Synchronous detection method and device for time division duplex repeater and electronic equipment

Abstract

The invention discloses a synchronous detection method and device for a time division duplex repeater and electronic equipment, the method is applied to the field of communication signal processing, and the method comprises the steps: carrying out the multi-branch differential correlation calculation of a local reference signal and a target signal according to a plurality of differential intervals, and obtaining a plurality of differential correlation results; performing normalization processing on each difference correlation result in the plurality of difference correlation results to obtain a plurality of normalized value sequences; determining weight information according to the signal-to-noise ratio information of the plurality of normalized value sequences, and fusing the plurality of differential correlation results to obtain a fusion result sequence; and determining a peak value in the fusion result sequence, and determining a synchronization point of the target signal and the local reference signal according to offset information of the peak value. Through application of the method and the device, the problem of large synchronization deviation caused by phase deviation caused by carrier frequency deviation when the user terminal receives the signal sent by the time division duplex repeater in a high-speed moving scene in the related technology is solved.

Description

Technical Field

[0001] This application relates to the field of communication signal processing, and more specifically, to a synchronization detection method, apparatus, and electronic equipment for a time-division duplex repeater. Background Technology

[0002] In the field of modern communications, Time Division Duplexing (TDD) technology has become an important mechanism for building high-speed mobile communication systems due to its advantage of efficiently utilizing spectrum resources. TDD achieves multiplexing of the same frequency band by alternately allocating uplink and downlink communication on the time axis, thereby improving spectrum utilization. However, the efficient operation of this mechanism highly depends on precise time slot synchronization to avoid potential conflicts and interference between uplink and downlink transmissions.

[0003] In high-speed mobile scenarios, such as vehicle communication on high-speed trains, urban rail transit, or highways, TDD repeaters face severe synchronization challenges. The increased relative speed between the user terminal and the base station causes a significant Doppler shift in the received signal, i.e., carrier frequency offset. This frequency offset phenomenon induces rapid phase changes in the received signal, making it difficult for traditional synchronization algorithms based on autocorrelation or single differential correlation techniques to capture clear and stable synchronization peaks. Especially under low signal-to-noise ratio (SNR) conditions, the broadening and attenuation of the synchronization peak become more significant, leading to a sharp decline in synchronization performance. This directly manifests as a large timing deviation between the user terminal and the TDD repeater, or even complete loss of synchronization, severely impacting communication quality and network performance.

[0004] There is currently no effective solution to the problem of large synchronization deviation caused by phase shift due to carrier frequency offset when a user terminal receives signals sent by a time-division duplex repeater in high-speed mobile scenarios. Summary of the Invention

[0005] The main objective of this application is to provide a synchronization detection method, apparatus, and electronic device for time-division duplex repeaters, in order to solve the problem in related technologies where, when a user terminal receives a signal sent by a time-division duplex repeater in a high-speed mobile scenario, the phase shift caused by the carrier frequency offset leads to a large synchronization deviation.

[0006] To achieve the above objectives, according to one aspect of this application, a synchronization detection method for a time-division duplex repeater is provided. The method includes: receiving a target signal transmitted by the time-division duplex repeater; performing multi-branch differential correlation calculation on a local reference signal and the target signal based on multiple differential intervals to obtain multiple differential correlation results, wherein the multiple differential intervals are calculated based on multiple constraints and the cumulative phase offset information of the differential product; normalizing each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences; determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences; fusing the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence; determining the peak value in the fused result sequence; and determining the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fused result sequence.

[0007] Furthermore, the multiple constraints include at least a first constraint and a second constraint. Before performing multi-branch differential correlation calculations on the local reference signal and the target signal based on multiple differential intervals, the above method further includes: calculating the cumulative phase offset information based on the first constraint to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the unsolved variables corresponding to the differential interval, constants, and signal sampling intervals, and the first constraint is used to limit the numerical range of the cumulative phase offset information; calculating the first numerical range based on the second constraint and a preset maximum frequency offset threshold to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficients, and the target coefficients are used to improve the accuracy of the signal after the occurrence of Doppler drift; and determining multiple differential intervals based on the integers contained in the second numerical range.

[0008] Further, determining multiple differential intervals based on integers included in the second numerical range includes: determining the device information of the data processing device, wherein the data processing device includes at least: field-programmable gate array logic units and digital signal processor units; conducting at least one simulation experiment based on the device information, signals received within a historical time period, and a preset number of differential branches, and calculating an evaluation index of the simulation experiment results; adjusting the preset number of differential branches according to the evaluation index to obtain a target number of differential branches; and filtering from integers included in the second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0009] Furthermore, each differential correlation result in the multiple differential correlation results is normalized to obtain multiple normalized value sequences, including: calculating the amplitude of each differential correlation result in the multiple differential correlation results; performing time delay processing on the local reference signal based on each of the multiple differential intervals to obtain multiple delayed reference signals; performing dot product calculations on the complex conjugate value of the local reference signal and the multiple delayed reference signals respectively, and summing the calculation results to obtain a first intermediate variable; calculating the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain multiple normalized value sequences.

[0010] Furthermore, before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, the above method further includes: determining the signal power of the signal peak corresponding to each normalized value sequence in the multiple normalized value sequences; determining the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; calculating the signal values ​​in each normalized value sequence other than the signal peak, and summing the calculation results to obtain the second intermediate variable corresponding to each normalized value sequence; and determining the signal-to-noise ratio information of multiple normalized value sequences based on the signal power of the signal peak corresponding to each normalized value sequence and the second intermediate variable corresponding to each normalized value sequence.

[0011] Furthermore, multiple differential correlation results are fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence, including: calculating the signal-to-noise ratio information of each normalized value sequence in multiple normalized value sequences based on an exponential function to obtain a third intermediate variable corresponding to each normalized value sequence; summing the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; determining the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables, and determining the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and weighting and fusing multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence.

[0012] Furthermore, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence. This includes: calculating the phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval, signal sampling interval, carrier frequency deviation of the target signal, and imaginary unit information corresponding to each normalized value sequence; when the carrier frequency deviation is known, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information to obtain a fused result sequence; when the carrier frequency deviation is unknown, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence.

[0013] To achieve the above objectives, according to another aspect of this application, a synchronization detection device for a time-division duplex repeater is provided. The device includes: a first calculation unit, configured to receive a target signal transmitted by the time-division duplex repeater, and perform multi-branch differential correlation calculations on a local reference signal and the target signal based on multiple differential intervals to obtain multiple differential correlation results, wherein the multiple differential intervals are calculated based on multiple constraints and the cumulative phase offset information of the differential product; a processing unit, configured to normalize each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences; a fusion unit, configured to determine the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, and fuse the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence; and a first determination unit, configured to determine the peak value in the fused result sequence and determine the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fused result sequence.

[0014] Furthermore, the multiple constraints include at least a first constraint and a second constraint. The aforementioned apparatus further includes: a second calculation unit, used to calculate the cumulative phase offset information according to the first constraint before performing multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the unsolved variable corresponding to the differential interval, constants, and signal sampling interval, and the first constraint is used to limit the numerical range of the cumulative phase offset information; a third calculation unit, used to calculate the first numerical range according to the second constraint and a preset maximum frequency offset threshold, to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficient, and the target coefficient is used to improve the accuracy of the signal after the occurrence of Doppler drift; and a second determination unit, used to determine multiple differential intervals based on the integers contained in the second numerical range.

[0015] Further, the second determining unit includes: a first determining subunit, used to determine the device information of the data processing device, wherein the data processing device includes at least: a field-programmable gate array (FPGA) logic unit and a digital signal processor (DSP) unit; a first calculating subunit, used to perform at least one simulation experiment based on the device information, signals received within a historical time period, and a preset number of differential branches, and calculate an evaluation index of the simulation experiment results; an adjusting subunit, used to adjust the preset number of differential branches according to the evaluation index to obtain a target number of differential branches; and a filtering subunit, used to filter among integers contained in a second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0016] Further, the processing unit includes: a second calculation subunit, used to calculate the amplitude of each differential correlation result among multiple differential correlation results; a third calculation subunit, used to perform delay processing on the local reference signal based on each differential interval among multiple differential intervals to obtain multiple delayed reference signals; to perform dot product calculation on the complex conjugate value of the local reference signal with the multiple delayed reference signals respectively, and to sum the calculation results to obtain a first intermediate variable; and a fourth calculation subunit, used to calculate the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain multiple normalized value sequences.

