A highly reliable and secure transmission method based on OTFS

Abstract

The present invention discloses a highly reliable and secure transmission method based on OTFS. For a source sequence at the transmitting end, a transmission sequence is obtained after sequentially undergoing LDPC encoding, QAM mapping, SVD precoding, inverse symplectic finite Fourier transform, Heisenberg transform, R-CP addition, D / A conversion, and up-conversion, which is then transmitted via a wireless channel. After a legitimate user's receiving end obtains the received sequence, down-conversion, A / D conversion, R-CP removal, Wigner transform, symplectic finite Fourier transform, SVD decoding, single-tap equalizer processing, QAM demapping, and LDPC decoding are performed to obtain a destination sequence. The present invention achieves highly reliable physical layer secure transmission in highly dynamic communication scenarios through precoding technology based on an OTFS equivalent matrix and error control coding technology based on LDPC, thereby improving system total throughput and spectrum efficiency, and reducing system peak-to-average ratio and transmission bit error rate, while simultaneously avoiding excessive computational complexity and time-frequency resource overhead.

Description

Technical Field

[0001] The present invention belongs to wireless communication technology, and in particular relates to a highly reliable and secure transmission method based on OTFS. Background Art

[0002] Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier modulation technology widely used in 4G and 5G. Its core idea is to convert high-speed serial data streams into low-speed parallel data streams by multiplexing orthogonal subcarriers. It can also effectively combat frequency selective fading and overcome inter-symbol interference (ISI). However, with the development of high-speed transportation and mobile communication services, the subcarrier orthogonality of traditional OFDM is destroyed in high-mobility scenarios due to the Doppler effect, thereby introducing inter-carrier interference (ICI), which seriously affects the performance of the communication system.

[0003] To combat the high Doppler effect in high-mobility scenarios, Orthogonal Time-Frequency-Space (OTFS) has been proposed as a new multi-carrier modulation scheme. It transforms the fast-varying time-frequency (TF) channel into a time-invariant sparse delay-Doppler (DD) channel through a pair of two-dimensional orthogonal transforms. Its characteristic is that each DD domain symbol is flattened over the entire TF domain, thereby achieving full time-frequency diversity gain, effectively combating the ICI caused by the Doppler effect and achieving better bit error rate performance than traditional OFDM in high-mobility scenarios.

[0004] Low-density parity check (LDPC) code is a linear block code with strong error correction capabilities. Its parity check matrix contains relatively few zero elements. This sparsity ensures that its decoding complexity and minimum code distance only increase linearly with code length, while for other typical block codes, they increase exponentially. Therefore, the code length of LDPC code can be designed to be very long without incurring excessive decoding complexity.

[0005] Due to the open nature of wireless channels, OTFS waveforms are susceptible to detection and interception by external eavesdroppers during air-interface transmission, leading to the leakage of private information. Therefore, OTFS communication systems urgently need a secure physical layer air-interface transmission method that also ensures good bit error rate performance. Summary of the Invention

[0006] To solve the problem of secure transmission of the OTFS physical layer and further improve transmission reliability, the present invention provides a highly reliable and secure transmission method based on OTFS. By using precoding technology based on the OTFS equivalent matrix and error control coding technology based on LDPC, highly reliable physical layer secure transmission can be achieved in highly dynamic communication scenarios, thereby improving the system's total throughput and spectrum efficiency, and reducing the system's peak-to-average ratio and transmission bit error rate, without incurring excessive computational complexity and time-frequency resource overhead.

