Fractional Fourier transform-based dispersion and time-frequency offset joint estimation method and fractional Fourier transform-based dispersion and time-frequency offset joint estimation system

Abstract

The invention provides a fractional order Fourier transform dispersion and time frequency offset joint estimation method and system, and the method comprises the steps: S1, inserting a training sequence in a transmitting end signal, and constructing a transmission signal flow; s2, finding the position of the training sequence at a receiving end through block-by-block search; s3, constructing a two-dimensional space based on the position of the training sequence, and searching to obtain a peak coordinate; the two-dimensional space comprises a transformation angle and a transformation domain; and S4, resolving by using the peak coordinates to obtain dispersion, time offset and frequency offset. The invention provides a dispersion and time-frequency offset joint estimation method based on fractional Fourier transform, which comprises the following steps of: designing a multifunctional training sequence at a transmitting end, and realizing joint estimation of three parameters at a receiving end by using fractional Fourier transform; and the training sequence overhead is reduced to 25% of that of the existing joint estimation algorithm based on the fractional Fourier transform principle.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter estimation of optical fiber communication systems, and in particular to a method and system for jointly estimating dispersion and time-frequency offset of fractional-order Fourier transform. Background Art

[0002] Optical fiber communication systems have become the main infrastructure of modern communication systems. The low loss and large bandwidth of optical fiber support the long-distance transmission of massive data. With the popularization of new services such as 5G communication, Internet of Things, cloud computing and artificial intelligence, global Internet data traffic has not only grown rapidly, but also presented new dynamic characteristics, which requires optical networks to be intelligently upgraded in terms of capacity and flexibility. Establishing an intelligent optical network with flexible modulation format, adjustable baud rate / bandwidth, high spectrum efficiency, high speed and large capacity has become one of the important trends in the development of optical fiber communication systems.

[0003] With the continuous development of optical networks, the digital signal parameter estimation technology that matches this will inevitably become a research direction. However, the current digital signal parameter estimation technology cannot decouple the estimation and compensation processes, and the response speed is slow during fast link switching, making it difficult to meet the application requirements of dynamic adaptation, flexibility and efficiency; the mainstream technology estimates a single parameter with a specific algorithm module, resulting in high estimation calculation overhead; in order to cope with different modulation formats, a corresponding training sequence needs to be designed for each parameter, which seriously reduces the spectrum efficiency of the system. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a method and system for jointly estimating dispersion and time-frequency offset of fractional Fourier transform.

[0005] A method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform provided by the present invention comprises:

[0006] Step S1: inserting a training sequence into the transmitting signal to construct a transmission signal stream;

[0007] Step S2: Find the location of the training sequence by searching block by block at the receiving end;

[0008] Step S3: construct a two-dimensional space based on the position of the training sequence, and search to obtain the peak coordinates;

[0009] The two-dimensional space includes a transformation angle and a transformation domain;

[0010] Step S4: using the peak coordinates to calculate the dispersion, time offset and frequency offset.

[0011] Preferably, step S1 includes inserting a training sequence at the beginning of each frame of the signal, and the generating formula is:

[0012]

[0013] Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-p·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0, 1).

[0014] Preferably, step S2 includes the following sub-steps:

[0015] Step S2.1: Calculate the detection parameter for each block of the receiving end signal. The calculation formula is as follows:

[0016]

[0017] Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving end signal;

[0018] Step S2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, a coarse positioning training sequence is obtained. The search formula is as follows:

[0019]

[0020] in, Indicates the index corresponding to the minimum detection parameter.