[0017] Furthermore, the aforementioned apparatus further includes: a third determining unit, configured to determine the signal peak value corresponding to each normalized value sequence in the plurality of normalized value sequences before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the plurality of normalized value sequences; a fourth determining unit, configured to determine the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; a fourth calculation unit, configured to calculate the signal values ​​in each normalized value sequence excluding the signal peak value, and sum the calculation results to obtain the second intermediate variable corresponding to each normalized value sequence; and a fifth determining unit, configured to determine the signal-to-noise ratio information of the plurality of normalized value sequences based on the signal peak value corresponding to each normalized value sequence, the number of effective data points of each normalized value sequence, and the second intermediate variable corresponding to each normalized value sequence.

[0018] Further, the fusion unit includes: a fifth calculation subunit, used to calculate the signal-to-noise ratio information of each normalized value sequence in multiple normalized value sequences based on an exponential function, to obtain a third intermediate variable corresponding to each normalized value sequence; a sixth calculation subunit, used to sum the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; a second determination subunit, used to determine the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables corresponding to each normalized value sequence, and to determine the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and a fusion subunit, used to perform weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fusion result sequence.

[0019] Furthermore, the fusion subunit includes: a calculation module for calculating phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval corresponding to each normalized value sequence, the signal sampling interval, the carrier frequency deviation of the target signal, and imaginary unit information; a first fusion module for weighted fusion of multiple normalized value sequences based on the weight information and phase compensation information corresponding to each differential correlation result when the carrier frequency deviation is known, to obtain a fusion result sequence; and a second fusion module for weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result when the carrier frequency deviation is unknown, to obtain a fusion result sequence.

[0020] To achieve the above objectives, according to one aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the above-described synchronization detection methods for time-division duplex repeaters, and when executed by a processor, implements the steps of the synchronization detection methods for time-division duplex repeaters in various embodiments of this application.

[0021] To achieve the above objectives, according to one aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including stored computer instructions, wherein, when the computer instructions are executed by a processor, any one of the above-described synchronization detection methods for a time-division duplex repeater is implemented.

[0022] To achieve the above objectives, according to one aspect of this application, an electronic device is provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described synchronization detection methods for a time-division duplex repeater.

[0023] This application employs the following steps: receiving a target signal transmitted by a time-division duplex repeater; performing multi-branch differential correlation calculations on the local reference signal and the target signal based on multiple differential intervals to obtain multiple differential correlation results, wherein the multiple differential intervals are calculated based on multiple constraints and the cumulative phase offset information of the differential product; normalizing each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences; determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, and fusing the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence; determining the peak value in the fused result sequence, and determining the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fused result sequence. This solves the problem in related technologies where, when a user terminal receives a signal transmitted by a time-division duplex repeater in a high-speed mobile scenario, the phase offset caused by the carrier frequency offset leads to a large synchronization deviation.

[0024] By performing multi-branch differential correlation calculations on the local reference signal and the received target signal based on multiple preset differential intervals, multiple differential correlation results reflecting signal correlation can be obtained. This ensures that the cumulative phase shift of the differential product does not lead to phase flipping, enabling accurate signal analysis and achieving the goal of avoiding synchronization deviation expansion, thus further improving the technical effect of synchronization detection accuracy. Next, by normalizing each differential correlation result, the outputs of different branches can be standardized, eliminating the impact of received power differences on synchronization detection. Then, by determining the weights of each normalized sequence based on their signal-to-noise ratio (SNR) information, and fusing the differential correlation results accordingly, the advantages of each branch can be combined, achieving the technical effect of enhancing the robustness and SNR tolerance of the synchronization algorithm. This improves synchronization performance in multiple scenarios and further ensures communication continuity and time slot alignment accuracy in high-speed mobile scenarios. Attached Figure Description

[0025] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0026] Figure 1 This is a flowchart of a synchronization detection method for a time-division duplex repeater provided according to Embodiment 1 of this application;

[0027] Figure 2 This is a flowchart illustrating an optional detection signal synchronization point for a time-division duplex repeater, provided according to Embodiment 1 of this application.

[0028] Figure 3This is a schematic diagram of a synchronization detection device for a time-division duplex repeater provided according to Embodiment 2 of this application;

[0029] Figure 4 This is a schematic diagram of a synchronization detection electronic device for a time-division duplex repeater provided according to Embodiment 5 of this application. Detailed Implementation

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] It should be noted that the user information (including but not limited to user device information, user personal information, collected data, used data, generated data, processed data, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, collected information, used information, generated information, processed information, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse. For example, this system has interfaces with relevant users or organizations. Before obtaining relevant information, it needs to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information only after receiving consent from the aforementioned user or organization.

[0032] It should be noted that this application provides users with a corresponding entry point for choosing to agree to or reject the automated decision-making results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0033] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Example 1

[0036] The present invention will now be described in conjunction with preferred implementation steps. Figure 1 This is a flowchart of a synchronization detection method for a time-division duplex repeater according to Embodiment 1 of this application, as shown below. Figure 1 As shown, the method includes the following steps:

[0037] Step S101: Receive the target signal sent by the time-division duplex repeater, perform multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, and obtain multiple differential correlation results. The multiple differential intervals are calculated based on the cumulative phase offset information of multiple constraints and differential products.

[0038] The execution entity in this embodiment can be a signal synchronization detection system for a time-division duplex repeater. Its core responsibility is to receive the target signal from the time-division duplex repeater and achieve accurate time slot synchronization. The system implements a multi-branch fusion differential correlation algorithm, performing sliding correlation calculations on the received signal and a preset signal sequence. Multiple differential branches are formed using multiple differential intervals, thereby stably and accurately detecting synchronization points in complex environments, ensuring communication continuity and time slot alignment accuracy between the repeater and the network. Especially in high-speed movement and low signal-to-noise ratio scenarios, the system significantly improves the robustness and efficiency of synchronization detection by weighted fusion of the branch results.

[0039] In this first embodiment, multiple differential intervals are first determined. This decision is based on consideration of carrier frequency offset, ensuring that the cumulative phase offset of the differential product between each branch remains within a safe range, avoiding phase flipping. Subsequently, using these multiple differential intervals, multi-branch differential correlation calculations are performed on the local reference signal and the target signal. Each differential interval corresponds to one differential branch, essentially processing different delayed versions of the target signal in parallel, calculating the phase difference with the reference signal, and evaluating correlation. This yields multiple differential correlation results corresponding to multiple differential branches, each result corresponding to one differential interval, reflecting the similarity of the signals under a specific delay. Through these steps, multi-angle evaluation of signal synchronization is achieved, enhancing the algorithm's adaptability and robustness in complex environments, ensuring accurate detection of synchronization points even in low signal-to-noise ratio and nonlinear Doppler scenarios.

[0040] For example, suppose the transmitted pilot sequence of known length N (i.e., the local reference signal mentioned above) is as follows: Where N is a positive integer. Due to the introduction of frequency offset, multipath delay, and Gaussian white noise, the received signal can be expressed as: ,in, For channel complex gain (which can be simplified to a constant); Carrier frequency offset (in Hz) refers to the difference between the frequencies of the transmitted and received signals; The channel delay value to be estimated (i.e., the synchronization point); The sampling interval for the time-division duplex repeater signal; It is additive white Gaussian noise; Indicates due to frequency offset The resulting change in signal phase with time n It is the imaginary unit, used to represent phase rotation.

[0041] In the process of performing sliding correlation on the target signal based on M differential intervals, the process of performing sliding correlation calculation on the differential branch corresponding to the i-th differential interval can be shown in Equation 1.

