Synchronization signal detection method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a synchronization signal detection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a group of time domain baseband signals; performing specified three-dimensional search on the group of time domain baseband signals to obtain a group of peak value pairs; converting the peak pair in the group of peak pairs into corresponding metric values, and performing grouping operation on the obtained group of metric values according to the corresponding S-PSS identifier and frequency offset hypothesis to obtain a plurality of metric value groups; respectively selecting M metric value groups from the plurality of metric value groups under each S-PSS identifier according to the maximum metric value in the metric value groups, and selecting N target metric values under each S-PSS identifier from the M metric value groups under each S-PSS identifier; and calculating a group of frequency offset estimation values based on the peak pairs corresponding to the N target metric values under each S-PSS identifier, and determining a group of signal synchronization parameters according to the group of frequency offset estimation values.

Description

Technical Field

[0001] This application relates to the field of wireless communication, and more specifically, to a method and apparatus for detecting synchronization signals, a storage medium, and an electronic device. Background Technology

[0002] With the rapid development of mobile communication, the traditional cellular network system centered on base stations has limitations, and device-to-device (D2D) communication has received increasing attention. Sidelink communication system is a D2D communication technology that enables direct communication between devices without base station intervention. To ensure effective communication, devices in a Sidelink system need to maintain a high degree of synchronization, including time and frequency synchronization. Therefore, the detection of the Sidelink Primary Synchronization Signal Identification (S-PSSID) is crucial in a Sidelink system. Furthermore, to ensure a stable connection between connected devices in a Sidelink system, timing synchronization and frequency offset synchronization are also required. S-PSSID, frequency offset, and timing can constitute the synchronization signal. However, S-PSSID and frequency offset cannot be obtained directly from the received signal. The receiver needs to perform correlation calculations with multiple pre-stored possible S-PSS sequences to find the sequence with the highest matching degree to determine S-PSSID. Frequency offset, on the other hand, needs to be indirectly derived through signal processing algorithms and mathematical calculations.

[0003] However, due to multipath redundancy and noise during signal transmission, the related technologies suffer from inaccurate synchronization signal detection. Summary of the Invention

[0004] This application provides a method and apparatus for detecting synchronization signals, a storage medium, and an electronic device to at least solve the technical problem of inaccurate synchronization signal detection in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for detecting a synchronization signal is provided, comprising: acquiring a set of time-domain baseband signals, wherein the set of time-domain baseband signals includes a sidelink primary synchronization signal (S-PSS); performing time-domain sliding correlation on the set of time-domain baseband signals using a time-domain window, performing a specified three-dimensional search on the set of time-domain baseband signals to obtain a set of peak pairs, wherein the specified three-dimensional search is a three-dimensional search of S-PSS identifier, frequency offset hypothesis, and timing position, wherein one of the peak pairs in the set of peak pairs includes a peak value of a sliding point determined during the time-domain sliding correlation process and a peak value of a time-domain window corresponding to the time-domain window matching the peak value of the sliding point; converting the peak pairs in the set of peak pairs into corresponding metric values, and according to the corresponding S-PSS identifier and frequency offset hypothesis... Suppose that a grouping operation is performed on the obtained set of metric values ​​to obtain multiple metric value groups under each S-PSS identifier; according to the maximum metric value in the metric value group, M metric value groups are selected from the multiple metric value groups under each S-PSS identifier, and N target metric values ​​under each S-PSS identifier are selected from the M metric value groups under each S-PSS identifier, where M and N are both integers greater than or equal to 1; a set of frequency offset estimates is calculated based on the peak values ​​corresponding to the N target metric values ​​under each S-PSS identifier, and a set of signal synchronization parameters is determined based on the set of frequency offset estimates, wherein each signal synchronization parameter in the set of signal synchronization parameters includes one frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position.

[0006] According to another aspect of the embodiments of this application, a synchronization signal detection device is also provided, comprising: an acquisition unit, configured to acquire a set of time-domain baseband signals, wherein the set of time-domain baseband signals includes a sidelink primary synchronization signal (S-PSS); a search unit, configured to perform time-domain sliding correlation on the set of time-domain baseband signals using a time-domain window, and perform a specified three-dimensional search on the set of time-domain baseband signals to obtain a set of peak pairs, wherein the specified three-dimensional search is a three-dimensional search of S-PSS identifier, frequency offset hypothesis, and timing position, wherein one of the peak pairs in the set of peak pairs includes a peak value of a sliding point determined during the time-domain sliding correlation process and a peak value of a time-domain window corresponding to the time-domain window matching the peak value of the sliding point; and a first execution unit, configured to convert the peak pairs in the set of peak pairs into corresponding metric values, and execute them according to the corresponding S-PSS identifier. The system assumes frequency offset and performs a grouping operation on the obtained set of measurement values ​​to obtain multiple measurement value groups under each S-PSS identifier. A selection unit is used to select M measurement value groups from the multiple measurement value groups under each S-PSS identifier according to the maximum measurement value in each measurement value group, and to select N target measurement values ​​under each S-PSS identifier from the M measurement value groups under each S-PSS identifier, where M and N are both integers greater than or equal to 1. A second execution unit is used to calculate a set of frequency offset estimates based on the peak values ​​corresponding to the N target measurement values ​​under each S-PSS identifier, and to determine a set of signal synchronization parameters based on the set of frequency offset estimates, wherein each signal synchronization parameter in the set of signal synchronization parameters includes one frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position.

[0007] In one exemplary embodiment, the apparatus further includes a processing unit, configured to perform filtering and downsampling processing on the time-domain baseband signals in the set of time-domain baseband signals after acquiring the set of time-domain baseband signals, to obtain the downsampled set of time-domain baseband signals, wherein the specified three-dimensional search is performed on the downsampled set of time-domain baseband signals.

[0008] In an exemplary embodiment, the search unit includes: a first execution module, configured to perform time-domain sliding using the time-domain window according to a specified step size, and after each time-domain sliding operation, using the sliding point corresponding to the time-domain window as the current sliding point and the signal sequence within the time-domain window in the set of time-domain baseband signals as the current received sequence, perform one or more search operations to obtain the set of peak pairs, wherein the local sequence matching the current received sequence is the current local sequence; and perform peak calculation based on the current received sequence and the current local sequence to obtain the current time-domain window peak value, wherein the current time-domain window peak value is the current sliding point. Peak value; Obtain a set of current multipath window peak values ​​corresponding to the current time-domain window peak value, wherein the set of current multipath window peak values ​​are time-domain window peak values ​​located within the multipath window, the window length of the time-domain window is greater than or equal to the sum of the sign length of the S-PSS and the multipath length of the multipath window, the multipath window is used to capture a first number of time-domain window peak values ​​of the multipath length preceding the current time-domain window peak value, the first number being an integer multiple of the multipath length; If there is a current multipath window peak value that satisfies a preset condition among the current multipath window peak values, the current time-domain window peak value and the current multipath window peak value that satisfies the preset condition are determined as a peak value pair.

[0009] In an exemplary embodiment, the apparatus further includes: a calculation unit, configured to, after obtaining a set of current multipath window peaks corresponding to the current time-domain window peak, calculate the ratio of each current multipath window peak in the set of current multipath window peaks to the current time-domain window peak, to obtain a peak ratio corresponding to each current multipath window peak, wherein the preset condition is that the corresponding peak ratio is greater than or equal to a preset peak ratio threshold.

