A load phase tracking method, device and medium based on training sequence

Abstract

The present invention relates to the field of wireless communication technology, and in particular to a method, device and medium for tracking the phase of a payload based on a training sequence. The method comprises the following steps: generating a local training sequence according to an FSK modulation mode; grouping the received training sequence according to a fixed symbol period, and correlating it with the conjugate of a local pre-phased training sequence; obtaining the phase angle of the correlation sequence using a CORDIC algorithm; accumulating and integrating the phase by group, and updating the phase of the phase modulation; obtaining the final phase deviation value under the current payload training sequence; accumulating and updating the local oscillator phase, and iteratively compensating the actual data of the payload. The present invention improves the estimation accuracy by uniformly inserting the pilot training sequence into the data block, thereby obtaining the average correlation value of the training sequence on multiple consecutive frames, and ensures continuous tracking of the data phase, effectively reducing the bit error rate, and improving the demodulation performance of the receiver.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a payload phase tracking method, device and medium based on a training sequence. Background Art

[0002] The most notable characteristic of wireless communications is the complexity and time-varying nature of their channels. To ensure distortion-free demodulation, the carriers modulated and demodulated by the transmitting and receiving devices must be perfectly aligned in frequency and phase. However, since the channel response is determined by the characteristics of the transmitting and receiving devices, as well as the propagation of the carrier in the wireless channel, device characteristics such as frequency deviation and drift, variations in the wireless propagation channel, and Doppler shift and fading caused by terminal movement all contribute to frequency skew and phase jitter. Specifically, the asynchrony of the local oscillator frequencies of the transmitting and receiving devices results in sampling frequency deviation and carrier frequency deviation. These frequency deviations produce phase shifts in the data symbols at the receiver that increase linearly with time. Therefore, the receiver must be able to capture and track channel variations to ensure the performance of data reception and demodulation.

[0003] In reality, the impact of carrier frequency offset can be attributed to the phase rotation of data symbols. To eliminate or suppress the effects of frequency offset, phase tracking is performed in the receiving device. Furthermore, due to the complex transmission path, signals are susceptible to strong interference, resulting in a low signal-to-noise ratio (SNR) at the receiving end. Therefore, achieving signal demodulation and decoding under conditions of large frequency offset and low SNR is a major design challenge for receivers.

[0004] Data-assisted carrier synchronization algorithms are typically used in receivers. These algorithms rely on a known training sequence between the transmitter and receiver for carrier synchronization. Pilot training sequences are evenly inserted into data blocks, and the average correlation value of the training sequence over multiple consecutive frames is calculated to improve estimation accuracy and ensure continuous tracking of the data phase. In digital communications, pseudorandom noise sequences (PN sequences) can be used as training sequences for phase estimation because they have good delay correlation and symmetric correlation.

[0005] Utilizing a frame structure that inserts training sequences at even intervals within the payload data and correlates the training sequences with the received signal, this process is continuously estimated in the time domain, giving the system strong resistance to fast fading. Furthermore, continuous estimation of multiple training sequences improves accuracy and, most importantly, ensures data phase tracking, effectively reducing the bit error rate and enhancing the receiver's demodulation performance. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention discloses a payload phase tracking method, device and medium based on a training sequence. The present invention realizes the tracking and correction of data phase under complex channel changes, ensures good bit error rate performance, and has a significant effect on improving demodulation performance.

[0007] The present invention is achieved through the following technical solutions:

[0008] A payload phase tracking method based on a training sequence comprises the following steps:

[0009] Step 1: Determine the training sequence position and actual data position of the received payload;

[0010] Step 2: Generate a local training sequence according to the FSK modulation mode and initialize the phase modulation phase of the local training sequence;

[0011] Step 3: Group the received training sequence according to a fixed symbol period and correlate it with the conjugate of the local pre-phased training sequence;

[0012] Step 4: Use the CORDIC algorithm to obtain the phase angle of the correlation sequence;

[0013] Step 5: Accumulate and integrate the phases by group and update the phase modulation phase to compensate for the phase deviation of the next set of training sequences;

[0014] Step 6: Accumulate and integrate the phase offset of each group in the training sequence to obtain the final phase offset value under the current load training sequence;

[0015] Step 7: Accumulate and update the local oscillator phase, and perform iterative compensation on the actual data of the payload.

[0016] Furthermore, to distinguish the training sequence position from the actual data position in the payload, the payload structure consists of a fixed-period pilot training sequence portion, which can be denoted as the P region, and a fixed-period actual data portion, which can be denoted as the D region. The pilot training sequence is inserted into the payload at equal intervals, so that a valid payload contains several P regions and D regions, and the number of P regions and D regions is the same. The P region is regarded as the pilot header of the D region.