[0042] (1)

[0043] in, Let be the difference interval of the i-th difference branch. This represents the output of the i-th difference branch at time index variable k. This represents the signal value received at the time index variable n of the target signal. This represents the signal value after applying sliding correlation processing to the target signal based on a time window k. This represents the differential delay interval of the target signal after sliding correlation processing. The complex conjugate value is then used to eliminate the influence of frequency offset. It is a known reference sequence (usually a pilot sequence, i.e., the local reference signal mentioned above) in a time-delay differential interval. The signal sample value after that, Represents pilot sequence The complex conjugate value is used to extract the amplitude and phase information of the signal. When a frequency offset exists, the phase of the target signal will change linearly relative to the transmitted signal, through... The calculation pair Differential processing can cancel out linear phase changes because the phase difference between adjacent samples will tend to be constant, thus not affecting the final product result. This is achieved through differential intervals. ,Signal With pilot sequence The phase difference and inner product calculation aims to remove the influence of frequency offset and find the synchronization point of the signal on multiple differential branches to improve the accuracy and robustness of synchronization detection.

[0044] Step S102: Normalize each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences.

[0045] In this first embodiment, each differential correlation result is normalized to obtain multiple normalized value sequences. The normalization operation calculates the energy of each branch signal and adjusts it to a uniform scale, aiming to eliminate the influence of signal strength differences on the correlation calculation results. This ensures the comparability of each branch signal during fusion, thereby improving the consistency and accuracy of synchronous detection. Through this step, the system can reduce the impact of signal strength fluctuations on the final fusion result, enabling the synchronous detection algorithm to remain stable and efficient even in complex scenarios such as low signal-to-noise ratio, multipath fading, and high Doppler shift.

[0046] Step S103: Determine the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, and fuse multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence.

[0047] In this first embodiment, after obtaining multiple normalized value sequences, the synchronization detection system further performs weighted fusion on these sequences based on the signal-to-noise ratio (SNR) information of each signal. SNR, as a key indicator of signal quality, reflects the ratio of effective information to noise in a signal. The system calculates the SNR of each normalized value sequence and assigns a corresponding fusion weight. Subsequently, according to their respective weights, the system combines multiple normalized value sequences to generate a fused result sequence. This fusion process aims to concentrate the information of high-quality signals while suppressing the negative impact of signals with low SNR, ensuring that the final fused result sequence can more accurately reflect the characteristics of the target signal and improve the accuracy and reliability of synchronization detection. Through the SNR-based weighted fusion strategy, the adaptability and robustness of the synchronization detection algorithm in complex environments are effectively enhanced.

[0048] For example, suppose multiple normalized value sequences are , The weight information corresponding to each difference correlation result is represented as follows: The fusion process of each difference correlation result can be represented as: , This represents the sequence of fusion results described above.

[0049] Step S104: Determine the peak value in the fusion result sequence, and determine the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fusion result sequence.

[0050] In this first embodiment, after weighted fusion of multiple normalized value sequences, a fusion result sequence is formed. This fusion result sequence integrates the correlation information of signals under different differential intervals, and its signal peak value can indicate the synchronization position of the target signal. Specifically, the system scans the fusion result sequence to find the point where the amplitude or energy reaches its maximum value. This peak point corresponds to the optimal matching position between the target signal and the local reference signal, i.e., the synchronization point. By determining the position of the signal peak value in the fusion result sequence, the accurate synchronization time of the target signal can be determined, achieving time slot alignment. This process relies on the accuracy of the previous signal processing, including differential, normalization, and weighted fusion, to ensure the robustness and reliability of the synchronization point determination, thereby ensuring stable and accurate communication synchronization between the TDD repeater and the network in high-speed mobile scenarios.

[0051] Specifically, the process of determining the peak value in the fusion result sequence and determining the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fusion result sequence can be expressed as:

[0052] ,in, This represents the parameter used to find the maximum value, where k is the time index variable. This indicates the value of the fused signal corresponding to the time index variable k. This represents the amplitude of the signal corresponding to the time index variable k. This represents the estimated value of the synchronization offset, i.e., the synchronization point of the signal. (Through...) Calculation, Determination The time index variable k corresponding to the maximum value is used as the estimated value of the signal synchronization point, i.e. .

[0053] In summary, the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application receives the target signal transmitted by the time-division duplex repeater, performs multi-branch differential correlation calculations on the local reference signal and the target signal based on multiple differential intervals, and obtains multiple differential correlation results. The multiple differential intervals are calculated based on the cumulative phase offset information of multiple constraints and differential products. Each differential correlation result is normalized to obtain multiple normalized value sequences. The weight information corresponding to each differential correlation result is determined based on the signal-to-noise ratio information of the multiple normalized value sequences, and the multiple differential correlation results are fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence. The peak value in the fused result sequence is determined, and the synchronization point between the target signal and the local reference signal is determined based on the offset information of the peak value in the fused result sequence. This solves the problem in related technologies where, in high-speed mobile scenarios, the phase offset caused by carrier frequency offset leads to large synchronization deviations when a user terminal receives a signal transmitted by a time-division duplex repeater.

[0054] By performing multi-branch differential correlation calculations on the local reference signal and the received target signal based on multiple preset differential intervals, multiple differential correlation results reflecting signal correlation can be obtained. This ensures that the cumulative phase shift of the differential product does not lead to phase flipping, enabling accurate signal analysis and achieving the goal of avoiding synchronization deviation expansion, thus further improving the technical effect of synchronization detection accuracy. Next, by normalizing each differential correlation result, the outputs of different branches can be standardized, eliminating the impact of received power differences on synchronization detection. Then, by determining the weights of each normalized sequence based on their signal-to-noise ratio (SNR) information, and fusing the differential correlation results accordingly, the advantages of each branch can be combined, achieving the technical effect of enhancing the robustness and SNR tolerance of the synchronization algorithm. This improves synchronization performance in multiple scenarios and further ensures communication continuity and time slot alignment accuracy in high-speed mobile scenarios.

[0055] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, the multiple constraints include at least: a first constraint and a second constraint. Before performing multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, the method further includes: calculating the cumulative phase offset information based on the first constraint to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the variable to be solved corresponding to the differential interval, constants, and signal sampling interval, and the first constraint is used to limit the numerical range of the cumulative phase offset information; calculating the first numerical range based on the second constraint and a preset maximum frequency offset threshold to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficient, and the target coefficient is used to improve the accuracy of the signal after the occurrence of Doppler drift; and determining multiple differential intervals based on the integers contained in the second numerical range.

[0056] In this first embodiment, to ensure the synchronization detection accuracy of the time-division duplex repeater in high-speed mobile scenarios, a phase continuity constraint, namely the first constraint condition mentioned above, is first set based on signal characteristics and system requirements. Based on this constraint, the cumulative phase offset information of the differential product is quantitatively evaluated, incorporating differential interval parameters, system constants, and signal sampling intervals in the calculation process. By comprehensively considering these variables, a first numerical range for the differential interval selection can be defined, ensuring that phase flipping does not occur during signal processing and maintaining the validity and continuity of the phase information.

[0057] Then, within the defined first numerical range, considering the extreme frequency offset scenarios that may occur in high-speed mobile environments, a second constraint is introduced: the numerical range of the target coefficient. This target coefficient is mainly used to limit the selection of the differential interval when Doppler drift occurs, i.e., when the carrier frequency offset exceeds a preset maximum threshold, aiming to balance the consumption between synchronization performance and processing resources. By combining this target coefficient with the maximum frequency offset threshold, the first numerical range is recalculated, thereby determining the second numerical range of the differential interval. This range better matches the dynamic characteristics of the channel in high-speed mobile scenarios.

[0058] Finally, based on the obtained second numerical range, the specific integer values ​​that can be achieved within it are identified, thereby determining multiple specific differential intervals. The selection of these differential intervals fully considers the efficiency and robustness of signal processing, ensuring that the synchronization algorithm can cope with different frequency offsets. Even in scenarios with drastic frequency offset changes, the optimal synchronization timing can be found through differential correlation operations of multiple branches.