[0007] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0008] A highly reliable and secure transmission method based on OTFS, for a sending end, the transmission method includes:

[0009] Step 1: The source bit sequence m[i] sent by each data frame is constructed into a 1×k matrix m, where k represents the number of bits contained in each data frame; then the matrix m is combined with the k×n standard generator matrix G LDPC Perform linear matrix multiplication to map the source bit sequence to the LDPC sparse code domain and obtain the new codeword c = mG LDPC ;

[0010] Step 2: Perform QAM constellation mapping on the new codeword c to obtain a mapped discrete constellation symbol d[i], and construct d[i] into a matrix d;

[0011] Step 3: Perform singular value decomposition on the DD domain equivalent channel matrix of the legal channel to obtain Among them U Bob represents the left singular value matrix, E Bob represents a diagonal matrix, V Bob represents the right singular value matrix, (·) H Represents the conjugate transposed matrix; then use the matrix V Bob Perform linear precoding on matrix d to obtain the coding matrix x DD ;

[0012] Step 4: transform the encoding matrix x DD By first mapping to the DD domain grid through serial-to-parallel conversion, we get the matrix X DD ;

[0013] Step 5: For the matrix X DD Perform inverse symplectic finite Fourier transform to obtain the time-frequency domain signal matrix X TF ;

[0014] Step 6, the X TF Perform Heisenberg transform and first obtain the matrix S in the time-delay domain TD , and then perform column vectorization to obtain the time domain vector s T;

[0015] Step 7: Use the time domain plus CP matrix to process the time domain vector to obtain the time domain signal vector

[0016] Step 8: transform the time domain signal vector After digital-to-analog conversion and up-conversion, the transmission signal is obtained and finally sent to the wireless legal channel through the antenna for air interface transmission.

[0017] Furthermore, for the legitimate user's receiving end:

[0018] Step 1′: After the legitimate user obtains the received signal from the legitimate channel, the baseband digital signal vector is obtained through analog-to-digital conversion and down-conversion.

[0019] Step 2′, for the vector Perform the CP removal operation to obtain the time domain signal vector r T,Bob ;

[0020] Step 3′, the time domain signal vector r T,Bob Perform inverse column vectorization to reconstruct the matrix R TD,Bob ; For the TD domain matrix R TD,Bob Perform Wigner transformation to obtain TF matrix Y TF,Bob ;

[0021] Step 4′, the matrix Y TF,Bob Perform symplectic finite Fourier transform to obtain the DD domain signal matrix Y DD,Bob , then for Y DD,Bob Perform column vectorization to obtain y DD,Bob ;

[0022] Step 5′, perform singular value decomposition on the DD domain matrix of the legal channel to obtain the left singular value matrix U Bob Perform SVD decoding and get

[0023] Step 6′, the diagonal matrix E is obtained by performing singular value decomposition on the DD domain matrix of the legal channel Bob For the y DD,Bob Perform single tap equalization and get the matrix d Bob ;

[0024] Step 7', the signal d Bob Perform QAM constellation demapping to obtain codeword c Bob ;

[0025] Step 8′, for the code word c Bob Perform LDPC decoding to obtain m Bob , mBob Converted into the destination bit sequence m Bob [i]; when the decoding is completely correct, this sequence is the same as the source bit sequence m[i].

[0026] Furthermore, G LDPC =[I k ,P k×(n-k) ], I k represents the k×k identity matrix, P k×(n-k) represents a k×(nk) sparse matrix, and n represents the encoded code length.

[0027] Furthermore, the pair matrix X DD Perform inverse symplectic finite Fourier transform to obtain the time-frequency domain signal matrix X TF , expressed as:

[0028] The inverse symplectic finite Fourier transform is obtained by combining the M-point DFT by column and the N-point IDFT by row, that is, in represents the M-point normalized discrete Fourier transform matrix, e represents a natural constant, j represents an imaginary unit, k and l represent the matrix F M The row and column index values ​​in the matrix F N Represents the N-point normalized discrete Fourier transform matrix.

[0029] Furthermore, the transmit filter matrix is ​​expressed as:

[0030] G tx =diag[g tx (0),g tx (T / M),...,g tx ((M-1)T / M)]

[0031] in,(·) H represents the conjugate transposed matrix, g tx (t) represents a transmit filter function, T=1 / Δf represents a time grid interval, Δf represents a subcarrier interval, and M represents the number of points of a normalized discrete Fourier transform.

