Large-frequency-offset-resistant synchronization signal detection method and device suitable for NR standard, equipment and medium

Abstract

The invention provides an anti-large-frequency-offset synchronization signal detection method and device suitable for an NR standard, equipment and a medium. Performing down-sampling processing on the time domain receiving signal, and separating a plurality of time domain signals of a frequency band where the PSS / SSS is located; performing PSS coarse screening detection processing on the plurality of time domain signals to obtain a first time domain signal passing the PSS coarse screening detection; performing PSS fine screening detection to obtain a second time domain signal passing the PSS fine screening detection; performing SSS detection to obtain a third time domain signal passing the SSS detection; and performing frequency offset calculation according to the third time domain signal to obtain a target frequency offset. The method has the advantage that the calculated frequency offset error is obviously reduced compared with a traditional processing scheme.

Description

Technical Field

[0001] The present application relates to the technical field of signal detection. Specifically, it relates to a method, device, equipment and medium for detecting large frequency offset resistant synchronization signals applicable to the NR standard. Background Art

[0002] The PSS / SSS signals defined by the NR standard are used for the cell existence determination and initial time-frequency synchronization measurement in the cell search process of the terminal. It is constructed based on the 127-point length frequency domain gold sequence, and has good cross-correlation characteristics and autocorrelation characteristics, which can support the terminal to complete relatively accurate cell existence determination, as well as time synchronization and frequency synchronization of the base station downlink signal. However, limited by the factor that the NR standard uses OFDM technology and is relatively sensitive to frequency offset, its frequency offset resistance ability is weak. Summary of the Invention

[0003] The present application aims to provide a method, device, equipment and medium for detecting large frequency offset resistant synchronization signals applicable to the NR standard to solve the problem of weak frequency offset resistance ability.

[0004] In a first aspect, the present application provides a method for detecting large frequency offset resistant synchronization signals applicable to the NR standard, including:

[0005] Performing downsampling processing on the time-domain received signal to separate multiple time-domain signals in the frequency band where PSS / SSS is located;

[0006] Performing PSS coarse screening detection processing on the multiple time-domain signals to obtain the first time-domain signal passing the PSS coarse screening detection, the total number of the first time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the first time-domain signal;

[0007] Performing PSS fine screening detection according to the first time-domain signal, the total number of the first time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the first time-domain signal to obtain the second time-domain signal passing the PSS fine screening detection, the total number of the second time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the second time-domain signal;

[0008] Performing SSS detection according to the second time-domain signal, the total number of the second time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the second time-domain signal to obtain the third time-domain signal passing the SSS detection, the total number of the third time-domain signals, as well as the SSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the third time-domain signal;

[0009] Based on the third time-domain signal, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal, perform frequency offset calculation to obtain the target frequency offset.

[0010] In some embodiments, performing PSS rough screening detection processing on multiple time-domain signals to obtain the first time-domain signals passing the PSS rough screening detection and the total number of the first time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signals includes:

[0011] Initialize the starting point sequence number of the time-domain signal used in calculating the first correlation sequence;

[0012] If the starting point sequence number does not meet the termination condition, calculate the first correlation sequence corresponding to each group of local PSS sequences respectively according to the local PSS sequences defined by the first preset number of NR standards;

[0013] According to each group of first correlation sequences, obtain the correlation weights of each group of first correlation sequences under different coarse frequency offset compensation values;

[0014] If the starting point sequence number is less than the starting point sequence number threshold, obtain the PSS rough screening existence weight and noise power corresponding to each group of first correlation sequences according to the correlation weights corresponding to each group of first correlation sequences;

[0015] Perform PSS rough screening detection on multiple time-domain signals according to the PSS rough screening existence weight and noise power corresponding to each group of first correlation sequences, to obtain the first time-domain signals passing the PSS rough screening detection and the total number of the first time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signals.

[0016] In some embodiments, the termination condition includes:

[0017]

[0018] Among them, i represents the starting point sequence number of r ds (m) when calculating the first correlation sequence, N rx represents the total number of sampling points of the time-domain signal received once, D ds represents the downsampling multiple, M pss represents the length of the calculated PSS correlation sequence, and the value is N fft / D ds ,N fft represents the number of points for FFT / IFFT performed by both the transmitter and the receiver.