[0010] In an exemplary embodiment, the peak value of the set of peak pairs is a complex value; the conversion unit includes: a second execution module, configured to merge the peak values ​​contained in each peak pair in the set of peak pairs, and perform square processing on the merged peak values ​​to obtain a metric value corresponding to each peak pair; and a merging module, configured to merge the metric values ​​corresponding to each peak pair based on the antenna corresponding to each peak pair to obtain the set of metric values.

[0011] In one exemplary embodiment, the apparatus further includes: a third execution unit, configured to perform a filtering operation on the obtained set of metrics according to preset filtering conditions before performing a grouping operation on the obtained set of metrics according to the corresponding S-PSS identifier and frequency offset hypothesis, to obtain the filtered set of metrics, wherein the grouping operation is performed on the filtered set of metrics, and the preset filtering conditions include at least one of the following: the filtered metrics are greater than or equal to a preset noise threshold; the filtered metrics are the largest within a preset multipath threshold range; the number of metrics filtered from the corresponding S-PSS identifier and frequency offset hypothesis in descending order is less than or equal to a second number; and the total number of filtered metrics is less than or equal to a third number.

[0012] In an exemplary embodiment, the second execution unit includes: a third execution module, configured to perform conjugate multiplication on the peak pairs corresponding to the same antenna among the peak pairs corresponding to the N target metric values ​​under each S-PSS identifier, and merge the conjugate multiplication results to obtain a set of merged values, wherein the merged values ​​in the set of merged values ​​are complex values; an acquisition module, configured to acquire the residual frequency offset according to the phase of each merged value in the set of merged values, to obtain the residual frequency offset corresponding to each merged value; and a determination module, configured to determine the frequency offset estimate corresponding to each merged value according to the frequency offset assumption corresponding to each merged value and the residual frequency offset corresponding to each merged value, to obtain the set of frequency offset estimates.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed by a processor.

[0014] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the steps in any of the method embodiments described above.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to perform the steps of any of the above method embodiments through the computer program.

[0016] By employing this application, through a specified three-dimensional search based on a time-domain window, and grouping and filtering based on metric values, only received signals with high reliability and quality can be retained, thereby removing the effects of multipath redundancy and noise. Therefore, the problem of inaccurate synchronization signal detection in related technologies can be solved, achieving the effect of improving the accuracy of synchronization signal detection. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of a synchronization signal detection method according to an embodiment of this application;

[0018] Figure 2 This is a flowchart illustrating an optional synchronization signal detection method according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of an optional synchronization signal detection method according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of another optional method for detecting a synchronization signal according to an embodiment of this application;

[0021] Figure 5 This is a structural block diagram of an optional synchronization signal detection device according to an embodiment of this application;

[0022] Figure 6 This is a computer system architecture block diagram of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0023] 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.

[0024] 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 so that the embodiments of this application described herein can be implemented in orders other than those illustrated or 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.

[0025] According to one aspect of the embodiments of this application, a method for detecting a synchronization signal is provided. Optionally, in this embodiment, the above-described method for detecting a synchronization signal may be applied, but is not limited to, to applications such as... Figure 1 The illustration shows a hardware environment comprising one set of terminal devices 102 and another set of terminal devices 102. Terminal devices 102 can connect to other terminal devices 102 wirelessly and can be used for communication between different terminal devices 102.

[0026] The aforementioned wireless communication may include, but is not limited to, at least one of the following: Wireless Fidelity (WIFI), Bluetooth, and Internet of Things (IoT) communication protocols. The terminal device 102 may be, but is not limited to, a mobile phone, tablet computer, in-vehicle terminal, etc.

[0027] The synchronization signal detection method in this embodiment is executed by the terminal device 102, or by a client installed on the terminal device 102. Figure 2 This is a flowchart illustrating an optional synchronization signal detection method according to an embodiment of this application, as shown below. Figure 2 As shown, the process of this method may include the following steps:

[0028] Step S202: Obtain a set of time-domain baseband signals, wherein the set of time-domain baseband signals includes the side link master synchronization signal S-PSS;

[0029] Step S204: Perform time-domain sliding correlation on a set of time-domain baseband signals using a time-domain window, and perform a specified three-dimensional search on the set of time-domain baseband signals to obtain a set of peak pairs. The specified three-dimensional search is a three-dimensional search of S-PSS identifier, frequency offset hypothesis, and timing position. One of the peak pairs in the set of peak pairs includes a peak value of a sliding point determined during the time-domain sliding correlation process and a peak value of a time-domain window corresponding to the peak value of the sliding point.

[0030] Step S206: Convert the peak pairs in a set of peak pairs into corresponding metric values, and perform a grouping operation on the obtained set of metric values ​​according to the corresponding S-PSS identifier and frequency offset assumption to obtain multiple metric value groups under each S-PSS identifier.

[0031] Step S208: According to the maximum metric value in the metric value group, select M metric value groups from the multiple metric value groups under each S-PSS identifier, and select N target metric values ​​under each S-PSS identifier from the M metric value groups under each S-PSS identifier, where M and N are both integers greater than or equal to 1.

[0032] Step S210: Calculate a set of frequency offset estimates based on the peak values ​​corresponding to the N target metrics under each S-PSS identifier, and determine a set of signal synchronization parameters based on the set of frequency offset estimates. Each signal synchronization parameter in the set of signal synchronization parameters includes a frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position.

[0033] The synchronization signal detection method in this embodiment can be applied to the field of wireless communication, specifically to scenarios where device-to-device communication is performed using the Sidelink communication system. Sidelink is a D2D communication technology that allows two peer-to-peer user nodes to communicate directly without the intervention of an intermediary entity. In a distributed network, user nodes can act as both servers and clients, achieving self-organized communication by sharing hardware resources. Sidelink is one type of D2D communication technology, enabling direct communication between devices without the intervention of a base station. In Sidelink, to ensure a stable connection between connected devices, it is necessary to detect the S-PSSID (S-PSS identifier) ​​and perform timing and frequency offset synchronization.

[0034] S-PSSID refers to the unique identifier carried by each master synchronization signal (S-PSS) in the Sidelink communication system. This identifier is periodically sent in the signal at a certain time and frequency. It is used by terminal devices to identify and distinguish different communication links or network nodes. It can help the receiving device quickly identify the identity of the sender, thereby establishing a preliminary synchronization relationship.

[0035] Timing synchronization refers to the alignment process between the receiver and the transmitter on the time axis. In wireless communication, due to differences in transmission distance, the arrival time of signals at the receiver will vary, which is called time delay. Furthermore, the propagation path of the signal may vary, causing the arrival time of the signal to be dispersed, i.e., multipath propagation. The goal of timing synchronization is to accurately measure and correct these time delays and multipath effects. To ensure correct data decoding and the normal operation of the entire communication system, timing synchronization is needed to ensure that the time reference of the received signal is consistent with that of the transmitted signal.

