Primary synchronization signal detection method and apparatus, and computer-readable storage medium

Abstract

The present disclosure provides a primary synchronization signal detection method, device and computer readable storage medium. The method comprises: performing segment compression preprocessing on three groups of local PSS time domain sequences respectively; performing down-sampling processing on a received signal; sliding point by point on a plurality of down-sampled data, and performing segment compression preprocessing on the time domain signal of the down-sampled data in each sliding correlation window; performing correlation operation on the time domain signal and the three groups of compressed time domain sequences to obtain three groups of time domain correlation values; searching for a maximum correlation peak value from the three groups of time domain correlation values, calculating the average value and the peak-to-average ratio value of the three groups of time domain correlation values; in the case that the ratio value is greater than or equal to a threshold value, determining the corresponding intra-cell identification group number and coarse synchronization time point according to the maximum correlation peak value; in the case that the ratio value is less than the threshold value, calculating a frequency domain correlation value, and determining the corresponding intra-cell identification group number and coarse synchronization time point according to the maximum correlation peak value of the frequency domain correlation value.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of communication technology, and particularly relates to a primary synchronization signal detection method, device and computer readable storage medium. BACKGROUND

[0002] Synchronization technology plays an important role in a communication system, and is necessary and prerequisite for the correct information transmission of the receiving and transmitting ends of the communication system. In a mobile communication system, synchronization technology also has a wide range of applications, such as terminal access to the network, small cell cooperative networking and the like. As a new generation of cellular mobile technology, the 5th-Generation (5G) is redesigned for synchronization signals in order to meet the requirements of enhanced Mobile Broadband (eMBB), ultra Reliable Low Latency Communication (uRLLC) and massive Machine Type of Communication (mMTC) and other scenarios, and introduces the concept of Synchronization Signal and PBCH block (SSB), which contains three parts of Primary Synchronization Signals (PSS), Secondary Synchronization Signals (SSS) and Physical Broadcast Channel (PBCH). The SSB carries the physical cell identity number, Master Information Block (MIB) and other key information, which is crucial for the subsequent cell access process.

[0003] Synchronization search is the first step of cell access to the network, and the detection performance directly affects the performance of the entire communication system. PSS is the first signal to be detected in the synchronization search process, and successful capture of PSS at the receiving end can obtain the cell identification group number and the coarse synchronization time point. The PSS cross-correlation detection algorithm is a commonly used classical detection algorithm. However, the PSS cross-correlation detection algorithm contains a large number of complex multiplication operations, which greatly consumes storage space and hardware resources, and has poor timeliness in operation, and it is difficult to meet the low latency requirements of 5G. SUMMARY

[0004] One of the technical problems solved by the present disclosure is to provide a primary synchronization signal detection method to reduce the complexity of primary synchronization signal detection.

[0005] According to one aspect of the present disclosure, a primary synchronization signal detection method is provided, comprising: performing segment compression preprocessing on three groups of local primary synchronization signal (PSS) time domain sequences respectively to obtain three groups of compressed time domain sequences; performing down-sampling processing on a received signal to obtain a plurality of down-sampled data; performing point-by-point sliding on the plurality of down-sampled data, and performing segment compression preprocessing on the time domain signals of the down-sampled data in each sliding correlation window; performing correlation operation on the time domain signals after segment compression preprocessing and the three groups of compressed time domain sequences to obtain three groups of time domain correlation values; searching for a maximum correlation peak value in the three groups of time domain correlation values, calculating an average value of the three groups of time domain correlation values, and calculating a ratio of the maximum correlation peak value to the average value; in a case where the ratio is greater than or equal to a threshold value, determining a corresponding intra-cell identification group number and a coarse synchronization time point according to the maximum correlation peak value; and in a case where the ratio is less than the threshold value, transforming the time domain signals of part of the time domain correlation values in the three groups of time domain correlation values into frequency domain signals, calculating frequency domain correlation values of the frequency domain signals and local PSS frequency domain sequences, and determining a corresponding intra-cell identification group number and a coarse synchronization time point according to a maximum correlation peak value of the frequency domain correlation values.

[0006] In some embodiments, the step of performing segment compression preprocessing on three groups of local primary synchronization signal (PSS) time domain sequences respectively to obtain three groups of compressed time domain sequences comprises: generating three groups of local PSS frequency domain sequences; performing N-point inverse fast Fourier transform (IFFT) transformation on the three groups of local PSS frequency domain sequences to obtain three groups of local PSS time domain sequences, wherein the i-th group of local PSS time domain sequence is represented as: wherein N≥1 and N is a positive integer, i=0, 1, 2; dividing each group of local PSS time domain sequence into K segments, and performing summation operation on each segment of local PSS time domain sequence, so that the length of each group of local PSS time domain sequence is compressed from N points to K points to obtain a compressed local PSS time domain sequence P i , i.e. wherein K is a positive integer and K

[0007] In some embodiments, the step of performing point-by-point sliding on the plurality of down-sampled data, and performing segment compression preprocessing on the time domain signals of the down-sampled data in each sliding correlation window comprises: performing sliding with a step length of 1 on L-point down-sampled data, and the sliding window length is N points, wherein L>N and L is a positive integer, when sliding to the l-th point, the N-point data in the sliding correlation window is represented as a sampling point PSS time domain sequence R l , i.e., R l = [r0, r1, r2, …, r N-2r N-1 , where l = 0, 1, 2, …, L-N, r0to r N-1 are elements of the sampling point PSS time domain sequence R l ; the sampling point PSS time domain sequence R l is divided into K segments, and a summation operation is performed on each segment of the sampling point PSS time domain sequence, so that the data length of the sampling point PSS time domain sequence R l is compressed from N points to K points, that is, a compressed sampling point PSS time domain sequence R' l is obtained, that is, R' K-1 = [r'0, r'1, …, r' j ], where r'0to r' jN / K+0 are elements of the compressed sampling point PSS time domain sequence R' jN / K+1 . jN / K+N / K-1 , …, +r K-1 , j = 0, 1, …, K-1, r'0to r' l are elements of the compressed sampling point PSS time domain sequence R' l .

[0008] In some embodiments, the step of performing a correlation operation on the segmented and compressed time domain signal and the three compressed time domain sequences to obtain three groups of time domain correlation values includes: performing a correlation operation on the compressed sampling point PSS time domain sequence R' i and the compressed local PSS time domain sequence P i,l to obtain a time domain correlation value pss_corr i,l , pss_corr l = |R' i · P H |, where i = 0, 1, 2, l = 0, 1, 2, …, L-N, (·) H is a conjugate transpose operation, and |·| is a modulus value operation.

[0009] In some embodiments, the relationship formula for calculating the ratio C of the maximum correlation peak value and the average value is: where max(pss_corr i,l ) is the maximum correlation peak value in the three groups of time domain correlation values, and mean(pss_corr i,l ) is the average value of the three groups of time domain correlation values.

[0010] In some embodiments, in the case where the ratio is greater than or equal to a threshold value, the step of determining the cell identification group number and the coarse synchronization time point according to the maximum correlation peak value includes: in the case where the ratio is greater than or equal to a threshold value, determining the cell identification group number The coarse synchronization time point is determined according to the position l of the sliding point corresponding to the maximum correlation peak value.