[0019] In some embodiments, based on the first time-domain signal, the total number of the first time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signal, PSS fine screening detection is performed to obtain the second time-domain signal that passes the PSS fine screening detection, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal, including:

[0020] Initialize the first detection sequence for PSS fine screening detection and the number of results that pass the PSS fine screening detection;

[0021] If the first detection sequence is less than the total number of the first time-domain signals and the number of results meets the result number preset condition, calculate the PSS coarse screening result index required for PSS fine screening processing corresponding to the first detection sequence;

[0022] Obtain the second correlation sequence according to the PSS coarse screening result index;

[0023] Obtain the correlation weights corresponding to the second correlation sequence under different fine frequency offset compensation values according to the second correlation sequence;

[0024] Obtain the PSS fine screening existence weight corresponding to the second correlation sequence according to the correlation weights corresponding to the second correlation sequence;

[0025] Perform PSS fine screening detection according to the PSS fine screening existence weight corresponding to the second correlation sequence to obtain the second time-domain signal that passes the PSS fine screening detection, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal.

[0026] In some embodiments, the result number preset condition is:

[0027] l fine_pss ≤N max_fine_pss ;

[0028] l fine_pss represents the number of results, and N max_fine_pss represents the maximum number of output results in the PSS fine screening detection process.

[0029] In some embodiments, based on the second time-domain signal, the total number of the second time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal, SSS detection is performed to obtain the third time-domain signal that passes the SSS detection, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal, including:

[0030] Initialize the second detection sequence for SSS detection;

[0031] If the second detection sequence is less than the total number of second time-domain signals, calculate the PSS detection result index required for the SSS detection process corresponding to the second detection sequence;

[0032] Based on the local SSS sequences defined by the second preset quantity, the time offset corresponding to the second time-domain signal, and the PSS detection result index, obtain the third correlation sequence corresponding to each local SSS sequence;

[0033] According to the third correlation sequence, obtain the correlation weight value of the third correlation sequence;

[0034] According to the correlation weight value of the third correlation sequence, obtain the SSS existence weight value corresponding to the third correlation sequence;

[0035] Based on the SSS existence weight value, perform SSS detection to obtain the third time-domain signals that pass the SSS detection, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signals.

[0036] In some embodiments, the second preset quantity is 336.

[0037] In some embodiments, according to the third time-domain signals, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signals, perform frequency offset calculation to obtain the target frequency offset, satisfying:

[0038]

[0039] 0≤l<l sss k ss_start ≤k<k ss_start +127

[0040] wherein, FO(l) represents the target frequency offset, N fft represents the number of points for the receiver to perform FFT, N cp represents the CP length, TO pss (l) represents the time offset corresponding to the l-th detection result that passes the PSS detection, FO pss (l) represents the frequency offset corresponding to the l-th detection result that passes the PSS detection, l sss represents the number of results that pass the SSS detection, k ss_start represents the starting RB index occupied by the PSS and the SSS in the frequency domain.

[0041] In a second aspect, the present application provides a large frequency offset resistant synchronization signal detection device applicable to the NR standard, including:

[0042] The downsampling processing module is used to perform downsampling processing on the time-domain received signal and separate multiple time-domain signals in the frequency band where PSS / SSS is located;

[0043] The PSS rough screening module is used to perform PSS rough screening detection processing on multiple time-domain signals to obtain the first time-domain signal that passes the PSS rough screening detection, the total number of the first time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signal;

[0044] The PSS fine screening module is used to perform PSS fine screening detection based on the first time-domain signal, the total number of the first time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signal, to obtain the second time-domain signal that passes the PSS fine screening detection, the total number of the second time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal;

[0045] The SSS detection module is used to perform SSS detection based on the second time-domain signal, the total number of the second time-domain signals, as well as the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal, to obtain the third time-domain signal that passes the SSS detection, the total number of the third time-domain signals, as well as the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal;

[0046] The target frequency offset module is used to perform frequency offset calculation based on the third time-domain signal, the total number of the third time-domain signals, as well as the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal to obtain the target frequency offset.

[0047] In a third aspect, the present application provides an electronic device, including: a memory and a processor;

[0048] The memory stores computer-executable instructions;

[0049] The processor executes the computer-executable instructions stored in the memory, enabling the processor to execute the method of the first aspect.

[0050] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method of the first aspect.