[0017] Furthermore, the specific implementation process of step 1 is:

[0018] Step 101: Frame header capture and tracking can determine the starting position of the payload;

[0019] Step 102: Determine the protocol data transmitted in the frame header, mainly including the number of bytes sent, encoding method, spread spectrum multiple, etc.

[0020] Step 103: The protocol information obtained from the above steps can be used to determine the symbol period length of the payload. The fixed periodicity and equal interval insertion of the P and D regions can then be used to determine the training sequence position and the actual data position.

[0021] Furthermore, the specific implementation process of step 2 is:

[0022] Step 201: Determine the modulation mode of the currently received frame;

[0023] Step 202: Generate a pseudo-random code using a pseudo-random Gold sequence as an original pilot sequence, and perform FSK-type modulation according to the modulation mode to obtain a local training sequence;

[0024] Step 203: Initialize the phase of the local training sequence to 0, and perform pre-phase modulation on the modulation sequence obtained in step 202 to generate a local pre-phase modulation training sequence.

[0025] Specifically, the local training sequence described in step 202 is generated by using a pseudo-random Gold sequence through FSK modulation, which can be expressed as Where l(n) is the generated local training sequence, g(n) is the pseudo-random code generated using the Gold sequence, Indicates the modulation phase of the current nth sequence.

[0026] Specifically, the phase modulation phase of the local training sequence described in step 203 is used to adjust the phase of the local training sequence associated with the received sequence, thereby achieving phase tracking. The process can be expressed as follows:

[0027]

[0028] Where l'(n) is the local training sequence after phase modulation, θ g The initial phase of each P region is set to θ g =0.

[0029] Furthermore, the specific implementation process of step 3 is:

[0030] Step 301: Divide the received training sequence into multiple groups according to a fixed symbol period, and perform phase estimation and tracking on a group-by-group basis;

[0031] Step 302: conjugate the local pre-phase training sequence of step 203;

[0032] Step 303: Perform a complex multiplication operation on the received training sequence and the local pre-phased training sequence to obtain a correlation signal.

[0033] Specifically, the fixed symbol period in step 301 is defined as N s, its value is set to N s =k s *S. S is defined as the number of training sequence symbols used for phase tracking, k s is the sampling multiple.

[0034] Specifically, the correlation signal calculation process described in step 303 can be expressed as z(n)=r(n)*l' * (n). In the formula, r(n) is the training sequence part of the received payload, which can be expressed as r(n)=r i (n)+jr q (n), r i (n) and r q (n) are the I and Q components of the received signal, and n is the sampling point index.

[0035] Furthermore, the CORDIC algorithm is used in step 4 to calculate the inverse tangent value. The implementation process can be achieved by iteratively rotating the original vector toward the positive half axis of the x-axis. The accumulated rotation angle is the desired inverse tangent angle. The calculation expression for the inverse tangent phase angle of the correlation sequence is:

[0036]

[0037] Where θ(n) is the arc tangent value corresponding to the nth correlation sequence, which represents the instantaneous phase.

[0038] Furthermore, the specific implementation process of step 5 is:

[0039] Step 501: The instantaneous phase obtained in step 4 is calculated by N in the group. s Sampling points are accumulated and integrated;

[0040] Step 502: Calculate the average value of the phase integral of the current group as the first N s Phase deviation value of each sampling point;

[0041] Step 503: Update the phase offset value to the phase modulation phase, and use it as the initial phase value of the next set of training sequences to compensate for the phase offset of subsequent training sequences.

[0042] Specifically, the process described in steps 501 and 502 can achieve the purpose of cumulative noise reduction, and the calculation process is:

[0043]

[0044] Where, represents the average phase value of group g.

[0045] Specifically, the phase adjustment phase in step 503 is updated as Where, represents the updated new phase, θ g The phase value of the previous group. When repeating the operation steps of the next group of training sequences, the new phase needs to be updated to the phase modulation phase.

[0046] Furthermore, the specific implementation process of step 6 is:

[0047] Step 601: average the average phase value of each group and the number of sampling points in the group;

[0048] Step 602: Accumulate the integral of the average value;

[0049] Step 603: After processing all groups in the current P region, the number of groups is averaged. If the P region is divided into only one group, the result of step 601 is used directly as the final phase deviation value. If the P region contains multiple groups, the accumulated integral value starting from the second group is divided by the corresponding number of groups.

[0050] Specifically, the phase deviation accumulation and integration process of each group in steps 601 and 602 is expressed as:

[0051]

[0052] Where, Φ s Indicates the phase deviation integral value, N g is the number of groups in the P area.