[0011] In some embodiments, the step of transforming the time domain signals of part of the time domain correlation values in the three groups of time domain correlation values into frequency domain signals comprises: sorting the time domain correlation values in each group of time domain correlation values in descending order respectively, retaining the first M time domain correlation values in each group after sorting as a candidate peak value set, taking out the time domain signals corresponding to the positions of the time domain correlation values in the candidate peak value set, transforming the time domain signals into frequency domain signals, and taking out PSS frequency domain data S 0,l ,pss_Corr 1,l ,pss_corr 2,l of the frequency domain signals according to a mapping rule, wherein the PSS frequency domain data S i,m is as follows: wherein i = 0, 1, 2, m = 0, 1, …, M-1, and M is a positive integer.

[0012] In some embodiments, the step of calculating the frequency domain correlation values of the frequency domain signals and the local PSS frequency domain sequence comprises: performing correlation calculation on the PSS frequency domain data S i,m of the frequency domain signals and the corresponding local PSS frequency domain sequence to obtain the frequency domain correlation values pss_corr' i,m , wherein the PSS frequency domain data S i,m is as follows: i,m i H wherein i = 0, 1, 2.

[0013] In some embodiments, the step of determining the cell identification group number and the coarse synchronization time point according to the maximum correlation peak value of the frequency domain correlation values comprises: searching for the maximum correlation peak value in the frequency domain correlation values pss_corr' i,m ; determining the cell identification group number according to the group number i corresponding to the maximum correlation peak value of the frequency domain correlation values pss_corr' i,m ; and finding the position l of the sliding point of the time domain correlation value in the candidate peak value set corresponding to the point of the i-th row and the m-th frequency domain correlation value according to the index value m corresponding to the maximum correlation peak value of the frequency domain correlation values pss_corr' i , thereby determining the coarse synchronization time point.

[0014] ​According to another aspect of the present disclosure, a primary synchronization signal detection device is provided, comprising: a first preprocessing unit configured to perform segment compression preprocessing on three groups of local primary synchronization signal (PSS) time domain sequences to obtain three groups of compressed time domain sequences; a down-sampling unit configured to perform down-sampling processing on a received signal to obtain a plurality of down-sampled data; a second preprocessing unit configured to slide the plurality of down-sampled data point by point, and perform segment compression preprocessing on the time domain signals of the down-sampled data in each sliding correlation window; a calculation unit configured to perform correlation calculation on the time domain signals after segment compression preprocessing and the three groups of compressed time domain sequences to obtain three groups of time domain correlation values, search for a maximum correlation peak value in the three groups of time domain correlation values, calculate an average value of the three groups of time domain correlation values, and calculate a ratio of the maximum correlation peak value to the average value; a first determination unit configured to determine a corresponding cell identification group number and a coarse synchronization time point according to the maximum correlation peak value in a case where the ratio is greater than or equal to a threshold value; and a second determination unit configured to, in a case where the ratio is less than the threshold value, transform the time domain signals of part of the time domain correlation values in the three groups of time domain correlation values into frequency domain signals, calculate frequency domain correlation values of the frequency domain signals and local PSS frequency domain sequences, and determine a corresponding cell identification group number and a coarse synchronization time point according to a maximum correlation peak value of the frequency domain correlation values.

[0015] In some embodiments, the first preprocessing unit is configured to generate three groups of local PSS frequency domain sequences, perform N-point inverse fast Fourier transform (IFFT) transformation on the three groups of local PSS frequency domain sequences to obtain three groups of local PSS time domain sequences, wherein the i-th group of local PSS time domain sequence is represented as: wherein N>1 and N is a positive integer, i=0, 1, 2; each group of local PSS time domain sequence is divided into K segments, and summation operation is performed on each segment of local PSS time domain sequence, so that the length of each group of local PSS time domain sequence is compressed from N points to K points, to obtain a compressed local PSS time domain sequence P i , i.e. wherein K is a positive integer and K<N.

[0016] In some embodiments, the second preprocessing unit is configured to slide the L-point down-sampled data with a step length of 1, and the sliding window length is N points, wherein L>N and L is a positive integer, when sliding to the l-th point, the N-point data in the sliding correlation window is represented as a sampling point PSS time domain sequence R l , i.e., R l =[r0, r1, r2, …, r N-2 , r N-1 ], wherein l=0, 1, 2, …, L-N, r0 to r N-1is an element of the sampling point PSS time domain sequence R l is divided into K segments, and a summation operation is performed on the sampling point PSS time domain sequence in each segment, so that the data length of the sampling point PSS time domain sequence R l is compressed from N points to K points, and a compressed sampling point PSS time domain sequence R' l is obtained, that is, R' l = [r'0, r'1,..., r'K-1], where r'j = rj + rj+K, j = 0, 1,..., K-1, r'0 to r'K-1 are elements of the compressed sampling point PSS time domain sequence R' K-1 . j jN / K+0 jN / K+1 jN / K+N / K-1 K-1 l

[0017] In some embodiments, the operation unit is configured to perform a correlation operation on the compressed sampling point PSS time domain sequence R' l and a compressed local PSS time domain sequence P i to obtain time domain correlation values pss_corr i,l , pss_corr i,l = |R' l · P i H |, where i = 0, 1, 2, l = 0, 1, 2,..., L-N, (·) H is a conjugate transpose operation, and |·| is a modulus value operation.

[0018] In some embodiments, the operation unit calculates a relationship of a ratio C of the maximum correlation peak value to the average value as follows: where max(pss_corr i,l ) is the maximum correlation peak value in the three groups of time domain correlation values, and mean(pss_corr i,l ) is the average value of the three groups of time domain correlation values.

[0019] In some embodiments, the first determination unit is configured to determine a cell identification group number according to the group number i corresponding to the maximum correlation peak value, and determine a coarse synchronization time point according to the position l of the sliding point corresponding to the maximum correlation peak value, when the ratio is greater than or equal to a threshold value.

[0020] In some embodiments, the second determination unit is configured to determine three groups of time domain correlation values pss_corr 0,l , pss_corr 1,l , and pss_corr 2,l .​​​​​The time-domain correlation values ​​in each group are sorted from largest to smallest. The top M time-domain correlation values ​​of each group are retained as a candidate peak set. The time-domain signals corresponding to the time-domain correlation values ​​in the candidate peak set are extracted, and the time-domain signals are transformed into frequency-domain signals. According to the mapping rules, the PSS frequency domain data S of the frequency-domain signals are extracted. i,m for: Where i = 0, 1, 2, m = 0, 1, ..., M-1, and M is a positive integer.

[0021] In some embodiments, the second determining unit is used to determine the PSS frequency domain data S of the frequency domain signal. i,m With the corresponding local PSS frequency domain sequence Perform relevant calculations to obtain the frequency domain correlation value pss_corr′. i,m For: pss_corr′ i,m =|S i,m ·O i H | where i = 0, 1, 2.