[0051] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present application are:

[0052] Two-step PSS detection processing including coarse screening detection and fine screening detection. During the coarse screening detection processing, the candidate PSS detection results are scanned and detected with a frequency offset compensation granularity of 0.5 times the subcarrier spacing, and the frequency offset value in units of 0.5 times the subcarrier spacing is obtained; during the fine screening detection, the candidate PSS detection results with high reliability are selected and scanned and detected with a smaller granularity of frequency offset compensation to detect the final PSS detection results, and a more accurate frequency offset measurement result is obtained. Such a design can effectively reduce the number of times of frequency offset compensation processing.

[0053] The frequency offset is calculated based on the frequency domain dimension correlation results of PSS and SSS signals based on time domain frequency offset compensation. Based on the frequency offset results detected by PSS, the PSS / SSS signals are frequency offset compensated in the time domain dimension, and then the frequency domain dimension correlation results are obtained through FFT transformation, and then the frequency offset is calculated. The frequency offset error calculated in this way is significantly reduced compared with the traditional processing scheme, and the error can be controlled within the range of 0.03 times the subcarrier spacing under the condition of a frequency offset of 2 times the single subcarrier spacing. Brief Description of the Drawings

[0054] Figure 1 It is a schematic diagram of traditional PSS / SSS detection processing;

[0055] Figure 2 It is a schematic diagram of traditional PSS detection processing;

[0056] Figure 3 It is a schematic diagram of traditional SSS detection processing;

[0057] Figure 4 It is a schematic flow chart of the anti-large frequency offset synchronization signal detection method applicable to the NR standard provided by the embodiment of the present application;

[0058] Figure 5 It is a schematic diagram of the PSS coarse screening detection process provided by the embodiment of the present application;

[0059] Figure 6 It is a schematic diagram of the PSS fine screening detection processing proposed by the embodiment of the present application;

[0060] Figure 7 It is a schematic diagram of the SSS detection processing proposed by the embodiment of the present application;

[0061] Figure 8 It is a schematic diagram of the structure of the anti-large frequency offset synchronization signal detection device applicable to the NR standard provided by the embodiment of the present application;

[0062] Figure 9 It is a schematic diagram of the structure of an electronic device provided by the present application. Detailed Embodiments

[0063] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can be arranged and designed in various different configurations.

[0064] Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application that is claimed, but merely represents selected embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts shall fall within the scope of protection of this application.

[0065] Embodiment

[0066] First, the prior art will be described. The PSS / SSS signals defined by the NR standard are used for the cell existence determination and initial time-frequency synchronization measurement in the cell search process of a terminal. They are constructed based on a 127-point length frequency-domain gold sequence, have good cross-correlation and auto-correlation characteristics, and can support the terminal to complete relatively accurate cell existence determination, as well as time synchronization and frequency synchronization of the base station downlink signal. However, limited by the fact that the NR standard uses OFDM technology, which is relatively sensitive to frequency offset, its anti-frequency-offset ability is weak, generally only about 0.2 times the subcarrier spacing.

[0067] Figure 1 The following is a schematic diagram of traditional PSS / SSS detection processing, including:

[0068] Step 101: Downsample the time-domain received signal r(n) to separate the time-domain signal r ds (m) in the frequency band where PSS / SSS is located, as shown in the following formula:

[0069]

[0070] where m represents the element number of the r ds (m) sequence, r ds (m) represents the signal in the frequency band where PSS / SSS is located separated after downsampling filtering, L ds represents the length of the downsampling filter coefficient sequence, h ds (k) represents the downsampling filter coefficient sequence, r(n) represents the time-domain received signal after phase compensation, 0 ≤ n < N rx , N rx represents the total number of sampling points of the time-domain signal received once, and its value is related to the signal sampling rate. N rxDenotes the total number of sampling points of the time-domain signal for a single reception. The value is related to the signal sampling rate, D ds Denotes the downsampling multiple, which needs to be divisible by N fft and is defaulted to 16.

[0071] Step 102: Based on r ds (m) perform PSS detection processing. Its main input is r ds (m), and the main outputs are {u pss (l)}, {TO pss (l)}, {FO pss (l)}, {SINR pss (l)}, {N pss (l)} and l pss , where l represents the sequence number of the PSS detection result. u pss (l) represents the PSS root sequence number of the l-th detection result passing the PSS detection, and its value range is {0, 1, 2}. TO pss (l) represents the corresponding time offset of the l-th detection result passing the PSS detection, with the unit of D ds T s T s represents the sampling interval. FO pss (l) represents the corresponding frequency offset of the l-th detection result passing the PSS detection, with the unit of subcarrier interval. SINR pss (l) represents the corresponding SINR of the l-th detection result passing the PSS detection. N pss (l) represents the corresponding noise power of the l-th detection result passing the PSS detection. l pss represents the number of results passing the PSS detection.

