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

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

A MIMO Space-Time Code Identification Method Based on Packet Extremum Model

technical field

The invention belongs to the field of signal identification and processing, and in particular relates to a MIMO space-time code identification method based on a packet extremum model.

Background technique

Signal identification is a classic subject in military and civilian fields such as communication reconnaissance and cognitive radio, and it is also an indispensable technical link in reconfigurable communication. Its tasks generally include estimation of the number of transmitting antennas, identification of space-time codes, and identification of modulation methods. link. Under non-cooperative conditions, space-time code identification is usually the premise and foundation of modulation and subsequent decoding. The existing methods can be mainly divided into two categories: likelihood ratio recognition and feature recognition. The performance of likelihood ratio recognition is the best, but it requires prior information of the signal and channel, and is easily affected by model mismatch, and the complexity is also high. , while feature recognition methods mainly include cyclostationary frequency detection method, fourth-order moment peak feature method, etc. These methods are slightly less complex, but have poor performance at low noise ratio.

The invention is based on the grouped extreme value model in the extreme value theory (EVT), based on the correlation spectrum of any two receiving antennas, selects specific feature quantities and thresholds, completes the identification of SM and STBC code types, and calculates the algorithm. The complexity is low, and it still has good performance at low signal-to-noise ratio. In particular, the method can overcome the disadvantage of algorithm failure caused by spectral line splitting in cyclostationary frequency detection.

SUMMARY OF THE INVENTION

In view of the deficiencies in the prior art, the present invention provides a MIMO space-time code identification method based on a grouping extremum model.

To achieve the above object, the present invention adopts the following technical solutions:

1) Calculate the delay correlation spectral modulus value of the observed signal between different receiving antennas;

2) Appropriately grouping the relevant spectral modulus values, and taking the maximum value of each grouping to construct a grouping maximum value sequence;

3) Based on the extreme value theory EVT, the normalized grouped maximum value sequence is obtained;

4) Extract the maximum value of the normalized grouping maximum value sequence as the identification feature;

5) Set the threshold for MIMO space-time code identification;

6) Compare the identification feature with the threshold to identify the SM and STBC codes in the MIMO signal.

In order to optimize the above technical solutions, the specific measures taken also include:

In step 1):

s(n)为信号向量，N为信号样本长度；In a MIMO transmission environment, there are L transmit antennas and P receive antennas, L>1, P>1, and the received signal vector of the receive antenna is: r(n)=H(n)s(n)+w( n), n=0,...,N-1, where H(n) is the Rayleigh fading channel matrix, w(n) is the additive white Gaussian noise, and the variance is

s(n) is the signal vector, and N is the length of the signal sample;

Denote the received signal of the i-th antenna as r _i (n), the received signal of the j-th antenna as r _j (n), and the delay amount as τ, then the correlation spectral modulus value is: U(k)=|DFT[ r _i (n)r _j (n-τ)]|, k=0,...,N-1, where i≠j, the signals involved in the correlation spectrum operation must come from different receiving antennas, τ>1.

In step 2):

Group the relative spectral modulus value U(k), divide it into K groups evenly, and take the maximum value γ _l for each grouping, l=0,...,K-1, and divide the K grouping maximum values Constitute a grouped maximal value sequence {γ _l }, l=0, . . . , K-1.

In step 3):

σ_z为相关谱序列的标准差。Based on the EVT method, the grouped maximal value sequence {γ _l } is normalized to obtain the normalized grouped maximal value sequence U'(l)=[γ _l -a _K ]/b _K ., l=0, ..., K-1, where, the normalization coefficient

σ _z is the standard deviation of the correlation spectrum sequence.

In step 4):

The maximal value of the normalized grouping maximal value sequence U'(l) is selected as the identification feature quantity, which is denoted as _Revt =max[U'(l)].

In step 5):

The threshold th _evt for MIMO space-time code identification is set, th _evt =-1n[-ln(1-P _fa )], where P _fa is the false alarm probability.

In step 6):

When _{Revt ≥ th evt ,} the pattern is STBC; otherwise, the pattern is SM.

The beneficial effects of the present invention are: by extracting the maximum value of the normalized grouping extrema of the correlation spectrum as the basis to complete the identification of the two code types of SM and STBC, only the received signals of any two receiving antennas need to be used, and no signal Prior information, it still has better performance at lower signal-to-noise ratio, and the method is easy to implement.

Description of drawings

FIG. 1 is a flowchart of the identification method of the present invention.

Figure 2 is a statistical histogram of the maximum value of the normalized grouped extremum sequence of the correlation spectrum under different code pattern conditions.

FIG. 3 is a performance comparison diagram of the present invention and the cyclostationary frequency detection method under the same simulation conditions.

Detailed ways

The present invention will now be described in further detail with reference to the accompanying drawings.

In the identification method of the present invention, the signals of two different receiving antennas are firstly selected, and the two are correlated by delay, and then the correlation spectral mode value is calculated, and the sequence of the correlation spectral mode value is appropriately grouped and the maximum value is taken to obtain Group the extreme value sequence and normalize it based on the EVT method, find the maximum value of the normalized grouping extreme value sequence as the identification feature, and then set the corresponding threshold, if the identification feature is greater than this threshold, then identify It is STBC code pattern, otherwise, it is recognized as SM code pattern.