[0022] In some embodiments, the second determining unit is used to search for the frequency domain correlation value pss_corr′. i,m The maximum correlation peak value in the frequency domain; based on the frequency domain correlation value pss_corr′ i,m The cell identifier group number is determined by the intra-group number i corresponding to the maximum correlation peak value. Value; and based on the frequency domain correlation value pss_corr′ i,m Find the index value m corresponding to the maximum correlation peak, find the position l of the sliding point of the time domain correlation value in the candidate peak set corresponding to the point of the m-th frequency domain correlation value in the i-th row, and thus determine the coarse synchronization time point.

[0023] According to another aspect of this disclosure, a master synchronization signal detection device is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute the method described above based on instructions stored in the memory.

[0024] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the method described above.

[0025] The above-disclosed method for detecting the master synchronization signal can reduce the complexity of master synchronization signal detection.

[0026] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] Referring to the drawings, the present disclosure can be more clearly understood according to the following detailed description with reference to the following illustrative embodiments. Like numbers may

[0029] Figure 1 is a flow chart illustrating a primary synchronization signal detection method according to some embodiments of the present disclosure;

[0030] Figure 2 is a flow chart illustrating a primary synchronization signal detection method according to some embodiments of the present disclosure;

[0031] Figure 3 is a mapping diagram of SSB according to some embodiments of the present disclosure;

[0032] Figure 4 is a structural block diagram of a primary synchronization signal detection apparatus according to some embodiments of the present disclosure;

[0033] Figure 5 is a structural block diagram of a primary synchronization signal detection apparatus according to some embodiments of the present disclosure;

[0034] Figure 6 is a structural block diagram of a primary synchronization signal detection apparatus according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0035] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. If it is considered that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0036] It should be understood, however, that the sizes of the components shown in the drawings are illustrative and not necessarily drawn to scale.

[0037] The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the present disclosure or its application or uses.

[0038] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein. However, where appropriate, such techniques, methods, and devices can be considered part of the present disclosure.

[0039] In all of the examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not as a limitation. Thus, other examples of exemplary embodiments can have different values.

[0040] It should be noted that like numerals and letters refer to like items throughout the drawings, and once an item is defined in one drawing, further discussion of the same item is not necessary in subsequent drawings.

[0041] Figure 1 is a flowchart illustrating a primary synchronization signal detection method according to some embodiments of the present disclosure. As shown in Figure 1 , the method comprises steps S102-S114.

[0042] In step S102, three groups of local PSS time domain sequences are respectively subjected to segment compression preprocessing to obtain three groups of compressed time domain sequences.

[0043] In some embodiments, the step S102 can comprise generating three groups of local PSS frequency domain sequences. For example, the three groups of PSS frequency domain sequences d PSS (n) can be generated by existing standard techniques, as follows:

[0044] d PSS (n) = 1-2x(u), (1)

[0045]

[0046] where x(i+7) = (x(i+4) + x(i)) mod 2, (3)

[0047] [x(6) x(5) x(4) x(3) x(2) x(1) x(0)] = [1 1 1 0 1 1 0]. (4)

[0048] The step S102 can also comprise subjecting the three groups of local PSS frequency domain sequences to N-point IFFT (Inverse Fast Fourier Transform) transformation to obtain three groups of local PSS time domain sequences, where the i-th group of local PSS time domain sequences is represented as:

[0049]

[0050] where N≥1, and N is a positive integer, i = 0, 1, 2. Here, N is the number of FFT (Fast Fourier Transform) points, i.e., the number of IFFT points.

[0051] The step S102 can also comprise dividing each group of local PSS time domain sequences into K segments, and performing summation operation on each segment of local PSS time domain sequences, so that the length of each group of local PSS time domain sequences is compressed from N points to K points, to obtain the compressed local PSS time domain sequences P i , i.e.

[0052]

[0053] wherein, K is a positive integer and K < N.

[0054] Three groups of compressed pre-processed local PSS time domain sequences can be stored by using local registers.

[0055] In step S104, the received signal is down-sampled to obtain a plurality of down-sampled data.

[0056] For example, the received signal is down-sampled, and L-point down-sampled data is stored for subsequent processing. Here, L > N, and L is a positive integer. For example, L is much larger than N.

[0057] In step S106, the plurality of down-sampled data is point-by-point slid, and the time domain signal of the down-sampled data in each sliding correlation window is segmented and compressed pre-processed. Here, point-by-point sliding refers to sliding with a step size of 1.

[0058] In some embodiments, step S106 can include sliding the L-point down-sampled data with a step size of 1, and the sliding window length is N points, wherein L > N, and L is a positive integer. When sliding to the lth point, the N-point data in the sliding correlation window is represented as a sampled PSS time domain sequence R l That is,

[0059] R l = [r0, r1, r2, …, r N-2 , r N-1 ], (6)

[0060] wherein l = 0, 1, 2, …, L-N, r0 to r N-1 are elements of the sampled PSS time domain sequence.

[0061] This step S106 can also include dividing the sampled PSS time domain sequence R l into K segments, and performing summation operation on each segment of the sampled PSS time domain sequence, so that the data length of the sampled PSS time domain sequence R l is compressed from N points to K points, that is, a compressed sampled PSS time domain sequence R′ l That is

[0062] R′ l = [r′0, r′1, …, r′ K-1 ],

[0063] wherein r′ j = r jN / K+0 + r jN / K+1 + … + rjN / K+N / K-1 j = 0, 1, …, K-1, r'0to r'K-1 K-1 are elements of the compressed sample point PSS time domain sequence R'. l are elements of the compressed sample point PSS time domain sequence R'.

[0064] In step S108, the segmented compressed pre-processed time domain signal is correlated with the three sets of compressed time domain sequences to obtain three sets of time domain correlation values. Here, the correlation operation refers to an operation of calculating a correlation value.

[0065] In some embodiments, step S108 can include correlating the compressed sample point PSS time domain sequence R' l with the compressed local PSS time domain sequence P i to obtain a time domain correlation value pss_corr i,l .

[0066] pss_corr i,l = |R' l · P i H |, (7)

[0067] where i = 0, 1, 2, l = 0, 1, 2, …, L-N, (·) H is a conjugate transpose operation, and |·| is a modulus value operation.

[0068] In step S110, the maximum correlation peak value in the three sets of time domain correlation values is searched from the three sets of time domain correlation values, the average value of the three sets of time domain correlation values is calculated, and the ratio of the maximum correlation peak value to the average value is calculated.

[0069] For example, the maximum correlation peak value max(pss_corr i,l ) in the three sets of time domain correlation values can be searched from the three sets of time domain correlation values. Here, the maximum correlation peak value is the maximum value in the correlation values (for example, the time domain correlation values or the frequency domain correlation values to be described later). Then, the average value mean(pss_corr corri,l ) of the three sets of time domain correlation values is calculated.

[0070] In some embodiments, the relationship of the ratio C of the maximum correlation peak value to the average value of the time domain correlation values is as follows:

[0071]

[0072] where max(pss_corr i,l ) is the maximum correlation peak value in the three sets of time domain correlation values, and mean(pss_corr i,l ) is the average value of the three sets of time domain correlation values. The ratio C can be referred to as a peak-to-average ratio value.

[0073] At step S112, in the case that the ratio is greater than or equal to the threshold value, the intra-cell identification group number and the coarse synchronization time point are determined according to the maximum correlation peak value.