[0036] Frequency offset synchronization refers to correcting the frequency deviation between the received and transmitted signals. Frequency offset, or frequency deviation, can be caused by inherent instability in the device's oscillator, temperature variations, voltage fluctuations, or the Doppler effect. This deviation causes phase rotation of the signal, affecting the accuracy of data demodulation and communication quality. During S-PSS detection, frequency offset synchronization can be achieved by estimating the frequency offset of the received signal relative to the transmitted signal and then adjusting the signal frequency accordingly (e.g., through a frequency compensation module in a digital signal processor). Accurate frequency offset estimation and compensation are crucial for ensuring high-quality, low-error-rate data transmission between terminal devices in the Sidelink system.

[0037] It should be noted that in related technologies, the S-PSSID, timing, and frequency offset mentioned above cannot be directly determined by measurement. S-PSSID and timing are determined by comparing the received S-PSS sequence with locally stored S-PSS sequences to find the S-PSS sequence with the highest matching degree. Frequency offset can usually be indirectly estimated using signal processing algorithms and mathematical calculations. However, due to the characteristics of signal sequences and the effects of multipath redundancy, noise, and frequency offset during signal transmission, the methods used in related technologies to determine S-PSSID, timing, and frequency offset suffer from inaccurate results.

[0038] To at least partially solve the above-mentioned technical problems, in this embodiment, by performing a specified three-dimensional search based on a time-domain window, and by grouping, sorting, and filtering based on metrics, only the received signals with high reliability can be retained, thereby removing the influence of multipath redundancy and noise and improving the accuracy of synchronization signal detection.

[0039] In this embodiment, a set of time-domain baseband signals is first acquired, wherein the set of time-domain baseband signals includes the sidelink master synchronization signal S-PSS. Here, the acquired set of time-domain baseband signals can come from the digital front-end (DFE). In a wireless communication system, after the received signal enters from the antenna, it can undergo a series of radio frequency front-end processing, such as filtering, amplification, and mixing, to convert the radio frequency signal into an intermediate frequency or baseband signal. The above process is usually completed jointly by the radio frequency front-end and the DFE, so the aforementioned set of time-domain baseband signals can be acquired from the DFE. Optionally, the received set of time-domain baseband signals may include K time-domain baseband signals, arranged according to the reception time as s(n), n=0,1,2,…,K-1. The time-domain baseband signals can be acquired based on a specified signal sampling rate. The signal sampling rate can be set based on the communication environment, the performance conditions of the terminal equipment, etc., and this embodiment does not limit this.

[0040] Following this, a set of time-domain baseband signals is subjected to time-domain sliding correlation using a time-domain window. A specified three-dimensional search is then performed on the set of time-domain baseband signals to obtain a set of peak pairs. The specified three-dimensional search includes the S-PSS identifier, frequency offset hypothesis, and timing position. Each peak pair in the set includes a peak value at a sliding point determined during the time-domain sliding correlation process and an in-window peak value within a time-domain window that matches the peak value at the sliding point. Here, time-domain sliding correlation is a method for detecting known signal sequences. In the Sidelink communication system, the receiver needs to compare the received S-PSS sequence with locally stored S-PSS sequences to find the S-PSS sequence with the highest matching degree to determine the S-PSS identifier. The time-domain sliding correlation process can be as follows: by sliding the locally stored S-PSS sequence across the sequence of received time-domain baseband signals, multiplying and accumulating each row to detect the existence of similar signal segments, thereby determining the matching degree of the compared signal sequences. This allows for the calculation of the corresponding peak value for each time-domain baseband signal and the determination of the closest S-PSS identifier. In addition, the specified three-dimensional search also includes the search for frequency offset hypothesis and timing position. Optionally, the frequency offset hypothesis can be determined by frequency domain analysis based on fast Fourier transform or time domain analysis based on autocorrelation, and the timing position can be determined by time domain synchronization based on autocorrelation or positioning position confirmation method based on peak detection. This embodiment does not limit this.

[0041] It should be noted that a peak pair includes the peak value of a sliding point determined during the time-domain sliding correlation process and an in-window peak value within a time-domain window that matches the peak value of the sliding point. During signal reception, since the same S-PSS signal may arrive at the receiver through different paths, this will cause signal delay and generate multiple overlapping S-PSS signals. This situation is called multipath redundancy. In this embodiment, the peak values ​​corresponding to the multiple overlapping S-PSS signals can be integrated into a peak pair for subsequent calculation, thereby eliminating the interference of multipath redundancy.

[0042] In this embodiment, peak pairs in a set of peak pairs are converted into corresponding metric values. Then, according to the corresponding S-PSS identifier and frequency offset assumption, the resulting set of metric values ​​is grouped to obtain multiple metric value groups under each S-PSS identifier. Here, the metric values ​​can reflect the signal strength and signal quality of the corresponding signal. Optionally, peak pairs can be converted into corresponding metric values ​​through energy calculation, correlation calculation, signal-to-noise ratio calculation, antenna combining, etc. Afterward, the resulting set of metric values ​​can be grouped according to the corresponding S-PSS identifier and frequency offset assumption to obtain multiple metric value groups under each S-PSS identifier, for example, as... Figure 3 As shown, there are corresponding S-PSS identifiers A and frequency offset hypothesis a for metric 1; S-PSS identifier A and frequency offset hypothesis b for metric 2; S-PSS identifier B and frequency offset hypothesis a for metric 3; S-PSS identifier B and frequency offset hypothesis a for metric 4; and S-PSS identifier B and frequency offset hypothesis b for metric 5. After grouping, we can obtain two groups of metrics with S-PSS identifier A and two groups of metrics with S-PSS identifier B.

[0043] Following this, based on the maximum metric value in each metric group, M metric groups can be selected from multiple metric groups under each S-PSS identifier. Then, from the M metric groups under each S-PSS identifier, N target metric values ​​can be selected for each S-PSS identifier, where M and N are both integers greater than or equal to 1. For example, ... Figure 3 As shown, when both M and N are 1, if metric 1 is greater than metric 2, greater than metric 3, greater than metric 4, and greater than metric 5, then firstly, a first metric group containing metric 1 is selected from the metric group identified as A in the S-PSS, and then a second metric group containing metric 3 and metric 4 is selected from the metric group identified as B in the S-PSS. Then, metric 1 is selected as the target metric from the first metric group, and metric 3 is selected as the target metric from the second metric group, finally obtaining metric 1 and metric 3 as the target metric values.

[0044] Through the above screening steps, the signal with the highest confidence and its corresponding target metric value can be selected for each S-PSS identifier. Then, a set of frequency offset estimates can be calculated based on the peak values ​​of the N target metric values ​​under each S-PSS identifier. Based on this set of frequency offset estimates, a set of signal synchronization parameters is determined. Each signal synchronization parameter in the set includes one frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position. Because of the aforementioned screening, a more accurate frequency offset estimate can be calculated based on the target metric value. Combined with the corresponding S-PSS identifier and timing position, a more accurate synchronization signal can be formed. Here, the method for calculating the frequency offset estimate can be set empirically, for example, it can be a phase difference-based method, the minimum mean square error algorithm, maximum likelihood estimation, etc. This embodiment does not limit this method.