[0072] Step 103: Based on the PSS detection result and r ds (m) perform SSS detection. Its main inputs are r ds (m), {u pss (l)}, {TO pss (l)}, {FO pss (l)}, {SINR pss (l)}, {N pss (l)} and l pss , and the main outputs are {u sss (l)}, {TO sss (l)}, {FO sss (l)}, {SINR sss (l)}, {N sss (l)} and l sss , where l represents the sequence number of the SSS detection result. u sss(l) represents the SSS root sequence number of the PSS detection result detected by the l-th SSS, and its value range is {0, 1,..., 335}. TO sss (l) represents the corresponding time offset of the detection result detected by the l-th SSS, and the unit is D ds T s , T s represents the sampling interval. FO sss (l) represents the corresponding frequency offset of the detection result detected by the l-th SSS, and the unit is the subcarrier interval. SINR sss (l) represents the corresponding SINR of the detection result detected by the l-th SSS. N sss (l) represents the corresponding noise power of the detection result detected by the l-th SSS. l sss represents the number of results detected by the SSS.

[0073] Figure 2 is a schematic diagram of traditional PSS detection processing. As shown in the figure, most PSS signal detections adopt the method of segmented correlation to overcome the influence of frequency offset. Its detection ability is greatly affected by frequency offset. Selecting appropriate number of segments (N seg ) and segment length (L seg ), the anti-frequency offset ability can generally only reach about 0.2 times of the subcarrier interval.

[0074] Step 201: Initialization j = 0, l pss = 0, i represents the starting point serial number of r ds (m) when calculating the correlation sequence, j represents the number of PSS detections, l pss represents the number of results detected by the PSS.

[0075] Step 202: If Among them, M pss represents the length of the calculated PSS correlation sequence, and its value must be N fft / D ds , then continue to execute Step 203; otherwise, jump to Step 2010.

[0076] Step 203: Based on 3 groups of local PSS sequences defined by NR standards (denoted as s pss (m, x), x = 0, 1, 2), calculate the corresponding 3 groups of correlation sequences respectively, denoted as c pss (m, x), x = 0, 1, 2, as shown in the following formula. Here, m represents the element serial number of these three groups of sequences, and x represents the serial number of these 3 groups of local PSS sequences,

[0077] c pss (m, x) = rds (i + m)·s pss (m, x)

[0078] 0 ≤ m < M pss 0 ≤ x < 3。

[0079] Step 204: Calculate the correlation weights of 3 groups of correlation sequences, denoted as C pss (i, x), x = 0, 1, 2, as shown in the following formula

[0080]

[0081] 0 ≤ x < 3;

[0082] where x represents the serial number of these 3 groups of correlation sequences, N seg represents the total number of sub - segments for segmented correlation, which should be divisible by M pss and M sss divisible, L seg represents the length of each sub - segment for segmented correlation, L seg = M pss / N seg = M sss / N seg 。M pss represents the length of the PSS correlation sequence to be calculated, and the value must be N fft / D ds 。M sss represents the length of the PSS correlation sequence to be calculated, and the value must be N fft / D ds 。

[0083] Step 205: If i meets the condition then calculate the PSS existence weights corresponding to 3 groups of correlation sequences, denoted as W pss (j, x), x = 0, 1, 2, and the noise power (denoted as P noise (j)), as shown in the following formula:

[0084]

[0085] 0 ≤ x < 3

[0086] x represents the serial number of these 3 groups of existence weights; otherwise, jump to Step 2010.

[0087] Step 206: Based on W pss (j, x) perform PSS detection, and record the corresponding detection results (u pss (l corse_pss ), TO pss (l pss ), FO pss (lpss )、SINR pss (l pss ) and N pss (l pss )), as shown in the following formula:

[0088]

[0089] Among them, N pss_thr Indicates the decision threshold for PSS detection. pss Indicates the length of the calculated PSS related sequence, the value must be N fft / D ds . N pss (l pss ) indicates the first pss The corresponding noise power of the detection result detected by PSS. noise (j) is expressed as noise power.