[0053] Specifically, the final phase deviation value in step 603 is expressed as:

[0054]

[0055] Furthermore, the specific implementation process of step 7 is:

[0056] Step 701: Update the local oscillator phase using the final phase offset value of the P-region training sequence;

[0057] Step 702: Read the corresponding NCO value according to the local oscillator phase;

[0058] Step 703: Starting from the position of the D region, the received payload sequence is iteratively compensated for the change caused by the phase offset, which is actually manifested as multiplying the received payload sequence by the local oscillator.

[0059] Specifically, the local oscillator phase described in step 701 needs to be updated in real time according to the sampling points of the load. At the same time, the phase superposition of all sampling points in the P area must also be considered. When starting to compensate the D area data, the phase superposition value of the P area is added. The specific process is expressed as follows:

[0060]

[0061] Where t represents the index value of the P area, It represents the accumulated phase deviation value of the first t P zones. In particular, when the next D zone starts to compensate, then for have:

[0062]

[0063] Where, Indicates the last phase of the D region with index value t, represents the initial phase of the D region with index value t+1, and That is, the phase superposition value of the P region with the index value t.

[0064] Specifically, the NCO value described in step 702 is implemented using sine and cosine functions based on the CORDIC algorithm, which is equivalent to the reverse process of the inverse tangent function based on the CORDIC algorithm. This implementation process can be achieved by iteratively rotating a point on the x-axis to the desired phase angle. The x-coordinate of the corresponding unit vector is the cosine value, and the y-coordinate is the sine value.

[0065] Specifically, the process of iteratively compensating the actual load data in step 703 is expressed as follows: The compensated data includes the actual data of the load and the training sequence data, and the phase deviation estimation is performed on the adjusted training sequence data. This is repeated, and the phase deviation estimation value of the current P zone data is used to correct the data of the current D zone and the next P zone.

[0066] A payload phase tracking device based on a training sequence includes: a data cache module for caching data to be processed, including P-zone and D-zone data, to wait for the completion of pre-phasing phase preparation of the P-zone data and to wait for calculation delays for phase estimation and cumulative noise reduction. The data is stored and read at the same bit rate;

[0067] Phase tracking module: used to control the correlation between the local sequence and the received sequence, obtain the local training sequence according to the modulation mode, read the orthogonal component corresponding to the pre-adjusted phase of the current group, and pre-adjust the phase of the local training sequence group and then correlate it with the received sequence to obtain the correlation value;

[0068] Accumulation noise reduction module: used to estimate the phase deviation of the training sequence. The corresponding arc tangent angle is calculated for the correlation value, and the integration is accumulated according to the number of group sampling points. The average phase deviation value within the group is obtained by accumulating the imaginary part of the average phase deviation value according to the number of groups to obtain the final phase deviation value of the training sequence.

[0069] Iterative compensation module: used to compensate the payload data phase, accumulate the phase deviation value of the iterative training sequence, and use the corresponding local oscillator signal to multiply the actual data part of the payload and the next training sequence in sequence to compensate the data phase.

[0070] A computer storage medium stores a load phase tracking device, which, when executed by at least one processor, implements the steps of any of the above-mentioned training sequence-based load phase tracking methods.

[0071] The present invention has the following beneficial effects:

[0072] 1) The present invention utilizes the correlation of the training sequence and directly correlates it with the local sequence to obtain an integral value. At the same time, based on the data frame structure with uniformly distributed loads of the training sequence, the phase is continuously estimated and iteratively compensated, which can effectively improve the accuracy of phase deviation estimation to ensure the correctness of subsequent demodulated data.

[0073] 2) Based on the correlation integral, the present invention pre-adjusts the phase of the local sequence and updates and compensates the phase adjustment in real time by grouping, thereby achieving the purpose of phase tracking.

[0074] 3) When calculating the inverse tangent angle and constructing the local oscillator signal, the present invention utilizes the idea of ​​successive approximation of the CORDIC algorithm and obtains the inverse tangent angle and the local oscillator signal through a cyclic calculation method, avoiding the large-scale use of storage units to achieve phase search and saving resource area. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 A simplified block diagram of a wireless communication system.

[0077] Figure 2 Implement a block diagram for the receiving device.

[0078] Figure 3 This is the NCO implementation diagram based on the CORDIC algorithm.

[0079] Figure 4 Schematic diagram of the multi-stage extractor.

[0080] Figure 5 Figure 2 is a schematic diagram of the payload frame structure.

[0081] Figure 6 Flowchart of the payload phase tracking method based on training sequence.

[0082] Figure 7 Generate a circuit diagram for a pseudo-random Gold sequence.

[0083] Figure 8 Schematic diagram of the structure of the payload phase tracking device based on the training sequence.