[0059] For example, the difference interval is The sliding correlation of the i-th differential correlation branch can be expressed as shown in Equation 1, where the cumulative phase shift information of the differential product can be represented as: ,in, This is the cumulative phase offset, representing the angle by which the signal phase accumulates its rotation over a given time interval due to carrier frequency offset. Carrier frequency offset is a frequency difference measured in Hertz (Hz). It is the difference interval, that is, the sequence interval used for difference correlation calculation. This indicates the sampling interval of the time-division duplex repeater, reflecting the speed and frequency of signal acquisition.

[0060] To avoid phase flipping, a first constraint condition needs to be set, which can be expressed as: Therefore, the maximum difference interval needs to satisfy: To account for Doppler drift, a second constraint is needed, namely, adding a safety factor. and set The range of values ​​for can be expressed as: Given that the maximum frequency offset is In the case of , the maximum difference interval is represented as: Therefore, multiple difference intervals The conditions that need to be met can be expressed as: The choice of the number of differential branches also needs to be made based on the resource situation in actual production activities, and a trade-off needs to be made between performance and resources.

[0061] Through the above steps, not only is the synchronization deviation problem caused by carrier frequency offset effectively overcome, but also the algorithm's adaptability and synchronization success rate in complex frequency offset environments are improved by reasonably configuring the differential interval, thereby ensuring the continuity of the communication system and the accuracy of time slot alignment. In particular, it has shown excellent stability under low signal-to-noise ratio and large frequency offset conditions, reducing the probability of communication interruption and improving the maintenance efficiency and economic efficiency of the equipment.

[0062] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, determining multiple differential intervals based on integers included in the second numerical range includes: determining the device information of a data processing device, wherein the data processing device includes at least: a field-programmable gate array (FPGA) logic unit and a digital signal processor (DSP) unit; performing at least one simulation experiment based on the device information, signals received within a historical time period, and a preset number of differential branches, and calculating an evaluation index of the simulation experiment results; adjusting the preset number of differential branches based on the evaluation index to obtain a target number of differential branches; and filtering from integers included in the second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0063] In this first embodiment, to optimize the synchronization detection performance of the time-division duplex (TDD) repeater, especially to enhance its adaptability in high-speed mobile and complex channel environments, it is first necessary to obtain the device information of the data processing equipment. The data processing equipment, as the physical platform for the synchronization algorithm, directly affects the algorithm's execution efficiency and reliability. Data processing equipment includes, but is not limited to, field-programmable gate array (FPGA) logic units and digital signal processor (DSP) units. The device information involved may include the number of data processing devices.

[0064] Then, based on the collected device information, combined with historical data and a preset number of differential branches, at least one simulation experiment is conducted. The historical data covers signal samples received under different scenarios, such as static, low-speed, high-speed movement, and multipath environments, providing rich test material for the simulation experiment. The preset number of differential branches represents the initial synchronization detection algorithm structure considered based on the device information. The purpose of the simulation experiment is to test the performance of the synchronization algorithm under different numbers of differential branches under various channel conditions, including key evaluation indicators such as synchronization success rate, synchronization deviation, and resource utilization, and to quantitatively analyze the experimental results.

[0065] Secondly, based on the evaluation indicators calculated from the simulation results, the preset number of differential branches is adjusted. For example, when the synchronization success rate is greater than a first preset threshold and the synchronization deviation is greater than a second preset threshold, the optimal number of differential branches used in the simulation experiment corresponding to the lowest resource utilization rate is determined from the simulation results, and this optimal number of differential branches is determined as the aforementioned target number of differential branches.

[0066] Finally, based on the determined target number of difference branches, a random selection method can be used to select from the set of integers included in the previously calculated second numerical range to determine the final multiple difference intervals.

[0067] Through the above steps, the technical effect of significantly improving the synchronous detection performance of TDD repeaters is achieved while ensuring the effective utilization of data processing equipment resources.

[0068] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, each differential correlation result in the plurality of differential correlation results is normalized to obtain a plurality of normalized value sequences, including: calculating the amplitude of each differential correlation result in the plurality of differential correlation results; performing delay processing on the local reference signal based on each differential interval in the plurality of differential intervals to obtain a plurality of delayed reference signals; performing dot product calculation on the complex conjugate value of the local reference signal with the plurality of delayed reference signals respectively, and summing the calculation results to obtain a first intermediate variable; calculating the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain a plurality of normalized value sequences.

[0069] In this first embodiment, to refine the synchronous detection process and ensure the consistency and accuracy of signal processing, the amplitude of each differential correlation result is first calculated. This operation aims to obtain the amplitude characteristics of the signal, remove the phase information of the signal, and facilitate the quantification and comparison of signal energy in subsequent steps. Specifically, the amplitude of each differential correlation result can be expressed as: ,in, This represents the signal value of the i-th differential branch at the time index variable k, which is the output of the i-th differential branch at the time index variable k in Formula 1 above. .

[0070] Next, a delayed version of the local reference signal is obtained. This delayed version is based on the differential interval corresponding to each differential correlation result; that is, the original local reference signal is shifted along the time axis by a shift length equal to the differential interval to generate the delayed local reference signal. A dot product operation is then performed on the complex conjugate values ​​of the delayed and undelayed local reference signals. This involves performing a complex multiplication on each corresponding element of the two sequences. This process essentially performs differential processing on the signal sequences to eliminate the influence of frequency offset. Each term of the dot product result is summed to obtain the first intermediate variable, which characterizes the correlation strength between the delayed signal and the reference signal.

[0071] Specifically, the calculation process of the first intermediate variable can be expressed as follows: ,in, This represents the difference interval corresponding to a certain difference correlation result. This represents the complex conjugate value of the local reference signal (i.e., the pilot sequence). This represents the local reference signal after the time delay differential interval.

[0072] Finally, based on the amplitude sequence of each differential correlation result, combined with the first intermediate variable generated above, we perform a fine normalization process to obtain a normalized value sequence corresponding to each differential correlation result. The normalization process aims to eliminate the interference caused by signal strength fluctuations in correlation discrimination, ensuring that comparisons between signals are based on a consistent signal energy level. Specifically, the processing logic involves dividing the amplitude sequence of each differential correlation result by its corresponding first intermediate variable. This operation essentially compares the instantaneous energy of the signal with the average energy of the entire signal segment, thereby generating a normalized value sequence that reflects the signal correlation strength and is unaffected by signal strength fluctuations.

[0073] Specifically, the calculation process of the normalized result of the i-th difference branch can be expressed as follows: ,in, This represents the magnitude of the i-th difference correlation result. This represents the first intermediate variable mentioned above.

[0074] Through the above steps, the technical effect of improving the accuracy and robustness of signal correlation discrimination under complex channel conditions is achieved. Specifically, the generation of the normalized value sequence not only eliminates the influence of random fluctuations in signal strength but also establishes comparability between signal strength fluctuations and channel variations. This enables the synchronization detection system to more accurately identify synchronization heads in the target signal, especially in environments with low signal-to-noise ratios or variable channels, thereby improving the robustness and efficiency of synchronization detection.

[0075] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, the method further includes: determining the signal power of the signal peak corresponding to each normalized value sequence in the multiple normalized value sequences; determining the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; calculating the signal values ​​other than the signal peak in each normalized value sequence and summing the calculation results to obtain a second intermediate variable corresponding to each normalized value sequence; and determining the signal-to-noise ratio information of multiple normalized value sequences based on the signal power of the signal peak corresponding to each normalized value sequence and the second intermediate variable corresponding to each normalized value sequence.

[0076] In this first embodiment, to quantitatively evaluate the proportional relationship between noise and signal components during signal processing and thus optimize the subsequent fusion strategy, the signal peak value of each normalized value sequence is first determined, and the signal power at the peak point is calculated based on the signal peak value. For example, the calculation process of the signal power of the signal peak value corresponding to each normalized value sequence can be expressed as follows: ,in, Represents the sequence of the i-th normalized values. Represents a sequence of normalized values The index of the maximum value. Indicates in The amplitude at the location, Indicates position The signal power.

