Symbol Synchronization and Frequency Offset Estimation Method and System for Quantum Noise Flow Encryption System

Abstract

The present application discloses a symbol synchronization and frequency offset estimation method and system for a quantum noise flow encryption system. The method includes: generating a conjugate symmetric sequence U based on a PN sequence, and generating a conjugate antisymmetric training sequence based on U; the sending end encrypts the training sequence using a pre-shared key, adds the encrypted training sequence to a data frame, the receiving end receives a data signal including the encrypted training sequence, and restores the sequence signals in the training sequence positions corresponding to different data positions based on the key; calculating the autocorrelation values and timing metric values of the corresponding restored sequences based on different strategies; taking the mean of the timing metric values of different strategies to obtain a comprehensive timing metric value, performing symbol synchronization of the quantum noise flow encryption system based on the comprehensive timing metric value, and performing frequency offset estimation based on the autocorrelation values at the symbol synchronization positions. The present application can improve the symbol synchronization accuracy and frequency offset estimation accuracy in the quantum noise flow encryption system, and improve the security of symbol frequency synchronization.

Description

Technical Field

[0001] This application belongs to the technical field of quantum noise stream encryption, and relates to a symbol synchronization and frequency offset estimation method and system applicable to a quantum noise stream encryption system. Background Art

[0002] The security of optical fiber communication networks has received increasing attention. Quantum Noise Stream Cipher (QNSC) technology can ensure the physical layer security of optical fiber communication networks. Quantum noise stream encryption optical communication systems include quantum noise stream encryption systems based on intensity, phase, and quadrature modulation. Quantum noise stream encryption systems can achieve high-speed and secure transmission of service data information. The essence of quantum noise stream encryption technology is to use quantum noise to hide the transmitted information and achieve encrypted transmission of optical signals. It has been proven to be an optical fiber communication physical layer encryption scheme with extremely high security performance. Encrypting the optical signal based on quantum noise, quantum noise is superimposed on a certain or certain physical states (such as amplitude, phase, frequency, etc.) of the effective signal to hide the transmitted information. For unauthorized parties, since the transmitted effective signal is hidden in the quantum noise, it is difficult to detect and identify the true state of the effective signal, so it is difficult to directly decrypt and obtain useful data information.

[0003] Quantum noise stream encryption systems need to solve the problem of the contradiction between accuracy and security in symbol frequency synchronization, that is, symbol frequency synchronization requires both accuracy and security. Existing quantum noise stream encryption systems are implemented based on widely used single-carrier technologies or OFDM single-carrier or multi-carrier technologies.

[0004] Regarding the accuracy of symbol frequency synchronization, synchronization technology is a problem that any quantum noise stream encryption system implemented by single-carrier technology and OFDM single-carrier or multi-carrier technology needs to solve. The performance of the synchronization algorithm directly affects the performance of the quantum noise stream encryption system. It can be said that without an accurate synchronization algorithm, reliable data transmission cannot be achieved. The role of symbol timing synchronization includes that the receiving end estimates the data frame position of the quantum noise stream encryption system implemented by single-carrier technology and OFDM single-carrier or multi-carrier technology through the synchronization algorithm. The carrier frequency also directly affects whether the receiving end of the quantum noise stream encryption system can accurately demodulate and recover the original data.

[0005] The quantum noise stream encryption system implemented by single-carrier technology is not sensitive to the offset of the carrier frequency. The frequency offset causes certain attenuation and phase rotation to the received signal, which can be solved by technologies such as equalization. However, the symbol synchronization error affects the transmission performance of the quantum noise stream encryption system implemented by single-carrier technology.

[0006] For a quantum noise flow encryption system implemented by OFDM single-carrier or multi-carrier technology, symbol timing synchronization is an important prerequisite for achieving high-quality and reliable transmission of high-rate data. Symbol timing synchronization estimates the accurate position of the OFDM symbol through a synchronization algorithm, enabling the OFDM system to obtain accurate sampling values. Additionally, carrier offset has a severe impact on the performance of a quantum noise flow encryption system implemented by OFDM technology. In an OFDM system, laser frequency offset in the transmitter causes inter-carrier interference (ICI), seriously affecting the transmission performance of a quantum noise flow encryption system implemented based on OFDM single-carrier or multi-carrier technology. Carrier frequency offset stems from the inconsistency between the signal laser in the transmitter and the local laser in the receiver. The deviation between the carrier frequencies of the transmitter and the receiver is a key factor affecting the performance of the quantum noise flow encryption system. A quantum noise flow encryption system implemented by OFDM single-carrier or multi-carrier technology requires a higher accuracy of frequency offset estimation and demands orthogonality between subcarriers. Since the orthogonality between subcarriers is lost, simply increasing the transmission power of the signal cannot significantly improve the system performance. Therefore, for a quantum noise flow encryption system implemented by OFDM single-carrier or multi-carrier technology, high-precision symbol and frequency synchronization must be achieved at the receiver end.

[0007] Therefore, all quantum noise flow encryption systems implemented by single-carrier technology, OFDM single-carrier or multi-carrier technology require precise symbol synchronization technology, and quantum noise flow encryption systems implemented by OFDM single-carrier or multi-carrier technology also require precise frequency offset estimation technology.

[0008] Regarding the security of symbol frequency synchronization, using constant-amplitude Chu sequences, zero-amplitude sequences, and fixed complex sequences for synchronization has a relatively high synchronization accuracy. However, the characteristics of the above sequences are obvious and are easily synchronized by Eve, being accurate but not secure enough. The training sequence based on PN random numbers does not have the constant-amplitude characteristic and is not a fixed complex sequence, with relatively high security. However, Eve can also judge the autocorrelation values of each part of the training sequence for symbol synchronization. When Eve identifies the synchronization position, she can correspondingly obtain data and key synchronization. Using the PN sequence for synchronization is neither accurate nor secure enough.

[0009] The existing symbol frequency synchronization algorithms have problems in the accuracy and security of symbol frequency synchronization when applied to quantum noise flow encryption systems. The symbol frequency synchronization algorithms used in existing quantum noise flow encryption systems do not comprehensively consider the accuracy and security of symbol frequency synchronization, but only consider the synchronization performance during the signal transmission process from the perspective of the accuracy of symbol frequency synchronization. To improve the security of quantum noise flow encrypted data information and increase the hiding effect of noise on optical information, quantum noise flow encrypted optical signals are often transmitted at a low OSNR. In the scenario of high-order modulation low-SNR optical signal transmission, the contradiction between the accuracy and security of symbol frequency synchronization in quantum noise flow encryption systems becomes more prominent. Therefore, symbol synchronization and frequency offset estimation techniques that consider the security of symbol frequency synchronization have become important techniques in quantum noise flow encryption systems implemented by single-carrier technology and OFDM single-carrier or multi-carrier technology.

[0010] The symbol frequency synchronization algorithms used in quantum noise flow encryption systems include the Schmidl algorithm, the Minn algorithm, the Park algorithm, and the symbol frequency synchronization algorithm based on zero-amplitude or constant-amplitude training sequences. To achieve symbol and frequency synchronization in quantum noise flow encryption systems, the OFDM frame structure of existing symbol frequency synchronization algorithms based on training sequences is as Figure 1 shown.

[0011] The Schmidl algorithm is a synchronization method based on training sequences, which can be used for symbol timing synchronization. The structure of the training sequence is as Figure 2 shown. This training sequence is composed of two identical sequences with a length of N / 2 in the time domain. Schmidl proposed using a sliding autocorrelation algorithm for symbol timing synchronization. The symbol timing metric is obtained by calculating the correlation value between the left N / 2 sequence and the right N / 2 sequence of the training sequence. The Schmidl algorithm calculates the autocorrelation value of the OFDM signal by using a sliding window with a length of L. This window can slide over time to calculate the autocorrelation value and timing metric of data signals at different positions. When the position of the sliding window is within the cyclic prefix, the timing metric obtains a maximum value, resulting in a plateau, which leads to inaccurate determination of the position of the OFDM symbol. Based on the training sequence with a repetitive structure, the Schmidl algorithm can estimate the fractional part of the frequency offset (FO) using the autocorrelation value at the symbol synchronization position. However, due to the peak plateau problem, the error of frequency offset estimation is relatively large. The method for estimating the frequency offset based on the autocorrelation value at the timing synchronization position of the Schmidl algorithm is as follows:

[0012]

[0013] is the frequency offset value, is the synchronization position nThe autocorrelation value of angle.