Fig. 1 shows a MIMO space-time code identification method based on a grouping extremum model, which specifically includes the following steps.

1. Calculate the relevant spectral mode value

Assuming that there are L transmit antennas and P receive antennas in the MIMO transmission environment, L>1, P>1, the received signal vector of the receive antennas is:

r(n)=H(n)s(n)+w(n), n=0,...,N-1

)，s(n)为信号向量，N为信号样本长度。In the formula: H(n) is the Rayleigh fading channel matrix, w(n) is the additive white Gaussian noise (variance is

), s(n) is the signal vector, and N is the length of the signal sample.

Denote the received signal of the i-th antenna as r _i (n) and the received signal of the j-th antenna as r _j (n), and the relevant spectral modulus is:

U(k)=|DFT[r _i (n)r _j (n-τ)]|, k=0,...,N-1

In the formula, i≠j is required, that is, the signals involved in the correlation spectrum operation must come from different receiving antennas, τ is the delay amount, and τ>1.

2. Constructing the grouped maximal value sequence

Group the relevant spectral modulus value U(k), divide it into K groups evenly (generally, the number of samples in each group is 5-15), and take the maximum value γ _l for each group, l=0, .. ., K-1, the K grouped maxima are formed into a sequence of grouped maxima {γ _l }, l=0, ..., K-1.

3. Normalization

Based on the EVT method, the grouped maximum sequence {γ _l } is normalized to obtain the normalized grouped maximum sequence:

U'(l)=[γ _l -a _K ]/b _K ., l=0, ..., K-1

其中σ_z为相关谱序列的标准差，实际中，σ_z由

估计得到，式中

是相关谱U(k)中去除3-5根大谱线后的统计平均值。Among them, the normalization coefficient

where σ _z is the standard deviation of the correlation spectrum sequence, in practice, σ _z is given by

can be estimated, where

is the statistical average after removing 3-5 large spectral lines in the correlation spectrum U(k).

Fourth, define the identification feature quantity

The maximal value of the normalized grouping maximal value sequence U'(l) is selected as the identification feature quantity, which is denoted as _Revt =max[U'(l)].

5. Threshold setting

Set the threshold th _evt for MIMO space-time code identification, which is calculated by the following formula:

th _evt = -ln[-ln(1- _Pfa )]

In the formula, P _fa is the false alarm probability, which is generally between 0.01 and 0.0001.

6. Pattern recognition

When _{Revt ≥ th evt ,} the pattern is STBC; otherwise, the pattern is SM.

Table 1 shows the average recognition accuracy of this method. Assuming that there are 3 transmitting antennas and 4 receiving antennas in the MIMO transmission environment, the first receiving antenna is related to the received signal of the second receiving signal, and the delay amount for 20 sample points. The channel is a Rayleigh fading channel matrix, and the additional noise is additive white Gaussian noise. The signal is divided into 4 time slots during transmission, and the number of symbols in each time slot is 1024 points. The modulation method used for the signal is QPSK modulation. The setting range of the signal-to-noise ratio is -3dB to 12dB, and the step size is 3dB. For each signal-to-noise ratio, 1000 simulations are performed for each of the two different code types, and the false alarm probability is taken as 0.0001.

It can be seen from Table 1 that the correct identification probability of this method under the above simulation conditions: when the signal-to-noise ratio is greater than -3dB, the identification correct rate of the two code patterns is above 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 signal-to-noise ratio conditions

Figure 2 shows the statistical histograms of the maximal values of the normalized packet maxima sequence of the two space-time coding systems obtained by simulation, SM and AL (that is, STBC codes when the number of transmitting and receiving antennas is 2, and the transmission time slot is 2). picture. The signal-to-noise ratio is set to 0dB, and 1000 simulations are performed for each of the two different code types. The conditions of other simulations are the same as those in Table 1. It can be seen from the figure that under different space-time code conditions, there are certain differences in the statistical histogram of the maximum value of the normalized grouped maximum value sequence of the correlation spectrum value, which provides a basis for the implementation of this algorithm.

Figure 3 shows the performance comparison between this method and the cyclostationary frequency detection method under the simulation conditions set in Table 1. It can be seen from FIG. 3 that the algorithm proposed by the present invention is slightly better than the cyclostationary frequency detection method.

The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions that belong to the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those skilled in the art, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present 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

s (N) is a signal vector, N is a signal sample length;

let the received signal of the ith antenna be r_i(n) the received signal of the jth antenna is r_j(n), if the delay is τ, the correlation spectrum module value is: u (k) ═ DFT [ r_i(n)r_j(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 R_evt；

5) Setting a threshold for identifying the MIMO space-time code;

setting a threshold th for MIMO space-time code recognition_evt，th_evt＝-ln[-ln(1-P_fa)]In the formula, P_faIs 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 is_evt≥th_evtIf 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 group_lK-1, forming K grouping maxima into a sequence of grouping maxima { γ ═ 0_l},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-a_K]/b_KK-1, where the normalization coefficients are 0

σ_zIs 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 R_evt＝max[U'(l)]。

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