[0077] Then, based on the number of sampling points of the target signal and the differential interval corresponding to each branch signal, the number of effective data points for each normalized value sequence is accurately calculated. Determining the number of effective data points is to eliminate interference from invalid data points introduced by the differential interval during signal processing, ensuring the accuracy of the signal-to-noise ratio (SNR) calculation. This calculation process, based on the length of the signal sequence and the differential interval, removes signal endpoints that cannot participate in the effective calculation, thereby obtaining the true range of signal data participating in the SNR calculation and providing an accurate signal sample base for SNR evaluation.

[0078] Secondly, for each normalized value sequence, statistical calculations are performed on all signal values ​​except for the signal peak, and the statistical results are summed to obtain the second intermediate variable. The second intermediate variable essentially reflects the total energy of the non-peak portion of the normalized value sequence, i.e., the cumulative value of noise and background energy. This calculation quantifies the noise energy by traversing the normalized value sequence and accumulating the squares of all signal points except for the peak. For example, the calculation process of the second intermediate variable can be represented as follows: ,in, This indicates the number of valid data points when calculating noise power. This indicates that it will be from k=0 to Within the range, except for the position where k=peakInd, for all The squared values ​​are accumulated to calculate the total energy of the non-peak portion of the i-th normalized value sequence.

[0079] Finally, based on the signal power of the signal peak and the second intermediate variable of each normalized value sequence obtained above, the signal-to-noise ratio (SNR) information of multiple normalized value sequences is accurately calculated. This SNR is the ratio between signal and noise energy, directly reflecting the signal quality performance in a noisy environment. Specifically, the calculation process for the SNR information of the i-th normalized value sequence can be expressed as follows: ,in, This represents the signal power corresponding to the peak value of the i-th normalized value sequence. It represents the total energy of the non-peak portion in the i-th normalized value sequence.

[0080] Through the above steps, the technical effect of quantitatively evaluating signal quality and guiding signal fusion strategies is achieved. Specifically, the signal evaluation mechanism based on signal-to-noise ratio can effectively identify useful signal components in the signal sequence and quantify noise interference. This not only helps improve the accuracy of signal fusion but also ensures that the synchronization detection results in complex environments are more accurate. It is a key link in achieving precise synchronization between TDD repeaters and base stations in high-speed mobile scenarios.

[0081] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, the weight information corresponding to each differential correlation result is determined based on the signal-to-noise ratio information of multiple normalized value sequences, and the multiple differential correlation results are fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence. This includes: calculating the signal-to-noise ratio information of each normalized value sequence based on an exponential function to obtain a third intermediate variable corresponding to each normalized value sequence; summing the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; determining the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables, and determining the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and weightedly fusing the multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence.

[0082] In this first embodiment, to construct an effective weight allocation mechanism to improve the accuracy and robustness of signal fusion, a mathematical model is first used to transform the signal-to-noise ratio (SNR) information of the normalized value sequence, aiming to obtain a numerical form more suitable as fusion weights. Based on the effect of the exponential function, the SNR information of each normalized value sequence is mapped to a new numerical space, generating the so-called third intermediate variable. The third intermediate variable corresponding to the i-th normalized value sequence can be expressed as: , This represents the signal-to-noise ratio (SNR) information of the i-th normalized value sequence. This transformation can not only amplify signal information with higher SNR, but also moderately suppress signal information with lower SNR, thereby presenting the differences between signals in a more intuitive form, which is convenient for subsequent weight allocation.

[0083] Then, the third intermediate variable generated from all normalized value sequences is summed to obtain the fourth intermediate variable. The fourth intermediate variable can be represented as: , represents the signal-to-noise ratio information of the i-th normalized value sequence, and M represents the number of difference branches corresponding to multiple difference intervals.

[0084] Secondly, the weight information corresponding to each normalized value sequence is calculated based on the third and fourth intermediate variables of each normalized value sequence, and the weight information corresponding to each difference correlation result is determined based on the correspondence between each normalized value sequence and each difference correlation result. The weight information corresponding to the i-th normalized value sequence can be expressed as: , Represents the third intermediate variable in the sequence of the i-th normalized values. This represents the fourth intermediate variable. The core of calculating the weight information lies in balancing the relationship between signal quality and signal diversity, ensuring that high-quality signals receive higher weights in the fusion result sequence, while signal diversity is not ignored, thereby achieving the optimal balance between signal quality and system stability in the fusion strategy.

[0085] Finally, based on the weight information corresponding to each differential correlation result, a weighted fusion operation is performed in conjunction with the phase compensation information of the target signal to generate the final fusion result sequence. This fusion process first uses phase compensation information to perform phase calibration on each normalized value sequence to ensure phase consistency between signals. Then, according to the weight information of each signal, the calibrated signals are weighted and superimposed. By adjusting the signal contribution through weights, effective concentration of signal energy and suppression of noise are achieved.

[0086] Through the above steps, the technical effect of optimizing the signal fusion process in complex channel environments is achieved. Specifically, the weight allocation mechanism based on signal-to-noise ratio information can identify and enhance the effective components in the signal while reducing the impact of noise, thus ensuring the quality of the fused signal. This fusion strategy not only improves the synchronization detection accuracy of TDD repeaters in high-speed mobile scenarios but also enhances the system's adaptability to changing channel conditions and interference environments.

[0087] Optionally, in the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence. This includes: calculating the phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval, signal sampling interval, carrier frequency deviation of the target signal, and imaginary unit information corresponding to each normalized value sequence; when the carrier frequency deviation is known, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information to obtain a fused result sequence; when the carrier frequency deviation is unknown, multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence.

[0088] In this first embodiment, to achieve accurate synchronization of a TDD repeater in high-speed mobile or complex channel environments, it is first necessary to calculate the phase compensation information of the target signal based on a series of key parameters. This calculation essentially compensates for the carrier frequency deviation, thereby eliminating signal phase distortion caused by frequency offset and improving phase consistency between signals. The parameters include the differential interval corresponding to each normalized value sequence, the signal sampling interval, the carrier frequency deviation of the target signal, and imaginary unit information, where the carrier frequency deviation is estimated in advance using other auxiliary methods. The specific process of calculating the phase compensation information involves mathematical modeling of the above parameters. Through formula derivation and calculation, a value capable of correcting the signal phase is obtained, providing a phase calibration basis for subsequent signal fusion. For example, the phase compensation information can be expressed as: ,in, It is a carrier frequency deviation. It is the difference interval of the i-th difference branch. It is the sampling interval. It is the imaginary unit, used to represent the phase of a sine wave.

[0089] Then, with the carrier frequency deviation known, weighted coherent fusion is performed based on the weight information and phase compensation information corresponding to each normalized value sequence. The goal of weighted coherent fusion is to maximize the signal-to-noise ratio while ensuring phase consistency between signals. In this fusion process, each normalized value sequence is assigned a specific weight, which reflects the signal quality and channel state, while the phase compensation information ensures that all signals are in the same phase state before fusion, avoiding signal energy loss caused by phase mismatch. Weighted coherent fusion achieves signal energy concentration and noise reduction through the complex product of weighted signals, generating a phase-aligned fusion result sequence with higher signal-to-noise ratio and stronger robustness.

[0090] For example, when the carrier frequency deviation is known, the calculation process of weighted coherent fusion based on the weight information and phase compensation information corresponding to each normalized value sequence can be expressed as follows: ,in, This represents the weight information corresponding to the i-th normalized value sequence. This indicates the phase compensation information mentioned above. This represents the sequence of fusion results.

[0091] Secondly, in scenarios where the carrier frequency deviation is unknown, we perform weighted incoherent fusion based solely on the weight information corresponding to each normalized value sequence. Weighted incoherent fusion focuses on the weighted summation of signal strengths, rather than phase consistency calibration, making it suitable for situations where precise frequency deviation information is difficult to obtain. This process also multiplies each signal by its corresponding weight, but directly superimposes the signal amplitudes to merge signal energy. While the output signal of weighted incoherent fusion may be slightly coarse due to the lack of phase compensation, it can still effectively concentrate signal energy through weight adjustment in complex environments with unknown frequency deviations, providing a relatively robust base signal for synchronous detection.