[0032] Furthermore, the time domain plus CP matrix is ​​expressed as:

[0033]

[0034] Among them, the matrix is the MN×MN identity matrix I MN The last l CP The matrix constructed by the submatrix of the row, where the length of CP is l CP It is generally designed to be larger than the maximum channel delay tap l max .

[0035] Furthermore, the pair of vectors Perform the CP removal operation to obtain the time domain signal vector r T,Bob , expressed as:

[0036] r T,Bob =H T,Bob s T +w T,Bob

[0037] in represents the time domain equivalent channel matrix before and after adding CP, represents the permutation matrix for the forward cyclic shift, represents a diagonal matrix, P represents the number of multipath channels between the transmitter and the receiver of the legitimate user, and h i 、 and denote the complex gain coefficient, integer delay tap and integer Doppler tap of the i-th path respectively, j is an imaginary unit, e is a natural constant, It represents a time domain noise vector that obeys a Gaussian distribution, with a mean of 0 and a variance of the noise power

[0038] Furthermore, the receiver filter matrix is ​​expressed as:

[0039] G rx =diag[g rx (0),g rx (T / M),...,g rx ((M-1)T / M)]

[0040] Among them, g rx (t) represents the receiving filter function, F M represents the M-point normalized discrete Fourier transform matrix.

[0041] Furthermore, any legal codeword c B ob all satisfy in(·) T represents the transposed matrix, H LDPC Represents the generator matrix G of the transmitter LDPC The corresponding check matrix.

[0042] Compared with the prior art, the present invention has the following technical features:

[0043] 1. Compared with traditional OFDM, the OTFS communication system adopted by the present invention has excellent performance in high mobility scenarios and can effectively resist the severe Doppler effect in high mobility channels.

[0044] 2. This paper utilizes the reciprocity of the duplex system wireless channel to perform singular value decomposition on the DD domain equivalent channel matrix unique to OTFS. Based on the difference in the channel matrices between legitimate users and eavesdroppers, a secure precoder is designed, which makes the transmission waveform have the characteristics of low interception, ensuring the security of wireless physical layer transmission.

[0045] 3. In the face of high-order cumulative amount detection by eavesdroppers, the phase and amplitude distribution of the air interface transmission waveform of the present invention is close to Gaussian white noise, which has good covert transmission characteristics.

[0046] 4. This invention combines LDPC coding with SVD precoding to form a serial concatenated code, leveraging the grouping properties of both OTFS and LDPC codecs. At the transmitter, the signal undergoes linear matrix multiplication with the LDPC generator matrix and the SVD precoding matrix. At the receiver, a single-tap linear equalizer is designed based on the diagonal matrix decomposed by the SVD. This results in low complexity for both the concatenated encoder and the single-tap equalizer in the system.

[0047] 5. This invention fully utilizes the low-density characteristics of the LDPC code domain to reduce the peak-to-average ratio of the OTFS system. The powerful error detection and correction capabilities of LDPC iterative decoding significantly improve the transmission reliability of the system.

[0048] 6. The present invention adopts the R-CP model unique to the OTFS system. Compared with the traditional F-CP model used in OFDM, it further improves spectrum efficiency and saves a large amount of time-frequency resource overhead. While improving the total system throughput, it can also maintain similar bit error rate performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a block diagram of a legal communication link;

[0050] Figure 2 The distribution diagram of the phase-amplitude statistical characteristics of the transmission waveform, where (a) is the amplitude distribution and (b) is the phase distribution;

[0051] Figure 3 The constellation diagram received by the eavesdropper, where (a) is the first-level eavesdropping user and (b) is the second-level eavesdropping user;

[0052] Figure 4 This is the system bit error rate curve simulation diagram. DETAILED DESCRIPTION