[0021] Preferably, step S3 includes the following sub-steps:

[0022] Step S3.1: training sequence for coarse positioning Perform fractional Fourier transform at different transform angles, with the angle range being (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transform angle-transform domain;

[0023] Step S3.2: Search the peak coordinates in the two-dimensional space using a variable step search method; perform a rough search using a preset search interval to obtain a rough result; perform further search within a certain range of the rough result until the set search accuracy is met;

[0024] Get the coordinates of the two peaks:

[0025] {φ opt1 ,Δn1} and {φ opt2 , Δn2}

[0026] Among them, φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

[0027] Preferably, step S4 includes the following sub-steps:

[0028] Step S4.1: Solve the following equations to obtain the dispersion length z1:

[0029] tanφ opt1 =tanφ sps +tanφ cd

[0030] tanφ sps =sps·tan(π / 2-α)

[0031]

[0032] Where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z:

[0033] z=(z1+z2) / 2;

[0034] Step S4.2: Solve the following equations to obtain the time offset Δt and the frequency offset Δf:

[0035] Δn1=μcosφ opt1 +ysinφ opt1

[0036] Δn2=μcosφ opt2 +ysinφ opt2

[0037]

[0038]

[0039] Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

[0040] A system for jointly estimating dispersion and time-frequency offset of fractional Fourier transform provided by the present invention comprises:

[0041] Module M1: inserts a training sequence into the transmitting signal to construct a transmission signal stream;

[0042] Module M2: Find the location of the training sequence by searching block by block at the receiving end;

[0043] Module M3: construct a two-dimensional space based on the position of the training sequence and search for the peak coordinates;

[0044] The two-dimensional space includes a transformation angle and a transformation domain;

[0045] Module M4: Use the peak coordinates to solve for dispersion, time offset and frequency offset.

[0046] Preferably, the module M1 includes inserting a training sequence at the beginning of each frame of the signal, and the generating formula is:

[0047]

[0048] Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0, 1).

[0049] Preferably, the module M2 includes the following submodules:

[0050] Module M2.1: Calculate the detection parameters of the receiving end signal block by block. The calculation formula is as follows:

[0051]

[0052] Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving end signal;

[0053] Module M2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, the coarse positioning training sequence is obtained The search formula is as follows:

[0054]

[0055] in, Indicates the index corresponding to the minimum detection parameter.

[0056] Preferably, the module M3 includes the following submodules:

[0057] Module M3.1: Coarse Positioning Training Sequence Perform fractional Fourier transform at different transform angles, with the angle range being (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transform angle-transform domain;

[0058] Module M3.2: Use variable step search to search for peak coordinates in two-dimensional space; use a preset search interval to perform a rough search to obtain a rough result; perform further search within a certain range of the rough result until the set search accuracy is met;

[0059] Get the coordinates of the two peaks:

[0060] {φ opt1 ,Δn1} and {φ opt2 , Δn2}

[0061] Among them, φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

[0062] Preferably, the module M4 includes the following submodules:

[0063] Module M4.1: Solve the following system of equations to obtain the dispersion length z1:

[0064] tanφ opt1 =tanφ sps +tanφ cd

[0065] tanφ sps =sps·tan(π / 2-α)

[0066]

[0067] Where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z:

[0068] z=(z1+z2) / 2;

[0069] Module M4.2: Solve the following equations to obtain the time offset Δt and frequency offset Δf:

[0070] Δn1=μcosφ opt1 +γsinφ opt1

[0071] Δn2=μcosφ opt2 +γsinφ opt2

[0072]

[0073]

[0074] Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

[0075] Compared with the prior art, the present invention has the following beneficial effects:

[0076] 1. The present invention proposes a method for joint estimation of dispersion and time-frequency offset based on fractional Fourier transform. A multifunctional training sequence is designed at the transmitting end, and fractional Fourier transform is used at the receiving end to realize joint estimation of three parameters. The training sequence overhead is reduced to 25% of the existing joint estimation algorithm also based on the principle of fractional Fourier transform.

[0077] 2. The estimation method proposed in the present invention is independent of the signal compensation process, and has a computational complexity reduced by more than 50% compared with similar algorithms. It has a sensitive response speed and high reliability during link switching; it adopts a block processing architecture and is suitable for hardware implementation.

[0078] 3. The present invention is based on training sequence estimation and can be applied to any modulation format. The system has strong compatibility and good applicability.