[0014] Although the Schmidl symbol frequency synchronization algorithm has good reliability under low signal-to-noise ratio (SNR), due to the existence of the timing measurement peak platform, the position of the OFDM symbol is not accurately judged, resulting in reduced symbol frequency synchronization accuracy. Minn corrects the peak platform problem by modifying the structure of the training sequence.

[0015] The training symbol structure used by the Minn algorithm is [AA -A -A], such as Figure 3 As shown, A represents a sequence with a length of L=N / 4, which is generated by IFFT modulation of an N / 4-point PN sequence. The method proposed by Minn obtains the timing metric by calculating the autocorrelation value of the cyclic repetitive structure of the training sequence. The Minn algorithm uses pseudo-random sequences with opposite relationships, which can make the timing metric form a maximum value, solving the peak platform problem in the Schmidl algorithm. However, due to the particularity of the training sequence structure, secondary peak interference is caused, which may cause misjudgment of the synchronization position. In addition, when the signal-to-noise ratio decreases, the secondary peak peak value in the timing metric will be close to the main peak peak value. Although the Minn algorithm solves the peak platform problem, the secondary peak interferes with the main peak, resulting in errors in the judgment of the symbol synchronization position, and the frequency offset estimation based on the autocorrelation value of the symbol synchronization position also has deviations. The method for estimating the frequency offset based on the autocorrelation value of the symbol synchronization position based on the Minn algorithm is as follows:

[0016]

[0017] is the frequency deviation value, Is the synchronization position n The autocorrelation value of angle.

[0018] In order to solve the above problems, Park et al. proposed a new symbol timing synchronization algorithm and designed a new training sequence structure, such as Figure 4 shown.

[0019] The new training sequence structure consists of four parts. C is a PN sequence obtained by IFFT modulation, with a length of N / 4. C* is the conjugate sequence of C, with a length of N / 4. D is the symmetric sequence of C, also with a length of N / 4. In the Park algorithm, the left and right halves of the training sequence are symmetric sequences at their respective midpoints. The first N / 4 point sequence and the third N / 4 point sequence are conjugate sequences. When the sliding window deviates from the correct position, the autocorrelation value drops rapidly. The Park algorithm solves the problem of the secondary peaks in the Minn algorithm. When the signal-to-noise ratio of the signal decreases, the judgment accuracy decreases, and symbol synchronization and frequency offset estimation based on symbol synchronization are incorrect.

[0020] Existing symbol frequency synchronization algorithms cannot achieve both accuracy and security when applied to quantum noise flow encryption systems. Accuracy and security restrict each other and are difficult to directly apply. For example, the classic Schmidl algorithm, Minn algorithm, and Park algorithm combined with the CHU sequence improve accuracy but reduce security. Constant amplitude zero autocorrelation (CAZAC) sequences have obvious constant amplitude, ideal autocorrelation, and good cross-correlation characteristics. CAZAC sequences are widely used for synchronization in single-carrier communication systems or OFDM communication systems. In particular, the CHU sequence, as a constant amplitude zero autocorrelation sequence, is widely used for synchronization in LTE systems and satellite optical communication systems. The existing method of using the Schmidl algorithm combined with the CHU sequence to achieve symbol frequency synchronization is based on symbol synchronization to achieve frequency synchronization, which can improve the accuracy of synchronization. However, the characteristics of this constant amplitude sequence are not secure enough. The classic Schmidl algorithm, Minn algorithm, and Park algorithm that use PN sequences as training sequences have relatively high security but low symbol frequency synchronization accuracy. For example, in the Schmidl algorithm that uses a PN sequence as a training sequence, since the PN sequence is randomly generated and the training sequences used in different data frames are different, it can improve the security of symbol frequency synchronization. However, the symbol frequency synchronization accuracy of the Schmidl algorithm is low. From the characteristics and application scenarios of quantum noise flow encryption systems, in order to improve the security of quantum noise flow encryption systems, data signals need to be transmitted at a low OSNR. Transmitting data signals at a low OSNR further reduces the synchronization accuracy.

[0021] In the existing symbol frequency synchronization algorithms, the Schmidl algorithm has a peak platform problem. Affected by the peak platform, the judgment of the OFDM symbol synchronization position is not accurate enough, which affects the frequency offset estimation using the autocorrelation value of the application timing metric peak position. There may be deviations in the frequency offset estimation, and the mean square error of the frequency offset estimation is relatively large. The Minn algorithm solves the peak platform problem, but the Minn algorithm has the problem of high side peak values. Due to the interference of side peaks, the symbol synchronization position may be misjudged, resulting in errors in the subsequent frequency offset estimation and affecting the accuracy of the frequency offset estimation. The Schmidl algorithm and the Minn algorithm have a certain judgment accuracy rate at low OSNR, and the synchronization position judgment accuracy rate in high OSNR scenarios has improved, but it cannot reach 100%, especially the Schmidl algorithm. The Park algorithm does not have the peak platform problem and also solves the problem of high side peak values of the Minn algorithm. The symbol synchronization accuracy rate of the Park algorithm at high OSNR is extremely high and can reach 100%. When the OSNR decreases, the main peak value of the Park algorithm is relatively low, and the symbol synchronization judgment accuracy rate of the Park algorithm decreases rapidly, resulting in errors in symbol frequency synchronization. The synchronization accuracy rate of the Schmidl algorithm, the Minn algorithm, and the Park algorithm can be further improved when using the CHU sequence as the training sequence compared to using the PN sequence as the training sequence. However, due to the characteristics of the training sequences and the corresponding timing metric methods used in the above algorithms, problems such as peak platforms, side peak interference, and low main peak values still exist. In addition, the timing metric calculation methods of the training sequences proposed in the above algorithms are relatively single and cannot fully extract the characteristics of the training sequences. The symbol frequency synchronization algorithm of the quantum noise flow encryption system needs to comprehensively consider the accuracy and security of symbol frequency synchronization. Due to the constant amplitude characteristic of the CHU sequence, the training sequences of the Schmidl algorithm, the Minn algorithm, and the Park algorithm are easily recognized, and the above algorithms have relatively high accuracy and low security when combined with the Chu sequence. The existing Schmidl, Minn, and Park algorithms based on the CAZAC sequence have more accurate synchronization performance than the symbol frequency synchronization algorithm based on the PN sequence. However, the constant amplitude characteristic of the CAZAC sequence is obvious and can be quickly and accurately recognized by unauthorized receivers, making it easy to be recognized and attacked by unauthorized receivers. The PN sequence does not have the constant amplitude characteristic, but its synchronization performance is relatively low. In addition, the training sequence based on the PN sequence is not encrypted, and the position of the training sequence can also be recognized through the correlation operation of the training sequence.

[0022] In summary, the existing algorithms cannot achieve both accuracy and security, and when directly applied to the high-order modulation low SNR optical signal transmission scenario of the quantum noise flow encryption system, the symbol frequency synchronization accuracy rate is low and it is not secure enough. Therefore, in the quantum noise flow encryption system, it is necessary to explore a high-performance symbol frequency synchronization algorithm implemented by the PN sequence.

[0023] The Chinese patent with the publication number CN113162882A proposes an autocorrelation OFDM symbol synchronization method based on conjugate antisymmetric training sequences. By using the mirror sequence, conjugate sequence, and opposite number sequence of the complex PN sequence to set the training sequence structure, the symbol synchronization accuracy of the OFDM system in high OSNR scenarios is significantly improved through the timing metric of the symmetric structure sequence. However, the proposed strategy cannot fully extract the training sequence features, and the synchronization accuracy is relatively low in low OSNR scenarios. Summary of the Invention

[0024] To address the deficiencies in the prior art, this application proposes a symbol synchronization and frequency offset estimation method and system for a quantum noise flow encryption system. Through the training sequence generated by the random number sequence and the corresponding symbol frequency joint synchronization strategy, it can combine the advantages of the Schmidl strategy, Minn strategy, and Park strategy, use a training sequence to fully extract the features of the training sequence, improve the symbol synchronization accuracy and frequency offset estimation accuracy in the quantum noise flow encryption system, and further improve the security performance of signal transmission by encrypting the training sequence with a key, achieving the hiding of the synchronization position from unauthorized receivers.