[0090] Step 207: According to the number of results that pass the PSS test this time (denoted as L pss (j)) Update l pss The value is as shown below:

[0091] l pss = l pss +L pss (j).

[0092] Step 208: Update j, j=j+1.

[0093] Step 209: Update i, i=i+1, and jump to Step 202.

[0094] Step 2010: End the process and feedback all test results, including {u pss (l)}、{TO pss (l)}, {FO pss (l)}, {SINR pss (l)}、{N pss (l)} and l pss , where l represents the serial number of the result detected by PSS.

[0095] Figure 3 It is a schematic diagram of traditional SSS detection processing, such as Figure 3 As shown, it can also overcome the frequency offset effect based on the segmented method, but the frequency offset effect can be further reduced based on the frequency offset measurement result of the PSS detection result (see Step 5 for details).

[0096] Step 301: Initialize j = 0, l sss= 0, where j represents the number of times of SSS detection, and l sss represents the number of results of SSS detection.

[0097] Step 302: If j < l pss , then continue to execute Step 303; otherwise, execute Step 3010.

[0098] Step 303: Calculate the required PSS detection result index for the j-th SSS detection process, denoted as l max_pss , as shown in the following formula, where l represents the element number of {SINR pss (l)}.

[0099]

[0100] Step 304: Based on the local SSS sequences (denoted as s sss (m, x), x = 0, 1,..., 335) defined by 336 groups of NR standards, calculate the corresponding 336 groups of correlation sequences, denoted as c sss (m, x), x = 0, 1,..., 335, as shown in the following formula:

[0101] c sss (m, x) = r ds (TO pss (l max_pss ) + m + 2M pss ) · s sss (m, x) 0 ≤ m < M sss

[0102] 0 ≤ x < 336

[0103] Here, m represents the element number of these 336 groups of sequences, and x represents the sequence number of these 336 groups of local SSS sequences

[0104] Step 305: Calculate the correlation weights of the 336 groups of correlation sequences c sss (m, x), denoted as C sss (k, x), as shown in the following formula:

[0105]

[0106] 0 ≤ x < 336;

[0107] where k represents the sequence number of different fine frequency offset compensation values, and x represents the sequence number of these 336 groups of correlation sequences.

[0108] Step 306: Calculate the SSS existence weights corresponding to the 336 groups of correlation sequences c sss (m, x), denoted as W sss(j, x):

[0109]

[0110] 0 ≤ x < 336;

[0111] where x represents the sequence number of these 336 groups of related sequences, N pss (l max_pss ) represents the corresponding noise power of the detection result of the l-th max_pss detection result passing the PSS detection.

[0112] Step 307: Perform SSS detection based on W sss (j, x). If the following conditions are met, record the corresponding detection results (u sss (l sss ), TO sss (l sss ), SINR sss (l sss ) and N sss (l sss ). As shown in the following formula, it is necessary to perform the following detection and determination on 336 groups of W sss (j, x):

[0113]

[0114] 0 ≤ x < 336

[0115] where N sss_thr represents the decision threshold of the SSS detection, and u corse_pss (l max_pss ) represents the PSS root sequence number of the detection result of the l-th max_pss detection result passing the PSS rough screening detection, and its value range is {0, 1, 2}.

[0116] Step 308: Update the value of l sss according to the number of detection results (denoted as L sss )(j)) passing the SSS detection this time, as shown in the following formula:

[0117] l sss = l sss + L sss (j).

[0118] Step 309: Update j and reset SINR pss (l max_pss ):, j = j + 1, SINR pss (l max_pss ) = 0 and jump to Step 302.

[0119] Step 3010: End the processing procedure and feedback all detection results, including {u sss (l)}, {FO sss (l)}, {TO sss (l)}, {SINR sss (l)}, {N sss (l)} and l sss , where l represents the serial number of the SSS detection result.