[0045] The embodiments provided in this application obtain a set of time-domain baseband signals, including a sidelink master synchronization signal (S-PSS). A time-domain sliding correlation is performed on the set of time-domain baseband signals using a time-domain window, and a specified three-dimensional search is conducted to obtain a set of peak pairs. The specified three-dimensional search is a three-dimensional search of the S-PSS identifier, frequency offset assumption, and timing position. One peak pair in the set includes a peak value at a sliding point determined during the time-domain sliding correlation process and a peak value at a time-domain window corresponding to the peak value at the sliding point. The peak pairs in the set are converted into corresponding metrics, and the obtained set of metrics is then analyzed according to the corresponding S-PSS identifier and frequency offset assumption. A grouping operation is performed to obtain multiple metric value groups under each S-PSS identifier. Based on the maximum metric value in each metric value group, M metric value groups are selected from the multiple metric value groups under each S-PSS identifier. Then, N target metric values ​​are selected from the M metric value groups under each S-PSS identifier, where M and N are integers greater than or equal to 1. A set of frequency offset estimates is calculated based on the peak pairs corresponding to the N target metric values ​​under each S-PSS identifier. Based on this set of frequency offset estimates, a set of signal synchronization parameters is determined. Each signal synchronization parameter in the set includes one frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position. Because a specified three-dimensional search based on a time-domain window, and grouping, sorting, and filtering based on metric values ​​are used, only received signals with high reliability and quality can be retained. This removes the influence of multipath redundancy and noise, solves the problem of inaccurate synchronization signal detection in related technologies, and improves the accuracy of synchronization signal detection.

[0046] In an exemplary embodiment, after acquiring a set of time-domain baseband signals, the method further includes: performing filtering and downsampling processing on the time-domain baseband signals in the set of time-domain baseband signals to obtain a set of downsampled time-domain baseband signals, wherein the specified three-dimensional search is performed on the set of downsampled time-domain baseband signals.

[0047] To improve the efficiency and accuracy of a specified 3D search, the time-domain baseband signal can be filtered and downsampled before the search. This filtering and downsampling process can limit bandwidth and prevent aliasing (removing high-frequency spurious signals), reduce data rate (reducing computational complexity and improving efficiency), suppress noise, perform matched filtering, and increase signal gain (improving signal energy output and reducing noise). These improvements enhance the efficiency of the subsequent specified 3D search and reduce noise interference.

[0048] Optionally, the specific method of filtering and downsampling processing and the downsampling factor can be set based on experience. For example, filtering and downsampling processing can use half-band filters, Hilbert converters, multi-stage downsampling filters, etc. The downsampling factor can be set based on the highest frequency component of the signal and the hardware processing capability of the terminal device. This embodiment does not limit this.

[0049] This embodiment demonstrates how filtering and downsampling before performing a specified 3D search can improve the efficiency and accuracy of the subsequent 3D search.

[0050] In an exemplary embodiment, the method is characterized by performing time-domain sliding correlation on a set of time-domain baseband signals using a time-domain window to perform a specified three-dimensional search on the set of time-domain baseband signals to obtain a set of peak pairs. This includes: performing time-domain sliding using a time-domain window at a specified step size, and after each time-domain sliding operation, taking the sliding point corresponding to the time-domain window as the current sliding point and the signal sequence within the time-domain window in the set of time-domain baseband signals as the current received sequence, performing the following search operation once to obtain a set of peak pairs, wherein the local sequence matching the current received sequence is the current local sequence; and calculating the peak value based on the current received sequence and the current local sequence to obtain the peak value. The previous time-domain window peak value, where the current time-domain window peak value is the peak value of the current sliding point; obtain a set of current multipath window peak values ​​corresponding to the current time-domain window peak value, where a set of current multipath window peak values ​​are time-domain window peak values ​​located within the multipath window, the window length of the time-domain window is greater than or equal to the sum of the sign length of the S-PSS and the multipath length of the multipath window, the multipath window is used to capture time-domain window peak values ​​of a specified number of multipath lengths before the current time-domain window peak value, the specified number being an integer multiple of the multipath length; if there is a current multipath window peak value that meets the preset conditions among the current multipath window peak values, the current time-domain window peak value and the current multipath window peak value that meets the preset conditions are determined as a peak pair.

[0051] To eliminate interference caused by multipath redundancy and obtain a set of peak pairs, in this embodiment, a set of peak pairs composed of overlapping signals caused by multipath redundancy is extracted from a set of time-domain baseband signals by performing time-domain sliding correlation on a set of time-domain baseband signals through a time-domain window.

[0052] In this embodiment, the temporal window and multipath window need to be initialized first. The temporal window needs to ensure that it can cover at least the length of the S-PSS symbol and the multipath length. That is, the window length of the temporal window is greater than or equal to the sum of the S-PSS symbol length and the multipath length of the multipath window. The multipath window is used to capture the temporal window peaks of a specified number of multipath lengths before the current temporal window peak. The specified number is an integer multiple of the multipath length. That is, the length of the multipath window is an integer multiple of the multipath length. For example, it can be 1 times the multipath length or 2 times the multipath length.

[0053] In this embodiment, a time-domain sliding operation is performed using a time-domain window with a specified step size. After each time-domain sliding operation, the sliding point corresponding to the time-domain window is taken as the current sliding point, and a search operation is performed using the signal sequence within the time-domain window from a set of time-domain baseband signals as the current received sequence. This yields a set of peak pairs, where the local sequence matching the current received sequence is the current local sequence. Here, the specified step size can be set empirically. A smaller specified step size results in higher accuracy for the specified three-dimensional search, but also lower efficiency. For example, it can be one, two, or three time-domain baseband signals, and can be set based on the requirements for computational precision, real-time requirements, and the performance of the terminal device. This embodiment does not impose any limitations on this. Similar to the aforementioned embodiment, time-domain sliding correlation is a method that can be used to detect known signal sequences. A local sequence is preset at the signal receiving end for comparison with the received sequence, and the current local sequence is one of these local sequences.

[0054] The search operation performed on the current sliding point includes: calculating the peak value based on the current received sequence and the current local sequence to obtain the current time-domain window peak value, where the current time-domain window peak value is the peak value of the current sliding point; obtaining a set of current multipath window peak values ​​corresponding to the current time-domain window peak value, where the set of current multipath window peak values ​​are time-domain window peak values ​​located within the multipath window; if there is a current multipath window peak value that meets a preset condition among the current multipath window peak values, then the current time-domain window peak value and the current multipath window peak value that meets the preset condition are determined as a peak pair. For example, such as... Figure 4As shown, at the current sliding point, the current local sequence is determined based on the range of the time-domain window. Peak values ​​are calculated using a correlator and the current received sequence to obtain the current time-domain window peak value. At the current sliding point, based on the range of the multipath window, all time-domain window peak values ​​within that range are considered as a set of current multipath window peak values. After this, if a current multipath window peak value that meets a preset condition exists, the current time-domain window peak value and the current multipath window peak value that meets the preset condition are identified as a peak pair; otherwise, the search operation for the next sliding point is directly performed without obtaining a peak pair. This search operation is repeated until sliding is completed on a set of time-domain baseband signals.

[0055] Optionally, the preset conditions can be set based on experience. For example, the current multipath window peak value and the current time domain window peak value have a sufficiently high similarity or the ratio of the current multipath window peak value to the current time domain window peak value is high. This embodiment does not limit this.

[0056] In this embodiment, by performing time-domain sliding correlation on a set of time-domain baseband signals through a time-domain window, detection efficiency can be improved, multipath interference can be eliminated, and the ability to resist multipath and noise can be enhanced.

