Symbol synchronization and frequency offset estimation method and system for quantum noise stream encryption system

Abstract

The invention discloses a symbol synchronization and frequency offset estimation method and system for a quantum noise stream encryption system, and the method comprises the steps: generating a conjugate symmetric sequence U based on a PN sequence, and generating a conjugate antisymmetric training sequence based on U; a sending end encrypts a training sequence by adopting a pre-shared key, the encrypted training sequence is added into a data frame, a receiving end receives a data signal comprising the encrypted training sequence, and sequence signals in training sequence positions corresponding to different data positions are recovered based on the key; calculating an autocorrelation value and a timing metric value of the corresponding recovered sequence based on different strategies; and averaging the timing metric values of different strategies to obtain a comprehensive timing metric value, performing symbol synchronization of the quantum noise stream encryption system based on the comprehensive timing metric value, and performing frequency offset estimation based on an autocorrelation value of a symbol synchronization position. The symbol synchronization accuracy and the frequency offset estimation precision in the quantum noise stream encryption system can be improved, and the safety of symbol frequency synchronization is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of quantum noise stream encryption, and relates to a symbol synchronization and frequency deviation estimation method and system suitable for a quantum noise stream encryption system. Background Art

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

[0003] The quantum noise stream encryption system needs to solve the contradiction between accuracy and security in symbol frequency synchronization, that is, symbol frequency synchronization needs to have both accuracy and security. The existing quantum noise stream encryption system is based on the widely used single carrier technology or OFDM single carrier or multi-carrier technology.

[0004] As for the accuracy of symbol frequency synchronization, synchronization technology is a problem that needs to be solved for any quantum noise stream encryption system implemented by single carrier technology and OFDM single carrier or multi-carrier technology. 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 the receiver estimating 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 receiver of the quantum noise stream encryption system can accurately demodulate and restore the original data.

[0005] The quantum noise stream encryption system implemented by single-carrier technology is insensitive to the carrier frequency offset. The frequency offset causes a certain attenuation and phase rotation on the received signal, which can be solved by equalization and other technologies. However, the symbol synchronization error affects the transmission performance of the quantum noise stream encryption system implemented by single-carrier technology.

[0006] For quantum noise stream encryption systems implemented by OFDM single-carrier or multi-carrier technology, symbol timing synchronization is an important prerequisite for high-speed data, high-quality and reliable transmission. Symbol timing synchronization estimates the accurate position of OFDM symbols through synchronization algorithms, allowing the OFDM system to obtain accurate sampling values. In addition, carrier offset has a serious impact on the performance of quantum noise stream encryption systems implemented by OFDM technology. The laser frequency offset in the OFDM system causes inter-carrier interference (ICI), which seriously affects the transmission performance of quantum noise stream encryption systems based on OFDM single-carrier or multi-carrier technology. The carrier frequency offset originates from the inconsistency between the signal laser at the transmitter and the local laser frequency at the receiver. The deviation of the carrier frequency between the transmitter and the receiver is a key factor affecting the performance of the quantum noise stream encryption system. The quantum noise stream encryption system implemented by OFDM single-carrier or multi-carrier technology requires a higher frequency offset estimation accuracy and requires that the subcarriers remain orthogonal. Since the subcarriers lose orthogonality, simply increasing the signal transmission power cannot significantly improve the performance of the system. Therefore, for the quantum noise stream encryption system implemented by OFDM single-carrier or multi-carrier technology, its receiving end must achieve high-precision symbol and frequency synchronization.

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

[0008] For the security of symbol frequency synchronization, constant amplitude Chu sequence, zero amplitude sequence and fixed complex sequence are used for synchronization, and the synchronization accuracy is high. However, the characteristics of the above sequences are obvious and can be easily synchronized by Eve, which is accurate but not safe. The training sequence based on PN random numbers does not have the constant amplitude characteristic and is not a fixed complex sequence. The security is relatively high, but Eve can also determine the autocorrelation value of each part of the training sequence for symbol synchronization. Eve identifies the synchronization position and obtains data and key synchronization accordingly. The synchronization using PN sequence is not accurate enough and not safe enough.

[0009] The existing symbol frequency synchronization algorithm applied to the quantum noise stream encryption system has problems with the accuracy of symbol frequency synchronization and the security of symbol frequency synchronization. The symbol frequency synchronization algorithm used in the existing quantum noise stream encryption system does not comprehensively consider the accuracy and security of symbol frequency synchronization, and only considers the synchronization performance during the signal transmission process from the accuracy of symbol frequency synchronization. In order to improve the security of quantum noise stream encrypted data information and increase the hiding effect of noise on optical information, quantum noise stream encrypted optical signals are often transmitted at low OSNR. In the high-order modulation low SNR optical signal transmission scenario, the contradiction between the accuracy and security of symbol frequency synchronization in the quantum noise stream encryption system is more prominent. Therefore, the symbol synchronization and frequency offset estimation technology considering the security of symbol frequency synchronization has become an important technology in the quantum noise stream encryption system implemented by single carrier technology and OFDM single carrier or multi-carrier technology.