[0053] The present invention proposes a highly reliable and secure transmission method based on OTFS. This method combines LDPC coding with OTFS precoding technology to form a serial inner and outer concatenated coding between the code domain and the symbol domain. This method fully combines the powerful error correction performance of LDPC codes with the reliable security performance of precoding. Based on this, a highly reliable and secure OTFS air interface transmission solution is designed. The specific technical solutions of the present invention are as follows:

[0054] The sending end includes the following steps:

[0055] Step 1: The source bit sequence sent by each data frame is m[i] (i=0, 1, ..., k-1), where k represents the number of bits contained in each data frame and i represents the i-th data bit; the source bit sequence m[i] is constructed into a 1×k matrix m, and then the matrix m is combined with the k×n standard generator matrix G LDPC Perform linear matrix multiplication to map the source bit sequence to the LDPC sparse code domain and obtain the new codeword c = mG LDPC , where G LDPC =[I k ,P k×(n-k) ], I k represents the k×k identity matrix, P k×(n-k) Represents a k×(nk) sparse matrix, n represents the encoded code length, and the dimension parameter setting principle of the generator matrix and its submatrices is to ensure that the source matrix m and the generator matrix G LDPC The dimensions of accord with the linear matrix multiplication rules.

[0056] Step 2: Perform QAM constellation mapping on the new codeword c to obtain the mapped discrete constellation symbol d[i] (i=0,1,...,R-1), where R represents the number of constellation symbols contained in each data frame, and i represents the i-th constellation symbol; construct the d[i] into a matrix d=[d0,d1,...d R-1 ] T , where element d i =d[i](i=0,1,...,R-1), the superscript T indicates the matrix transpose.

[0057] Step 3: Based on the reciprocity of the wireless channel, both the legitimate transmitter and the legitimate user's receiver of the time division duplex system can obtain channel state information. The DD domain equivalent channel matrix of the legitimate channel (i.e., the wireless channel between the transmitter and the legitimate user's receiver) is subjected to singular value decomposition (SVD) to obtain Among them U Bob represents the left singular value matrix, E Bob represents a diagonal matrix, V Bob represents the right singular value matrix, (·)H represents the conjugate transposed matrix; in general, the equivalent channel matrix The acquisition can be estimated by receiving a known pilot sequence, and then using the matrix V Bob Perform linear precoding on the matrix d in step 2 to obtain the coding matrix x DD =V Bob d.

[0058] Step 4: For the subsequent two-dimensional transformation of OTFS modulation in the DD domain, the coding matrix By serial-to-parallel conversion, it is first mapped to the M×N DD domain grid to obtain the matrix Represents an M×N dimensional complex space, the matrix X DD Each row represents the constellation symbol information carried by a subcarrier, so there are M subcarriers, and the entire matrix X DD The constellation symbol information carried is an OTFS symbol, so the OTFS symbol length is N.

[0059] Step 5: For the matrix X DD Perform inverse symplectic finite Fourier transform (ISFFT) to obtain the time-frequency domain signal matrix X TF , the transformation can be obtained by combining M-point DFT by column and N-point IDFT by row, that is in represents the M-point normalized discrete Fourier transform matrix, e represents a natural constant, j represents an imaginary unit, k and l represent the matrix F M The row and column index values ​​​​in the matrix F N The same applies;

[0060] Step 6, the X TF Perform the Heisenberg transform and first obtain the matrix in the time-delay (TD) domain Among them G tx =diag[g tx (0),g tx (T / M),...,g tx ((M-1)T / M)] represents the transmit filter matrix, (·) H represents the conjugate transposed matrix, g tx (t) represents the transmit filter function, which can be flexibly designed by referring to the existing pulse shaping functions in common communication systems. T = 1 / Δf represents the time grid interval, and Δf represents the subcarrier interval. Then, column vectorization is performed to obtain the time domain vector s T =vec(STD ), where vec(·) represents a column quantization operation. Steps 5 and 6 on the transmitter side combine the standard OTFS modulation process, transforming the signal from the delay-Doppler domain to the time-frequency domain and then to the time domain. The intermediate transformation to the time-frequency domain is retained, facilitating integration with other technologies applicable to traditional OFDM. This, to a certain extent, ensures the compatibility of the present invention with other technologies. This is for the same reason as steps 3 and 4 on the receiver side below.