[0057] In an exemplary embodiment, after obtaining a set of current multipath window peaks corresponding to the current time-domain window peak, the method further includes: calculating the ratio of each current multipath window peak to the current time-domain window peak in the set of current multipath window peaks, to obtain the peak ratio corresponding to each current multipath window peak, wherein the preset condition is that the corresponding peak ratio is greater than or equal to a preset peak ratio threshold.

[0058] To determine the peak pair corresponding to the current time-domain window peak, we can determine whether a peak pair can be formed based on the ratio of the current time-domain window peak to each peak in the current multipath window peak. Here, since S-PSS is transmitted periodically in the time domain, the intensity of multiple peaks should remain relatively consistent without significant differences. Therefore, peaks with a peak ratio greater than or equal to a preset peak ratio threshold can be identified as a peak pair.

[0059] In this embodiment, during the sliding of the time-domain window, the magnitudes of multiple multipath window peaks and the current sliding point peak can be continuously monitored. If the peak ratio of one of the current multipath window peaks to the current time-domain window peak satisfies a preset condition, the current sliding point can be considered a candidate S-PSS identifier, and the current multipath window peak and the current time-domain window peak that satisfy the preset condition can be saved as a peak pair.

[0060] Here, the preset condition is that the corresponding peak ratio is greater than or equal to the preset peak ratio threshold. The specific value of the peak ratio threshold can be set based on experience. For example, it can be 0.5, 0.6 or other values. This embodiment does not limit this.

[0061] In this embodiment, by determining whether the preset conditions are met based on the peak ratio and a preset peak ratio threshold, subsequent S-PSS identifiers can be selected to form peak pairs, thereby achieving accurate multipath signal identification.

[0062] In an exemplary embodiment, the peak value of a set of peak pairs is a complex value; converting the peak pairs in a set of peak pairs into corresponding metric values ​​includes: merging the peak values ​​contained in each peak pair in the set of peak pairs, and performing a square operation on the merged peak values ​​to obtain the metric value corresponding to each peak pair; and merging the metric values ​​corresponding to each peak pair based on the antenna corresponding to each peak pair to obtain a set of metric values.

[0063] In order to convert peak pairs into corresponding metrics, in this embodiment, a set of metrics that can reflect signal strength and quality can be obtained through merging, sum of squares, and antenna merging operations.

[0064] In this embodiment, the peaks contained in each peak pair in a set of peak pairs are merged, and the merged peaks are squared to obtain the metric value corresponding to each peak pair. Optionally, the specific method of merging can be set based on experience, for example, multiplying the peaks in the peak pair by phase alignment, or other more complex signal processing algorithms. This embodiment does not limit this. The squaring operation can convert complex values ​​into a metric value that can represent the signal or energy intensity, while reducing the complexity of the signal.

[0065] In a system with multiple antennas, the terminal device receives peak pair information from multiple antennas. To integrate this information and improve synchronization performance, in this embodiment, the metric values ​​corresponding to each peak pair can be merged based on the antenna corresponding to each peak pair to obtain a set of metric values.

[0066] This embodiment converts peak pairs into corresponding metric values, which can intuitively show the signal strength corresponding to each peak pair, thus simplifying signal processing and analysis.

[0067] In an exemplary embodiment, before performing a grouping operation on the obtained set of metrics according to the corresponding S-PSS identifier and frequency offset hypothesis, the method further includes: performing a filtering operation on the set of metrics according to preset filtering conditions to obtain a filtered set of metrics, wherein the grouping operation is performed on the filtered set of metrics, and the preset filtering conditions include at least one of the following: the filtered metrics are greater than or equal to a preset noise threshold; the filtered metrics are the largest within a preset multipath threshold range; the number of metrics filtered from the metrics with the same corresponding S-PSS identifier and frequency offset hypothesis in descending order is less than or equal to a second number; and the total number of filtered metrics is less than or equal to a third number.

[0068] In this embodiment, to improve detection efficiency, a filtering operation can be performed before grouping. The filtering conditions include at least one of the following: to eliminate noise interference, a noise threshold can be preset, and the filtered metric values ​​are greater than or equal to the preset noise threshold; to eliminate multipath redundancy, a multipath threshold can be preset, and the filtered metric values ​​are the largest within the preset multipath threshold range; to reduce redundant data, the number of metric values ​​filtered from those with the same S-PSS identifier and frequency offset assumption can be less than or equal to a second number, in descending order; to reduce computational complexity, a retention upper limit for metric values ​​can be preset, and the total number of filtered metric values ​​is less than or equal to a third number, i.e., a retention upper limit for metric values. It should be noted that the above filtering operations can be performed individually or in combination; that is, the filtering conditions can include one or more of the above four conditions.

[0069] Within a preset multipath threshold range, the largest metric value is selected. Here, the preset multipath threshold is a time-domain window used to remove multipath redundancy. In the aforementioned embodiments, after converting peak pairs into corresponding metric values, each metric value has its corresponding timing position, located on a set of time-domain baseband signals. Metric values ​​that are multipath signals are distributed relatively close together and can be covered by the preset multipath threshold. To eliminate multipath redundancy, a set of metric values ​​can be slidably filtered across a set of time-domain baseband signals using the preset multipath threshold, retaining the largest metric value among those simultaneously covered by the same preset multipath threshold range.

[0070] Here, the preset multipath threshold is similar to the multipath window in the previous embodiment, and is used to capture a preset number of time-domain baseband signals before and after each metric value. In order to ensure that the preset multipath threshold can cover the metric values ​​that are multipaths to each other, the length of the preset multipath threshold (i.e., the preset number) is an integer multiple of the multipath length. The preset number can be set according to experience, for example, it can be 1 or 2 times the multipath length. This embodiment does not limit this.

[0071] This embodiment demonstrates how screening before subsequent steps can reduce the number of metrics entering the subsequent grouping operation, thereby improving processing efficiency.

[0072] In an exemplary embodiment, a set of frequency offset estimates is calculated based on the peak pairs corresponding to the N target metrics under each S-PSS identifier, including: performing conjugate multiplication on the peak pairs corresponding to the same antenna among the peak pairs corresponding to the N target metrics under each S-PSS identifier, and merging the conjugate multiplication results to obtain a set of merged values, wherein the merged values ​​in the set of merged values ​​are complex values; obtaining the residual frequency offset according to the phase of each merged value in the set of merged values ​​to obtain the residual frequency offset corresponding to each merged value; and determining the frequency offset estimate corresponding to each merged value according to the frequency offset assumption corresponding to each merged value and the residual frequency offset corresponding to each merged value to obtain a set of frequency offset estimates.

[0073] In order to calculate an accurate set of frequency offset estimates based on the filtered target metric values, the peak pairs can be multiplied by conjugate, combined, and phase analyzed to calculate a set of frequency offset estimates.

[0074] In this embodiment, firstly, the peak pairs corresponding to the same antenna among the N target metric values ​​under each S-PSS identifier are conjugate multiplied, and the conjugate multiplication results are merged to obtain a set of merged values. The merged value in this set is a complex value. Here, conjugate multiplication multiplies two complex signals, inverting the phase of one of the signals. This helps eliminate phase differences in the signals, highlighting the frequency characteristics of the signals and facilitating subsequent frequency offset estimation. The result of conjugate multiplication is a complex value. For multiple antennas at the same sliding point, multiple complex value results can be merged to obtain a single complex value. Optionally, merging can be performed using methods such as summation, averaging, or maximum ratio merging; this embodiment does not limit this method.