[0092] For example, when the carrier frequency deviation is unknown, the calculation process of weighted coherent fusion based on the weight information corresponding to each normalized value sequence can be expressed as follows: ,in, This represents the weight information corresponding to the i-th normalized value sequence. This represents the sequence of fusion results.

[0093] Through the above steps, the technical effect of improving the accuracy and stability of signal fusion in complex environments is achieved. Specifically, the calculation of phase compensation information ensures the phase consistency between signals, while the signal-to-noise ratio-based weighting mechanism improves the overall quality of the fusion result sequence by weighting the signal energy. Whether coherent or incoherent fusion, it can effectively identify the synchronization head, even under harsh conditions such as low signal-to-noise ratio or large frequency offset. This synchronization strategy significantly enhances the synchronization performance of TDD repeaters, reduces the risk of communication interruption, and ensures communication continuity in high-speed scenarios.

[0094] Optionally, in this first embodiment, Figure 2 This is a flowchart illustrating an optional detection signal synchronization point for a time-division duplex repeater, provided according to Embodiment 1 of this application. Figure 2 As shown, firstly, based on the signal's frequency offset characteristics, signal-to-noise ratio, and repeater hardware resources (such as the processing capabilities of FPGA and DSP), the interval parameters of multiple differential correlation branches are determined. This step ensures that the synchronization algorithm can adapt to high-speed movement and complex channel environments. By selecting appropriate differential intervals, it can capture potential synchronization information under different time delays or frequency offsets. Then, the sliding correlation results of multiple differential correlation branches are calculated. This process involves performing mathematical operations on the received signal and a pre-stored local reference signal to detect the appearance of synchronization headers in the signal. By calculating correlation peaks in parallel across multiple differential intervals, the algorithm's ability to combat multipath effects and frequency offsets can be effectively improved, providing preliminary synchronization clues for subsequent signal processing.

[0095] Secondly, the differential correlation results of multiple differential branches are normalized to eliminate the impact of signal strength fluctuations on synchronization detection. By calculating and adjusting the signal amplitude, the differential correlation results of different differential branches are ensured to be comparable, thereby more accurately identifying the peak characteristics of the synchronization signal. Further calculation of the signal-to-noise ratio (SNR) information of the differential branches allows for a more objective assessment of signal quality, providing a quantified weight reference for subsequent weighted fusion and ensuring the algorithm's adaptability and synchronization accuracy in low SNR environments. Based on the SNR of each differential branch, a soft-max strategy is used for weight allocation, ensuring that high-quality signal branches contribute more to the fusion process, while the weights of low-quality branches are appropriately reduced, thus improving the overall synchronization performance and robustness of the system.

[0096] Finally, based on the weight information corresponding to each differential correlation result, the differential correlation results of all branches are weighted and fused to generate a fused result sequence. The maximum correlation peak in the fused result sequence is identified, synchronization point determination is performed, the frame boundary of the signal is determined, and high-precision synchronization is achieved.

[0097] The flowchart clearly illustrates the entire process of the multi-branch fusion differential correlation synchronization detection method from signal preprocessing to final synchronization decision through the progressive steps described above. Each step aims to improve the robustness and accuracy of synchronization detection, ultimately achieving stable and high-precision synchronization in complex environments and high-speed moving scenarios.

[0098] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0099] Example 2

[0100] Embodiment 2 of this application also provides a synchronization detection device for a time-division duplex repeater. It should be noted that the synchronization detection device for a time-division duplex repeater in Embodiment 2 of this application can be used to execute the synchronization detection method for a time-division duplex repeater provided in Embodiment 1 of this application. The synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application will be described below.

[0101] Figure 3 This is a schematic diagram of a synchronization detection device for a time-division duplex repeater provided according to Embodiment 2 of this application. Figure 3 As shown, the device includes: a first computing unit 301, a processing unit 302, a fusion unit 303, and a first determining unit 304.

[0102] Specifically, the first calculation unit 301 is used to receive the target signal sent by the time division duplex repeater, perform multi-branch differential correlation calculation on the local reference signal and the target signal according to multiple differential intervals, and obtain multiple differential correlation results. The multiple differential intervals are calculated based on the cumulative phase offset information of multiple constraints and differential products.

[0103] The processing unit 302 is used to normalize each of the multiple differential correlation results to obtain multiple normalized value sequences.

[0104] The fusion unit 303 is used to determine the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, and to fuse multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fusion result sequence.

[0105] The first determining unit 304 is used to determine the peak value in the fusion result sequence and determine the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fusion result sequence.

[0106] The synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application receives the target signal sent by the time-division duplex repeater through a first calculation unit 301, performs multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, and obtains multiple differential correlation results. The multiple differential intervals are calculated based on the cumulative phase offset information of multiple constraints and differential products. A processing unit 302 normalizes each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences. A fusion unit 303 determines the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, and fuses the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fusion result sequence. A first determination unit 304 determines the peak value in the fusion result sequence and determines the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fusion result sequence. This solves the problem in related technologies where, in high-speed mobile scenarios, the phase offset caused by carrier frequency offset leads to large synchronization deviations when a user terminal receives a signal sent by a time-division duplex repeater.

[0107] By performing multi-branch differential correlation calculations on the local reference signal and the received target signal based on multiple preset differential intervals, multiple differential correlation results reflecting signal correlation can be obtained. This ensures that the cumulative phase shift of the differential product does not lead to phase flipping, enabling accurate signal analysis and achieving the goal of avoiding synchronization deviation expansion, thus further improving the technical effect of synchronization detection accuracy. Next, by normalizing each differential correlation result, the outputs of different branches can be standardized, eliminating the impact of received power differences on synchronization detection. Then, by determining the weights of each normalized sequence based on their signal-to-noise ratio (SNR) information, and fusing the differential correlation results accordingly, the advantages of each branch can be combined, achieving the technical effect of enhancing the robustness and SNR tolerance of the synchronization algorithm. This improves synchronization performance in multiple scenarios and further ensures communication continuity and time slot alignment accuracy in high-speed mobile scenarios.

[0108] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the aforementioned multiple constraints include at least: a first constraint and a second constraint. The device further includes: a second calculation unit, used to calculate the cumulative phase offset information based on the first constraint before performing multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the variable to be solved corresponding to the differential interval, constants, and signal sampling interval, and the first constraint is used to limit the numerical range of the cumulative phase offset information; a third calculation unit, used to calculate the first numerical range based on the second constraint and a preset maximum frequency offset threshold, to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficient, and the target coefficient is used to improve the accuracy of the signal after the occurrence of Doppler drift; and a second determination unit, used to determine multiple differential intervals based on the integers contained in the second numerical range.

[0109] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the second determining unit includes: a first determining subunit, used to determine the device information of the data processing device, wherein the data processing device includes at least: a field-programmable gate array (FPGA) logic unit and a digital signal processor (DSP) unit; a first calculating subunit, used to perform at least one simulation experiment based on the device information, the signal received within a historical time period, and a preset number of differential branches, and calculate the evaluation index of the simulation experiment results; an adjusting subunit, used to adjust the preset number of differential branches according to the evaluation index to obtain a target number of differential branches; and a filtering subunit, used to filter among integers contained in a second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0110] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the processing unit 302 includes: a second calculation subunit for calculating the amplitude of each differential correlation result among multiple differential correlation results; a third calculation subunit for performing delay processing on the local reference signal based on each differential interval among multiple differential intervals to obtain multiple delayed reference signals; performing dot product calculation on the complex conjugate value of the local reference signal with the multiple delayed reference signals respectively, and summing the calculation results to obtain a first intermediate variable; and a fourth calculation subunit for calculating the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain multiple normalized value sequences.