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

[0011] The Schmidl algorithm is a synchronization method based on a training sequence. This method can be used for symbol timing synchronization. The training sequence structure is as follows: Figure 2 As shown. This training sequence is composed of two identical sequences of length N / 2 in the time domain. Schmidl proposed to use the sliding autocorrelation algorithm for symbol timing synchronization. The symbol timing metric is obtained by calculating the correlation 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 of length 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, so a platform will appear, which leads to inaccurate position judgment of the OFDM symbol. Based on the training sequence with a repetitive structure, the Schmidl algorithm can use the autocorrelation value of the symbol synchronization position to estimate the fractional part of the frequency offset (FO), but the error of the frequency offset estimation is large due to the peak platform problem. The method for frequency offset estimation based on the autocorrelation value of the timing synchronization position of the Schmidl algorithm is as follows: , is the frequency deviation value, Is the synchronization position n The autocorrelation value of angle.

[0012] 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.

[0013] 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: , is the frequency deviation value, Is the synchronization position n The autocorrelation value of angle.

[0014] 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.

[0015] The new training sequence structure consists of four parts. C is the PN sequence obtained by IFFT modulation, and its length is N / 4. C* is the conjugate sequence of C, and its length is N / 4. D is the symmetric sequence of C, and its length is also 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 peak of the Minn algorithm. When the signal-to-noise ratio decreases, the judgment accuracy decreases, and errors occur in symbol synchronization and frequency offset estimation based on symbol synchronization.

[0016] The existing symbol frequency synchronization algorithm cannot achieve both accuracy and security when applied to the quantum noise stream encryption system. Accuracy and security restrict each other and are difficult to apply directly. For example, the classic Schmidl algorithm, Minn algorithm and Park algorithm combined with CHU sequence improve accuracy and reduce security. The constant amplitude zero autocorrelation sequence (CAZAC) sequence has obvious constant amplitude, ideal autocorrelation and good cross-correlation characteristics. CAZAC sequence is widely used in the synchronization of single carrier communication system or OFDM communication system, especially CHU sequence as a constant amplitude zero autocorrelation sequence is widely used in the synchronization of LTE system and satellite optical communication system. The existing widely used Schmidl algorithm combined with CHU sequence to achieve symbol frequency synchronization method is based on symbol synchronization to achieve frequency synchronization, which can improve the accuracy of synchronization, but this constant amplitude sequence feature is obviously not secure enough. The classic Schmidl algorithm, Minn algorithm and Park algorithm that use PN sequence as training sequence have relatively high security but low symbol frequency synchronization accuracy. For example, the Schmidl algorithm uses a PN sequence as a training sequence. Since the PN sequence is randomly generated, different data frames use different training sequences, which 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 the quantum noise stream encryption system, in order to improve the security of the quantum noise stream encryption system, low OSNR transmission data signals are required, and low OSNR transmission data signals further reduce the synchronization accuracy.

[0017] Among the existing symbol frequency synchronization algorithms, the Schmidl algorithm has a peak platform problem. Affected by the peak platform, the OFDM symbol synchronization position judgment is not accurate enough, which affects the frequency offset estimation of the autocorrelation value of the timing measurement peak position. The frequency offset estimation may be biased, and the mean square error of the frequency offset estimation is large. The Minn algorithm solves the peak platform problem, but the Minn algorithm has the problem of high secondary peak value. Due to the interference of the secondary peak, the symbol synchronization position may be misjudged, resulting in errors in the subsequent frequency offset estimation, affecting the accuracy of the frequency offset estimation. The Schmidl algorithm and the Minn algorithm have a certain judgment accuracy in low OSNR, and the synchronization position judgment accuracy 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 secondary peak value of the Minn algorithm. The symbol synchronization accuracy of the Park algorithm is particularly high at high OSNR, which can reach 100%. When the OSNR decreases, the main peak of the Park algorithm is low, the symbol synchronization judgment accuracy of the Park algorithm decreases rapidly, and symbol frequency synchronization errors occur. The Schmidl algorithm, Minn algorithm and Park algorithm can further improve the synchronization accuracy by using CHU sequence as training sequence compared with using PN sequence as training sequence. However, due to the characteristics of the training sequence and the corresponding timing measurement method used by the above algorithms, the problems of peak platform, secondary peak interference and low main peak still exist. In addition, the training sequence timing measurement calculation method proposed by the above algorithms is relatively simple and cannot fully extract the characteristics of the training sequence. The symbol frequency synchronization algorithm of the quantum noise stream encryption system needs to comprehensively consider the accuracy and security of symbol frequency synchronization. Due to the constant amplitude feature of the CHU sequence, the training sequence of the Schmidl algorithm, Minn algorithm and Park algorithm is easy to be identified. The above algorithms combined with the Chu sequence have higher accuracy and lower security. The existing Schmidl, Minn and Park algorithms based on CAZAC sequence have more accurate synchronization performance than the symbol frequency synchronization algorithm based on PN sequence. However, the constant amplitude feature of the CAZAC sequence is obvious, which can be quickly and accurately identified by the unauthorized receiving end, and is easy to be identified and attacked by the unauthorized receiving end. The PN sequence does not have the constant amplitude feature, but the 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 identified through the correlation operation of the training sequence.