[0075] Following this, the residual frequency offset is obtained based on the phase of each merged value in a set of merged values, thus obtaining the residual frequency offset corresponding to each merged value. Based on the phase information of the merged values, the residual frequency offset corresponding to each merged value can be calculated. The phase information is directly related to the frequency offset of the signal; therefore, by analyzing the phase of the merged values, the residual frequency offset of the signal can be accurately estimated.

[0076] Finally, based on the frequency offset assumption and residual frequency offset corresponding to each combined value, a frequency offset estimate for each combined value is determined, resulting in a set of frequency offset estimates. Combining the frequency offset assumption (which can be a preset integer multiple of the frequency offset value) with the calculated residual frequency offset allows for the determination of the frequency offset estimate for each combined value. The frequency offset estimate represents the actual frequency offset between the receiving terminal device and the transmitting device, and is a key parameter for signal demodulation and synchronization algorithms.

[0077] In this embodiment, a set of frequency offset estimates is calculated through conjugate multiplication, combined value calculation, and phase analysis, which can improve the accuracy of frequency offset estimation and optimize the performance of synchronization and signal demodulation.

[0078] The method for detecting synchronization signals in the embodiments of this application will be explained below with reference to optional examples. In this optional example, SSB SCS = 15kHz (that is, the subcarrier spacing (SCS) of the Sidelink synchronization signal block (SSB) is 15kHz) is taken as an example.

[0079] Step 1: Obtain a 20ms time-domain baseband signal containing S-PSS from the DFE to obtain a set of time-domain baseband signals s(n), n=0,1,2,…,K-1; K=76800. The sampling rate f used in this optional example is... s =3.84MHz.

[0080] Step 2: Perform half-band filtering and 2x downsampling on this set of time-domain baseband signals.

[0081] Step 3: Perform time-domain sliding correlation on the downsampled signal, perform a three-dimensional search for S-PSS identification, frequency offset assumption (in this optional example, the frequency offset assumption interval is 7.5 kHz), and timing position, and filter the peak values, including:

[0082] First, define a time-domain window with a length of 155 (=S - PSS symbol length + multipath length =L). S +L MP =137+18), define a multi-path window, the length of the multi-path window is 36 (=2L) MP ).

[0083] During the temporal sliding correlation process, the peak values ​​of the first 36 current multipath windows and the current temporal window peak value (denoted as P) are continuously compared. c If there exists a peak within the window whose peak power ratio to the current time-domain window peak power is greater than the threshold T1 = 0.5, then the current sliding point is considered a candidate S-PSS identifier, and the maximum peak value (denoted as P) of all peak values ​​within the multipath window that meet the conditions is saved. b ).

[0084] Finally, after sliding the correlation across all time-domain sample locations, a maximum of J = 120 S-PSS peak pairs (P) can be selected. c and P b), and the S-PSS identifier, frequency offset assumption, and timing position corresponding to the peak. Here, 120 peak pairs may include: 2 S-PSS identifiers, 3 frequency offset assumptions, and 20 timing positions;

[0085] Step 4: Combine the peak pairs obtained in Step 3, square them, and then perform antenna combining to obtain the metric value. Retain the metric value that satisfies all of the following preset conditions:

[0086] The measured value is greater than the preset noise threshold T2 = 1.

[0087] Among the metrics with the same S-PSSID and frequency offset assumption, only the largest metric within the multipath threshold of 4 is retained. The maximum number of retained metrics is 30, including: 2 S-PSSIDs, 3 frequency offset assumptions, and 5 timings.

[0088] Step 5: Post-process the metrics retained in Step 4, including:

[0089] First, the metrics are grouped according to the S-PSS identifier. Measured values ​​with the same S-PSS identifier are grouped together, resulting in 2 groups of metrics.

[0090] Then, within each S-PSS identifier group, the frequency offset assumptions are grouped together, with the same frequency offset assumptions grouped together, resulting in 3 frequency offset assumption groups;

[0091] Within each S-PSS identifier group, the following processing is performed sequentially: the maximum metric values ​​of each frequency offset hypothesis group are compared, the top M=2 frequency offset hypothesis groups are selected, and then all metric values ​​of these two groups are mixed and sorted, retaining the top N=5 metric values ​​and their corresponding S-PSS identifiers, timing, frequency offset hypothesis values ​​and related peak pairs.

[0092] Step 6: Calculate the residual frequency offset using the peak values ​​corresponding to the metrics retained in Step 5, including:

[0093] First, multiply the peak values ​​of each day's chart by their conjugates, as shown in formula (1):

[0094]

[0095] Where conj represents conjugate, · represents scalar dot product, and i represents antenna label;

[0096] Then antenna merging is performed, as shown in formula (2):

[0097]

[0098] Where R represents the number of antennas;

[0099] The residual frequency offset is obtained from the phase of the combined value. The unit of residual frequency offset is Hz, as shown in formula (3):

[0100]

[0101] Here, angle() represents the phase of a complex number in radians, and t represents the time interval between two S-PSS symbols in seconds;

[0102] Finally, the frequency offset estimate f is obtained by combining the frequency offset assumption. The unit of the frequency offset estimate is hz, as shown in formula (4):

[0103]

[0104] Where k represents the frequency offset hypothesis (value range -1, 0, 1), and the unit is 7.5 kHz.

[0105] This optional example demonstrates how to eliminate multipath redundancy in the acquisition of synchronization signals, thereby improving the final synchronization effect.

[0106] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0108] According to another aspect of the embodiments of this application, a synchronization signal detection device is also provided. This synchronization signal detection device can be used to implement the synchronization signal detection method provided in the above embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0109] Figure 5 This is a structural block diagram of an optional synchronization signal detection device according to an embodiment of this application, such as... Figure 5 As shown, the detection device for the synchronization signal includes:

[0110] The acquisition unit 502 is used to acquire a set of time-domain baseband signals, wherein the set of time-domain baseband signals includes the side link master synchronization signal S-PSS;

[0111] Search unit 504 is used to perform time-domain sliding correlation on a set of time-domain baseband signals of the device by using a time-domain window, and to perform a specified three-dimensional search on a set of time-domain baseband signals of the device to obtain a set of peak pairs. The specified three-dimensional search of the device is a three-dimensional search of S-PSS identifier, frequency offset hypothesis and timing position. One of the peak pairs in the set of peak pairs of the device includes a peak value of a sliding point determined in the process of performing time-domain sliding correlation and a peak value of a time-domain window corresponding to the device time-domain window that matches the peak value of a sliding point of the device.

[0112] The first execution unit 506 is used to convert the peak pairs in a set of peak pairs in the device into corresponding measurement values, and perform a grouping operation on the obtained set of measurement values ​​according to the corresponding S-PSS identifier and frequency offset assumption to obtain multiple measurement value groups under each S-PSS identifier.