[0111] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the device further includes: a third determining unit, used to determine the signal peak value corresponding to each normalized value sequence in the multiple normalized value sequences before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences; a fourth determining unit, used to determine the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; a fourth calculation unit, used to calculate the signal values ​​in each normalized value sequence other than the signal peak value, and sum the calculation results to obtain the second intermediate variable corresponding to each normalized value sequence; and a fifth determining unit, used to determine the signal-to-noise ratio information of the multiple normalized value sequences based on the signal peak value corresponding to each normalized value sequence, the number of effective data points of each normalized value sequence, and the second intermediate variable corresponding to each normalized value sequence.

[0112] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the fusion unit 303 includes: a fifth calculation subunit, used to calculate the signal-to-noise ratio information of each normalized value sequence in multiple normalized value sequences based on an exponential function, to obtain a third intermediate variable corresponding to each normalized value sequence; a sixth calculation subunit, used to sum the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; a second determination subunit, used to determine the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables corresponding to each normalized value sequence, and to determine the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and a fusion subunit, used to perform weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fusion result sequence.

[0113] Optionally, in the synchronization detection device for a time-division duplex repeater provided in Embodiment 2 of this application, the aforementioned fusion subunit includes: a calculation module, used to calculate the phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval corresponding to each normalized value sequence, the signal sampling interval, the carrier frequency deviation of the target signal, and the imaginary unit information; a first fusion module, used to perform weighted fusion of multiple normalized value sequences based on the weight information and phase compensation information corresponding to each differential correlation result when the carrier frequency deviation is known, to obtain a fusion result sequence; and a second fusion module, used to perform weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result when the carrier frequency deviation is unknown, to obtain a fusion result sequence.

[0114] The synchronization detection device for a time-division duplex repeater includes a processor and a memory. The first calculation unit 301, processing unit 302, fusion unit 303 and first determination unit 304 mentioned above are all stored in the memory as program units. The processor executes the program units stored in the memory to realize the corresponding functions.

[0115] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can reduce signal synchronization deviation between the user terminal and the time-division duplex repeater.

[0116] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0117] Embodiment 3 of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a synchronization detection method for a time-division duplex repeater.

[0118] Embodiment 4 of the present invention provides a processor for running a program, wherein the program executes a synchronization detection method for a time-division duplex repeater.

[0119] Figure 4 This is a schematic diagram of a synchronization detection electronic device for a time-division duplex repeater according to Embodiment 5 of this application. Figure 4As shown, Embodiment 5 of the present invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: receiving a target signal transmitted by a time-division duplex repeater; performing multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals to obtain multiple differential correlation results, wherein the multiple differential intervals are calculated based on multiple constraints and the cumulative phase offset information of the differential product; normalizing each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences; determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, and fusing the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence; determining the peak value in the fused result sequence, and determining the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fused result sequence.

[0120] When the processor executes the program, it also implements the following steps: multiple constraints include at least a first constraint and a second constraint. Before performing multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, the above method further includes: calculating the cumulative phase offset information based on the first constraint to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the variable to be solved corresponding to the differential interval, constants, and signal sampling interval, and the first constraint is used to limit the numerical range of the cumulative phase offset information; calculating the first numerical range based on the second constraint and a preset maximum frequency offset threshold to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficient, and the target coefficient is used to improve the accuracy of the signal after the occurrence of Doppler drift; determining multiple differential intervals based on the integers contained in the second numerical range.

[0121] When the processor executes the program, it also performs the following steps: determining multiple differential intervals based on integers contained in the second numerical range, including: determining the device information of the data processing device, wherein the data processing device includes at least: field-programmable gate array logic units and digital signal processor units; performing at least one simulation experiment based on the device information, signals received within a historical time period, and a preset number of differential branches, and calculating the evaluation index of the simulation experiment results; adjusting the preset number of differential branches according to the evaluation index to obtain a target number of differential branches; and filtering from integers contained in the second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0122] When the processor executes the program, it also performs the following steps: normalizing each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences, including: calculating the amplitude of each differential correlation result in the multiple differential correlation results; performing delay processing on the local reference signal based on each differential interval in the multiple differential intervals to obtain multiple delayed reference signals; performing dot product calculations on the complex conjugate value of the local reference signal with the multiple delayed reference signals respectively, and summing the calculation results to obtain a first intermediate variable; calculating the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain multiple normalized value sequences.

[0123] When the processor executes the program, it also performs the following steps: Before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, the above method further includes: determining the signal power of the signal peak corresponding to each normalized value sequence in the multiple normalized value sequences; determining the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; calculating the signal values ​​in each normalized value sequence other than the signal peak, and summing the calculation results to obtain the second intermediate variable corresponding to each normalized value sequence; determining the signal-to-noise ratio information of multiple normalized value sequences based on the signal power of the signal peak corresponding to each normalized value sequence and the second intermediate variable corresponding to each normalized value sequence.

[0124] When the processor executes the program, it also performs the following steps: determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, and fusing multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence, including: calculating the signal-to-noise ratio information of each normalized value sequence based on an exponential function to obtain a third intermediate variable corresponding to each normalized value sequence; summing the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; determining the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables, and determining the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and performing weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence.

[0125] The processor, when executing the program, also performs the following steps: weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fusion result sequence, including: calculating the phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval corresponding to each normalized value sequence, the signal sampling interval, the carrier frequency deviation of the target signal, and imaginary unit information; when the carrier frequency deviation is known, weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information to obtain a fusion result sequence; when the carrier frequency deviation is unknown, weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result to obtain a fusion result sequence.

[0126] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0127] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: receiving a target signal transmitted by a time-division duplex repeater; performing multi-branch differential correlation calculations on a local reference signal and a target signal based on multiple differential intervals to obtain multiple differential correlation results, wherein the multiple differential intervals are calculated based on multiple constraints and the cumulative phase offset information of differential products; normalizing each differential correlation result in the multiple differential correlation results to obtain multiple normalized value sequences; determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, and fusing the multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence; determining the peak value in the fused result sequence, and determining the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fused result sequence.

[0128] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: multiple constraints, including at least a first constraint and a second constraint, and before performing multi-branch differential correlation calculations on the local reference signal and the target signal based on multiple differential intervals, the above method further includes: calculating the cumulative phase offset information based on the first constraint to obtain a first numerical range corresponding to the differential interval, wherein the cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the unsolved variable corresponding to the differential interval, constants, and signal sampling interval, and the first constraint is used to limit the numerical range of the cumulative phase offset information; calculating the first numerical range based on the second constraint and a preset maximum frequency offset threshold to obtain a second numerical range corresponding to the differential interval, wherein the second constraint includes the numerical range of the target coefficient, the target coefficient being used to improve the accuracy of the signal after the occurrence of Doppler drift; and determining multiple differential intervals based on the integers contained in the second numerical range.

[0129] When executed on a data processing device, it is also suitable to execute an initialization program with the following method steps: determining multiple differential intervals based on integers contained in a second numerical range, including: determining device information of the data processing device, wherein the data processing device includes at least: field-programmable gate array logic units and digital signal processor units; performing at least one simulation experiment based on the device information, signals received within a historical time period, and a preset number of differential branches, and calculating an evaluation index of the simulation experiment results; adjusting the preset number of differential branches according to the evaluation index to obtain a target number of differential branches; and filtering from integers contained in the second numerical range based on the target number of differential branches to obtain multiple differential intervals.

[0130] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: normalizing each differential correlation result among multiple differential correlation results to obtain multiple normalized value sequences, including: calculating the amplitude of each differential correlation result among multiple differential correlation results; delaying the local reference signal based on each differential interval among multiple differential intervals to obtain multiple delayed reference signals; performing dot product calculations on the complex conjugate value of the local reference signal and the multiple delayed reference signals respectively, and summing the calculation results to obtain a first intermediate variable; calculating the normalized value sequence corresponding to each differential correlation result based on the amplitude of each differential correlation result and the first intermediate variable to obtain multiple normalized value sequences.