[0079] Other beneficial effects of the present invention will be explained in the specific implementation manner through the introduction of specific technical features and technical solutions. Through the introduction of these technical features and technical solutions, those skilled in the art should be able to understand the beneficial technical effects brought about by the technical features and technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0081] Figure 1 The present invention is a flow chart of the method.

[0082] Figure 2 It is a schematic diagram of the coarse positioning training sequence of the receiving end in the present invention.

[0083] Figure 3 This is an example diagram of the transformation angle-transformation domain two-dimensional distribution in the present invention.

[0084] Figure 4 This is a flow chart of the variable step length search in the present invention.

[0085] Figure 5 This is a graph showing the estimated error results of the present invention applied to the optical fiber transmission experimental system. DETAILED DESCRIPTION

[0086] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0087] Reference Figure 1 As shown, a method for jointly estimating dispersion and time-frequency offset based on fractional Fourier transform includes:

[0088] Step S1: inserting a training sequence into the transmitting signal to construct a transmission signal stream;

[0089] Specifically, a training sequence is inserted at the beginning of each frame of the signal, and the generation formula is as follows:

[0090]

[0091] Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0,1).

[0092] Step S2: Find the rough position of the training sequence by searching block by block at the receiving end;

[0093] Step S2.1: Calculate the detection parameter for each block of the receiving end signal. The calculation formula is as follows:

[0094]

[0095] Among them, |·| represents the amplitude of the signal, Rk(t) represents the k-th block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving signal.

[0096] Step S2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, a coarse positioning training sequence is obtained. The search formula is as follows:

[0097]

[0098] in, Indicates the index corresponding to the minimum detection parameter.

[0099] Step S3: construct a two-dimensional space consisting of transformation angles and transformation domains, and search to obtain peak coordinates;

[0100] Step S3.1: training sequence for coarse positioning Perform fractional Fourier transform at different transformation angles within an angle range of (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transformation angle-transformation domain.

[0101] Step S3.2: Use variable step search to search for peak coordinates in two-dimensional space: first set a larger search interval to perform a coarse search to obtain a coarse result, then perform a small range search near the coarse value, and so on, until the search accuracy is met and the two peak coordinates {φ opt1 ,Δn1} and {φ opt2 ,Δn2}, where φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

[0102] Step S4: using the peak coordinates to calculate the dispersion, time offset and frequency offset.

[0103] Step S4.1: Solve the following equations to obtain the dispersion length z1:

[0104] tanφ opt1 =tanφ sps +tanφ cd

[0105] tanφ sps =sps·tan(π / 2-α)

[0106]

[0107] where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z:

[0108] z=(z1+z2) / 2

[0109] Step S4.2: Solve the following equations to obtain the time offset Δt and the frequency offset Δf:

[0110] Δn1=μcosφ opt1 +γsinφ opt1

[0111] Δn2=μcosφ opt2 +γsinφ opt2

[0112]

[0113]

[0114] Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

[0115] The present invention proposes a method for joint estimation of dispersion and time-frequency offset based on fractional Fourier transform. A multifunctional training sequence is designed at the transmitting end, and fractional Fourier transform is used at the receiving end to realize joint estimation of three parameters. The training sequence overhead is reduced to 25% of the existing joint estimation algorithm which is also based on the principle of fractional Fourier transform.

[0116] The above is a basic embodiment of the present invention. The technical solution of the present invention is further described below through a preferred embodiment.

[0117] Example 1

[0118] In this embodiment, the transmission experiment system parameters used are: single-carrier dual-polarization coherent optical transmission, symbol rate 60GBaud, modulation format 16QAM, single-span transmission distance 80km, and maximum number of transmission spans 20.

[0119] Reference Figure 1 As shown, a method for jointly estimating dispersion and time-frequency offset based on fractional Fourier transform includes:

[0120] A method for jointly estimating dispersion and time-frequency offset based on fractional Fourier transform provided by the present invention includes:

[0121] Step S1: inserting a training sequence into the transmitting signal to construct a transmission signal stream;

[0122] Specifically, a training sequence is inserted at the beginning of each frame of the signal, and the generation formula is as follows:

[0123]

[0124] Where S(t) represents a DC signal, whose length is Ns , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, and P is the order of the fractional Fourier transform.