[0113] The selection unit 508 is used to select M measurement value groups from multiple measurement value groups under each S-PSS identifier of the device according to the maximum measurement value in the measurement value group, and to select N target measurement values ​​under each S-PSS identifier of the device from the M measurement value groups under each S-PSS identifier of the device, where M and N are both integers greater than or equal to 1;

[0114] The second execution unit 510 is used to calculate a set of frequency offset estimates based on the peak values ​​corresponding to the N target metrics under each S-PSS identifier of the device, and to determine a set of signal synchronization parameters based on the set of frequency offset estimates of the device. Each signal synchronization parameter in the set of signal synchronization parameters of the device includes a frequency offset estimate and the corresponding S-PSS identifier and timing position.

[0115] It should be noted that the acquisition unit 502 in this embodiment can be used to execute the above step S202, the search unit 504 in this embodiment can be used to execute the above step S204, the first execution unit 506 in this embodiment can be used to execute the above step S206, the selection unit 508 in this embodiment can be used to execute the above step S208, and the second execution unit 510 in this embodiment can be used to execute the above step S210.

[0116] The embodiments provided in this application obtain a set of time-domain baseband signals, including a sidelink master synchronization signal (S-PSS). A time-domain sliding correlation is performed on the set of time-domain baseband signals using a time-domain window, and a specified three-dimensional search is conducted to obtain a set of peak pairs. The specified three-dimensional search is a three-dimensional search of the S-PSS identifier, frequency offset assumption, and timing position. One peak pair in the set includes a peak value at a sliding point determined during the time-domain sliding correlation process and a peak value at a time-domain window corresponding to the peak value at the sliding point. The peak pairs in the set are converted into corresponding metrics, and the obtained set of metrics is then analyzed according to the corresponding S-PSS identifier and frequency offset assumption. The values ​​are grouped to obtain multiple metric value groups under each S-PSS identifier. Based on the maximum metric value in each metric value group, M metric value groups are selected from the multiple metric value groups under each S-PSS identifier. Then, N target metric values ​​are selected from the M metric value groups under each S-PSS identifier, where M and N are integers greater than or equal to 1. A set of frequency offset estimates is calculated based on the peak pairs corresponding to the N target metric values ​​under each S-PSS identifier. Based on this set of frequency offset estimates, a set of signal synchronization parameters is determined. Each signal synchronization parameter in the set includes one frequency offset estimate from the set of frequency offset estimates and the corresponding S-PSS identifier and timing position. Because of the specified three-dimensional search based on a time-domain window, and the grouping, sorting, and filtering based on metric values, only the received signals with high reliability and quality can be retained. This removes the influence of multipath redundancy and noise, solves the problem of inaccurate synchronization signal detection in related technologies, and improves the accuracy of synchronization signal detection.

[0117] In one exemplary embodiment, the above apparatus further includes: a processing unit, configured to perform filtering and downsampling processing on the time-domain baseband signals in the set of time-domain baseband signals after acquiring a set of time-domain baseband signals, to obtain a set of downsampled time-domain baseband signals, wherein the specified three-dimensional search is performed on the set of downsampled time-domain baseband signals.

[0118] In an exemplary embodiment, the search unit includes: a first execution module, configured to perform time-domain sliding using a time-domain window at a specified step size, and after each time-domain sliding operation, using the sliding point corresponding to the time-domain window as the current sliding point and the signal sequence within the time-domain window from a set of time-domain baseband signals as the current received sequence, perform one or more search operations to obtain a set of peak pairs, wherein the local sequence matching the current received sequence is the current local sequence; and perform peak calculation based on the current received sequence and the current local sequence to obtain the current time-domain window peak value, wherein the current time-domain window peak value is the current sliding peak value. The peak value of the point; obtain a set of current multipath window peak values ​​corresponding to the current time-domain window peak value, wherein the set of current multipath window peak values ​​are time-domain window peak values ​​located within the multipath window, the window length of the time-domain window is greater than or equal to the sum of the sign length of the S-PSS and the multipath length of the multipath window, the multipath window is used to capture time-domain window peak values ​​of a first number of multipath lengths before the current time-domain window peak value, the first number being an integer multiple of the multipath length; if there is a current multipath window peak value that meets the preset conditions among the current multipath window peak values, the current time-domain window peak value and the current multipath window peak value that meets the preset conditions are determined as a peak pair.

[0119] In an exemplary embodiment, the above-described apparatus further includes: a calculation unit, configured to, after obtaining a set of current multipath window peaks corresponding to the current time-domain window peak, calculate the ratio of each current multipath window peak to the current time-domain window peak in the set of current multipath window peaks, to obtain the peak ratio corresponding to each current multipath window peak, wherein the preset condition is that the corresponding peak ratio is greater than or equal to a preset peak ratio threshold.

[0120] In an exemplary embodiment, the peak value of a set of peak pairs is a complex value; the conversion unit includes: a second execution module, configured to merge the peak values ​​contained in each peak pair in the set of peak pairs, and perform squaring on the merged peak values ​​to obtain a metric value corresponding to each peak pair; and a merging module, configured to merge the metric values ​​corresponding to each peak pair based on the antenna corresponding to each peak pair to obtain a set of metric values.

[0121] In one exemplary embodiment, the apparatus further includes: a third execution unit, configured to perform a filtering operation on a set of metrics according to preset filtering conditions before performing a grouping operation on the obtained set of metrics according to the corresponding S-PSS identifier and frequency offset hypothesis, to obtain a filtered set of metrics, wherein the grouping operation is performed on the filtered set of metrics, and the preset filtering conditions include at least one of the following: the filtered metrics are greater than or equal to a preset noise threshold; the filtered metrics are the largest within a preset multipath threshold range; the number of metrics filtered from the corresponding S-PSS identifier and frequency offset hypothesis in descending order is less than or equal to a second number; and the total number of filtered metrics is less than or equal to a third number.

[0122] In an exemplary embodiment, the second execution unit includes: a third execution module, configured to perform conjugate multiplication on the peak pairs corresponding to the same antenna among the peak pairs corresponding to the N target metric values ​​under each S-PSS identifier, and merge the conjugate multiplication results to obtain a set of merged values, wherein the merged values ​​in the set of merged values ​​are complex values; an acquisition module, configured to acquire the residual frequency offset based on the phase of each merged value in the set of merged values, thereby obtaining the residual frequency offset corresponding to each merged value; and a determination module, configured to determine the frequency offset estimate corresponding to each merged value based on the frequency offset assumption corresponding to each merged value and the residual frequency offset corresponding to each merged value, thereby obtaining a set of frequency offset estimates.

[0123] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0124] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes the steps in any of the above method embodiments when it is run.

[0125] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0126] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor is configured to perform the steps of any of the method embodiments described above via the computer program. In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0127] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0128] According to another aspect of the embodiments of this application, a computer program product is also provided, comprising a computer program / instructions containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit 601, it performs various functions provided in the embodiments of this application. The sequence numbers of the embodiments of this application above are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0129] Figure 6 A schematic block diagram of a computer system architecture for implementing embodiments of the present application is shown. Figure 6 As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which performs various appropriate actions and processes based on programs stored in ROM 602 or loaded into RAM 603 from storage section 608. Random access memory 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0130] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card, such as a local area network card or modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0131] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit 601, it performs various functions defined in the system of this application.