[0074] Here, the threshold value (which can be referred to as a peak-to-average ratio threshold) can be set according to actual needs, and those skilled in the art can understand that the scope of the present disclosure is not limited to the specific value of the threshold value.

[0075] For example, V t represents a peak-to-average ratio threshold, and if the peak-to-average ratio C is greater than or equal to the threshold V t , then the intra-cell identification group number i corresponding to the maximum correlation peak value and the position l of the sliding point can be determined , and the coarse synchronization time point.

[0076] In some embodiments, the step S112 includes: in the case that the ratio is greater than or equal to the threshold value, determining the intra-cell identification group number i according to the maximum correlation peak value corresponding to the intra-cell identification group number i , and determining the coarse synchronization time point according to the position l of the sliding point corresponding to the maximum correlation peak value. That is, The time point of l is the coarse synchronization time point.

[0077] At step S114, in the case that the ratio is less than the threshold value, the time domain signal of part of the time domain correlation values in the three groups of time domain correlation values is transformed into a frequency domain signal, the frequency domain correlation value of the frequency domain signal and the local PSS frequency domain sequence is calculated, and the intra-cell identification group number and the coarse synchronization time point corresponding to the maximum correlation peak value of the frequency domain correlation value are determined.

[0078] In some embodiments, the step of transforming the time domain signal of part of the time domain correlation values in the three groups of time domain correlation values into a frequency domain signal includes: sorting the time domain correlation values in each group of the three groups of time domain correlation values pss_corr 0,l , pss_corr 1,l , and pss_corr 2,l from large to small respectively, retaining the first M time domain correlation values in each group after sorting as a candidate peak value set, taking out the time domain signal corresponding to the position of the time domain correlation value in the candidate peak value set, transforming the time domain signal into a frequency domain signal, and taking out the PSS frequency domain data S i,m of the frequency domain signal according to the mapping rule as follows:

[0079]

[0080] wherein i = 0, 1, 2, and m = 0, 1, …, M-1, and M is a positive integer.

[0081] In some embodiments, the step of calculating the frequency domain correlation value of the frequency domain signal with the local PSS frequency domain sequence comprises: correlating the PSS frequency domain data S i,m with the corresponding local PSS frequency domain sequence to obtain the frequency domain correlation value pss_corr' i,m is:

[0082] pss_corr' i,m = |S i,m · O i H |, (10)

[0083] wherein i = 0, 1, 2.

[0084] It should be noted that the local PSS frequency domain sequence O i here is the local PSS frequency domain sequence d PSS (n) described above.

[0085] In some embodiments, the step of determining the corresponding cell identification group number and the coarse synchronization time point according to the maximum correlation peak value of the frequency domain correlation value comprises: searching for the maximum correlation peak value in the frequency domain correlation value pss_corr' i,m ; determining the cell identification group number according to the group number i corresponding to the maximum correlation peak value of the frequency domain correlation value pss_corr' i,m ; and finding the position l of the sliding point of the time domain correlation value in the candidate peak value set of the point corresponding to the i-th row and the m-th frequency domain correlation value according to the index value m corresponding to the maximum correlation peak value of the frequency domain correlation value pss_corr' i,m , thereby determining the coarse synchronization time point.

[0086] Here, the corresponding time domain correlation value in the candidate peak value set can be found according to the index value m corresponding to the maximum correlation peak value of the frequency domain correlation value pss_corr' i,m , and the position l of the sliding point in the time domain correlation value (pss_corr 0,l , pss_corr 1,l , or pss_corr 2,l ) can be determined according to the time domain correlation value, and the coarse synchronization time point can be determined according to the position l of the sliding point.

[0087] In the above embodiments, the size of the frequency domain correlation value can be compared first, and the maximum value in the frequency domain correlation value can be found, and the position of the sliding point of the time domain correlation value can be determined according to the maximum value.

[0088] For example, taking M = 2 as an example, the following is explained:

[0089] (1) Sort each group of time-domain correlation values respectively and retain the first two time-domain correlation values, and store the position l, i.e. a 3-row 2-column array of position l, for example: {[550 800]; [600, 100]; [700, 900]}.

[0090] (2) According to the above six l positions, the time-domain signal is transformed into the frequency domain, and the frequency-domain correlation value is calculated, for example: {[0.2 0.4]; [1, 0.8]; [0.5 0.7]}, and the maximum value of the frequency-domain correlation value is found to be 1, and the corresponding row i is 1 (i starts from 0), and m = 0.

[0091] (3) According to the values of i and m, the l in the array in step (1) is taken, i.e. l = 600, which is the coarse synchronization time point.

[0092] At this point, the primary synchronization signal detection method according to some embodiments of the present disclosure is provided. The method comprises: performing segment compression preprocessing on three groups of local PSS time-domain sequences to obtain three groups of compressed time-domain sequences; performing down-sampling processing on a received signal to obtain a plurality of down-sampled data; sliding the plurality of down-sampled data point by point, and performing segment compression preprocessing on the time-domain signal of the down-sampled data in each sliding correlation window; performing correlation operation on the segment compression preprocessed time-domain signal and the three groups of compressed time-domain sequences to obtain three groups of time-domain correlation values; searching for the maximum correlation peak value in the three groups of time-domain correlation values, calculating the average value of the three groups of time-domain correlation values, and calculating the ratio of the maximum correlation peak value to the average value; in the case that the ratio is greater than or equal to a threshold value, determining the corresponding cell identification group number and coarse synchronization time point according to the maximum correlation peak value; and in the case that the ratio is less than the threshold value, transforming the time-domain signal of part of the time-domain correlation values in the three groups of time-domain correlation values into a frequency-domain signal, calculating the frequency-domain correlation value of the frequency-domain signal and the local PSS frequency-domain sequence, and determining the corresponding cell identification group number and coarse synchronization time point according to the maximum correlation peak value of the frequency-domain correlation value. The method can reduce the complexity of primary synchronization signal detection.

[0093] Compared with related technologies, the above method of the present disclosure has the following advantages:

[0094] Compared with the PSS cross-correlation algorithm in related technologies, the method of the present disclosure can multiply the number of complex multiplication operations by performing segment compression preprocessing on the data in the sliding window, greatly reducing the calculation complexity and improving the calculation time efficiency.

[0095] The above method of the present disclosure has self-adaptability to different channel conditions by introducing a threshold decision mechanism, which can effectively reduce the calculation complexity and improve the detection efficiency while ensuring the detection performance.

[0096] The length of the three groups of PSS time domain sequences stored in the PSS cross-correlation algorithm of the related technology is the number of FFT points, the method of the present disclosure respectively performs segmentation and compression preprocessing on the three groups of PSS time domain sequences, and the length of the three groups of PSS time domain sequences stored is the number of segments, thereby effectively saving storage space.

[0097] Figure 2 is a flow chart illustrating a primary synchronization signal detection method according to some other embodiments of the present disclosure. Figure 3 is a mapping schematic diagram of SSB according to some embodiments of the present disclosure. The following will be described in combination with Figure 2 and Figure 3 The primary synchronization signal detection method according to some embodiments of the present disclosure will be described in detail. The method includes steps S202 to S222.