[0061] Step 7: To combat ICI and ISI caused by time spreading, the R-CP (Reduced Cyclic Prefix) model is used, which has less overhead than the F-CP (Full Cyclic Prefix) model in traditional OFDM systems. This improves spectrum efficiency and system throughput. Specifically, the time domain vector is processed using the time domain plus CP matrix to obtain the time domain signal vector: in Represents the time domain plus CP matrix, matrix is the MN×MN identity matrix I MN The last l CP The matrix constructed by the submatrix of the row, where the length of CP is l CP It is generally designed to be slightly larger than the maximum channel delay tap l max .

[0062] Step 8: transform the time domain signal vector After digital-to-analog conversion and up-conversion, the transmission signal is obtained and finally sent to the wireless channel via the antenna for air interface transmission. The present invention is mainly described by taking a multipath fading channel as an example.

[0063] For the receiving end of a legitimate user, the following steps are included:

[0064] Step 1′: After the legitimate user obtains the received signal from the legitimate channel, the baseband digital signal vector is obtained through analog-to-digital conversion and down-conversion.

[0065] Step 2′, for the vector Perform the CP removal operation to obtain the time domain signal vector r T,Bob =H T,Bob s T +w T,Bob ,in represents the time domain equivalent channel matrix before and after adding CP, represents the permutation matrix for the forward cyclic shift, represents a diagonal matrix, P represents the number of multipath channels between the transmitter and the receiver of the legitimate user, and h i 、 and denote the complex gain coefficient, integer delay tap and integer Doppler tap of the i-th path respectively, It represents a time domain noise vector that obeys a Gaussian distribution, with a mean of 0 and a variance of the noise power

[0066] Step 3′, the time domain signal vector r T,Bob Perform inverse column vectorization to reconstruct into an M×N matrix R TD,Bob =vec -1 (r T,Bob );For the TD domain matrix R TD,Bob Perform Wigner transformation to obtain TF matrix Y TF,Bob =F M G rx R TD,Bob , where the receiver filter matrix can be expressed as G rx =diag[g rx (0),g rx (T / M),...,g rx ((M-1)T / M)], g rx (t) represents the receiving filter function, F M represents the M-point normalized discrete Fourier transform matrix.

[0067] The two-step cascade transformation method used in this solution takes into account:

[0068] From the perspective of the standard modulation process, without loss of generality, the two-step transformation is a standard modulation transmission process of the OTFS technology. From the perspective of transform domain signal processing, at the transmitting end, the delay-Doppler domain signal is transformed into the time-frequency domain through ISFFT, and then the signal is transformed into the time domain through Heisenberg transform. In the time-frequency domain, some traditional OFDM processing techniques can be used to ensure compatibility with other technologies to a certain extent; the same is true for the receiving end.

[0069] Step 4′, the matrix Y TF,Bob Perform symplectic finite Fourier transform (SFFT) to obtain the DD domain signal matrix Then perform column vectorization to get y DD,Bob =vec(Y DD,Bob ); At this time, the DD domain signal reception vector can be expressed as where x DD =vec(X DD ), X DD is the matrix described in step 4 at the sending end, and represent the equivalent channel matrix and noise vector of the legal channel in the DD domain respectively.

[0070] Step 5′, the matrix U obtained in step 3 at the sending end Bob Perform SVD decoding and get

[0071] Step 6′, the matrix E obtained in step 3 at the sending end Bob For the y DD,Bob Perform single-tap equalization and get After further simplification, the signal The second term is the noise term that obeys the Gaussian distribution.

[0072] Step 7', the signal d Bob Perform QAM constellation demapping to obtain codeword c Bob .