[0084] Figure 9 Diagram of the overall implementation framework of payload phase tracking based on training sequence. DETAILED DESCRIPTION

[0085] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0086] The sampling frequency offset and carrier frequency offset in wireless communication system receivers described in the background technology cause data symbols to have a phase offset that increases linearly with time. Although cyclic prefixes or other pilot headers are provided in the receiver to capture and track the received signal, thereby correcting for coarse and fine frequency offsets, when the payload data is long, factors such as residual frequency offset or channel noise interference still affect the phase changes of subsequent payload data, leading to errors in the demodulation of subsequent data symbols and an increase in the bit error rate.

[0087] Example 1

[0088] Based on this, the embodiment of the present invention provides a payload phase tracking solution based on a training sequence. For this solution, see Figure 1 , provides a wireless communication system applicable to the embodiment of the present invention, which includes a transmitting device and a receiving device. The transmitting device encodes, modulates and processes the source data, moves it to the corresponding carrier frequency band for transmission, and the receiving device captures, demodulates, decodes and processes the received signal to restore the source data of the transmitting device. Figure 1 As shown, the receiving device can perform the following Figure 2 The received signal processing flow shown is as follows:

[0089] exist Figure 2 In the embodiment provided, the specific implementation steps are as follows:

[0090] Step a: Digital down-conversion of the data sequence x(n) sampled and quantized by the ADC can be obtained by multiplying the signal sequence x(n) with a rotating complex vector, that is, The rotation complex vector is the two orthogonal local oscillator sequences cos(w c n) and sin(w cn), which can be generated by a numerically controlled oscillator (NCO). Traditional NCOs are implemented using a ROM (read-only memory) lookup table. The corresponding sine and cosine values ​​are calculated based on the NCO's sine and cosine phases and stored in the ROM. Another method for implementing the NCO is based on the CORDIC algorithm. The basic idea is to use successive approximation to calculate trigonometric functions, requiring only addition, subtraction, and shift operations, making it easy to implement.

[0091] Specifically, the CORDIC algorithm can be understood in the coordinate system as starting from the starting point (x i ,y i ) rotate to the target point (x j ,y j ) can be achieved by refining the multi-step rotation, agreeing that each step only rotates a fixed angle θ n , then each step of rotation has the following relationship:

[0092]

[0093] By extraction, the above formula can also be written as:

[0094]

[0095] Considering a fixed θ n In this case, cosθ n In fact, it is also a fixed value, which can be replaced by a constant in implementation. n =arctan(2 -n ), then:

[0096]

[0097] The above formula is the rotation iteration formula. It can be found that there are only addition, subtraction and shift operations. n is the number of iterations or the cycle. In addition, the angle accumulation parameter z is introduced. n , which is used to judge the difference with the target angle, then z n+1 =z n -d(n)θ n .

[0098] The above formula can be understood as follows: if the angle rotated in the next iteration is greater than the target angle, it is necessary to rotate in the opposite direction; otherwise, continue to rotate in the same direction. Then d(n) is the direction appropriate, satisfying:

[0099]

[0100] Iterate according to the above process until convergence to the target point. Figure 3The NCO implementation diagram based on the CORDIC algorithm shown in the figure sets the number of iterations to 16. The starting point of rotation is determined by the input angle quadrant. Subsequently, new rotation points are obtained in sequence according to the iterative formula until the iteration is completed and the corresponding sine and cosine signals are output.

[0101] Step b: To reduce the data sampling rate, the down-converted signal is digitally low-pass filtered and decimated. Filtering is to remove high-frequency components from the signal to prevent aliasing; decimation is to extract one D data from the original sampling sequence to form a new sequence. In this embodiment of the present invention, in order to meet the requirements of multi-level rate processing, the following is proposed: Figure 4 The multi-stage extractor structure shown.

[0102] Step c: Use the local synchronization header data to correlate with the downsampled signal to capture the frame header position for subsequent tracking and frequency offset correction.

[0103] Step d: It is worth noting that this step involves payload phase tracking. As can be seen from the preceding and following steps, the input signal is the synchronized initial payload position data, and the output is the corrected data after phase tracking.

[0104] Specifically, in order to intuitively express the above load structure, the embodiment of the present invention provides Figure 5 The payload structure diagram is shown in Figure 1. The payload consists of a fixed-period pilot training sequence portion, which can be denoted as the P region, and a fixed-period actual data portion, which can be denoted as the D region. Pilot training sequences are inserted into the payload at equal intervals, resulting in a payload containing an equal number of P regions and D regions. The P region is considered the pilot header of the D region.

[0105] Step e: Demodulate and despread the data after phase tracking adjustment.