[0098] Before step S202, three groups of compressed and preprocessed local PSS time domain sequences are stored in advance.

[0099] In step S202, the received signal is subjected to down-sampling processing, and L-point down-sampled data is stored for subsequent processing.

[0100] In step S204, the L-point down-sampled data is subjected to sliding with a step of 1, and the sliding window length is N points. When sliding to the lth point, the N-point data in the sliding correlation window is represented as a sampled point PSS time domain sequence R l , that is,

[0101] R l = [r0, r1, r2, …, r N-2 , r N-1 ],

[0102] wherein, l = 0, 1, 2, …, L-N, r0 to r N-1 are elements of the sampled point PSS time domain sequence.

[0103] In step S206, the sampled point PSS time domain sequence R l is divided into K segments, and a summation operation is performed on each segment of the sampled point PSS time domain sequence. Thus, the data length is compressed from N points to K points, and a compressed sampled point PSS time domain sequence R′ l is obtained, that is

[0104] R′ l = [r′0, r′1, …, r′ K-1 ],

[0105] wherein, r′ j = r jN / K+0 + r jN / K+1 + … + r jN / K+N / K-1 , j = 0, 1, …, K-1.

[0106] In step S208, the compressed sample point PSS time domain sequence R' l is correlated with the compressed local PSS time domain sequence P i to obtain a time domain correlation value pss_corr i,l ,

[0107] pss_corr i,l = |R′ l · P i H |.

[0108] In step S210, the maximum correlation peak max(pss_corr i,l ) in the three sets of time domain correlation values is searched from the three sets of time domain correlation values, the average value mean(pss corri,l ) of the three sets of time domain correlation values is calculated, and the ratio of the maximum correlation peak to the average value, i.e. the peak-to-average ratio C, is calculated:

[0109] In step S212, a peak-to-average ratio threshold V t is introduced, and it is judged whether the peak-to-average ratio C is less than the threshold value V t . If the peak-to-average ratio C is less than the threshold value V t , the process enters step S214, i.e. the step of blindly searching the three sets of PSS time domain sequences is started to be executed. If the peak-to-average ratio C is greater than or equal to the threshold value V t , the process enters step S222, i.e. the cell identification group number is determined according to the group number i corresponding to the maximum correlation peak, and the coarse synchronization time point is determined according to the position l of the sliding point corresponding to the maximum correlation peak.

[0110] In step S214, the time domain correlation values in each of the three sets of time domain correlation values pss_corr 0,l , pss_corr 1,l , pss_corr 2，l are respectively sorted from large to small, and the first M time domain correlation values in each set after sorting are reserved as a candidate peak value set.

[0111] In step S216, the time domain signal corresponding to the position of the time domain correlation value in the candidate peak value set is taken out, the time domain signal is transformed to the frequency domain through FFT, and the PSS frequency domain data S i,m of the frequency domain signal is taken out according to the mapping rule as shown in formula (3): Figure 3 wherein i=0, 1, 2, m=0, 1, …, M-1, and M is a positive integer.

[0112] Figure 3 ​​OFDM (Orthogonal Frequency Division Multiplexing) symbol and the number of FFT points are shown in the middle. For example, Figure 3 The frequency domain data of the PSS is shown from point 56 to point 182.

[0113] In step S218, the PSS frequency domain data S i,m is correlated with the corresponding local PSS frequency domain sequence to obtain the frequency domain correlation value pss_corr' i,m .

[0114] pss_corr' i,m = |S i,m · O i H |, i = 0, 1, 2.

[0115]

[0116] In step S220, the maximum correlation peak value in the frequency domain correlation value pss_corr' i,m is searched.

[0117] In step S222, the cell identification group number is determined according to the group number i corresponding to the maximum correlation peak value of the frequency domain correlation value pss_corr' i，m ; and the position l of the sliding point of the time domain correlation value in the candidate peak value set of the point corresponding to the ith row and mth frequency domain correlation value is found according to the index value m corresponding to the maximum correlation peak value of the frequency domain correlation value pss_corr' i,m , so as to determine the coarse synchronization time point.

[0118] So far, the primary synchronization signal detection method according to some embodiments of the present disclosure is provided. In the method, the received signal is subjected to downsampling processing, and the time domain signal in each sliding correlation window is subjected to segment compression preprocessing; the three groups of local PSS time domain sequences are also subjected to segment compression preprocessing, and the processed data is stored; the processed received signal and the locally stored compressed time domain sequence are subjected to correlation operation; the peak-to-average ratio value is calculated and a threshold is introduced; if the peak-to-average ratio value meets the threshold requirement (the peak-to-average ratio value is greater than or equal to the threshold value), it is determined that the detection is correct, and the group number and the coarse synchronization time point corresponding to the maximum correlation peak value are output; if the peak-to-average ratio value does not meet the threshold requirement (the peak-to-average ratio value is less than the threshold value), the three groups of PSS sequences are blindly detected, that is, the first M correlation values in descending order are reserved in the correlation results of the three groups of local sequences, the corresponding time domain signals are taken out, subjected to FFT transformation to the frequency domain, subjected to frequency domain correlation calculation with the corresponding frequency domain PSS sequence, and the group number and the coarse synchronization time point corresponding to the maximum correlation peak value in the frequency domain are output.​

[0119] The method of the present disclosure can multiply reduce the complex multiplication operation amount and reduce the storage space of the local PSS time domain sequence in the process of calculating the correlation peak point by point by segmenting and compressing the data in the sliding window, and the threshold decision processing can reduce the calculation complexity while ensuring the detection performance, so as to achieve the purposes of reducing the algorithm implementation complexity, improving the PSS detection time efficiency, saving the storage space and hardware resources.

[0120] The following is verified by using the Matlab software simulation platform. For example, the system related parameter configuration table 1 is shown in the following table:

[0121] Table 1: Exemplary system related parameter configuration

[0122]

[0123]

[0124] The wireless channel adopts the extended vehicle channel model (EVA) channel, and the moving speed is 30 km / h.

[0125] The above method of the present disclosure is simulated on the Matlab software by using the above parameters, and the related parameter settings in the detection algorithm process are as follows: the length of the reserved down-sampling data is L=38400, the length of the sliding window is N=256, the number of FFT points is 256, the number of segments of the data in the sliding window and the local PSS time domain sequence is K=128, the peak-to-average ratio threshold Vt=5, and the maximum correlation peak value of each group reserved is M=3. Under such parameter settings, the method of the present disclosure is compared with the cross-correlation algorithm of the related art as follows:

[0126] In terms of detection performance: when the PSS detection accuracy is 90%, the performance of the method of the present disclosure is close to that of the cross-correlation algorithm.

[0127] In terms of calculation complexity: the method of the present disclosure reduces the calculation complexity by about half compared with the traditional cross-correlation algorithm, and improves the PSS detection efficiency.

[0128] In terms of storage space: the length of the sliding window and the PSS time domain sequence of the cross-correlation algorithm of the related art is 256 points, and the method of the present disclosure stores the data length of K=128 by segmenting and summing, thereby saving half of the storage resources.