[0018] In summary, the existing algorithms cannot achieve both accuracy and security. When directly applied to the high-order modulation low SNR optical signal transmission scenario of the quantum noise stream encryption system, the symbol frequency synchronization accuracy is low and not secure enough. Therefore, it is necessary to explore high-performance symbol frequency synchronization algorithms implemented by PN sequences in quantum noise stream encryption systems.

[0019] A Chinese patent with publication number CN113162882A proposes an autocorrelation OFDM symbol synchronization method based on a conjugate antisymmetric training sequence. The training sequence structure is set by using the mirror sequence, conjugate sequence and opposite sequence of the complex PN sequence. The timing measurement of the symmetric structure sequence significantly improves the symbol synchronization accuracy of the OFDM system in high OSNR scenarios. However, the proposed strategy cannot fully extract the training sequence features, and the synchronization accuracy is low in low OSNR scenarios. Summary of the invention

[0020] In order to solve the deficiencies in the prior art, the present application proposes a symbol synchronization and frequency deviation estimation method and system for a quantum noise stream encryption system. The training sequence generated by a random number sequence and the corresponding symbol frequency joint synchronization strategy can combine the advantages of the Schmidl strategy, the Minn strategy and the Park strategy, and use a training sequence to fully extract the characteristics of the training sequence, thereby improving the symbol synchronization accuracy and frequency deviation estimation accuracy in the quantum noise stream encryption system. The key encryption training sequence can further improve the security performance of signal transmission and achieve the hiding of the synchronization position from unauthorized receiving ends.

[0021] This application adopts the following technical solution.

[0022] The first aspect of the present application proposes a symbol synchronization and frequency offset estimation method for a quantum noise stream encryption system, comprising: 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 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; 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 values ​​obtained by the timing measurement strategy, Schmidl strategy, and Minn strategy of the symmetrical structure training sequence.

[0023] Preferably, 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.

[0024] Preferably, the step 2 of 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.

[0025] Preferably, in step 3, the receiving end recovers the first nThe 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 security numbers -1, 1, -i, i for the decryption key; (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 security numbers in the corresponding decryption key 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 amplitude encryption actions in the training sequence positions corresponding to the data positions perform the corresponding inverse actions, and accordingly restore the 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.

[0026] Preferably, the timing measurement strategy of the symmetric structure training sequence in step 4 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. , , data.

[0027] Preferably, the Schmidl strategy in step 4 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; , They represent the first n The training sequence position corresponding to the data position , data.

[0028] Preferably, the Minn strategy in step 4 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. , , , , data.

[0029] Preferably, the calculation formula for frequency offset estimation in step 6 is as follows: (13) in, is the frequency deviation estimate; is calculated based on the Schmidl strategy. n The autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; 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; 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.

[0030] The second aspect of the present application proposes a symbol synchronization and frequency deviation estimation system for a quantum noise stream encryption system, comprising: 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.

[0031] Compared with the prior art, the present application is a symbol and frequency joint synchronization technology that has no constant amplitude or zero amplitude characteristics, a high main peak, a low secondary peak, and no peak platform problem. It comprehensively considers the mutual constraints between accuracy and security, solves the problem that the training sequence in the quantum noise stream encryption system is easily identified, can improve the symbol synchronization and frequency offset estimation accuracy in the quantum noise stream encryption system, improve the security and comprehensive performance of signal transmission, and can be applied to wireless communication systems and optical communication systems.

[0032] Compared with the prior art, the beneficial effects of this application include at least: 1. The present application proposes a training sequence suitable for symbol synchronization frequency offset estimation in a quantum noise stream encryption system, and the training sequence is generated by using a conjugate symmetric sequence U composed of a PN sequence and a conjugate sequence, a reverse sequence and a mirror sequence of the conjugate symmetric sequence U.

[0033] The training sequence structure is generated by a random number sequence, namely a PN sequence, which has no constant amplitude characteristic and is suitable for low OSNR quantum noise stream encryption systems. It has significant accuracy and security in low OSNR scenarios.

[0034] The present application adopts multiple timing metric calculation strategies to calculate the timing metric of a training sequence, and implements multiple training sequences based on one training sequence. This 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.

[0035] 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 peak value of the main peak 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 stream encryption system.

[0036] The proposed algorithm using PN sequence as training sequence can achieve the performance of the algorithm based on Chu sequence as training sequence. Due to the random generation of PN sequence and the randomness of key, the training sequences generated in different data frames are different, which can improve the security of symbol frequency synchronization.

[0037] The specific position of the training sequence is applied with the opposite number or conjugate operator, and the training sequence also conforms to the structure of Schmidl, Minn and symmetric structure training sequence. The training sequence combined with the corresponding strategy can fully extract the characteristics of the training sequence and achieve accurate symbol frequency synchronization.