[0125] In this embodiment, the training sequence length N s is set to 1024, the fractional Fourier transform order P is set to 0.5, and the transformation angle α is set to π / 4.

[0126] Step S2: The rough positioning training sequence is shown as follows Figure 2 As shown, the rough position of the training sequence is found by searching block by block at the receiving end;

[0127] Step S2.1: Calculate the detection parameter for each block of the receiving end signal. The calculation formula is as follows:

[0128]

[0129] Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving signal.

[0130] In this embodiment, N s Set to 1024, and the receiving end sampling rate sps is set to 1.

[0131] Step S2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, a coarse positioning training sequence is obtained. The search formula is as follows:

[0132]

[0133] in, Indicates the index corresponding to the minimum detection parameter.

[0134] Step S3: construct a two-dimensional space consisting of transformation angles and transformation domains, and search to obtain peak coordinates;

[0135] Step S3.1: training sequence for coarse positioning Perform fractional Fourier transform at different transformation angles within an angle range of (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transformation angle-transformation domain.

[0136] Step S3.2: Use variable step search to search for peak coordinates in two-dimensional space: first set a larger search interval to perform a coarse search to obtain a coarse result, then perform a small range search near the coarse value, and so on, until the search accuracy is met and the two peak coordinates {φ opt1 ,Δn1} and {φ opt2 ,Δn2}, where φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

[0137] In this embodiment, a wide search range of (-π / 2, π / 2) with a step size of 0.01π is used in the initial stage, followed by a fine search in a small range of (-0.1π, 0.1π) with a step size of 0.001π. The final peak coordinates are obtained after two rounds of searching. α is π / 4, N s The transformation angle-transformation domain two-dimensional distribution formed by the coarse positioning training sequence with 1024 and sps of 1 is as follows Figure 3 As shown in the figure, the variable step search process is as follows Figure 4 shown.

[0138] Step S4: using the peak coordinates to calculate the dispersion, time offset and frequency offset.

[0139] Step S4.1: Solve the following equations to obtain the dispersion length z1:

[0140] tanφ opt1 =tanφ sps +tanφ cd

[0141] tanφ sps =sps·tan(π / 2-α)

[0142]

[0143] where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z:

[0144] z=(z1+z2) / 2

[0145] Step S4.2: Solve the following equations to obtain the time offset Δt and the frequency offset Δf:

[0146] Δn1=μcosφ opt1+ysinφ opt1

[0147] Δn2=μcosφ opt2 +ysinφ opt2

[0148]

[0149]

[0150] Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

[0151] In this embodiment, the group velocity dispersion parameter β2 is set to -21.6826 ps / nm / km, and the symbol period T is set to N s Set to 1024, and the receiving end signal sampling rate sps is set to 1.

[0152] Figure 5 The estimation error results of the combined estimation method of dispersion and time-frequency offset based on fractional Fourier transform applied to the optical fiber transmission experimental system are shown in the figure. Each scenario is tested 50 times and the estimated error is averaged. The total number of spans is 20. Combined with the back-to-back scenario, there are 21 test scenarios with a total of 1050 tests. The left figure is a histogram of the probability distribution of the time offset estimation error, and the right figure is a curve of the dispersion estimation error and the frequency offset estimation error changing with the number of transmission spans.

[0153] The estimation method proposed in the present invention is independent of the signal compensation process, and has a computational complexity reduced by more than 50% compared with similar algorithms. It has a sensitive response speed and high reliability during link switching. It adopts a block processing architecture and is suitable for hardware implementation.