[0025] This application adopts the following technical solutions.

[0026] The first aspect of this application proposes a symbol synchronization and frequency offset estimation method for a quantum noise flow encryption system, including:

[0027] Step 1: Generate a conjugate symmetric sequence U based on the PN sequence, and generate a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U;

[0028] Step 2: The sender encrypts the training sequence using a pre-shared key, adds the encrypted training sequence to the data frame, and the data frame is transmitted in the data transmission channel;

[0029] Step 3: The receiver receives the data signal including the training sequence, uses the pre-shared key to recover the sequence signal at the training sequence position corresponding to the n th data position in the received data signal, and obtains a recovered sequence based on the sequence signal at the training sequence position corresponding to the n th data position;

[0030] Step 4: Calculate the autocorrelation value and timing metric value of the recovered sequence of the sequence signal at the training sequence position corresponding to the n th data position respectively based on the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetric structure training sequence;

[0031] Step 5: Take the average of the timing metric values obtained from the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetric structure training sequence to obtain the comprehensive timing metric value at different data positions, and perform symbol synchronization based on the comprehensive timing metric;

[0032] Step 6: Perform frequency offset estimation based on the autocorrelation values obtained from the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetric structure training sequence.

[0033] Preferably, in Step 1, the method for generating the conjugate symmetric sequence U based on the PN sequence is as follows:

[0034]

[0035] A = B.*exp(- j π(N / 8 - 1) l / N / 8)

[0036] where denotes the inverse fast Fourier transform of sequence A;

[0037] B is an N / 8-point PN sequence;

[0038] l = 0, 1, ..., N / 8 - 1;

[0039] ".*" is the dot product operator;

[0040] N is the length of the training sequence;

[0041] The conjugate-antisymmetric training sequence structure generated based on the conjugate symmetric sequence U is [-U, -U, -U*, U', -U', U*, U, U];

[0042] where -U is the opposite sequence of U, U* is the conjugate sequence of U, -U* is the opposite sequence of the conjugate sequence of U, U' is the mirror sequence of U, and -U' is the opposite sequence of the mirror sequence of U.

[0043] Preferably, the encryption of the training sequence using the pre-shared key in Step 2 includes:

[0044] (1) Encrypt the amplitude of the symbol with the serial number n in the training sequence using the pre-shared key. The encryption method is as follows: Based on the 2-bit keys 00, 01, 10, and 11, perform operations of not taking the inverse of the real and imaginary parts and the amplitude of the complex number corresponding to the data signal symbol in the training sequence, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary parts and the amplitude of the complex number corresponding to the data signal symbol in the training sequence;

[0045] (2) Encrypt the amplitude of the symbol with the serial number nPhase encryption of the complex numbers corresponding to the symbols, with the encryption method being: based on the 2-bit keys 00, 01, 10, and 11, multiply the complex numbers corresponding to the data signal symbols in the training sequence that have undergone amplitude encryption by the secure numbers -1, 1, i, and -i respectively.

[0046] Preferably, in step 3, the receiving end restores the sequence signal in the training sequence position corresponding to the n th data position in the received data signal based on the pre-shared key, specifically including:

[0047] (1) The receiving end finds the secure numbers in the corresponding decryption sequence based on the 2-bit 0 / 1 encryption key used by the sending end, where the secure numbers -1, 1, i, and -i corresponding to the encryption key correspond to the decryption secure numbers -1, 1, -i, and i;

[0048] (2) The receiving end multiplies the complex numbers corresponding to the different data signal symbols in the training sequence position corresponding to the n th data position by the corresponding secure numbers in the decryption sequence to restore the phases of the complex numbers corresponding to the different data signal symbols in the training sequence position corresponding to the n th data position;

[0049] (3) The receiving end finds the inverse actions corresponding to the amplitude encryption actions of the different data signal symbols in the training sequence position corresponding to the n th data position based on the 2-bit 0 / 1 encryption key used by the sending end;

[0050] (4) The receiving end performs the corresponding inverse actions on the amplitudes of the different data signal symbols in the training sequence position corresponding to the n th data position, and correspondingly restores the amplitudes of the complex numbers corresponding to the different data signal symbols in the training sequence position corresponding to the n th data position, specifically as follows:

[0051] The inverse actions corresponding to the actions of not taking the inverse of the real and imaginary parts and amplitudes of the complex number corresponding to the data signal symbol, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary parts and amplitudes all correspond to not taking the inverse of the real and imaginary parts and amplitudes of the complex number corresponding to the data signal symbol, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary parts and amplitudes.

[0052] Preferably, the formula for calculating the autocorrelation value and the timing metric value of the sequence restored from the sequence signal in the training sequence position corresponding to the n th data position in the timing metric strategy of the symmetric structure training sequence is as follows:

[0053] (3)

[0054] (4)

[0055] (5)

[0056] wherein,

[0057] represents the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n -th data position calculated based on the timing metric strategy of the symmetric structure training sequence;

[0058] represents half of the sequence energy value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n -th data position, and is used to normalize;

[0059] represents the timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n -th data position calculated based on the timing metric strategy of the symmetric training structure sequence;

[0060] , , respectively represent the , , -th data in the training sequence position corresponding to the n-th data position recovered at the receiving end.

[0061] Preferably, the formulas for calculating the autocorrelation value and the timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n -th data position by the Schmidl strategy in step 4 are as follows:

[0062] (6)

[0063] (7)

[0064] (8)

[0065] wherein, represents the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n -th data position calculated based on the Schmidl strategy;

[0066] represents n half of the sequence energy value of the sequence recovered from the sequence signal at the training sequence position corresponding to the -th data position, and is used to

[0067] Indicates the timing metric value of the sequence with sequence signal recovery among the training sequence positions corresponding to the n th data position calculated based on the Schmidl strategy;

[0068] , respectively represent the n th data in the training sequence positions corresponding to the th and th data positions recovered by the receiver.

[0069] Preferably, the calculation formulas for the autocorrelation value and the timing metric value of the sequence with sequence signal recovery among the training sequence positions corresponding to the n th data position calculated by the Minn strategy described in step 4 are as follows:

[0070] (9)

[0071] (10)

[0072] (11)

[0073] where is the autocorrelation value of the sequence with sequence signal recovery among the training sequence positions corresponding to the n th data position calculated based on the Minn strategy;

[0074] represents half of the sequence energy value of the sequence with sequence signal recovery among the training sequence positions corresponding to the n th data position, and is used to normalize;

[0075] represents the timing metric value of the sequence with sequence signal recovery among the training sequence positions corresponding to the n th data position calculated based on the Minn strategy;

[0076] , , , , respectively represent the th, th, th, th, th data in the training sequence positions corresponding to the nth data position recovered by the receiver.

[0077] Preferably, the calculation formula for the frequency offset estimation described in step 6 is as follows,

[0078] (13)

[0079] Wherein, is the frequency offset estimation value;

[0080] is the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position calculated based on the Schmidl strategy;

[0081] is the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position calculated based on the Minn strategy;

[0082] represents the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position calculated based on the timing metric strategy of the training sequence with a symmetric structure.