[0038] The training sequence conforms to the structural characteristics of the training sequence of the Schmidl repetitive structure and the Minn cyclic repetitive structure and the characteristics of the symmetric structure training sequence. When the signal-to-noise ratio decreases, the training sequence can maintain the autocorrelation performance, the autocorrelation value is high, and the main peak peak value is less affected by the decrease in the signal-to-noise ratio. The present application synthesizes the proposed training sequence timing metric and can fully extract the characteristics of the training sequence.

[0039] 2. In the application scenario of quantum noise stream encryption system, this application proposes for the first time to use the key shared by the sender and the receiver in the quantum noise stream encryption system to encrypt and decrypt the amplitude and phase of the training sequence, thereby improving the security of symbol frequency synchronization and achieving both accuracy and security in symbol frequency synchronization.

[0040] The amplitude and phase encryption of the training sequence using the key shared by the transmitter and receiver in the quantum noise stream encryption system reduces the correlation between different parts of the training sequence. The autocorrelation value of the encrypted training sequence is low, and the training sequence has higher security. The encrypted training sequence does not have a constant amplitude feature. The training sequence is the same as the data signal. The training sequence is hidden in the signal data to improve the security of symbol frequency synchronization.

[0041] Due to the role of the key, unauthorized parties cannot recover the amplitude and phase of the training sequence without the key, making it difficult to achieve symbol frequency synchronization, thereby improving the security of symbol frequency synchronization and achieving higher security while improving synchronization accuracy.

[0042] The amplitude and phase of the complex number corresponding to the symbol in the training sequence used for symbol frequency synchronization are encrypted, the amplitude of the training sequence is inverted and encrypted, and the phase of the training sequence is rotated and encrypted, which improves the security of symbol frequency synchronization and data information.

[0043] 3. The present application proposes a symbol synchronization and timing measurement scheme suitable for a quantum noise stream encryption system. The present application combines a training sequence with a corresponding timing measurement strategy based on a symmetric structure training sequence, a Schmidl strategy, and a Minn strategy. The autocorrelation value of the ±N / 4 position of the symbol synchronization position is low, thereby eliminating the interference of the secondary peaks at the ±N / 4 position of the symbol synchronization position; the Minn strategy and the Schmidl strategy have been inherited in the low OSNR main peak with high peak value and strong anti-interference capability, and the symbol synchronization strategy based on a symmetric structure training sequence has been inherited in the high OSNR synchronization position with high judgment accuracy, low secondary peak value, and high main peak value.

[0044] The present application solves the problem of frequency offset estimation errors in the Minn strategy. In the proposed strategy, when the symbol synchronization position obtained by symbol synchronization is within the protection 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 problems of the Schmidl strategy; and solves the problem of frequency offset estimation errors caused by symbol synchronization errors in the symbol frequency synchronization strategy based on a symmetrical structure. Compared with the symbol frequency synchronization method based on a symmetrical structure training sequence, the present application can more fully extract the characteristics of the training sequence, has a higher synchronization judgment accuracy, and can more accurately estimate the frequency offset.

[0045] Compared with the strategy based on symmetric structure training sequence, the present invention has higher synchronization position judgment accuracy at low OSNR. Compared with the strategy based on repetitive structure training sequence such as Schmidl strategy and Minn strategy, the present invention has higher synchronization position judgment accuracy at high OSNR.

[0046] 4. The present application proposes a frequency deviation estimation scheme suitable for a quantum noise stream encryption system. Multiple autocorrelation values ​​and timing metrics are calculated for the training sequence using multiple classical timing measurement strategies. The average values ​​of the multiple autocorrelation values ​​and timing metrics are calculated to obtain a comprehensive autocorrelation value and timing metric value. The advantages of multiple timing measurement strategies are combined to fully extract the characteristics of the training sequence. When deviating from the correct symbol synchronization position, the autocorrelation value and timing metric values ​​of the Schmidl strategy, the Minn strategy, and the strategy based on the symmetric structure training sequence are low, and the secondary peaks of the above strategies appear at different positions, so the amplitude mean of the timing metric of the above strategies is low. Near the correct symbol synchronization position, the autocorrelation value and timing metric values ​​of the above strategies are high, which can achieve accurate frequency deviation estimation.

[0047] 5. Due to the accurate symbol synchronization performance, the present application realizes accurate frequency offset estimation based on symbol synchronization. The present application uses the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetrical structure training sequence to extract the characteristics of a training sequence 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 characteristics using the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetrical structure training sequence all describe the same frequency offset information, and the frequency offset calculated based on the sum of multiple autocorrelation values ​​has a higher accuracy rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is the OFDM frame structure used for symbol synchronization; Figure 2 is the training sequence structure of the Schmidl algorithm; Figure 3 is the training sequence structure of the Minn algorithm; Figure 4 is the training sequence structure of the Park algorithm; Figure 5 The training sequence structure proposed for this application; Figure 6 is the training sequence corresponding to the nth data position; Figure 7 This application discloses a symbol frequency joint synchronization process in a quantum noise stream encryption system based on a PN sequence. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. The embodiments described in this application are only embodiments of a part of the present application, rather than all embodiments. Based on the spirit of the present application, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present application.

