antenna is rj(n), if the delay is τ, the correlation spectrum module value is: u (k) ═ DFT [ ri(n)rj(n-τ)]I, k is 0., N-1, where i ≠ j, the signals participating in the correlation spectrum calculation must be from different receiving antennas, τ > 1;
2) grouping the related spectrum module values, and taking the maximum value of each group to construct a group maximum value sequence;
3) obtaining a normalized grouping maximum sequence based on the extreme value theory EVT;
4) extracting the maximum value of the normalized group maximum value sequence as the identification feature quantity Revt；
5) Setting a threshold for identifying the MIMO space-time code;
setting a threshold th for MIMO space-time code recognitionevt，thevt＝-ln[-ln(1-Pfa)]In the formula, PfaIs the false alarm probability;
6) comparing the identification characteristic quantity with a threshold, and identifying two codes of SM and STBC in the MIMO signal;
when R isevt≥thevtIf so, the code type is STBC; otherwise, the code pattern is SM.
2. The method of claim 1, wherein the MIMO space-time code identification method based on the grouping extremum model comprises: in step 2):
grouping the correlation spectrum modulus values U (K), uniformly dividing the correlation spectrum modulus values into K groups, and taking a maximum value gamma for each grouplK-1, forming K grouping maxima into a sequence of grouping maxima { γ ═ 0l},l＝0,...,K-1。
3. The method of claim 2, wherein the MIMO space-time code identification method based on the grouping extremum model comprises: in step 3):
based on EVT method, will divideSequence of group maxima [ gamma ]lNormalizing to obtain a normalized grouping maximum value sequence U' (l) [ [ gamma ] ]l-aK]/bKK-1, where the normalization coefficients are 0
Figure FDA0002428357040000011
Figure FDA0002428357040000021
σzIs the standard deviation of the relevant spectral sequences.
4. The method of claim 3, wherein the MIMO space-time code identification method based on the grouped extreme value model comprises: in step 4):
the maximum value of the normalized grouping maximum value sequence U' (l) is selected as an identification characteristic quantity which is marked as Revt＝max[U'(l)]。
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106357369A (en) * 2016-09-22 2017-01-25 金陵科技学院 Method for identifying MIMO (multiple input multiple output) code types on basis of above-threshold features of correlation spectra
CN106443604A (en) * 2016-09-22 2017-02-22 金陵科技学院 Verification method for blind processing result of LFM/BPSK hybrid modulation signal

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106357369A (en) * 2016-09-22 2017-01-25 金陵科技学院 Method for identifying MIMO (multiple input multiple output) code types on basis of above-threshold features of correlation spectra
CN106443604A (en) * 2016-09-22 2017-02-22 金陵科技学院 Verification method for blind processing result of LFM/BPSK hybrid modulation signal

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"On the Identification of SM and Acoded SC-FDMA Signals:A Statistical-Based Approach";Yahia A;《IEEE Transactions on Vehicular Technology》;20160127;全文 *
"一种改进的BPSK/QPSK信号调制识别算法";杨莉;《电讯技术》;20170828;全文 *

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