[0073] Step 8′, for the code word c Bob Perform LDPC decoding to obtain m Bob , so that any legal codeword c Bob All satisfied in(·) T represents the transposed matrix, Represents the matrix G generated in step 1 at the sending end LDPC The corresponding check matrix, where the parameters n, k and matrix P are the same as those in step 1 of the sending end; the decoded m Bob Converted into the destination bit sequence m Bob [i] (i = 0, 1, ..., k-1), when the decoding is completely correct, this sequence is the same as the source bit sequence m[i] (i = 0, 1, ..., k-1).

[0074] LDPC decoding algorithms can use both soft-decision and hard-decision decoding. To further reduce decoding complexity, this paper primarily illustrates hard-decision decoding. In practice, using soft-decision decoding achieves higher coding gain at the expense of increased decoding complexity, further improving system transmission reliability.

[0075] The safety performance analysis of the solution of the present invention is as follows:

[0076] The eavesdropping model in this invention takes into account two levels of eavesdroppers: a primary eavesdropper and a secondary eavesdropper. For the primary eavesdropper, we consider a scenario that is extremely unfavorable to the legitimate link. We assume that the primary eavesdropper has prior knowledge of the legitimate link's OTFS transmission method and system parameters, but is unaware of the proposed cascade coding scheme. For the secondary eavesdropper, we consider a scenario that is extremely unfavorable to the legitimate link. We assume that the secondary eavesdropper is fully aware of the proposed secure transmission method based on cascade coding.

[0077] The receiving process of the first-level eavesdropper is as follows:

[0078] In steps 1-4, the first-level eavesdropper can perform the same processing as the legitimate user's receiving end from step 1' to step 4', and obtain the baseband digital signal vector The time domain signal vector r after removing CP T,E ve1=H T,E ve1s T +w T, E ve1, TD domain signal matrix R TD,Eve1 =vec -1 (r T,Eve1 ), TF domain signal matrix Y TF,Eve1 =F M G rx R TD,Eve1 , DD domain signal matrix DD domain signal vector The DD equivalent channel matrix and DD equivalent noise vector of the first-level eavesdropper are and w T,Eve1 represents the time domain noise vector of the first-level eavesdropper, H T,Eve1 Represents the time domain equivalent channel matrix of the first-level eavesdropper channel;

[0079] Step 5: DD domain vector y of the rectangular wave received signal TF,Eve1 For DD domain equalization, Zero Forcing (ZF) equalization and Minimum Mean Square Error (MMSE) equalization can generally be used. The ZF equalization formula is: The ZF equilibrium matrix is It represents the signal vector obtained after the first-level eavesdropper performs ZF equalization. The MMSE equalization formula is The MMSE equalization matrix is represents the signal vector obtained after the first-level eavesdropper performs MMSE equalization, and ρ represents the average signal-to-noise ratio. In this invention, the more optimal MMSE equalization method is mainly used as an example;

[0080] Step 6: equalize the signal Perform QAM constellation demapping to obtain the received codeword c Eve1 ;

[0081] Step 7: Receive codeword c Eve1 Perform LDPC decoding in the same way as in step 8′ at the receiving end of the legitimate user to obtain mEve1 ; The decoded m Eve1 Converted into the destination bit sequence m Eve1 [i](i=0,1,...,k-1). So far, the first-level eavesdropper has completed all the processing procedures at its receiving end. Since it is unable to know the concatenated coding transmission method proposed in this invention, even if it adopts the better MMSE equalization, it still cannot recover the source bit sequence m[i](i=0,1,...,k-1), that is, the bit sequence m[i] obtained by the first-level eavesdropper decoding. Eve1 [i] (i=0, 1, ..., k-1) is different from the source bit sequence m[i] (i=0, 1, ..., k-1), so the eavesdropping fails.