[0050] Embodiment 1 of the present application provides a method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system, such as Figure 7 As shown, including: 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; Further preferably, the training sequence structure used in the present application is a training sequence structure composed of a conjugate symmetric sequence U and a mirror sequence, a conjugate sequence and a reverse sequence of U. The present application uses a conjugate symmetric sequence obtained from a random number (Pseudo-Noise, PN) sequence as the sequence U for symbol synchronization and frequency offset estimation, such as Figure 5 shown.

[0051] The present application generates a conjugate symmetric sequence U based on a random number PN sequence.

[0052] The complex conjugate symmetric sequence U is obtained by IFFT operation of A. The function expression of generating sequence A is: A= B.*exp(- j π(N / 8-1) l / N / 8) (1) The B sequence is a random real number sequence of N / 8 points, where N is the length of the training sequence. l = 0, 1, ..., N / 8-1. “.*” is the dot multiplication operator. The expression of the U sequence is, (2) The training sequence structure proposed in the present application is an antisymmetric 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 of length 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, and 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.

[0053] 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; The present application proposes a symbol frequency synchronization method suitable for quantum noise stream encryption system with coexistence of accuracy and security. The present application realizes the security of symbol frequency synchronization based on the encryption of training sequence amplitude and phase. The application scenario of the present application is quantum noise stream encryption system, and the legal communicating parties have a pre-shared key to encrypt data, and the pre-shared key can also be used to encrypt the training sequence.

[0054] The transmitter uses a key of length 4N to encrypt the amplitude and phase of the training sequence.

[0055] The encryption of different symbol amplitudes in the training sequence is implemented by a 2-bit 0 / 1 security key. The pre-shared key between the transmitter and the receiver of the quantum noise stream encryption system is implemented by professional key distribution technology and key synchronization technology, and the key complies with relevant test standards. In order to vividly illustrate the symbol frequency synchronization method of the present application, the key used for the amplitude encryption of the Nth symbol in the training sequence is 11 as an example, that is, the 2-bit key 11 represents the action of inverting all the real and imaginary amplitudes of the complex numbers corresponding to the data signal symbols in the training sequence.

[0056] The amplitude inversion action keeps 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, it means that the absolute value of the real part, imaginary part, real part and imaginary part of the complex number corresponding to the current encrypted data signal symbol is subtracted from the maximum value 1 normalized by the absolute value of the amplitude to obtain the amplitude of the real part and imaginary part of the complex number corresponding to the encrypted data signal symbol.

[0057] The phase encryption of the complex numbers corresponding to different data signal symbols in the training sequence is implemented by a 2-bit 0 / 1 security key. For example, the key used for the phase encryption of the complex number corresponding to the Nth symbol in the training sequence is 01, that is, the 2-bit key 01 means that the complex number corresponding to the amplitude-encrypted data signal symbol in the training sequence is multiplied by the security number 1.

[0058] 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; Further preferably, the receiving end of the present application has a pre-shared key of the quantum noise stream encryption system, which can decrypt the training sequence and restore the amplitude and phase of different data signal symbols in the training sequence.

[0059] The length of the key 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 key. Figure 6 As shown in the figure, in order to vividly illustrate the mechanism of using sliding window to achieve symbol synchronization, in order to recover the first n Take the Nth symbol in the training sequence corresponding to the data position as an example, that is, the example recovers the n +Amplitude and phase of the symbols of the N data positions.

[0060] The decryption process of this application first restores n The phase of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the data position is then recovered. n The amplitude of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the data position.

[0061] (1) The receiver finds the corresponding security number in the decryption sequence based on the pre-shared key and the 2-bit 0 / 1 security key used by the sender. The security numbers -1, 1, i, -i used in the encryption key correspond to the security numbers -1, 1, -i, i in the decryption key respectively.

[0062] (2) The receiving end n The Nth sequence signal (i.e., the Nth sequence signal) in the training sequence position corresponding to the data position n+N data signal symbols) multiplied by the corresponding decryption key security number can recover the n +The phase of the complex number corresponding to the N data signal symbols.

[0063] The security numbers -1, 1, i, -i corresponding to the encryption key of the data signal symbol correspond to the decryption key security numbers -1, 1, -i, i respectively. The 2-bit decryption keys 00, 01, 10, 11 correspond to the decryption actions of the complex numbers corresponding to the encrypted data signal symbols in the training sequence multiplied by the security numbers -1, 1, -i, i respectively. n The phase encryption and decryption of the complex number corresponding to the Nth symbol in the training sequence corresponding to the data position is performed using key 01 as an example, that is, the 2-bit key 01 represents the phase encryption of the Nth symbol. n The Nth sequence signal (i.e., the Nth sequence signal) in the training sequence corresponding to the data position n +N data signal symbols) multiplied by the security number 1.

[0064] (3) Next, based on the pre-shared key used by the sender, the receiver finds the first n The inverse action corresponding to the Nth data signal symbol amplitude encryption action in the training sequence position corresponding to the data position; (4) The receiving end performs the inverse action corresponding to the encryption action of the different data signal symbol amplitudes at the sending end, and can restore the first n The amplitude of the complex number corresponding to the Nth data signal symbol in the training sequence position corresponding to the data position.