[0132] It should be noted that, Figure 6 The computer system 600 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0133] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0134] The above are merely preferred embodiments of this application and are not intended to limit 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 principles of this application should be included within the protection scope of this application.

Claims

1. A method of detecting a synchronization signal, characterized by, The method comprises: obtaining a set of time domain baseband signals, wherein the set of time domain baseband signals comprises a sidelink primary synchronization signal (S-PSS); performing a specified three-dimensional search on the set of time domain baseband signals by using time domain windows to perform time domain sliding correlation on the set of time domain baseband signals, to obtain a set of peak value pairs, wherein the specified three-dimensional search is a three-dimensional search of S-PSS identifiers, frequency offset hypotheses, and timing positions, and one peak value pair in the set of peak value pairs comprises a peak value of one sliding point determined in the process of time domain sliding correlation and one time domain window peak value corresponding to the time domain window matched with the peak value of the one sliding point; converting the peak value pairs in the set of peak value pairs into corresponding metric values, and performing grouping operation on the obtained set of metric values according to corresponding S-PSS identifiers and frequency offset hypotheses, to obtain a plurality of sets of metric values under each S-PSS identifier; selecting M sets of metric values from the plurality of sets of metric values under each S-PSS identifier according to maximum metric values in the sets of metric values, and selecting N target metric values under each S-PSS identifier from the M sets of metric values under each S-PSS identifier, wherein M and N are both integers greater than or equal to 1; calculating a set of frequency offset estimation values based on the peak value pairs corresponding to the N target metric values under each S-PSS identifier, and determining a set of signal synchronization parameters according to the set of frequency offset estimation values, wherein each signal synchronization parameter in the set of signal synchronization parameters comprises one frequency offset estimation value in the set of frequency offset estimation values and corresponding S-PSS identifier and timing position.

2. The method of claim 1, wherein, After the step of obtaining the set of time domain baseband signals, the method further comprises: performing filtering and downsampling processing on the time domain baseband signals in the set of time domain baseband signals to obtain the set of time domain baseband signals after downsampling, wherein the specified three-dimensional search is performed on the set of time domain baseband signals after downsampling.

3. The method of claim 1, wherein, The step of performing the specified three-dimensional search on the set of time domain baseband signals by using time domain windows to perform time domain sliding correlation on the set of time domain baseband signals to obtain a set of peak value pairs comprises: performing time domain sliding by using the time domain windows according to a specified step size, and after each time domain sliding is performed, taking the sliding point corresponding to the time domain window as a current sliding point, and taking the signal sequence in the set of time domain baseband signals located in the time domain window as a current received sequence to perform the following search operation once, to obtain the set of peak value pairs, wherein the local sequence matched with the current received sequence is a current local sequence: performing peak value calculation based on the current received sequence and the current local sequence to obtain a current time domain window peak value, wherein the current time domain window peak value is the peak value of the current sliding point; obtain a group of current multipath window peaks corresponding to the current time domain window peak, wherein the group of current multipath window peaks are time domain window peaks located in a multipath window, a window length of the time domain window is greater than or equal to a sum of a symbol length of the S-PSS and a multipath length of the multipath window, the multipath window is used to capture time domain window peaks of a first number of the multipath length before the current time domain window peak, and the first number is an integral multiple of the multipath length; in a case where, in the current multipath window peaks, there is a current multipath window peak satisfying a preset condition, determine the current time domain window peak and the current multipath window peak satisfying the preset condition as a peak pair.

4. The method of claim 3, wherein, after the obtaining of the group of current multipath window peaks corresponding to the current time domain window peak, the method further comprises: respectively calculate a ratio of each current multipath window peak in the group of current multipath window peaks to the current time domain window peak to obtain a peak ratio corresponding to the each current multipath window peak, wherein the preset condition is that the corresponding peak ratio is greater than or equal to a preset peak ratio threshold.

5. The method of claim 1, wherein, the peaks of the group of peak pairs are complex values; and the converting of the peak pairs in the group of peak pairs into corresponding metric values comprises: respectively merge the peaks included in each peak pair in the group of peak pairs, and perform square processing on the merged peaks to obtain a metric value corresponding to the each peak pair; merge the metric values corresponding to the each peak pair based on antennas corresponding to the each peak pair to obtain the group of metric values.

6. The method of claim 1, wherein, before the performing of the grouping operation on the obtained group of metric values according to the corresponding S-PSS identifier and frequency offset assumption, the method further comprises: perform a screening operation on the group of metric values according to a preset screening condition to obtain a screened group of metric values, wherein the grouping operation is performed on the screened group of metric values, and the preset screening condition comprises at least one of the following: a screened metric value is greater than or equal to a preset noise threshold; a screened metric value is maximum within a preset multipath threshold range; a number of screened metric values in a metric value corresponding to the same S-PSS identifier and frequency offset assumption is less than or equal to a second number in a descending order; and a total number of screened metric values is less than or equal to a third number.

7. The method according to any one of claims 1 to 6, characterized in that, the calculating of the group of frequency offset estimation values based on the peak pairs corresponding to the N target metric values under the each S-PSS identifier comprises: conjugate multiply the peak pairs corresponding to the same antenna in the peak pairs corresponding to the N target metric values under the each S-PSS identifier, and merge the conjugate multiplication results to obtain a group of merged values, wherein a merged value in the group of merged values is a complex value; obtain a residual frequency offset according to a phase of each merged value in the group of merged values to obtain a residual frequency offset corresponding to the each merged value; determine a frequency offset estimation value corresponding to the each merged value according to a frequency offset assumption corresponding to the each merged value and the residual frequency offset corresponding to the each merged value to obtain the group of frequency offset estimation values.

8. An apparatus for detecting a synchronization signal, characterized by comprising: ​ The acquisition unit is configured to acquire a set of time domain baseband signals, wherein the set of time domain baseband signals comprises a sidelink primary synchronization signal (S-PSS); The searching unit is configured to perform a specified three-dimensional search on the set of time domain baseband signals by using time domain window time domain sliding correlation, to obtain a set of peak value pairs, wherein the specified three-dimensional search is a three-dimensional search of S-PSS identification, frequency offset hypothesis and timing position, and one peak value pair in the set of peak value pairs comprises a peak value of one sliding point determined in the process of time domain sliding correlation and a time domain window peak value corresponding to the time domain window matched with the one sliding point; The first execution unit is configured to convert the peak value pairs in the set of peak value pairs into corresponding metric values, and perform grouping operation on the obtained set of metric values according to corresponding S-PSS identification and frequency offset hypothesis, to obtain a plurality of metric value groups under each S-PSS identification; The selecting unit is configured to select M metric value groups from the plurality of metric value groups under each S-PSS identification according to the maximum metric value in the metric value group, and select N target metric values under each S-PSS identification from the M metric value groups under each S-PSS identification, wherein M and N are both integers greater than or equal to 1; The second execution unit is configured to calculate a set of frequency offset estimation values based on the peak value pairs corresponding to the N target metric values under each S-PSS identification, and determine a set of signal synchronization parameters according to the set of frequency offset estimation values, wherein each signal synchronization parameter in the set of signal synchronization parameters comprises one frequency offset estimation value in the set of frequency offset estimation values and corresponding S-PSS identification and timing position.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method in any one of claims 1 to 7.

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