[0154] The present invention also provides a system for jointly estimating dispersion and time-frequency offset of a fractional-order Fourier transform. The system for jointly estimating dispersion and time-frequency offset of a fractional-order Fourier transform can be implemented by executing the process steps of the method for jointly estimating dispersion and time-frequency offset of a fractional-order Fourier transform, that is, a person skilled in the art can understand the method for jointly estimating dispersion and time-frequency offset of a fractional-order Fourier transform as a preferred implementation mode of the system for jointly estimating dispersion and time-frequency offset of a fractional-order Fourier transform.

[0155] Specifically, a fractional-order Fourier transform dispersion and time-frequency offset joint estimation system includes:

[0156] Module M1: inserts a training sequence into the transmitting signal to construct a transmission signal stream;

[0157] Module M2: Find the location of the training sequence by searching block by block at the receiving end;

[0158] Module M3: construct a two-dimensional space based on the position of the training sequence and search for the peak coordinates;

[0159] The two-dimensional space includes a transformation angle and a transformation domain;

[0160] Module M4: Use the peak coordinates to solve for dispersion, time offset and frequency offset.

[0161] The module M1 includes inserting a training sequence at the beginning of each frame of the signal, and the generation formula is:

[0162]

[0163] Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0, 1).

[0164] The module M2 includes the following submodules:

[0165] Module M2.1: Calculate the detection parameters of the receiving end signal block by block. The calculation formula is as follows:

[0166]

[0167] Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving end signal;

[0168] Module M2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, the coarse positioning training sequence is obtained The search formula is as follows:

[0169]

[0170] in, Indicates the index corresponding to the minimum detection parameter.

[0171] The module M3 includes the following submodules:

[0172] Module M3.1: Coarse Positioning Training Sequence Perform fractional Fourier transform at different transform angles, with the angle range being (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transform angle-transform domain;

[0173] Module M3.2: Use variable step search to search for peak coordinates in two-dimensional space; use a preset search interval to perform a rough search to obtain a rough result; perform further search within a certain range of the rough result until the set search accuracy is met;

[0174] Get the coordinates of the two peaks:

[0175] {φ opt1 ,Δn1} and {φ opt2 , Δn2}

[0176] Among them, φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

[0177] The module M4 includes the following submodules:

[0178] Module M4.1: Solve the following system of equations to obtain the dispersion length z1:

[0179] tanφ opt1 =tanφ sps +tanφ cd

[0180] tanφ sps =sps·tan(π / 2-α)

[0181]

[0182] Where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z:

[0183] z=(z1+z2) / 2;

[0184] Module M4.2: Solve the following equations to obtain the time offset Δt and frequency offset Δf:

[0185] Δn1=μcosφ opt1 +γsinφ opt1

[0186] Δn2=μcosφopt2 +γsinφ opt2

[0187]

[0188]

[0189] Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

[0190] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0191] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for jointly estimating dispersion and time-frequency offset based on fractional Fourier transform, characterized in that: include: Step S1: inserting a training sequence into the transmitting signal to construct a transmission signal stream; Step S2: Find the location of the training sequence by searching block by block at the receiving end; Step S3: construct a two-dimensional space based on the position of the training sequence, and search to obtain the peak coordinates; The two-dimensional space includes a transformation angle and a transformation domain; Step S4: using the peak coordinates to calculate the dispersion, time offset and frequency offset.

2. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 1, characterized in that: The step S1 includes inserting a training sequence at the beginning of each frame of the signal, and the generating formula is: Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0,1).

3. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 2, characterized in that: The step S2 comprises the following sub-steps: Step S2.1: Calculate the detection parameter for each block of the receiving end signal. The calculation formula is as follows: Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving end signal; Step S2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, a coarse positioning training sequence is obtained. The search formula is as follows: in, Indicates the index corresponding to the minimum detection parameter.

4. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 3, characterized in that: The step S3 comprises the following sub-steps: Step S3.1: training sequence for coarse positioning Perform fractional Fourier transform at different transform angles, with the angle range being (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transform angle-transform domain; Step S3.2: Search the peak coordinates in the two-dimensional space using a variable step search method; perform a rough search using a preset search interval to obtain a rough result; perform further search within a certain range of the rough result until the set search accuracy is met; Get the coordinates of the two peaks: {φ opt1 Δn1} and {φ opt2 , Δn2} Among them, φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

5. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 4, characterized in that: The step S4 comprises the following sub-steps: Step S4.1: Solve the following equations to obtain the dispersion length z1: man opt1 =tanφ sps ++tanφ cd tanf sps =sps·tan(π / 2-α) Where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z: z=(z1+z2) / 2; Step S4.2: Solve the following equations to obtain the time offset Δt and the frequency offset Δf: Δn1=μcosφ opt1 +γsinφ opt1 Δn2=μcosφ opt2 +γsinφ opt2 Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

6. A method for jointly estimating dispersion and time-frequency offset based on fractional Fourier transform, characterized in that: include: Module M1: inserts a training sequence into the transmitting signal to construct a transmission signal stream; Module M2: Find the location of the training sequence by searching block by block at the receiving end; Module M3: construct a two-dimensional space based on the position of the training sequence and search for the peak coordinates; The two-dimensional space includes a transformation angle and a transformation domain; Module M4: Use peak coordinates to solve for dispersion, time offset, and frequency offset.

7. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 6, characterized in that: The module M1 includes inserting a training sequence at the beginning of each frame of the signal, and the generation formula is: Where S(t) represents a DC signal, whose length is N s , represents the fractional Fourier transform of the signal with a rotation angle of α = P·π / 2, It represents the fractional Fourier transform of the signal with a rotation angle of -α=-P·π / 2, TS(t) is the training sequence, P is the order of the fractional Fourier transform, and its value range is (0,1).

8. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 7, characterized in that: The module M2 includes the following submodules: Module M2.1: Calculate the detection parameters of the receiving end signal block by block. The calculation formula is as follows: Among them, |·| represents the amplitude of the signal, R k (t) represents the kth block of received signals, and the block length is N s sps, sps is the sampling rate of the receiving end signal; Module M2.2: After the block-by-block detection parameter calculation of the single-frame receiving end signal is completed, the coarse positioning training sequence is obtained The search formula is as follows: in, Indicates the index corresponding to the minimum detection parameter.

9. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 8, characterized in that: The module M3 includes the following submodules: Module M3.1: Coarse Positioning Training Sequence Perform fractional Fourier transform at different transform angles, with the angle range being (-π / 2, π / 2), and form a two-dimensional distribution with the signal amplitude as the vertical axis on the search plane of the transform angle-transform domain; Module M3.2: Use variable step search to search for peak coordinates in two-dimensional space; use a preset search interval to perform a rough search to obtain a rough result; perform further search within a certain range of the rough result until the set search accuracy is met; Get the coordinates of the two peaks: {φ opt1 , Δn1} and {φ opt2 , Δn2} Among them, φ opt1 With φ opt2 are the fractional Fourier transform angles corresponding to the two peaks, Δn1 and Δn2 are the coarse positioning training sequences The peak center offset in the corresponding fractional Fourier transform domain.

10. The method for jointly estimating dispersion and time-frequency offset of fractional Fourier transform according to claim 9, characterized in that: The module M4 includes the following submodules: Module M4.1: Solve the following system of equations to obtain the dispersion length z1: man opt1 =tanφ sps +tanφ cd tanf sps =sps·tan(π / 2-α) Where β2 is the group velocity dispersion parameter and T is the symbol period. opt1 Replace with φ opt2 , α is replaced by -α, and the dispersion length z2 can be solved. The average of z1 and z2 is used to obtain the final estimate of the dispersion length z: z=(z1+z2) / 2; Module M4.2: Solve the following equations to obtain the time offset Δt and frequency offset Δf: Δn1=μcosω opt1 +γsinφ opt1 Δn2=μcosφ opt2 +γsinφ opt2 Among them, μ and γ are the number of digital sampling points corresponding to the time offset and frequency offset respectively, round represents the nearest integer operation, and Δt and Δf are the final estimation results of the time offset and frequency offset.

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