[0083] A symbol synchronization and frequency offset estimation system for a quantum noise flow encryption system is proposed in the second aspect of the present application, including:

[0084] A training sequence generation module, configured to generate a conjugate symmetric sequence U based on a PN sequence, and generate a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U;

[0085] An encryption transmission module, configured to encrypt the training sequence by using a pre-shared key at the sending end, add the encrypted training sequence to a data frame, and transmit the data frame in a data transmission channel;

[0086] A decryption and recovery module, configured to receive a data signal including a training sequence at the receiving end, use the pre-shared key to recover the sequence signal at the training sequence position corresponding to the n th data position in the received data signal, and obtain a recovered sequence based on the sequence signal at the training sequence position corresponding to the n th data position;

[0087] A calculation module, configured to calculate the autocorrelation value and the timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position respectively based on the timing metric strategy, the Schmidl strategy, and the Minn strategy of the training sequence with a symmetric structure;

[0088] A symbol synchronization module, configured to obtain a comprehensive timing metric value for different data positions by taking the average of the timing metric values obtained by the timing metric strategy, the Schmidl strategy, and the Minn strategy of the training sequence with a symmetric structure, and perform symbol synchronization based on the comprehensive timing metric;

[0089] The frequency offset estimation module is used to perform frequency offset estimation based on the autocorrelation values obtained from the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetric structure training sequence.

[0090] Compared with the prior art, the present application is a symbol and frequency joint synchronization technology without the characteristics of constant amplitude or zero amplitude, with a high main peak value, a low secondary peak value, and no peak value platform problem. It comprehensively considers the mutual restriction relationship between accuracy and security, solves the problem that the training sequence in the quantum noise flow encryption system is easily recognized, can improve the symbol synchronization and frequency offset estimation accuracy in the quantum noise flow encryption system, improve the security and comprehensive performance of signal transmission, and can be applied to wireless communication systems and optical communication systems.

[0091] Compared with the prior art, the beneficial effects of the present application at least include:

[0092] 1. The present application proposes a training sequence for symbol synchronization frequency offset estimation applicable to the quantum noise flow encryption system, and uses a conjugate symmetric sequence U composed of PN sequences, the conjugate sequence of the conjugate symmetric sequence U, the opposite sequence, and the mirror sequence to generate the training sequence.

[0093] The training sequence structure is generated by a random number sequence, i.e., a PN sequence, without the characteristic of constant amplitude, is applicable to the quantum noise flow encryption system with low OSNR, and has significant accuracy and security in the low OSNR scenario.

[0094] The present application uses multiple timing metric calculation strategies to calculate the timing metric of a training sequence, realizes multiple training sequences based on one training sequence, is more accurate than using multiple training sequences, does not increase the length of the training sequence, can improve accuracy and security at the same time, and has higher transmission efficiency.

[0095] The proposed training sequence structure combined with the key can achieve a rapid decrease in the autocorrelation values of different parts of the training sequence when the sliding window deviates from the correct position; when the signal-to-noise ratio of the optical fiber channel decreases, the training sequence can maintain the autocorrelation performance, and the main peak value is less affected by the decrease in the signal-to-noise ratio, which can improve the accuracy of symbol synchronization and frequency offset estimation in the quantum noise flow encryption system.

[0096] The proposed algorithm using the PN sequence as the training sequence can achieve the performance of the algorithm using the Chu sequence as the training sequence. Due to the random generation of the PN sequence and the randomness of the key, the training sequences generated in different data frames are different, which can improve the security of symbol frequency synchronization.

[0097] Applying the opposite number or conjugate operator at specific positions of the training sequence, the training sequence also conforms to the structures of the Schmidl, Minn, and symmetric structure training sequences. The training sequence combined with the corresponding strategy can fully extract the characteristics of the training sequence and achieve accurate symbol frequency synchronization.

[0098] The training sequence conforms to the structural characteristics of the Schmidl repetitive structure and the Minn cyclic repetitive structure, as well as the characteristics of the symmetric structure training sequence. When the signal-to-noise ratio of the signal decreases, the training sequence can maintain its autocorrelation performance, with a relatively high autocorrelation value, and the peak value of the main peak is less affected by the decrease in the signal-to-noise ratio. The present application synthesizes the timing metrics of the proposed training sequence, and can fully extract the features of the training sequence.

[0099] 2. In the application scenario of the quantum noise flow encryption system, the present application first proposes to encrypt and decrypt the amplitude and phase of the training sequence using the key shared by the transmitter and receiver in the quantum noise flow encryption system, improving the security of symbol frequency synchronization and achieving both accuracy and security in symbol frequency synchronization.

[0100] Encrypting the amplitude and phase of the training sequence using the key shared by the transmitter and receiver in the quantum noise flow encryption system reduces the correlation between different parts of the training sequence. The autocorrelation value of the encrypted training sequence is relatively low, and the training sequence has high security. The encrypted training sequence does not have the characteristic of constant amplitude. Like the data signal, the training sequence is hidden in the signal data, improving the security of symbol frequency synchronization.

[0101] Due to the role of the key, unauthorized parties without the key cannot recover the amplitude and phase of the training sequence, making it difficult to achieve symbol frequency synchronization, improving the security of symbol frequency synchronization, and having high security on the premise of improving the synchronization accuracy.

[0102] The amplitude and phase of the complex number corresponding to the symbol in the training sequence for symbol frequency synchronization are encrypted. The amplitude of the training sequence is encrypted by taking the opposite, and the phase of the training sequence is encrypted by rotation, improving the security of symbol frequency synchronization and data information.

[0103] 3. The present application proposes a symbol synchronization and timing metric scheme applicable to the quantum noise flow encryption system. The present application combines the training sequence with the timing metric strategies, Schmidl strategy, and Minn strategy based on the symmetric structure training sequence. The autocorrelation value at the ±N / 4 positions of the symbol synchronization position is relatively low, eliminating the interference of the secondary peaks at the ±N / 4 positions of the symbol synchronization position; inheriting the advantages of the Minn strategy and the Schmidl strategy in having a high main peak value at low OSNR and strong anti-interference ability, and inheriting the advantages of the symbol synchronization strategy based on the symmetric structure training sequence in having a high judgment accuracy of the synchronization position, a low secondary peak value, and a high main peak value at high OSNR.

[0104] This application solves the problem of incorrect frequency offset estimation in the Minn strategy. In the proposed strategy, when the symbol synchronization position obtained by symbol synchronization is within the guard interval, the obtained training sequence data is not a cyclic shift of the training sequence, eliminating the peak platform effect and large frequency offset error of the Schmidl strategy; it solves the problem of incorrect frequency offset estimation caused by symbol synchronization error in the symbol frequency synchronization strategy based on the symmetric structure. This application can extract the features of the training sequence more fully compared with the symbol frequency synchronization method based on the symmetric structure training sequence, has a higher synchronization judgment accuracy rate, and can perform frequency offset estimation more accurately.

[0105] This application has a higher synchronization position judgment accuracy rate at low OSNR compared with the strategy based on the symmetric structure training sequence. This application has a higher synchronization position judgment accuracy rate at high OSNR compared with the strategies based on the repeated structure training sequence such as the Schmidl strategy and the Minn strategy.

[0106] 4. This application proposes a frequency offset estimation scheme applicable to the quantum noise flow encryption system. Multiple autocorrelation values and timing metrics are calculated for the training sequence using multiple classical timing metric strategies, and the average values of the multiple autocorrelation values and timing metrics are calculated to obtain comprehensive autocorrelation values and timing metric values, integrating the advantages of multiple timing metric strategies and fully extracting the features of the training sequence. When deviating from the correct symbol synchronization position, the autocorrelation values and timing metric values of the Schmidl strategy, the Minn strategy, and the strategy based on the symmetric structure training sequence are relatively low, and the side peaks of the above strategies appear at different positions, so the amplitude mean values of the timing metrics of the above strategies are relatively low. Near the correct symbol synchronization position, the autocorrelation values and timing metrics of the above strategies are relatively high, enabling accurate frequency offset estimation.