[0129] Therefore, the PSS detection method of the present disclosure has detection performance close to that of the cross-correlation algorithm of the related art, effectively reduces the calculation complexity, improves the PSS detection time efficiency, and saves the storage space resources.

[0130] Figure 4 is a structural block diagram illustrating a primary synchronization signal detection apparatus according to some embodiments of the present disclosure. As shown in Figure 4 , the primary synchronization signal detection apparatus comprises a first preprocessing unit 402, a down-sampling unit 404, a second preprocessing unit 406, a calculation unit 408, a first determination unit 410 and a second determination unit 412.

[0131] The first preprocessing unit 402 is configured to perform segment compression preprocessing on three groups of local primary synchronization signal (PSS) time domain sequences respectively to obtain three groups of compressed time domain sequences.

[0132] In some embodiments, the first preprocessing unit 402 is configured to generate three groups of local PSS frequency domain sequences, perform N-point IFFT transformation on the three groups of local PSS frequency domain sequences to obtain three groups of local PSS time domain sequences, wherein the i-th group of local PSS time domain sequence is represented as: wherein N≥1 and N is a positive integer, i=0, 1, 2; each group of local PSS time domain sequence is divided into K segments, and a summation operation is performed on each segment of local PSS time domain sequence, so that the length of each group of local PSS time domain sequence is compressed from N points to K points to obtain a compressed local PSS time domain sequence P i , i.e. wherein K is a positive integer and K

[0133] The down-sampling unit 404 is configured to perform down-sampling processing on a received signal to obtain a plurality of down-sampling data.

[0134] The second preprocessing unit 406 is configured to slide each point of the plurality of down-sampling data, and perform segment compression preprocessing on the time domain signal of the down-sampling data in each sliding correlation window.

[0135] In some embodiments, the second preprocessing unit 406 is configured to slide the L-point down-sampling data with a step of 1, and the sliding window length is N points, wherein L>N and L is a positive integer, when sliding to the l-th point, the N-point data in the sliding correlation window is represented as a sampled PSS time domain sequence R l , i.e. R l =[r0, r1, r2, …, r N-2 , r N-1 ], wherein l=0, 1, 2, …, L-N, r0 to r N-1 are elements of the sampled PSS time domain sequence; the sampled PSS time domain sequence R l is divided into K segments, and a summation operation is performed on each segment of the sampled PSS time domain sequence to compress the length of the sampled PSS time domain sequence R lThe data length is compressed from N points to K points, resulting in the compressed sampled point PSS time-domain sequence R′. l That is, R′ l =[r′0, r′1,…,r′ K-1 ], where r′ j =r jN / K+0 +r jN / K+1 +,…,+r jN / K+N / K-1 j = 0, 1, ..., K-1, r′0 to r′ K-1 R′ is the compressed sampling point PSS time-domain sequence l Element.

[0136] The arithmetic unit 408 is used to perform correlation operations on the segmented compressed preprocessed time-domain signal and three sets of compressed time-domain sequences to obtain three sets of time-domain correlation values, search for the maximum correlation peak value among the three sets of time-domain correlation values, calculate the average value of the three sets of time-domain correlation values, and calculate the ratio of the maximum correlation peak value to the average value.

[0137] In some embodiments, the arithmetic unit 408 is used to process the compressed sample point PSS time-domain sequence R′. l With the compressed local PSS time-domain sequence P i Perform relevant calculations to obtain the time-domain correlation value pss_corr i,l pss_corr i,l =|R′ l ·P i H |, where i = 0, 1, 2, l = 0, 1, 2, ..., LN, (·) H This is the conjugate transpose operation, and |·| is the modulo operation.

[0138] In some embodiments, the calculation unit 408 calculates the relationship C of the ratio of the maximum correlation peak to the average value as follows: Where max(pss_corr) i,l The mean(pss_corr) is the maximum correlation peak value among the three sets of time-domain correlation values. i，l ) represents the average value of the three sets of time-domain correlation values.

[0139] The first determining unit 410 is used to determine the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value when the ratio is greater than or equal to the threshold value.

[0140] In some embodiments, the first determining unit 410 is configured to determine the cell identifier group number based on the group number i corresponding to the maximum correlation peak value when the ratio is greater than or equal to a threshold value. The coarse synchronization time point is determined based on the position l of the sliding point corresponding to the maximum correlation peak value.

[0141] The second determining unit 412 is used to transform the time domain signals of some of the time domain correlation values ​​in the three sets of time domain correlation values ​​into frequency domain signals when the ratio is less than the threshold value, calculate the frequency domain correlation value between the frequency domain signal and the local PSS frequency domain sequence, and determine the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value of the frequency domain correlation value.

[0142] In some embodiments, the second determining unit 412 is used to determine the three sets of time-domain correlation values ​​pss_corr 0,l ,pss_corr 1,l ,pss_corr 2，l The time-domain correlation values ​​in each group are sorted from largest to smallest. The top M time-domain correlation values ​​of each group are retained as a candidate peak set. The time-domain signals corresponding to the time-domain correlation values ​​in the candidate peak set are extracted, and the time-domain signals are transformed into frequency-domain signals. According to the mapping rules, the PSS frequency domain data S of the frequency-domain signals are extracted. i，m for: Where i = 0, 1, 2, m = 0, 1, ..., M-1, and M is a positive integer.

[0143] In some embodiments, the second determining unit 412 is used to determine the PSS frequency domain data S of the frequency domain signal. i，m With the corresponding local PSS frequency domain sequence Perform relevant calculations to obtain the frequency domain correlation value pss_corr′. i，m For: pss_corr′ i,m =|S i,m ·O i H | where i = 0, 1, 2.

[0144] In some embodiments, the second determining unit 412 is used to search for the frequency domain correlation value pss_corr′. i，m The maximum correlation peak value in the frequency domain; based on the frequency domain correlation value pss_corr′ i，m The cell identifier group number is determined by the intra-group number i corresponding to the maximum correlation peak value. Value; and based on the frequency domain correlation value pss_corr′ i，m Find the index value m corresponding to the maximum correlation peak, find the position l of the sliding point of the time domain correlation value in the candidate peak set corresponding to the point of the m-th frequency domain correlation value in the i-th row, and thus determine the coarse synchronization time point.

[0145] So far, the primary synchronization signal detection device according to some embodiments of the present disclosure is provided. In the device, the complex multiplication amount can be reduced by several times by segmenting and compressing the data in the sliding window, and the storage space of the local PSS time domain sequence can be reduced. The threshold decision processing can reduce the calculation complexity while ensuring the detection performance, so as to achieve the purposes of reducing the algorithm implementation complexity, improving the PSS detection time efficiency, saving the storage space and hardware resources.

[0146] Figure 5 is a structural block diagram illustrating a primary synchronization signal detection device according to some other embodiments of the present disclosure. The primary synchronization signal detection device includes a memory 510 and a processor 520. Wherein:

[0147] The memory 510 can be a disk, a flash memory or any other non-volatile storage medium. The memory is used to store Figure 1 and / or Figure 2 instructions in the corresponding embodiments.

[0148] The processor 520 is coupled to the memory 510 and can be implemented as one or more integrated circuits, such as a microprocessor or a microcontroller. The processor 520 is used to execute the instructions stored in the memory, and can reduce the complexity of the primary synchronization signal detection.