[0106] Step f: Finally, complete the descrambling and decoding of the soft bit data.

[0107] Based on the above steps, a complete embodiment of the receiving device can be constructed. Specifically, in the above step d, the following can be performed: Figure 6 The method for payload phase tracking based on a training sequence shown includes:

[0108] S601: Determine the training sequence position and actual data position of the received payload.

[0109] In this embodiment, the specific implementation process of the above steps is as follows: the frame header capture and tracking in the aforementioned step c can determine the starting position of the payload; at the same time, the protocol data transmitted in the frame header is determined, mainly including information such as the number of bytes sent, the encoding method, and the spread spectrum multiple; the protocol information obtained from the above steps can be used to determine the symbol period length of the payload, and then the fixed periodicity and equal-interval insertion of the P zone and the D zone can be used to determine the training sequence position and the actual data position.

[0110] S602: Generate a local training sequence according to the FSK modulation mode, and initialize the phase modulation of the local training sequence.

[0111] In this embodiment, the specific implementation process of the above steps is: determining the modulation mode of the currently received frame; using a pseudo-random Gold sequence to generate a pseudo-random code as an original pilot sequence, and performing corresponding FSK-type modulation according to the modulation mode to obtain a local training sequence; initializing the phase of the local training sequence to 0, and pre-modulating the above sequence to generate a local pre-phased training sequence.

[0112] In the above, the modulation mode in this embodiment includes BFSK, MSK, GFSK, and GMSK. The original sequence is generated by a pseudo-random Gold sequence. The generation method can be referred to Figure 7 The circuit shown. Based on the initial value of the polynomial, two m sequences m1(n) and m2(n) are constructed. The two sequences have the same length and the same code rate. The local pseudo-random code g(n) sequence is formed by multiplying the optimal pairs. Figure 7 In the scheme, two sequences m1(n) and m2(n) are generated by shifting and XORing 31 registers according to their initial states and valid enable signals, and the two results are XORed to generate the final pseudo-random sequence signal.

[0113] The original sequence also needs to be modulated by FSK to generate a local training sequence, which is: Where l(n) is the generated local training sequence, Indicates the modulation phase of the current nth sequence.

[0114] To conserve implementation resources and taking into account the regularity of P-zone data, the embodiment of the present invention stores the local training sequences generated by the above process in ROM according to the modulation mode, eliminating the need for independent computation. Compared to BFSK and MSK, MSK incorporates differential coding on top of the original sequence. Therefore, the quadrature components only need to be modified by determining the current differential coding value after reading the ROM table. Furthermore, GFSK and GMSK, unlike the previous two modes, incorporate Gaussian filtering preprocessing, requiring separate data storage in ROM.

[0115] In the embodiment, the phase modulation phase of the local training sequence is used to adjust the phase of the local training sequence to achieve phase tracking. The process can be expressed as:

[0116]

[0117] Where l'(n) is the local training sequence after phase modulation, θ g The initial phase of each P region is set to θ g = 0. Specifically, the same architecture used to generate the NCO signal using the CORDIC algorithm can be used to generate the phase modulation signal here, and a complex multiplier can be used to complete the pre-phase modulation of the local training sequence.

[0118] S603: Group the received training sequence according to a fixed symbol period, and correlate it with the conjugate of the local pre-phased training sequence.

[0119] In this embodiment, the above steps are specifically implemented as follows: dividing the received training sequence portion into multiple groups according to a fixed symbol period, and performing phase estimation and tracking on a group-by-group basis; taking a conjugate of the local pre-phase-adjusted training sequence in step 602; and performing a complex multiplication operation on the received training sequence and the local pre-phase-adjusted training sequence to obtain a correlation signal.

[0120] The fixed symbol period is defined as N s , its value is set to N s =k s *S. S is defined as the number of training sequence symbols used for phase tracking, k s is the sampling multiple.

[0121] Specifically, the correlation signal calculation process can be expressed as z(n)=r(n)*l' * (n). In the formula, r(n) is the training sequence part of the received payload, which can be expressed as r(n)=r i (n)+jr q (n), r i (n) and r q (n) are the I and Q components of the received signal, and n is the sampling point index.

[0122] S604: Calculate the phase angle of the correlation sequence using the CORDIC algorithm.

[0123] Specifically, the CORDIC algorithm is used to calculate the inverse tangent value. The implementation process can be achieved by iterating the original vector to rotate toward the positive half axis of the x-axis. The accumulated rotation angle is the required inverse tangent angle. In this embodiment, the same iterative circuit as that used to generate the NCO signal can be used. The difference is that the initial value after determining the quadrant is determined by the input coordinate value, and the output is the final rotation angle accumulated parameter z n As the angle to be sought. The calculation expression of the arc tangent phase angle of the correlation sequence is:

[0124]

[0125] Where θ(n) is the arc tangent value corresponding to the nth correlation sequence, which represents the instantaneous phase.