[0065] The inverse actions of the actions of not negating the amplitude of the real and imaginary parts of the complex numbers corresponding to the data signal symbols, negating the amplitude of the real and imaginary parts, negating the amplitude of the imaginary parts, and negating the amplitude of all the real and imaginary parts, respectively correspond to not negating the amplitude of the real and imaginary parts of the complex numbers corresponding to the data signal symbols, negating the amplitude of the real and imaginary parts, negating the amplitude of the imaginary parts, and negating the amplitude of all the real and imaginary parts. The 2-bit decryption keys 00, 01, 10, and 11 respectively correspond to the actions of not negating the amplitude of the real and imaginary parts of the complex numbers corresponding to the data signal symbols in the training sequence, negating the amplitude of the real and imaginary parts, negating the amplitude of the real and imaginary parts, and negating the amplitude of all the real and imaginary parts.

[0066] First n The amplitude encryption and decryption of the Nth data signal symbol in the training sequence corresponding to the data position is performed using key 11 as an example, that is, the 2-bit key 11 corresponds to the Nth data signal symbol in the training sequence corresponding to the data position. n +The action of negating the amplitudes of all the real and imaginary parts of the complex numbers corresponding to N data signal symbols.

[0067] The receiving end subsequently performs symbol frequency synchronization based on the sequence autocorrelation value and timing metric value calculated from the sequence signal decrypted and restored in the training sequence position corresponding to the different data positions.

[0068] 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; Further preferably, the present application calculates the timing metric based on the characteristics that the training sequence complies with a symmetric structure training sequence, a Schmidl repetitive structure training sequence, and a Minn cyclic repetitive structure training sequence.

[0069] (1) The receiving end uses the timing measurement strategy based on the symmetrical structure training sequence to 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 metric strategy based on the symmetric structure training sequence calculates the timing metric value of the decrypted and recovered sequence. The opposite operator is added 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 the conjugate symmetric sequence. The timing metric calculated based on the conjugate symmetric training sequence structure is as follows: (3) (4) (5) in, Indicates the recovery n Signal data; r ( n ) and the training sequence [-U, -U, -U*, U', -U', U*, U, U] are as follows: r ( n +1) to r ( n +N / 8) represents the first n -U in the training sequence corresponding to the data position; r ( n +N / 8+1) to r ( n +N / 4) represents the first n -U in the training sequence corresponding to the data position; r ( n +N / 4+1) to r ( n+N / 4+N / 8+1) represents the first n -U* in the training sequence corresponding to the data position; And so on.

[0070] 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 is obtained by calculating the correlation value of the left half sequence and the right half sequence of the sequence; 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 processing; 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. , , data.

[0071] (2) The receiving end uses the timing measurement strategy based on the Schmidl algorithm to 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 metric value of the sequence recovered by decryption is calculated based on the Schmidl strategy, and the opposite operator is added to the first, second, third, and fifth N / 8 point sequences of the recovered sequence, and the conjugate operator is added to the fifth, sixth, seventh, and eighth N / 8 point sequences of the recovered 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 a repeating structure. The sequence [U, U, U*, U*, U, U, U*, U*] obtained after conversion conforms to the structure of the repeating structure training sequence of the classic Schmidl algorithm. Therefore, the Schmidl timing metric calculation method can be applied to calculate the timing metric, and the timing metric calculated based on the Schmidl training sequence structure is as follows: (6) (7) (8) The present application calculates the timing metric based on the characteristic that the training sequence conforms to the Schmidl training sequence structure.

[0072] In the above formula, is calculated by the Schmidl 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 The autocorrelation value is half of the sequence energy value recovered from the sequence signal in the training sequence position corresponding to the data position. pass 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 , data.

[0073] (3) The timing measurement strategy based on the Minn algorithm at the receiving end uses formulas (9)-(11) to calculate the 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 metric value of the decrypted and recovered sequence is calculated based on the Minn strategy, and the opposite operator is added to the first, second, third, sixth, seventh, and eighth N / 8 point sequences of the recovered sequence, and the conjugate operator is added 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 of the training sequence proposed in this application consists of two sequences of repeated structures, and the right half also consists of two sequences of repeated structures. The sequence [U, U, U, U, -U, -U, -U, -U] obtained after conversion conforms to the structure of the cyclic repetitive structure training sequence of the classic Minn algorithm, so the Minn timing metric calculation method can be used to calculate the timing metric. The timing metric calculated based on the Minn training sequence structure is as follows: (9) (10) (11) in, is calculated by Minn strategy. nThe autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; Indicates n The autocorrelation value is half of the sequence energy value recovered from the sequence signal in the training sequence position corresponding to the data position. pass 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. , , , , data.