[0107] 5. Due to the accurate symbol synchronization performance of this application, accurate frequency offset estimation is achieved based on symbol synchronization. For the training sequence, the autocorrelation values of a training sequence are extracted using the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetric structure training sequence respectively to obtain multiple autocorrelation values. Since the training sequence is affected by the same frequency offset, the autocorrelation values obtained by extracting the training sequence features using the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetric structure training sequence all describe the same frequency offset information, and the frequency offset calculated based on the sum value of multiple autocorrelation values has a relatively high accuracy rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 is the OFDM frame structure for symbol synchronization;

[0109] Figure 2 is the training sequence structure of the Schmidl algorithm;

[0110] Figure 3 It is the training sequence structure of the Minn algorithm;

[0111] Figure 4 It is the training sequence structure of the Park algorithm;

[0112] Figure 5 It is the training sequence structure proposed in this application;

[0113] Figure 6 It is the training sequence corresponding to the nth data position;

[0114] Figure 7 It is the symbol frequency joint synchronization process in the quantum noise flow encryption system based on the PN sequence in this application. Specific implementation manners

[0115] To make the objectives, technical solutions and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. The embodiments described in this application are only a part of the embodiments of this application, rather than all embodiments. Based on the spirit of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0116] Embodiment 1 of this application provides a method for symbol synchronization and frequency offset estimation of a quantum noise flow encryption system, as Figure 7 shown, including:

[0117] Step 1: Generate a conjugate symmetric sequence U based on the PN sequence, and generate a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U;

[0118] Further preferably, the training sequence structure used in this application is a training sequence structure composed of the conjugate symmetric sequence U, the mirror sequence of U, the conjugate sequence, and the opposite number sequence. This application uses the conjugate symmetric sequence obtained from the random number (Pseudo-Noise, PN) sequence as the sequence U for symbol synchronization and frequency offset estimation, as Figure 5 shown.

[0119] This application generates a conjugate symmetric sequence U based on the random number PN sequence.

[0120] The complex conjugate symmetric sequence U is obtained by the IFFT operation of A, and the function expression for generating the sequence A is

[0121] A = B.*exp(- j π(N / 8 - 1) l / N / 8) (1)

[0122] The B sequence is an N / 8-point random number real sequence, and N is the length of the training sequence. l= 0, 1, ..., N / 8 - 1. ".*" is the dot product operator. The expression of the U sequence is

[0123] (2)

[0124] The training sequence structure proposed in this application is an anti-symmetric sequence, and the structure of the training sequence is [-U, -U, -U*, U', -U', U*, U, U]. The training sequence consists of 8 parts, and the sequence U is obtained by the IFFT operation of a real PN sequence with a length of N / 8. -U is the opposite sequence of U, U* is the conjugate sequence of U, -U* is the opposite sequence of the conjugate sequence of U, U' is the mirror sequence of U, that is, the reverse sequence. -U' is the opposite sequence of the mirror sequence of U, that is, the mirror opposite sequence.

[0125] Step 2: The sender encrypts the training sequence using a pre-shared key, adds the encrypted training sequence to the data frame, and the data frame is transmitted in the data transmission channel;

[0126] This application proposes a symbol frequency synchronization method that coexists accuracy and security for a quantum noise flow encryption system. This application realizes the security of symbol frequency synchronization based on the encryption of the amplitude and phase of the training sequence. The application scenario of this application is a quantum noise flow encryption system. The legitimate communication parties have a pre-shared key for data encryption, and the pre-shared key can also be used to encrypt the training sequence.

[0127] The sender uses a 4N-length key to encrypt the amplitude and phase of the training sequence.

[0128] The encryption of the amplitudes of different symbols in the training sequence is realized by a 2-bit 0 / 1 security key. The pre-shared key between the sender and the receiver of the quantum noise flow encryption system is realized by professional key distribution technology and key synchronization technology, and the key meets relevant test standards. To illustrate the symbol frequency synchronization method of this application vividly, taking the key used for encrypting the amplitude of the Nth symbol in the training sequence as 11 as an example, that is, the 2-bit key 11 represents the action of taking the opposite of the real and imaginary parts of the complex number corresponding to the data signal symbol in the training sequence.

[0129] The action of taking the opposite of the amplitude keeps the positive and negative signs of the real or imaginary part of the complex number corresponding to the data signal symbol unchanged. When the 2-bit key is 01, 10, 11, they respectively represent subtracting the real part, imaginary part, and the absolute values of the real and imaginary parts of the complex number corresponding to the currently encrypted data signal symbol from the maximum value 1 normalized by the absolute value of the amplitude, to obtain the amplitudes of the real and imaginary parts of the complex number corresponding to the encrypted data signal symbol.

[0130] The phase encryption of the complex numbers corresponding to different data signal symbols in the training sequence is achieved by a 2-bit 0 / 1 security key. Taking the key used for encrypting the phase of the complex number corresponding to the Nth symbol in the training sequence as 01 as an example, that is, the 2-bit key 01 means multiplying the complex number corresponding to the data signal symbol that has undergone amplitude encryption in the training sequence by the security number 1.

[0131] Step 3: The receiving end receives the data signal including the training sequence, and uses the pre-shared key to recover the sequence signal in the training sequence position corresponding to the n th data position in the received data signal, and obtains a recovered sequence based on the sequence signal in the training sequence position corresponding to the n th data position;

[0132] Further preferably, the receiving end of the present application has the pre-shared key of the quantum noise flow encryption system, and can decrypt the training sequence to recover the amplitudes and phases of different data signal symbols in the training sequence.

[0133] The key length used by the receiving end to decrypt the training sequence is 4N. The sending end and the receiving end pre-share the encryption and decryption keys. As Figure 6 shown, in order to vividly illustrate the mechanism of symbol synchronization using a sliding window, taking the recovery of the Nth symbol in the training sequence corresponding to the n th data position as an example, that is, the example recovers the amplitudes and phases of the symbols at the n +Nth data position.

[0134] The decryption process of the present application first recovers the phase of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the n th data position, and then recovers the amplitude of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the n th data position.

[0135] (1) Based on the pre-shared key, the receiving end uses the 2-bit 0 / 1 security key used by the sending end to find the security number in the corresponding decryption sequence. The security numbers -1, 1, i, -i used by the encryption key respectively correspond to the decryption security numbers -1, 1, -i, i.

[0136] (2) The receiving end multiplies the Nth sequence signal (i.e., the complex number corresponding to the n th data position corresponding to the training sequence position) in the training sequence position corresponding to the n +Nth data signal symbol by the corresponding decryption security number to be able to recover the phase of the complex number corresponding to the n +Nth data signal symbol.

[0137] The security digits corresponding to the encryption key of the data signal symbol, -1, 1, i, -i, respectively, correspond to the decryption security digits -1, 1, -i, i. The 2-bit decryption keys 00, 01, 10, 11 respectively correspond to the decryption actions of multiplying the complex numbers of the encrypted data signal symbols in the training sequence by the security digits -1, 1, -i, i. Taking the encryption and decryption of the phase of the complex number corresponding to the Nth symbol in the training sequence corresponding to the n th data position using the key 01 as an example, that is, the 2-bit key 01 means multiplying the complex number by the security digit 1 for the Nth sequence signal (i.e., the n th data position) in the training sequence corresponding to the n +Nth data signal symbol.

[0138] (3) Next, based on the pre-shared key used by the transmitting end, the receiving end finds the inverse action corresponding to the encryption action of the amplitude of the Nth data signal symbol in the training sequence position corresponding to the n th data position based on the 2-bit 0 / 1 encryption key used;

[0139] (4) When the receiving end executes the inverse actions corresponding to the encryption actions of the amplitudes of different data signal symbols at the transmitting end, it can correspondingly restore the amplitude of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the n th data position.

[0140] The inverse actions of not taking the inverse of the real and imaginary part amplitudes of the complex number corresponding to the data signal symbol, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary part amplitudes all correspond to not taking the inverse of the real and imaginary part amplitudes of the complex number corresponding to the data signal symbol, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary part amplitudes all. The 2-bit decryption keys 00, 01, 10, 11 respectively correspond to the actions of not taking the inverse of the real and imaginary part amplitudes of the complex number corresponding to the data signal symbol in the training sequence, taking the inverse of the real part amplitude, taking the inverse of the imaginary part amplitude, and taking the inverse of the real and imaginary part amplitudes all.

[0141] Taking the encryption and decryption of the amplitude of the Nth data signal symbol in the training sequence corresponding to the n th data position using the key 11 as an example, that is, the 2-bit key 11 corresponds to the action of taking the inverse of the real and imaginary part amplitudes of the complex number corresponding to the n +Nth data signal symbol.

[0142] Subsequently, the receiving end calculates the autocorrelation value and the timing metric value based on the sequence recovered by decrypting the sequence signal in the training sequence positions corresponding to different data positions for symbol frequency synchronization.