[0149] In some embodiments, as shown in Figure 6 , the primary synchronization signal detection device 600 includes a memory 610 and a processor 620. The processor 620 is coupled to the memory 610 through a BUS bus 630. The primary synchronization signal detection device 600 can also be connected to an external storage device 650 through a storage interface 640 to call external data, and can also be connected to a network or another computer system (not shown) through a network interface 660, which will not be described in detail here.

[0150] In this embodiment, the data instructions are stored in the memory, and the above instructions are processed by the processor, so as to reduce the complexity of the primary synchronization signal detection.

[0151] In some embodiments, the present disclosure also provides a computer readable storage medium having computer program instructions stored thereon, which are executed by a processor to implement Figure 1 and / or Figure 2The steps of the method in the corresponding embodiments. Those skilled in the art should understand that the embodiments of the present disclosure can be provided as a method, device, or computer program product. Therefore, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0152] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in one or more flows and / or blocks.

[0153] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in one or more flows and / or blocks.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks ​ an apparatus that implements the functions specified in one or more flows and / or blocks.

[0155] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein according to the above description.

[0156] While certain embodiments of the disclosure have been described herein in detail as presently preferred, many modifications and variations thereof will be apparent to those skilled in the art, without departing from the scope and spirit of the disclosure. It is to be understood that those skilled in the art will be able to devise many embodiments of the disclosure which, while not explicitly described or shown herein, embody the principles of the disclosure and are included within its spirit and scope. Accordingly, all such suitable modifications and equivalents should be considered as within the scope of the disclosure. The scope of the disclosure is to be indicated by the appended claims, rather than the foregoing description, and all changes that come within the meaning and range of equivalents are intended to be embraced therein.

Claims

1. A method for detecting a master synchronization signal, comprising: The three sets of local master synchronization signal PSS time-domain sequences were segmented and compressed preprocessed to obtain three sets of compressed time-domain sequences. The received signal is downsampled to obtain multiple downsampled data. The multiple downsampled data are slid point by point, and the time-domain signal of the downsampled data within the relevant window at each slide is preprocessed by segmented compression. The segmented compressed preprocessed time-domain signal is correlated with the three sets of compressed time-domain sequences to obtain three sets of time-domain correlation values; The maximum correlation peak value is obtained from the three sets of time-domain correlation values. The average value of the three sets of time-domain correlation values ​​is calculated, and the ratio of the maximum correlation peak value to the average value is calculated. If the ratio is greater than or equal to the threshold value, the corresponding cell identifier group number and coarse synchronization time point are determined based on the maximum correlation peak value; as well as If the ratio is less than the threshold value, the time domain signals of some of the time domain correlation values ​​in the three sets of time domain correlation values ​​are transformed into frequency domain signals. The frequency domain correlation value between the frequency domain signal and the local PSS frequency domain sequence is calculated. The corresponding cell identifier group number and coarse synchronization time point are determined based on the maximum correlation peak value of the frequency domain correlation value.

2. The method for detecting the master synchronization signal according to claim 1, wherein, The steps for performing segmented compression preprocessing on the three sets of local PSS time-domain sequences to obtain three sets of compressed time-domain sequences include: Generate three sets of local PSS frequency domain sequences; Perform an N-point inverse fast Fourier transform (IFFT) on the three sets of local PSS frequency domain sequences to obtain three sets of local PSS time domain sequences, wherein the i-th set of local PSS time domain sequences is represented as: Where N≥1, and N is a positive integer, i=0,1,2; Each local PSS time-domain sequence is divided into K segments. The summation operation is performed on each segment to compress the length of each local PSS time-domain sequence from N points to K points, resulting in the compressed local PSS time-domain sequence P. i ,Right now in, K is a positive integer and K < N.

3. The method for detecting the master synchronization signal according to claim 2, wherein, The steps of sliding the multiple downsampled data points point by point and performing segmented compression preprocessing on the time-domain signal of the downsampled data within each sliding window include: The downsampled data at point L is subjected to a sliding window with a step size of 1 and a sliding window length of N points, where L > N and L is a positive integer. When the sliding window reaches point l, the N points of data within the sliding correlation window are represented as the time-domain sequence R of the sampled point PSS. l ,Right now, R l = [r0, r1, r2, ..., r N-2 r N-1 ], where l = 0, 1, 2, ..., LN, r0 to r N-1 These are elements of the time-domain sequence of the sampled PSS. The sampling point PSS time-domain sequence R l Divide the sampled data into K segments, and sum the PSS time-domain sequences of each segment to make the PSS time-domain sequence R of the sampled data segments equal to the sum of the PSS time-domain sequences of the sampled data segments. l The data length is compressed from N points to K points, resulting in the compressed sampled point PSS time-domain sequence R′. l That is, R′ l =[r′0, r′1,..., r′ K-1 ], Where, r′ j =r jN / K+0 +r jN / K+1 +,...,+r jN / K+N / K-1 j = 0, 1, ..., K-1, r′0 to r′ K-1 R′ is the compressed sampling point PSS time-domain sequence l Element.

4. The method for detecting the master synchronization signal according to claim 3, wherein, The steps of performing correlation operations between the segmented compressed preprocessed time-domain signal and the three sets of compressed time-domain sequences to obtain three sets of time-domain correlation values ​​include: The compressed sampling point PSS time-domain sequence R′ l With the compressed local PSS time-domain sequence P i Perform relevant calculations to obtain the time-domain correlation value pss_corr i，l , pss_corr i，l =|R′ l ·P i H |, Where i = 0, 1, 2, l = 0, 1, 2, ..., LN, (·) H This is the conjugate transpose operation, and |·| is the modulo operation.

5. The method for detecting the master synchronization signal according to claim 4, wherein, The formula for calculating the ratio C of the maximum correlation peak to the average value is: Where max(pss_corr) i，l The mean(pss_corr) is the maximum correlation peak value among the three sets of time-domain correlation values. i，l ) represents the average value of the three sets of time-domain correlation values.

6. The method for detecting the master synchronization signal according to claim 5, wherein, When the ratio is greater than or equal to the threshold value, the steps for determining the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value include: If the ratio is greater than or equal to the threshold value, the cell identifier group number is determined according to the group number i corresponding to the maximum correlation peak value. The coarse synchronization time point is determined based on the position l of the sliding point corresponding to the maximum correlation peak value.

7. The method for detecting the master synchronization signal according to claim 5, wherein, The steps for transforming the time-domain signals of some of the three sets of time-domain correlation values ​​into frequency-domain signals include: The three sets of time-domain correlation values ​​pss_corr 0，l pss_corr 1，l pss_corr 2，l The time-domain correlation values ​​in each group are sorted from largest to smallest. The top M time-domain correlation values ​​of each group are retained as a candidate peak set. The time-domain signals corresponding to the time-domain correlation values ​​in the candidate peak set are extracted, and the time-domain signals are transformed into frequency-domain signals. According to the mapping rules, the PSS frequency domain data S of the frequency-domain signals are extracted. i，m for: Where i = 0, 1, 2, m = 0, 1, ..., M-1, and M is a positive integer.