[0074] Step 5: Based on the timing measurement strategy of the symmetrical structure training sequence, the Schmidl strategy and the Minn strategy, the average of the timing measurement values ​​is obtained, the comprehensive timing measurement values ​​of different data positions are calculated by formula (12), and symbol synchronization is performed based on the comprehensive timing measurement; Further preferably, the comprehensive timing metric value calculation formula is as follows: (12) For the timing measurement strategy of the Minn or symmetrical structure training sequence, due to the combination of the training sequence of the present application and the corresponding timing measurement method, the autocorrelation value of the ±N / 4 position of the symbol synchronization position is low, which solves the problem of secondary 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 protection interval). In the strategy proposed in the present application, when the symbol synchronization position obtained by symbol synchronization is within the protection interval, the obtained training sequence data is not a cyclic shift of the training sequence, eliminating the peak platform effect.

[0075] As shown in formula (12), Depend on , , The arithmetic mean is obtained. When deviating from the correct symbol synchronization position, the timing metric values ​​of Schmidl, Minn and the strategies based on symmetric structure training sequences are low, so the amplitude mean of the timing metric of the above strategies is low. Near the correct symbol synchronization position, the above strategies have timing metrics of different shapes, and the values ​​of the timing metrics are high, so the mean of the timing metrics of the above strategies is high and there is no platform problem. The present application reasonably sets the training sequence structure by using the conjugate symmetric sequence U and its mirror sequence, conjugate sequence and opposite sequence. 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 reduction in the signal-to-noise ratio.

[0076] Symbol sync position It is obtained from the following formula: , The maximum value position of the timing measurement value is the position of the training sequence. The position of the training sequence is determined, and the synchronization position of the data frame is determined based on the training sequence, thereby achieving symbol synchronization of the signal.

[0077] Step 6: The autocorrelation values ​​of the strategy based on the symmetric structure training sequence, the Schmidl strategy, and the Minn strategy are used to estimate the frequency offset using formula (13).

[0078] Further preferably, the training sequence structure proposed in the present application conforms to the characteristics of Schmidl, Minn and symmetric structure training sequence structures at the same time. , and The autocorrelation values ​​obtained by extracting the same training sequence features using the Schmidl strategy, the Minn strategy, and the symmetric structure training sequence strategy. Therefore, all describe the same frequency offset information. Based on the properties of geometric progression, the frequency offset estimation proposed in this application Autocorrelation , and The calculation formula for frequency offset estimation is as follows: (13) in The range is [-ππ], and the method proposed in this application estimates the fractional part of the frequency offset The range is [-0.5 0.5].

[0079] Embodiment 2 of the present application provides a symbol synchronization and frequency offset estimation system for a quantum noise stream encryption system, including: 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 calculate the average of the timing measurement values ​​obtained by the timing measurement strategy, Schmidl strategy, and Minn strategy based on the symmetric structure training sequence, 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.

[0080] Compared with the prior art, the present application is a symbol and frequency joint synchronization technology without constant amplitude or zero amplitude characteristics, high main peak peak, low secondary peak peak, and no peak platform problem. It solves the problem of rapid recognition of training sequences in quantum noise stream encryption systems, comprehensively considers the mutual constraints between accuracy and security, can improve the symbol synchronization and frequency offset estimation accuracy in quantum noise stream encryption systems, improve the security and comprehensive performance of signal transmission, and can be applied to wireless communication systems and optical communication systems.

[0081] Compared with the prior art, the beneficial effects of this application include at least: 1. The present application proposes a training sequence suitable for symbol synchronization frequency offset estimation in a quantum noise stream encryption system, and the training sequence is generated by using a conjugate symmetric sequence U composed of a PN sequence and a conjugate sequence, a reverse sequence and a mirror sequence of the conjugate symmetric sequence U.

[0082] The training sequence structure is generated by a random number sequence, namely a PN sequence, which has no constant amplitude characteristic and is suitable for low OSNR quantum noise stream encryption systems. It has significant accuracy and security in low OSNR scenarios.

[0083] The present application adopts multiple timing metric calculation strategies to calculate the timing metric of a training sequence, and implements multiple training sequences based on one training sequence. This 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.

[0084] 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 peak value of the main peak 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 stream encryption system.

[0085] The proposed algorithm using PN sequence as training sequence can achieve the performance of the algorithm based on Chu sequence as training sequence. Due to the random generation of PN sequence and the randomness of key, the training sequences generated in different data frames are different, which can improve the security of symbol frequency synchronization.

[0086] The specific position of the training sequence is applied with the opposite number or conjugate operator, and the training sequence also conforms to the structure of Schmidl, Minn and symmetric structure training sequence. The training sequence combined with the corresponding strategy can fully extract the characteristics of the training sequence and achieve accurate symbol frequency synchronization.

[0087] The training sequence conforms to the structural characteristics of the training sequence of the Schmidl repetitive structure and the Minn cyclic repetitive structure and the characteristics of the symmetric structure training sequence. When the signal-to-noise ratio decreases, the training sequence can maintain the autocorrelation performance, the autocorrelation value is high, and the main peak peak value is less affected by the decrease in the signal-to-noise ratio. The present application synthesizes the proposed training sequence timing metric and can fully extract the characteristics of the training sequence.

[0088] 2. In the application scenario of quantum noise stream encryption system, this application proposes for the first time to use the key shared by the sender and the receiver in the quantum noise stream encryption system to encrypt and decrypt the amplitude and phase of the training sequence, thereby improving the security of symbol frequency synchronization and achieving both accuracy and security in symbol frequency synchronization.