[0143] Step 4: Calculate the nThe autocorrelation value and timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to each data position;

[0144] Further preferably, the present application calculates the timing metric based on the characteristics that the training sequence conforms to a symmetric structure training sequence, a Schmidl repeated structure training sequence, or a Minn cyclic repeated structure training sequence.

[0145] (1) The receiving end uses formulas (3)-(5) to calculate the autocorrelation value and timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position based on the timing metric strategy of the symmetric structure training sequence:

[0146] Calculate the timing metric value of the decrypted and recovered sequence based on the timing metric strategy of the symmetric structure training sequence. Add an opposite operator to the first, second, third, and fifth N / 8 point sequences of the recovered sequence. Since U is a conjugate symmetric sequence, the converted sequence [U, U, U*, U', U', U*, U, U] conforms to the structure of a conjugate symmetric sequence. The timing metric calculated based on the conjugate symmetric training sequence structure is as follows:

[0147] (3)

[0148] (4)

[0149] (5)

[0150] Where, represents the n th recovered signal data; r ( n ) has the following relationship with the training sequence [-U, -U, -U*, U', -U', U*, U, U]:

[0151] r ( n + 1) to r ( n + N / 8) represents -U in the training sequence corresponding to the n th data position of the recovered sequence;

[0152] r ( n + N / 8 + 1) to r ( n + N / 4) represents -U in the training sequence corresponding to the n th data position of the recovered sequence;

[0153] r ( n+(N / 4 + 1) to r ( n +(N / 4 + N / 8 + 1) represents the -U* in the training sequence corresponding to the n th data position of the recovered sequence;

[0154] And so on.

[0155] represents the autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the n th data position calculated based on the timing metric strategy of the symmetric structure training sequence, obtained by calculating the correlation value between the left half sequence and the right half sequence of the sequence;

[0156] represents half of the sequence energy value of the sequence recovered from the sequence signal in the training sequence position corresponding to the n th data position, used for normalization processing;

[0157] represents the timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the n th data position calculated based on the timing metric strategy of the symmetric training structure sequence;

[0158] 、 、 respectively represent the 、 、 rd data in the training sequence position corresponding to the nth data position recovered by the receiving end.

[0159] (2) The receiving end uses the timing metric strategy based on the Schmidl algorithm and calculates the autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the n th data position using formulas (6)-(8):

[0160] Calculate the timing metric value of the decrypted and restored sequence based on the Schmidl strategy. Add the opposite operator to the first, second, third, and fifth N / 8 point sequences of the restored sequence, and add the conjugate operator to the fifth, sixth, seventh, and eighth N / 8 point sequences of the restored sequence. Since U is a conjugate symmetric sequence, the left half sequence and the right half sequence in the training sequence proposed in this application are of a repetitive structure. The sequence [U, U, U*, U*, U, U, U*, U*] obtained after transformation conforms to the structure of the repetitive structure training sequence of the classical Schmidl algorithm. Therefore, the Schmidl timing metric calculation method can be applied to calculate the timing metric. The timing metric calculated based on the Schmidl training sequence structure is as follows:

[0161] (6)

[0162] (7)

[0163] (8)

[0164] This application calculates the timing metric based on the characteristic that the training sequence conforms to the Schmidl training sequence structure.

[0165] In the above formula, is the autocorrelation value of the sequence of the restored sequence signal in the training sequence position corresponding to the n th data position calculated by the Schmidl strategy;

[0166] represents half of the sequence energy value of the sequence of the restored sequence signal in the training sequence position corresponding to the n th data position. The autocorrelation value is normalized by ;

[0167] represents the timing metric value of the sequence of the restored sequence signal in the training sequence position corresponding to the n th data position calculated based on the Schmidl strategy;

[0168] and respectively represent the n th and th and th data in the training sequence position corresponding to the

[0169] (3) The receiving end uses the timing metric strategy based on the Minn algorithm and calculates the nAutocorrelation values and timing metric values of the sequences recovered from the sequence signals at the training sequence positions corresponding to the data positions:

[0170] Calculate the timing metric values of the decrypted and recovered sequences based on the Minn strategy. Add the opposite operator to the first, second, third, sixth, seventh, and eighth N / 8 point sequences of the recovered sequence, and add the conjugate operator to the third, fourth, fifth, and sixth N / 8 point sequences of the recovered sequence. Since U is a conjugate symmetric sequence, the left half sequence of the training sequence proposed in this application consists of two sequences with repeated structures, and the right half sequence also consists of two sequences with repeated structures. The sequence [U, U, U, U, -U, -U, -U, -U] obtained after conversion conforms to the structure of the cyclic repeated structure training sequence of the classical Minn algorithm, so the Minn timing metric calculation method can be applied to calculate the timing metric. The timing metric calculated based on the Minn training sequence structure is as follows:

[0171] (9)

[0172] (10)

[0173] (11)

[0174] Wherein, is the autocorrelation value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position calculated by the Minn strategy;

[0175] represents half of the sequence energy value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position, and the autocorrelation value is normalized by ;

[0176] represents the timing metric value of the sequence recovered from the sequence signal at the training sequence position corresponding to the n th data position calculated by the Minn strategy;

[0177] , , , , respectively represent the , , , , th data at the training sequence position corresponding to the nth data position recovered at the receiving end.

[0178] Step 5: Based on the timing metric strategies of the symmetric structure training sequence, the Schmidl strategy, and the Minn strategy, obtain the mean value of the timing metric. Calculate the comprehensive timing metric values at different data positions according to formula (12), and perform symbol synchronization based on the comprehensive timing metric.

[0179] Further preferably, the formula for calculating the comprehensive timing metric value is as follows:

[0180] (12)

[0181] For the timing metric strategy of the Minn or symmetric structure training sequence, due to the combination of the training sequence in this application and the corresponding timing metric method, the autocorrelation value at the ±N / 4 position of the symbol synchronization position is relatively low, solving the problem of side peak interference. For the Schmidl strategy, a part of the end of the training sequence is used as the prefix of the data frame (i.e., the guard interval). In the strategy proposed in this application, when the symbol synchronization position obtained by symbol synchronization is within the guard interval, the obtained training sequence data is not a cyclic shift of the training sequence, eliminating the peak platform effect.

[0182] As shown in formula (12), from 、 、 the arithmetic mean is obtained. When deviating from the correct symbol synchronization position, the timing metric values of the Schmidl, Minn, and symmetric structure training sequence-based strategies are relatively low. Therefore, the amplitude mean of the timing metric of the above strategies is relatively low. Near the correct symbol synchronization position, the above strategies have different-shaped timing metrics, and the timing metric values are relatively high. Therefore, the mean value of the timing metric of the above strategies is relatively high without a platform problem. In this application, by using the conjugate symmetric sequence U and the mirror sequence of U, the conjugate sequence, and the opposite sequence, the training sequence structure is reasonably set. When the sliding window deviates from the correct position, the autocorrelation value of the training sequence drops rapidly. When the signal-to-noise ratio of the signal transmission channel decreases, the training sequence can maintain the autocorrelation performance, and the peak value of the main peak is less affected by the decrease in the signal-to-noise ratio.

[0183] Symbol synchronization position is obtained by the following formula:

[0184]

[0185] The position of the maximum value of the timing metric value is the position of the training sequence. When the training sequence position is determined, the synchronization position of the data frame can be determined based on the training sequence, realizing the symbol synchronization of the signal.

[0186] Step 6: The frequency offset is estimated by formula (13) based on the autocorrelation values of the symmetric structure training sequence-based strategy, the Schmidl strategy, and the Minn strategy.

[0187] Further preferably, the training sequence structure proposed in this application simultaneously conforms to the characteristics of the Schmidl, Minn, and symmetric structure training sequence structures. 、 and represent the autocorrelation values obtained from the same training sequence features extracted by the Schmidl strategy, Minn strategy, and symmetric structure training sequence strategy. Therefore, the same frequency offset information is fully described. By the properties of the geometric sequence, the frequency offset estimation proposed in this application 、 and can be obtained, and the calculation formula for frequency offset estimation is as follows:

[0188] (13)

[0189] where ranges from [-π, π], and the fractional part of the frequency offset estimation proposed in this application ranges from [-0.5, 0.5].