[0131] When executed on a data processing device, it is also suitable to execute an initialization procedure with the following steps: before determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, the above method further includes: determining the signal power of the signal peak corresponding to each normalized value sequence in the multiple normalized value sequences; determining the number of effective data points of each normalized value sequence based on the number of sampling points of the target signal and the differential interval corresponding to each normalized value sequence; calculating the signal values ​​in each normalized value sequence other than the signal peak, and summing the calculation results to obtain the second intermediate variable corresponding to each normalized value sequence; determining the signal-to-noise ratio information of the multiple normalized value sequences based on the signal power of the signal peak corresponding to each normalized value sequence and the second intermediate variable corresponding to each normalized value sequence.

[0132] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: determining the weight information corresponding to each differential correlation result based on the signal-to-noise ratio information of multiple normalized value sequences, and fusing multiple differential correlation results based on the weight information corresponding to each differential correlation result to obtain a fused result sequence, including: calculating the signal-to-noise ratio information of each normalized value sequence based on an exponential function to obtain a third intermediate variable corresponding to each normalized value sequence; summing the third intermediate variables corresponding to each normalized value sequence to obtain a fourth intermediate variable; determining the weight information corresponding to each normalized value sequence based on the third and fourth intermediate variables, and determining the weight information corresponding to each differential correlation result based on the mapping relationship between each normalized value sequence and each differential correlation result; and performing weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fused result sequence.

[0133] When executed on a data processing device, it is also suitable to execute an initialization program with the following steps: weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain a fusion result sequence, including: calculating the phase compensation information of the target signal based on multiple parameters, wherein the multiple parameters include at least: the differential interval, signal sampling interval, carrier frequency deviation of the target signal, and imaginary unit information corresponding to each normalized value sequence; when the carrier frequency deviation is known, weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result and the phase compensation information to obtain a fusion result sequence; when the carrier frequency deviation is unknown, weighted fusion of multiple normalized value sequences based on the weight information corresponding to each differential correlation result to obtain a fusion result sequence.

[0134] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0138] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0139] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0140] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0141] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0142] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A synchronization detection method for a time-division duplex repeater, characterized in that, include: The system receives the target signal transmitted by the time-division duplex repeater, performs multi-branch differential correlation calculation on the local reference signal and the target signal based on multiple differential intervals, and obtains multiple differential correlation results. The multiple differential intervals are calculated based on the cumulative phase offset information of multiple constraints and differential products. Each of the multiple differential correlation results is normalized to obtain multiple normalized value sequences; The weight information corresponding to each differential correlation result is determined based on the signal-to-noise ratio information of the multiple normalized value sequences, and the multiple differential correlation results are fused based on the weight information corresponding to each differential correlation result to obtain a fused result sequence. Determine the peak value in the fusion result sequence, and determine the synchronization point between the target signal and the local reference signal based on the offset information of the peak value in the fusion result sequence.

2. The method according to claim 1, characterized in that, The plurality of constraints includes at least: a first constraint and a second constraint. Before performing multi-branch differential correlation calculations on the local reference signal and the target signal based on multiple differential intervals, the method further includes: The cumulative phase offset information is calculated based on the first constraint condition to obtain the first numerical range corresponding to the differential interval. The cumulative phase offset information is calculated based on the carrier frequency offset of the target signal, the variable to be solved corresponding to the differential interval, the constant, and the signal sampling interval. The first constraint condition is used to limit the numerical range of the cumulative phase offset information. The first numerical range is calculated based on the second constraint and the preset highest frequency offset threshold to obtain the second numerical range corresponding to the difference interval. The second constraint includes the numerical range of the target coefficient, which is used to improve the accuracy of the signal after the occurrence of Doppler drift. The plurality of difference intervals are determined based on the integers contained in the second numerical range.

3. The method according to claim 2, characterized in that, Determining the plurality of difference intervals based on the integers contained in the second numerical range includes: The equipment information of the data processing device is determined, wherein the data processing device includes at least: a field-programmable gate array (FPGA) logic unit and a digital signal processor (DSP) unit; Based on the device information, the signals received within the historical time period, and the preset number of differential branches, at least one simulation experiment is conducted, and the evaluation index of the simulation experiment results is calculated. The preset number of differential branches is adjusted according to the evaluation index to obtain the target number of differential branches; The plurality of difference intervals are obtained by filtering from integers within the second numerical range based on the target number of difference branches.

4. The method according to claim 1, characterized in that, Each of the multiple differential correlation results is normalized to obtain multiple normalized value sequences, including: Calculate the magnitude of each differential correlation result among the multiple differential correlation results; The local reference signal is delayed based on each of the plurality of differential intervals to obtain a plurality of delayed reference signals; The complex conjugate value of the local reference signal is multiplied by the plurality of delayed reference signals, and the results are summed to obtain the first intermediate variable. Based on the magnitude of each difference correlation result and the first intermediate variable, the normalized value sequence corresponding to each difference correlation result is calculated to obtain the plurality of normalized value sequences.

5. The method according to claim 1, characterized in that, Before determining the weight information corresponding to each difference correlation result based on the signal-to-noise ratio information of the multiple normalized value sequences, the method further includes: Determine the signal power of the signal peak corresponding to each of the plurality of normalized value sequences; The number of valid data points for each normalized value sequence is determined based on the number of sampling points of the target signal and the difference interval corresponding to each normalized value sequence. The signal values ​​other than the signal peak value in each normalized value sequence are calculated, and the calculation results are summed to obtain the second intermediate variable corresponding to each normalized value sequence. The signal-to-noise ratio information of the multiple normalized value sequences is determined based on the signal power of the signal peak corresponding to each normalized value sequence and the second intermediate variable corresponding to each normalized value sequence.

6. The method according to claim 1, characterized in that, The multiple difference correlation results are fused based on the weight information corresponding to each difference correlation result to obtain a fused result sequence, including: The signal-to-noise ratio of each normalized value sequence in the plurality of normalized value sequences is calculated based on the exponential function to obtain the third intermediate variable corresponding to each normalized value sequence; Summing the third intermediate variable corresponding to each normalized value sequence yields the fourth intermediate variable; The weight information corresponding to each normalized value sequence is determined based on the third and fourth intermediate variables corresponding to each normalized value sequence, and the weight information corresponding to each difference correlation result is determined based on the mapping relationship between each normalized value sequence and each difference correlation result. The multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain the fused result sequence.

7. The method according to claim 6, characterized in that, The multiple normalized value sequences are weighted and fused based on the weight information corresponding to each differential correlation result and the phase compensation information of the target signal to obtain the fused result sequence, including: The phase compensation information of the target signal is calculated based on multiple parameters, wherein the multiple parameters include at least: the differential interval corresponding to each normalized value sequence, the signal sampling interval, the carrier frequency deviation of the target signal, and the imaginary unit information; Given that the carrier frequency deviation is known, the multiple normalized value sequences are weighted and fused according to the weight information corresponding to each differential correlation result and the phase compensation information to obtain the fused result sequence. When the carrier frequency deviation is unknown, the multiple normalized value sequences are weighted and fused according to the weight information corresponding to each differential correlation result to obtain the fused result sequence.

8. A synchronization detection device for a time-division duplex repeater, characterized in that, include: The first calculation unit is used to receive the target signal sent by the time-division duplex repeater, and perform multi-branch differential correlation calculation on the local reference signal and the target signal according to multiple differential intervals to obtain multiple differential correlation results. Each differential interval represents the distance between the signal data points for differential calculation. The multiple differential intervals are calculated based on multiple constraints and the phase offset information of the target signal. The processing unit is used to normalize each of the multiple differential correlation results to obtain multiple normalized value sequences. The fusion unit is used to perform weighted fusion of the multiple normalized value sequences based on the signal-to-noise ratio information of the multiple normalized value sequences to obtain a fusion result sequence; The first determining unit is used to determine the synchronization point of the target signal based on the signal peak value in the fusion result sequence.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes stored computer instructions, wherein, when executed by a processor, the computer instructions implement the synchronization detection method for a time-division duplex repeater as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the synchronization detection method for a time-division duplex repeater as described in any one of claims 1 to 7.

Images