[0126] S605: Accumulate and integrate the phases in groups, and update the phase modulation phase to compensate for the phase deviation of the next group of training sequences.

[0127] In this embodiment, the specific implementation process of the above steps is: the instantaneous phase obtained in step 604 is calculated according to the N s The sampling points are accumulated and integrated; the average value of the phase integral of the current group is obtained as the first N s The phase deviation value of each sampling point is updated to the phase modulation phase, which is used as the initial phase value of the next set of training sequences to compensate for the phase deviation of subsequent training sequences.

[0128] Specifically, the cumulative integration and averaging process can achieve the purpose of cumulative noise reduction, and its calculation can be expressed as:

[0129]

[0130] Where, represents the average phase value of group g.

[0131] The phase modulation phase is updated as Where θ g+1 represents the updated new phase, θ g The phase value of the previous group. When repeating the operation steps of the next group of training sequences, the new phase needs to be updated to the phase modulation phase.

[0132] S606: Accumulate and integrate the phase offsets of each group of the training sequence to obtain a final phase offset value of the current load training sequence.

[0133] In this embodiment, the above steps are specifically implemented as follows: averaging the average phase value of each group with the number of sampling points in that group; accumulating the integral of this average; and after processing all groups in the current P region, averaging the number of groups again. If the P region is divided into only one group, its average value is directly used as the final phase deviation value. If the P region contains multiple groups, the integral value accumulated starting from the second group is divided by the corresponding number of groups.

[0134] Specifically, the cumulative integration process of each group of phase deviations is expressed as:

[0135]

[0136] Where, Φ s Indicates the phase deviation integral value, N g is the number of groups in the P area.

[0137] The final phase deviation value is expressed as:

[0138]

[0139] S607: Accumulate and update the local oscillator phase, and perform iterative compensation on the actual data of the payload.

[0140] In this embodiment, the specific implementation process of the above steps is: updating the local oscillator phase based on the final phase offset value of the training sequence in the P zone; reading the corresponding NCO value based on the local oscillator phase; and iteratively compensating for changes caused by the phase offset of the received payload sequence starting from the D zone, which is actually manifested as multiplying the received payload sequence by the local oscillator.

[0141] Specifically, the local oscillator phase needs to be updated in real time according to the sampling points of the load. At the same time, the phase superposition of all sampling points in the P area must also be considered. When starting to compensate the D area data, the phase superposition value of the P area is added. The specific process is expressed as follows:

[0142]

[0143] Where t represents the index value of P area and D area, It represents the accumulated phase deviation value of the first t P zones. In particular, when the next D area data starts to be compensated, have:

[0144]

[0145] Where, Indicates the phase corresponding to the last data in the D area with index value t. represents the initial phase of the D region with index value t+1, and where That is, the phase superposition value of the P region with the index value t.

[0146] The process of iterative compensation of actual load data is expressed as in, The NCO based on the CORDIC algorithm still reads the phase value to obtain the orthogonal data, which is then multiplied with the D area data in sequence.

[0147] Based on the above steps, the phase tracking of the payload can be completed. The multiplication result is the new data after the final phase modulation of the D area, which is also used as the data for subsequent demodulation and despreading.

[0148] Example 2

[0149] like Figure 8 As shown, the embodiment of the present invention also provides a device for tracking the payload phase based on the training sequence, referring to Figure 8 The device includes a data buffer module 801, a phase tracking module 802, an accumulation noise reduction module 803 and an iterative compensation module 804. Among them, the data buffer module 801 is used to buffer the data to be processed, including P-zone and D-zone data. First, it waits for the pre-phase preparation of the P-zone data to be completed, and second, it waits for the calculation delay of phase estimation and cumulative noise reduction. The data storage and reading maintain the same code rate. The phase tracking module 802 is used to control the correlation between the local sequence and the received sequence, obtain the local training sequence according to the modulation mode, read the orthogonal component corresponding to the pre-phase of the current group, and correlate the local training sequence group with the received sequence after pre-phase adjustment to obtain the correlation value. The cumulative noise reduction module 803 is used to estimate the phase deviation of the training sequence, calculate the corresponding inverse tangent angle of the correlation value, and accumulate and integrate it according to the number of group sampling points to obtain the average phase deviation value within the group. The imaginary part of the average phase deviation value is accumulated according to the number of groups to obtain the final training sequence phase deviation value. The iterative compensation module 804 is used to compensate the payload data phase. It accumulates the phase deviation value of the iterative training sequence and multiplies the actual data portion of the payload with the next training sequence in sequence using the corresponding local oscillator signal to compensate the data phase.