[0190] Embodiment 2 of this application provides a symbol synchronization and frequency offset estimation system for a quantum noise flow encryption system, including:

[0191] A training sequence generation module, configured to generate a conjugate symmetric sequence U based on a PN sequence, and generate a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U;

[0192] An encryption transmission module, configured to encrypt the training sequence using a pre-shared key at the sending end, add the encrypted training sequence to a data frame, and transmit the data frame in a data transmission channel;

[0193] A decryption and recovery module, configured to receive a data signal including a training sequence at the receiving end, use the pre-shared key to recover the sequence signal in the training sequence position corresponding to the n th data position in the received data signal, and obtain a recovered sequence based on the sequence signal in the training sequence position corresponding to the n th data position;

[0194] A calculation module, configured to calculate the autocorrelation value and timing metric value of the recovered sequence of the sequence signal in the training sequence position corresponding to the n th data position respectively based on the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetric structure training sequence;

[0195] A symbol synchronization module, which is used to calculate the mean value of the timing metric values obtained based on the timing metric strategies, Schmidl strategy, and Minn strategy of the symmetric structure training sequence, obtain the comprehensive timing metric values at different data positions, and perform symbol synchronization based on the comprehensive timing metric;

[0196] A frequency offset estimation module, which is used to estimate the frequency offset based on the autocorrelation values obtained from the timing metric strategies, Schmidl strategy, and Minn strategy of the symmetric structure training sequence.

[0197] Compared with the prior art, the present application is a symbol and frequency joint synchronization technology without the characteristics of constant amplitude or zero amplitude, with a high main peak value, a low secondary peak value, and no peak platform problem, solves the problem that the training sequence in the quantum noise flow encryption system is quickly recognized, comprehensively considers the mutual restriction relationship between accuracy and security, can improve the symbol synchronization and frequency offset estimation accuracy in the quantum noise flow encryption system, improve the security and comprehensive performance of signal transmission, and can be applied to wireless communication systems and optical communication systems.

[0198] Compared with the prior art, the beneficial effects of the present application at least include:

[0199] 1. The present application proposes a training sequence for symbol synchronization and frequency offset estimation applicable to the quantum noise flow encryption system, and uses a conjugate symmetric sequence U composed of PN sequences, the conjugate sequence of the conjugate symmetric sequence U, the opposite sequence, and the mirror sequence to generate the training sequence.

[0200] The training sequence structure is generated by a random number sequence, i.e., a PN sequence, without the characteristic of constant amplitude, is applicable to the quantum noise flow encryption system with low OSNR, and has remarkable accuracy and security in the low OSNR scenario.

[0201] The present application uses multiple timing metric calculation strategies to calculate the timing metric of a training sequence, realizes multiple training sequences based on one training sequence, is more accurate than using multiple training sequences, does not increase the length of the training sequence, can improve accuracy and security at the same time, and has higher transmission efficiency.

[0202] The proposed training sequence structure combined with the key can quickly reduce the autocorrelation values of different parts of the training sequence when the sliding window deviates from the correct position; when the signal-to-noise ratio of the optical fiber channel decreases, the training sequence can maintain the autocorrelation performance, and the main peak value is less affected by the decrease in the signal-to-noise ratio, which can improve the accuracy of symbol synchronization and frequency offset estimation in the quantum noise flow encryption system.

[0203] The proposed algorithm using the PN sequence as the training sequence can achieve the performance of the algorithm using the Chu sequence as the training sequence. Due to the random generation of the PN sequence and the randomness of the key, the training sequences generated in different data frames are different, which can improve the security of symbol frequency synchronization.

[0204] The opposite number or conjugate operator is applied to specific positions of the training sequence, and the training sequence also conforms to the structures of the Schmidl, Minn, and symmetric structure training sequences. The training sequence combined with the corresponding strategy can fully extract the characteristics of the training sequence and achieve accurate symbol frequency synchronization.

[0205] The training sequence conforms to the structural characteristics of the Schmidl repetitive structure, Minn cyclic repetitive structure, and symmetric structure training sequence. When the signal-to-noise ratio of the signal decreases, the training sequence can maintain its autocorrelation performance, with a relatively high autocorrelation value, and the peak value of the main peak is less affected by the decrease in the signal-to-noise ratio. The present application synthesizes the proposed timing metric of the training sequence, which can fully extract the features of the training sequence.

[0206] 2. In the application scenario of the quantum noise flow encryption system, the present application first proposes to encrypt and decrypt the amplitude and phase of the training sequence using the shared key between the transmitter and receiver in the quantum noise flow encryption system, improving the security of symbol frequency synchronization and achieving both accuracy and security in symbol frequency synchronization.

[0207] Encrypting the amplitude and phase of the complex numbers corresponding to the symbols in the training sequence for symbol frequency synchronization reduces the correlation between different parts of the training sequence. The autocorrelation value of the encrypted training sequence is relatively low, and the training sequence has high security. The encrypted training sequence does not have the characteristic of constant amplitude. Like the data signal, the training sequence is hidden in the signal data, improving the security of symbol frequency synchronization.

[0208] Due to the role of the key, unauthorized parties without the key cannot restore the amplitude and phase of the training sequence, making it difficult to achieve symbol frequency synchronization, improving the security of symbol frequency synchronization, and having high security on the premise of improving the synchronization accuracy.

[0209] The amplitude and phase of the complex numbers corresponding to the symbols in the training sequence for symbol frequency synchronization are encrypted. The amplitude of the training sequence is encrypted by taking the opposite, and the phase of the training sequence is encrypted by rotation, improving the security of symbol frequency synchronization and data information.

[0210] 3. The present application proposes a symbol synchronization and timing metric scheme applicable to the quantum noise flow encryption system. The present application combines the training sequence with the corresponding timing metric strategies based on symmetric structure training sequences, Schmidl strategies, and Minn strategies. The autocorrelation value at the ±N / 4 positions of the symbol synchronization position is relatively low, eliminating the interference of the secondary peaks at the ±N / 4 positions of the symbol synchronization position; inheriting the advantages of the Minn strategy and Schmidl strategy in having a high main peak value at low OSNR and strong anti-interference ability, and inheriting the advantages of the symbol synchronization strategy based on symmetric structure training sequences in having a high judgment accuracy of the synchronization position at high OSNR, a low secondary peak value, and a high main peak value.

[0211] This application solves the problem of incorrect frequency offset estimation in the Minn strategy. In the proposed strategy, when the symbol synchronization position obtained by symbol synchronization is within the guard interval, the obtained training sequence data is not a cyclic shift of the training sequence, eliminating the peak platform effect and large frequency offset error of the Schmidl strategy; it solves the problem of incorrect frequency offset estimation caused by symbol synchronization error in the symbol frequency synchronization strategy based on the symmetric structure. This application can extract the features of the training sequence more fully compared with the symbol frequency synchronization method based on the symmetric structure training sequence, has a higher synchronization judgment accuracy rate, and can perform more accurate frequency offset estimation.

[0212] This application has a higher synchronization position judgment accuracy rate at low OSNR compared with the strategy based on the symmetric structure training sequence. This application has a higher synchronization position judgment accuracy rate at high OSNR compared with the strategies based on the repeated structure training sequence such as the Schmidl strategy and the Minn strategy.

[0213] 4. This application proposes a frequency offset estimation scheme applicable to the quantum noise stream encryption system. Multiple autocorrelation values and timing metrics are calculated for the training sequence using multiple classical timing metric strategies, and the average values of the multiple autocorrelation values and timing metrics are calculated to obtain comprehensive autocorrelation values and timing metric values, integrating the advantages of multiple timing metric strategies and fully extracting the features of the training sequence. When deviating from the correct symbol synchronization position, the autocorrelation values and timing metric values of the Schmidl strategy, the Minn strategy, and the strategy based on the symmetric structure training sequence are relatively low, and the side peaks of the above strategies appear at different positions, so the amplitude mean of the timing metrics of the above strategies is relatively low. Near the correct symbol synchronization position, the autocorrelation values and timing metrics of the above strategies are relatively high, enabling accurate frequency offset estimation.