8. The method for detecting the master synchronization signal according to claim 7, wherein, The steps for calculating the frequency domain correlation value between the frequency domain signal and the local PSS frequency domain sequence include: PSS frequency domain data S of the frequency domain signal i，m With the corresponding local PSS frequency domain sequence Perform relevant calculations to obtain the frequency domain correlation value pss_corr′. i，m for: pss_corr′ i，m =|S i，m ·O i H |, Where i = 0, 1, 2.

9. The method for detecting the master synchronization signal according to claim 8, wherein, The steps for determining the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value of the frequency domain correlation value include: Search for the frequency domain correlation value pss_corr′ i，m The maximum correlation peak in; Based on the frequency domain correlation value pss_corr′ i，m The cell identifier group number is determined by the intra-group number i corresponding to the maximum correlation peak value. Value; and Based on the frequency domain correlation value pss_corr′ i，m Find the index value m corresponding to the maximum correlation peak, find the position l of the sliding point of the time domain correlation value in the candidate peak set corresponding to the point of the m-th frequency domain correlation value in the i-th row, and thus determine the coarse synchronization time point.

10. A master synchronization signal detection device, comprising: The first preprocessing unit is used to perform segmented compression preprocessing on the three sets of local PSS time-domain sequences to obtain three sets of compressed time-domain sequences. The downsampling unit is used to downsample the received signal to obtain multiple downsampled data. The second preprocessing unit is used to slide the multiple downsampled data point by point, and to perform segmented compression preprocessing on the time domain signal of the downsampled data within the relevant window for each slide. The processing unit is used to perform correlation operations on the segmented compressed preprocessed time-domain signal and the three sets of compressed time-domain sequences to obtain three sets of time-domain correlation values, search for the maximum correlation peak value among the three sets of time-domain correlation values, calculate the average value of the three sets of time-domain correlation values, and calculate the ratio of the maximum correlation peak value to the average value. The first determining unit is used to determine the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value when the ratio is greater than or equal to the threshold value. as well as The second determining unit is used to transform the time-domain signals of some of the time-domain correlation values ​​in the three sets of time-domain correlation values ​​into frequency-domain signals when the ratio is less than the threshold value, calculate the frequency-domain correlation value between the frequency-domain signal and the local PSS frequency-domain sequence, and determine the corresponding cell identifier group number and coarse synchronization time point based on the maximum correlation peak value of the frequency-domain correlation value.

11. The master synchronization signal detection device according to claim 10, wherein, The first preprocessing unit generates three sets of local PSS frequency domain sequences, performs an N-point IFFT transform on the three sets of local PSS frequency domain sequences, and obtains three sets of local PSS time domain sequences, wherein the i-th set of local PSS time domain sequences is represented as: Where N≥1, and N is a positive integer, i=0,1,2; Each local PSS time-domain sequence is divided into K segments. The summation operation is performed on each segment to compress the length of each local PSS time-domain sequence from N points to K points, resulting in the compressed local PSS time-domain sequence P. i ,Right now in, K is a positive integer and K < N.

12. The main synchronization signal detection device according to claim 11, wherein, The second preprocessing unit is used to slide the L-point downsampled data with a step size of 1. The sliding window length is N points, where L > N and L is a positive integer. When sliding to the l-th point, the N-point data within the sliding correlation window are represented as the sampling point PSS time-domain sequence R. l ,Right now, R l = [r0, r1, r2, ..., r N-2 r N-1 ], where l = 0, 1, 2, ..., LN, r0 to r N-1 These are elements of the time-domain sequence of the sampled PSS. The sampling point PSS time-domain sequence R l Divide the sampled data into K segments, and sum the PSS time-domain sequences of each segment to make the PSS time-domain sequence R of the sampled data segments equal to the sum of the PSS time-domain sequences of the sampled data segments. l The data length is compressed from N points to K points, resulting in the compressed sampled point PSS time-domain sequence R′. l That is, R′ l =[r′0, r′1,..., r′ K-1 ], Where, r′ j =r jN / K+0 +r jN / K+1 +,...,+r jN / K+N / K-1 j = 0, 1, ..., K-1, r′0 to r′ K-1 R′ is the compressed sampling point PSS time-domain sequence l Element.

13. The main synchronization signal detection device according to claim 12, wherein, The processing unit is used to process the compressed sample point PSS time-domain sequence R′. l With the compressed local PSS time-domain sequence P i Perform relevant calculations to obtain the time-domain correlation value pss_corr i，l , pss_corr i，l =|R′ l ·P i H |, Where i = 0, 1, 2, l = 0, 1, 2, ..., LN, (·) H This is the conjugate transpose operation, and |·| is the modulo operation.

14. The master synchronization signal detection device according to claim 13, wherein, The calculation unit calculates the ratio C of the maximum correlation peak to the average value using the following formula: Where max(pss_corr) i，l The mean(pss_corr) is the maximum correlation peak value among the three sets of time-domain correlation values. i，l ) represents the average value of the three sets of time-domain correlation values.

15. The master synchronization signal detection device according to claim 14, wherein, The first determining unit is used to determine the cell identifier group number based on the group number i corresponding to the maximum correlation peak value when the ratio is greater than or equal to a threshold value. The coarse synchronization time point is determined based on the position l of the sliding point corresponding to the maximum correlation peak value.

16. The master synchronization signal detection device according to claim 14, wherein, The second determining unit is used to determine the three sets of time-domain correlation values ​​pss_corr 0，l pss_corr 1，l pss_corr 2，l The time-domain correlation values ​​in each group are sorted from largest to smallest. The top M time-domain correlation values ​​of each group are retained as a candidate peak set. The time-domain signals corresponding to the time-domain correlation values ​​in the candidate peak set are extracted, and the time-domain signals are transformed into frequency-domain signals. According to the mapping rules, the PSS frequency domain data S of the frequency-domain signals are extracted. i，m for: Where i = 0, 1, 2, m = 0, 1, ..., M-1, and M is a positive integer.

17. The master synchronization signal detection device according to claim 16, wherein, The second determining unit is used to determine the PSS frequency domain data S of the frequency domain signal. i，m With the corresponding local PSS frequency domain sequence Perform relevant calculations to obtain the frequency domain correlation value pss_corr′. i，m for: pss_corr′ i，m =|S i，m ·O i H |, Where i = 0, 1, 2.

18. The master synchronization signal detection device according to claim 17, wherein, The second determining unit is used to search for the frequency domain correlation value pss_corr′. i，m The maximum correlation peak value in the frequency domain; based on the frequency domain correlation value pss_corr′ i，m The cell identifier group number is determined by the intra-group number i corresponding to the maximum correlation peak value. Value; and based on the frequency domain correlation value pss_corr′ i，m Find the index value m corresponding to the maximum correlation peak, find the position l of the sliding point of the time domain correlation value in the candidate peak set corresponding to the point of the m-th frequency domain correlation value in the i-th row, and thus determine the coarse synchronization time point.

19. A master synchronization signal detection device, comprising: Memory; as well as A processor coupled to the memory, the processor being configured to execute the method as described in any one of claims 1 to 9 based on instructions stored in the memory.

20. A computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the method as claimed in any one of claims 1 to 9.

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