[0150] Example 3

[0151] In this embodiment, for the payload phase tracking method executed in the receiving device, Figure 9 A specific implementation block diagram of an embodiment of the present invention is provided. In conjunction with the aforementioned implementation steps, CORDIC-based DDS and arctan implementations experience a fixed-period output delay due to loop iteration issues. Therefore, after data arrives, it is necessary to wait for NCO data preparation to complete or for the average phase deviation calculation to complete. Therefore, data should be cached after phase tracking is initiated.

[0152] refer to Figure 9Taking into account factors such as computational delay, random access memories (RAM1) and RAM2 are used to store the payload's P-zone and D-zone data, respectively, to delay and wait for data processing. It's worth noting that RAM1 stores the first P-zone data and the phase-modulated P-zone data, while RAM2 stores the D-zone data and all other P-zone data except the first. The storage timing of RAM1 and RAM2 is also based on the payload's training sequence position and actual data location. The valid bit of the P-zone data serves as the write enable for RAM1, and correspondingly, the valid bit of the D-zone data serves as the write enable for RAM2.

[0153] Example 4

[0154] The present invention also provides a computer storage medium comprising instructions for causing a computer device (such as a personal computer, server, or network device) to execute all or part of the steps described in the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a removable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0155] In summary, the present invention improves estimation accuracy by evenly inserting pilot training sequences into data blocks and averaging the correlation values ​​of the training sequences across multiple consecutive frames. This ensures continuous tracking of the data phase, effectively reducing the bit error rate and enhancing receiver demodulation performance. Furthermore, based on the successive approximation principle of the CORDIC algorithm, the present invention uses a cyclic iteration approach to calculate the inverse tangent angle and obtain the local oscillator signal, avoiding the large-scale use of memory cells and conserving resource space.

[0156] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention 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. However, 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 invention.

Claims

1. A load phase tracking method based on training sequence, characterized in that: The following steps are involved: Step 1: Determine the training sequence position and actual data position of the received payload; Step 2: Generate a local training sequence according to the FSK modulation mode, and initialize the phase modulation phase of the local training sequence; Step 3: Group the received training sequence according to a fixed symbol period and correlate it with the conjugate of the local pre-phased training sequence; Step 4: Use the CORDIC algorithm to obtain the phase angle of the correlation sequence; Step 5: Accumulate and integrate the phases by group, and update the phase modulation phase to compensate for the phase deviation of the next set of training sequences; Step 6: Accumulate and integrate the phase offset of each group in the training sequence to obtain the final phase offset value under the current load training sequence; Step 7: Accumulate and update the local oscillator phase, and iteratively compensate the actual data of the load; Repeating the above steps can complete the phase tracking and data iteration compensation of all loads; The step 7 comprises the following sub-steps: Step 701: Update the local oscillator phase according to the final phase deviation value of the P-region training sequence; Step 702: Read the corresponding NCO value according to the local oscillator phase, where the NCO value is the digital controlled oscillator value; Step 703: for the received payload sequence, starting from the D region, iteratively compensate for the change caused by the phase deviation, which is actually manifested as multiplying the received payload sequence by the local oscillator; The process of iteratively compensating the load data is expressed as: ; Indicates The corresponding receiving payload sequence after iterative compensation of sampling points; Indicates The corresponding receiving payload sequence before iterative compensation of sampling points; Indicates The local oscillator phase corresponding to each sampling point; the compensation data includes the actual data of the load and the training sequence data, and the phase deviation of the adjusted training sequence data is estimated, and this is repeated, and the data of the current D zone and the next P zone are corrected with the phase deviation estimation value of the current P zone data; The payload structure in step 1 is composed of a fixed-period pilot training sequence part and a fixed-period actual data part, wherein the fixed-period pilot training sequence part is recorded as a P region and the fixed-period actual data part is recorded as a D region.

2. The load phase tracking method based on training sequence according to claim 1, characterized in that: The pilot training sequence is inserted into the payload at equal intervals, so that a payload contains several P zones and D zones, and the number of P zones and D zones is the same, and the P zone is regarded as the pilot header of the D zone; The step 1 comprises the following sub-steps: Step 101: Frame header capture and tracking can determine the starting position of the payload; Step 102: determining the protocol data transmitted in the frame header, wherein the protocol data includes the number of bytes to be sent, the encoding method and the spread spectrum multiple information; Step 103: The protocol data obtained from the above steps can determine the symbol period length of the payload, and then the training sequence position and the actual data position can be determined by using the fixed periodicity and equal interval insertion of the P zone and the D zone.