[0082] The receiving process of the secondary eavesdropper is as follows:

[0083] In steps 1-8, the secondary eavesdropper can perform the same processing flow as the legitimate user's receiving end, thereby obtaining the receiving signals at all levels under the eavesdropping channel conditions, which are the baseband digital signal vectors in order. The time domain signal vector r after removing CP T,Eve2 =H T,Eve2 s T +w T,Eve2 , TD domain signal matrix R TD,Eve2 =vec -1 (r T,Eve2 ), TF domain signal matrix Y TF,Eve2 =F M G rx R TD,Eve2 , DD domain signal matrix DD domain signal vector y DD,Eve2 =vec(Y DD,Eve2 ), signal vector after SVD decoding Signal vector after single-tap equalization For the d Eve2 Further processing yields The DD equivalent channel matrix and DD equivalent noise vector of the secondary eavesdropper are and w T,Eve2 represents the time domain noise vector of the secondary eavesdropper, H T,Eve2 The time domain equivalent channel matrix of the secondary eavesdropper channel is represented by the SVD decomposition of the DD domain equivalent channel matrix of the secondary eavesdropper's eavesdropping channel. Due to the difference between the equivalent channel matrix of the eavesdropping channel and the legitimate channel, the secondary eavesdropper can only obtain the degraded version of d Eve2 and d Eve2 , and it is impossible to further eliminate the transmitter according to the legal channel matrix Designed precoding matrix VBob Finally, the secondary eavesdropper will perform LDPC decoding in the same way as the legitimate user's receiving end in step 8' to obtain the destination bit sequence m Eve2 [i](i=0,1,...,k-1). So far, the secondary eavesdropper has completed all the same processing procedures as the legitimate user's receiving end, but still cannot successfully recover the source bit m[i](i=0,1,...,k-1), that is, the bit sequence m[i] obtained by the secondary eavesdropper's decoding. Eve2 [i] (i=0, 1, ..., k-1) is different from the source bit sequence m[i] (i=0, 1, ..., k-1), so the eavesdropping fails.

[0084] The solution of the present invention supports OTFS transmission under arbitrary waveforms by selecting different transmission filter matrices G tx and the receive filter matrix G rx The transmission waveform can be changed appropriately. In order to fit the actual communication system implementation and simulation analysis of engineers, the simulation uses rectangular waves to simplify the Heisenberg transform and Wigner transform. Under the rectangular wave condition, there is a relationship G tx =G rx =I M , where I M represents the M×M identity matrix;

[0085] Depend on Figure 2 and Figure 3 It can be seen that the amplitude distribution of the transmission waveform of the present invention is similar to Rayleigh distribution, and its phase distribution is similar to homogeneous distribution. This makes the phase-amplitude distribution characteristics of its constellation diagram extremely similar to Gaussian white noise, making it impossible for an eavesdropper to distinguish the waveform of the useful signal from Gaussian white noise.

[0086] Figure 4 The transmission reliability from the legitimate sender to the legitimate user, the first-level eavesdropper, and the second-level eavesdropper was simulated. The simulation conditions adopted the EVA channel model in the 3GPP standard. The multipath delay and relative power were: [0 30 150 310370 710 1090 1730 2510]ns and [0 -1.5 -1.4 -3.6 -0.6 -9.1 -7-12-16.9]dB, respectively. The maximum speed of the simulated channel was 500km / h. The simulation results show that the scheme of the present invention has good transmission reliability and security. Neither the first-level eavesdropper nor the second-level eavesdropper can decipher the information transmitted on the legitimate link.