[0089] The amplitude and phase encryption of the training sequence using the key shared by the transmitter and receiver in the quantum noise stream encryption system reduces the correlation between different parts of the training sequence. The autocorrelation value of the encrypted training sequence is low, and the training sequence has higher security. The encrypted training sequence does not have a constant amplitude feature. The training sequence is the same as the data signal. The training sequence is hidden in the signal data to improve the security of symbol frequency synchronization.

[0090] Due to the role of the key, unauthorized parties cannot recover the amplitude and phase of the training sequence without the key, making it difficult to achieve symbol frequency synchronization, thereby improving the security of symbol frequency synchronization and achieving higher security while improving synchronization accuracy.

[0091] The amplitude and phase of the complex number corresponding to the symbol in the training sequence used for symbol frequency synchronization are encrypted, the amplitude of the training sequence is inverted and encrypted, and the phase of the training sequence is rotated and encrypted, which improves the security of symbol frequency synchronization and data information.

[0092] 3. The present application proposes a symbol synchronization and timing measurement scheme suitable for a quantum noise stream encryption system. The present application combines a training sequence with a corresponding timing measurement strategy based on a symmetric structure training sequence, a Schmidl strategy, and a Minn strategy. The autocorrelation value of the ±N / 4 position of the symbol synchronization position is low, thereby eliminating the interference of the secondary peaks at the ±N / 4 position of the symbol synchronization position; the Minn strategy and the Schmidl strategy have been inherited in the low OSNR main peak with high peak value and strong anti-interference capability, and the symbol synchronization strategy based on a symmetric structure training sequence has been inherited in the high OSNR synchronization position with high judgment accuracy, low secondary peak value, and high main peak value.

[0093] The present application solves the problem of frequency offset estimation errors in the Minn strategy. In the proposed strategy, when the symbol synchronization position obtained by symbol synchronization is within the protection 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 problems of the Schmidl strategy; and solves the problem of frequency offset estimation errors caused by symbol synchronization errors in the symbol frequency synchronization strategy based on a symmetrical structure. Compared with the symbol frequency synchronization method based on a symmetrical structure training sequence, the present application can more fully extract the characteristics of the training sequence, has a higher synchronization judgment accuracy, and can more accurately estimate the frequency offset.

[0094] Compared with the strategy based on symmetric structure training sequence, the present invention has higher synchronization position judgment accuracy at low OSNR. Compared with the strategy based on repetitive structure training sequence such as Schmidl strategy and Minn strategy, the present invention has higher synchronization position judgment accuracy at high OSNR.

[0095] 4. The present application proposes a frequency deviation estimation scheme suitable for a quantum noise stream encryption system. Multiple autocorrelation values ​​and timing metrics are calculated for the training sequence using multiple classical timing measurement strategies. The average values ​​of the multiple autocorrelation values ​​and timing metrics are calculated to obtain a comprehensive autocorrelation value and timing metric value. The advantages of multiple timing measurement strategies are combined to fully extract the characteristics of the training sequence. When deviating from the correct symbol synchronization position, the autocorrelation value and timing metric values ​​of the Schmidl strategy, the Minn strategy, and the strategy based on the symmetric structure training sequence are low, and the secondary peaks of the above strategies appear at different positions, so the amplitude mean of the timing metric of the above strategies is low. Near the correct symbol synchronization position, the autocorrelation value and timing metric values ​​of the above strategies are high, which can achieve accurate frequency deviation estimation.

[0096] 5. Due to the accurate symbol synchronization performance, the present application realizes accurate frequency offset estimation based on symbol synchronization. The present application uses the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetrical structure training sequence to extract the characteristics of a training sequence 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 characteristics using the Schmidl strategy, the Minn strategy, and the autocorrelation value calculation strategy of the symmetrical structure training sequence all describe the same frequency offset information, and the frequency offset calculated based on the sum of multiple autocorrelation values ​​has a higher accuracy rate.

[0097] Embodiment 3 of the present 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.

[0098] Embodiment 4 of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when the program is executed by a processor.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application rather than to limit it. Although the present application has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present application can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present application are within the scope of protection 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; 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 values ​​obtained by the timing measurement strategy, Schmidl strategy, and Minn 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 security numbers -1, 1, -i, i for the decryption key; (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 security numbers in the corresponding decryption key 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 amplitude encryption actions in the training sequence positions corresponding to the data positions perform the corresponding inverse actions, and accordingly restore the 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. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: Step 4 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. , , data.

6. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: Step 4: 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; , They represent the first n The training sequence position corresponding to the data position , data.

7. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: Step 4: 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. , , , , data.

8. The method for symbol synchronization and frequency deviation estimation of a quantum noise stream encryption system according to claim 1, characterized in that: The calculation formula for frequency offset estimation in step 6 is as follows: （13） in, is the frequency deviation estimate; is calculated based on the Schmidl strategy. n The autocorrelation value of the sequence recovered from the sequence signal in the training sequence position corresponding to the data position; 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; 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.

9. 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 8, 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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