3. The load phase tracking method based on training sequence according to claim 1, characterized in that: The step 2 includes the following sub-steps: Step 201: Determine the modulation mode of the current received frame; Step 202: Use the pseudo-random Gold sequence to generate a pseudo-random code as an original pilot sequence, and perform corresponding FSK-type modulation according to the modulation mode to obtain a local training sequence; Step 203: Initialize the phase of the local training sequence to 0, and perform pre-phase modulation on the modulation sequence obtained in step 202 to generate a local pre-phase modulation training sequence; The local training sequence is generated by using a pseudo-random Gold sequence through FSK modulation, which is expressed as ; In the formula, is the generated local training sequence, is a pseudo-random code generated using the Gold sequence, Indicates the current The modulation phase of the sequence.

4. The load phase tracking method based on training sequence according to claim 1, characterized in that: The step 3 includes the following sub-steps: Step 301: Divide the received training sequence part into multiple groups according to the fixed symbol period, and perform phase estimation and tracking in groups as units; Step 302: conjugate the local pre-phase training sequence of step 203; Step 303: perform a complex multiplication operation on the received training sequence and the local pre-phased training sequence to obtain a correlation signal; Wherein, the fixed symbol period is defined as , whose value is set to , Defined as the number of training sequence symbols used for phase tracking, is the sampling multiple.

5. The load phase tracking method based on training sequence according to claim 1, characterized in that: The step 4 uses the CORDIC algorithm to calculate the inverse tangent value, and the implementation process can be iterated from the original vector to The positive half axis of the axis is rotated to approximate, and the accumulated rotation angle is the required inverse tangent angle. The calculation expression of the inverse tangent phase angle of the related sequence is: In the formula, For the The arc tangent value corresponding to the correlation sequence represents the instantaneous phase; No. The q-path components of a correlation sequence, For the The i-way component of a correlation sequence.

6. The load phase tracking method based on training sequence according to claim 1, characterized in that: The step 5 comprises the following sub-steps: Step 501: The instantaneous phase obtained in step 4 is calculated by the group The sampling points are accumulated and integrated; Step 502: Calculate the average value of the phase integral of the current group as the previous Phase deviation value of each sampling point; Step 503: Update the phase deviation value to the phase modulation phase, and use it as the initial phase value of the next set of training sequences to compensate for the phase deviation of subsequent training sequences; The phase modulation phase is updated as follows: ; In the formula, represents the new phase after update, is the phase value of the previous group, represents the average phase value of the gth group; when the operation steps of the next set of training sequences are repeated subsequently, the new phase needs to be updated to the phase modulation phase in step 2.

7. The load phase tracking method based on training sequence according to claim 1, characterized in that: The step 6 comprises the following sub-steps: Step 601: average the average phase value of each group and the number of sampling points in the group; Step 602: Accumulate the integral of the above average value; Step 603: After processing all the groups in the current P zone, average the number of groups; If the P region is divided into only one group, the result of step 601 is directly used as the final phase deviation value; if there are multiple groups in the P region, the integral value accumulated starting from the second group is divided by the corresponding number of groups.

8. A load phase tracking device based on a training sequence, characterized in that: include: Data cache module: used to cache the data to be processed, including P zone and D zone data, the P zone is the pilot training sequence part of the fixed period, and the D zone is the actual data part of the fixed period; first, wait for the pre-phase preparation of the P zone data to be completed, and second, wait for the calculation delay of phase estimation and cumulative noise reduction, and the data storage and reading maintain the same code rate; Phase tracking module: used to control the correlation between the local training sequence and the received training sequence, obtain the local training sequence according to the modulation mode, read the orthogonal component corresponding to the pre-adjusted phase of the current group, and correlate the local training sequence group with the received training sequence after pre-adjusting the phase to obtain the correlation value; Accumulation noise reduction module: used for estimating the phase deviation of the training sequence, obtaining the corresponding arc tangent angle of the correlation value, and accumulating the integral according to the number of grouped sampling points, and averaging to obtain the average phase deviation value within the group; The imaginary part of the average phase deviation value is accumulated according to the number of groups, so as to obtain the final training sequence phase deviation value; Iterative compensation module: used to compensate the phase of the payload data, accumulate the phase deviation value of the iterative training sequence, and use the corresponding local oscillator signal to multiply the actual data part of the payload and the next training sequence in sequence to compensate the data phase.

9. A computer storage medium, characterized in that The computer storage medium stores a load phase tracking device, which, when executed by at least one processor, implements the load phase tracking method based on a training sequence as described in any one of claims 1 to 7.

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