[0087] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A highly reliable and secure transmission method based on OTFS, characterized in that: For the transmitting end, the transmission method includes: Step 1: The source bit sequence sent by each data frame Constructed as Matrix , Indicates the number of bits contained in each data frame; then the matrix and The standard generator matrix Perform linear matrix multiplication to map the source bit sequence to the LDPC sparse code domain to obtain a new codeword ; Step 2: Perform QAM constellation mapping to obtain the mapped discrete constellation symbols ,Will Constructed as a matrix ; Step 3: Perform singular value decomposition on the DD domain equivalent channel matrix of the legal channel to obtain ,in represents the left singular value matrix, represents a diagonal matrix, represents the right singular value matrix, represents the conjugate transposed matrix; then use the matrix Pair Matrix Perform linear precoding to obtain the coding matrix ; Step 4: The coding matrix By first mapping to the DD domain grid through serial-to-parallel conversion, the matrix ; Step 5: Matrix Perform inverse symplectic finite Fourier transform to obtain the time-frequency domain signal matrix ; Step 6: Perform the Heisenberg transform and first obtain the matrix in the time-delay domain , and then perform column vectorization to obtain the time domain vector ; Step 7: Use the time domain plus CP matrix to process the time domain vector to obtain the time domain signal vector ; Step 8: transform the time domain signal vector After digital-to-analog conversion and up-conversion, the transmitted signal is finally sent to the wireless legal channel through the antenna for air interface transmission; For the receiving end, the transmission method includes: Step 1′: After the legitimate user's receiving end obtains the received signal from the legitimate channel, it obtains the baseband digital signal vector through analog-to-digital conversion and down-conversion ; Step 2′, for the vector Perform the CP removal operation to obtain the time domain signal vector ; Step 3′, the time domain signal vector Perform inverse column vectorization to reconstruct it into a TD domain matrix ; For the TD domain matrix Perform Wigner transformation to obtain TF matrix ; Step 4′, the matrix Perform symplectic finite Fourier transform to obtain the DD domain signal matrix , then Column vectorization is performed to obtain ; Step 5′, perform singular value decomposition on the DD domain matrix of the legal channel to obtain the left singular value matrix Perform SVD decoding and get ; Step 6′, the diagonal matrix obtained by singular value decomposition of the DD domain matrix of the legal channel Regarding the Perform single-tap equalization to obtain the matrix ; Step 7', the signal Perform QAM constellation demapping to obtain codewords ; Step 8', the code word Perform LDPC decoding to obtain ,Will Converted into a destination bit sequence ; When the decoding is completely correct, the sequence is consistent with the source bit sequence same.

2. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: , express The identity matrix, Indicates a sparse matrices, Indicates the encoded code length.

3. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: The pair matrix Perform inverse symplectic finite Fourier transform to obtain the time-frequency domain signal matrix , expressed as: The inverse symplectic finite Fourier transform is done column by column Point DFT and do it row by row The point IDFT combination transformation is obtained, that is, ,in express The point-normalized discrete Fourier transform matrix, represents a natural constant, represents the imaginary unit, and Represents matrices respectively The row and column index values ​​in the matrix express N Point-normalized discrete Fourier transform matrix.

4. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: The transmit filter matrix is ​​expressed as: in, represents the conjugate transposed matrix, represents the transmit filter function, represents the time grid interval, represents the subcarrier spacing, M Represents the number of points in the normalized discrete Fourier transform.

5. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: The time domain plus CP matrix is ​​expressed as: Among them, the matrix is Identity matrix Middle and last The matrix constructed by the submatrix of the row, where the length of CP is Designed to be larger than the maximum channel delay tap .

6. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: The pair of vectors Perform the CP removal operation to obtain the time domain signal vector , expressed as: in represents the time domain equivalent channel matrix before and after adding CP, represents the permutation matrix for the forward cyclic shift, represents a diagonal matrix, Indicates the number of paths in the multipath channel between the sender and the receiver of the legitimate user, 、 and Respectively represent The complex gain coefficients, integer delay taps and integer Doppler taps of each path, j is the imaginary unit, e is a natural constant, It represents a time domain noise vector that obeys a Gaussian distribution, with a mean of 0 and a variance of the noise power .

7. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: The receiver filter matrix is ​​expressed as: in, represents the receiving filter function, express Point-normalized discrete Fourier transform matrix.

8. The highly reliable and secure transmission method based on OTFS according to claim 1, characterized in that: Any legal codeword All satisfied ,in represents the transposed matrix, Represents the generator matrix of the transmitter The corresponding check matrix.

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