[0214] 5. Due to the accurate symbol synchronization performance of this application, accurate frequency offset estimation is achieved based on symbol synchronization. This application uses the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetric structure training sequence to extract the features of a training sequence respectively to obtain multiple autocorrelation values. Since the training sequence is affected by the same frequency offset, the autocorrelation values obtained by extracting the training sequence features using the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetric structure training sequence all describe the same frequency offset information, and the frequency offset calculated based on the sum value of the multiple autocorrelation values has a relatively high accuracy rate.

[0215] Embodiment 3 of this application provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor operates based on the instructions to execute the steps of the method.

[0216] Example 4 of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method are implemented.

[0217] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them. Although the present application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present application can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present application belongs to the protection scope of the claims of the present application.

Claims

1. A symbol synchronization and frequency deviation estimation method for a quantum noise stream encryption system, characterized in that: include: Step 1: Generate a conjugate symmetric sequence U based on the PN sequence, and generate a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U; Step 2: The sending end encrypts the training sequence using a pre-shared key, adds the encrypted training sequence to the data frame, and transmits the data frame in the data transmission channel; Step 3: The receiving end receives a data signal including a training sequence and uses the pre-shared key to recover the first n The sequence signal in the training sequence position corresponding to the data position is based on the n A recovered sequence is obtained by using the sequence signal in the training sequence position corresponding to the data position; Step 4: Calculate the first n The autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; The timing measurement strategy of the symmetric structure training sequence calculates the n The formulas for the autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position are as follows: （3） （4） （5） in, represents the first time calculated based on the timing measurement strategy of the symmetric structure training sequence n The autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; Indicates n Half of the sequence energy value recovered from the sequence signal in the training sequence position corresponding to the data position is used to Perform normalization; represents the first time-based metric strategy based on the symmetric training sequence. n A timing metric value of a sequence recovered from a sequence signal in a training sequence position corresponding to a data position; , , They represent the nth data position recovered by the receiving end and the training sequence position corresponding to the nth data position. , , individual data; The Schmidl strategy calculates n The formulas for the autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position are as follows: （6） （7） （8） in, represents the first n The autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; Indicates n Half of the sequence energy value recovered from the sequence signal in the training sequence position corresponding to the data position is used to Perform normalization; represents the first n A timing metric value of a sequence recovered from a sequence signal in a training sequence position corresponding to a data position; , Respectively represent the first n The training sequence position corresponding to the data position , individual data; The Minn strategy calculates n The calculation formulas for the autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position are as follows: （9） （10） （11） in, is calculated based on the Minn strategy. n The autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; Indicates n Half of the sequence energy value recovered from the sequence signal in the training sequence position corresponding to the data position is used to Perform normalization; Indicates the first n A timing metric value of a sequence recovered from a sequence signal in a training sequence position corresponding to a data position; , , , , They represent the nth data position recovered by the receiving end and the training sequence position corresponding to the nth data position. , , , , individual data; Step 5: The timing metric values ​​obtained by the timing metric strategy, Schmidl strategy, and Minn strategy of the symmetrical structure training sequence are averaged to obtain comprehensive timing metric values ​​of different data positions, and symbol synchronization is performed based on the comprehensive timing metric; Step 6: Perform frequency offset estimation based on the autocorrelation value obtained by the timing measurement strategy, Schmidl strategy, and Minn strategy of the symmetrical structure training sequence; the calculation formula of the frequency offset estimation is as follows: （13） in, is the frequency deviation estimate; is the autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the nth data position calculated based on the Schmidl strategy; is the autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the nth data position calculated based on the Minn strategy; It represents the autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the nth data position calculated based on the timing measurement strategy of the symmetrical structure training sequence.

2. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: In step 1, the method of generating the conjugate symmetric sequence U based on the PN sequence is: A= B.*exp(- j π(N / 8-1) l / N / 8) in, Indicates the inverse fast Fourier transform of sequence A; B is the N / 8-point PN sequence; l = 0, 1, ..., N / 8-1； ".*" is the dot product operator; N is the length of the training sequence; The structure of the conjugate antisymmetric training sequence generated based on the conjugate symmetric sequence U is [-U, -U, -U*, U', -U',U*, U, U]; Among them, -U is the opposite number sequence of U, U* is the conjugate sequence of U, -U* is the opposite number sequence of the conjugate sequence of U, U' is the mirror image sequence of U, and -U' is the opposite number sequence of the mirror image sequence of U.

3. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: Step 2, encrypting the training sequence using a pre-shared key, includes: (1) Use the pre-shared key to train the sequence number n The encryption method is as follows: based on the 2-bit keys 00, 01, 10, and 11, the complex numbers corresponding to the data signal symbols in the training sequence are respectively subjected to the following actions: not negating the amplitude of the real and imaginary parts, negating the amplitude of the real part, negating the amplitude of the imaginary part, and negating the amplitude of all the real and imaginary parts; (2) Use the pre-shared key to train the sequence number n The phase encryption of the complex number corresponding to the symbol of the data signal is performed by multiplying the complex number corresponding to the amplitude-encrypted data signal symbol in the training sequence by the security numbers -1, 1, i, -i based on the 2-bit keys 00, 01, 10, 11 respectively.

4. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: Step 3: The receiving end recovers the first n The sequence signal in the training sequence position corresponding to the data position specifically includes: (1) The receiving end finds the corresponding security number in the decryption sequence based on the 2-bit 0 / 1 encryption key used by the sending end, where the security numbers -1, 1, i, -i corresponding to the encryption key correspond to the decryption security numbers -1, 1, -i, i; (2) The receiving end n The complex numbers corresponding to the different data signal symbols in the training sequence positions corresponding to the data positions are multiplied by the corresponding security numbers in the decryption sequence to recover the n The phases of the complex numbers corresponding to different data signal symbols in the training sequence positions corresponding to the data positions; (3) The receiver finds the first n The corresponding inverse action of the encryption action of different data signal symbol amplitudes in the training sequence position corresponding to the data position; (4) The receiving end n The different data signal symbol amplitudes in the training sequence positions corresponding to the data positions perform corresponding inverse actions and restore the first n The amplitudes of the complex numbers corresponding to different data signal symbols in the training sequence positions corresponding to the data positions are as follows: The inverse action of the action in which the amplitudes of the real and imaginary parts of the complex numbers corresponding to the data signal symbols are not negated, the real part amplitude is negated, the imaginary part amplitude is negated, and the amplitudes of all real and imaginary parts are negated corresponds to the action in which the amplitudes of the real and imaginary parts of the complex numbers corresponding to the data signal symbols are not negated, the real part amplitude is negated, the imaginary part amplitude is negated, and the amplitudes of all real and imaginary parts are negated.

5. A symbol synchronization and frequency deviation estimation system for a quantum noise stream encryption system, using the method according to any one of claims 1 to 4, characterized in that: The system comprises: A training sequence generating module, used for generating a conjugate symmetric sequence U based on the PN sequence, and generating a conjugate antisymmetric training sequence based on the conjugate symmetric sequence U; An encryption transmission module is used for the sending end to encrypt the training sequence using a pre-shared key, add the encrypted training sequence to the data frame, and the data frame is transmitted in the data transmission channel; The decryption recovery module is used for receiving a data signal including a training sequence at the receiving end, and recovering the first n The sequence signal in the training sequence position corresponding to the data position is based on the n A recovered sequence is obtained by using the sequence signal in the training sequence position corresponding to the data position; The calculation module is used to calculate the timing measurement strategy, Schmidl strategy and Minn strategy based on the symmetric structure training sequence. n The autocorrelation value and timing metric value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; A symbol synchronization module is used to obtain the average of the timing measurement values ​​obtained by the timing measurement strategy, Schmidl strategy and Minn strategy of the symmetrical structure training sequence to obtain the comprehensive timing measurement values ​​of different data positions, and perform symbol synchronization based on the comprehensive timing measurement; The frequency offset estimation module is used to estimate the frequency offset based on the autocorrelation value obtained by the timing measurement strategy, Schmidl strategy and Minn strategy of the symmetrical structure training sequence.

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