[0120] For convenient query, the meanings of the above relevant symbols are summarized as follows:

[0121] r(n) represents the time-domain received signal after phase compensation, 0 ≤ n < N rx , N rx represents the total number of sampling points of the time-domain signal for a single reception, and its value is related to the signal sampling rate;

[0122] r ds (m) represents the signal in the frequency band where the PSS / SSS is separated after downsampling filtering;

[0123] N fft represents the number of points for FFT / IFFT performed by both the transmitter and the receiver, and the default value is 4096;

[0124] D ds represents the downsampling multiple, which needs to be divisible by N fft , and the default value is 16;

[0125] {h ds (k)} represents the sequence of downsampling filter coefficients, which is a sequence with a length of L ds ;

[0126] M pss represents the length of the PSS-related sequence calculated, and the value must be N fft / D ds ;

[0127] M sss represents the length of the PSS-related sequence calculated, and the value must be N fft / D ds ;

[0128] s pss (m,x) represents the x-th group of PSS time-domain signals generated locally after the same downsampling process, where x is the PSS root sequence number, 0 ≤ m < M pss , 0 ≤ x < 3;

[0129] s sss (m,x) represents the x-th group of SSS time-domain signals generated locally after the same downsampling process, where x is the SSS root sequence number, 0 ≤ m < Msss , 0 ≤ x < 336;

[0130] l pss represents the number of results detected by PSS;

[0131] l sss represents the number of results detected by SSS;

[0132] N pss_thr represents the decision threshold for PSS detection;

[0133] N sss_thr represents the decision threshold for SSS detection;

[0134] N seg represents the total number of segments for segment - by - segment correlation, and it should be divisible by M pss and M sss exactly;

[0135] L seg represents the segment length for segment - by - segment correlation, L seg = M pss / N seg = M sss / N seg ;

[0136] u pss u(l) represents the PSS root sequence number of the l - th detection result detected by PSS, and its value range is {0, 1, 2};

[0137] u sss u(l) represents the SSS root sequence number of the l - th PSS detection result detected by SSS, and its value range is {0, 1,..., 335};

[0138] FO pss FO(l) represents the corresponding frequency offset of the l - th detection result detected by PSS, with the unit of sub - carrier spacing;

[0139] TO pss TO(l) represents the corresponding time offset of the l - th detection result detected by PSS, with the unit of D ds T s , T s represents the sampling interval;

[0140] TO sss TO(l) represents the corresponding time offset of the l - th detection result detected by SSS, with the unit of D ds T s ,T s represents the sampling interval;

[0141] SINR pss(l) represents the corresponding SINR of the detection result of the l-th PSS detection;

[0142] SINR sss (l) represents the corresponding SINR of the detection result of the l-th SSS detection;

[0143] N pss (l) represents the corresponding noise power of the detection result of the l-th PSS detection;

[0144] N sss (l) represents the corresponding noise power of the detection result of the l-th SSS detection.

[0145] In summary, since the frequency offset will destroy the correlation characteristics of the PSS and SSS signals, the anti-frequency offset ability of the traditional PSS / SSS detection and processing scheme is weak, and the approximate ability is 0.2 times the subcarrier spacing. Considering the actual high-speed mobile application scenario, it is not sufficient. Generally, due to cost considerations, the crystal oscillator accuracy adopted by the terminal is generally poor. Calculated according to 1 ppm, in the typical 4.9 GHz frequency band, the maximum frequency offset caused by the crystal oscillator alone is 4.9 kHz. At this time, considering the 350 km / h mobile scenario (high-speed rail) under the condition of a 30 kHz subcarrier spacing, the maximum Doppler frequency offset caused by high-speed movement is about 1.6 kHz. That is, the maximum frequency offset of the PSS / SSS signal received by the terminal at this time can reach 6.5 kHz, exceeding 0.2 times the subcarrier spacing, making it difficult to ensure the correct reception of the PSS / SSS. Obviously, the above-listed conventional processing schemes are difficult to meet the application requirements in the high-speed mobile application scenario.

[0146] In addition, most of the traditional PSS / SSS detection schemes are calculated based on the phase difference in the time domain. At this time, the frequency offset measurement result has a large error under low signal-to-noise ratio conditions.

[0147] Figure 4 The flowchart of the anti-large frequency offset synchronization signal detection method applicable to the NR standard provided by the embodiments of the present application is as follows Figure 4 shown, including:

[0148] Step 401: Perform downsampling processing on the time-domain received signal to separate multiple time-domain signals in the frequency band where the PSS / SSS is located.

[0149] Specifically, perform downsampling processing on the time-domain received signal r(n) to separate the time-domain signal r ds (m) in the frequency band where the PSS / SSS is located, as shown in the following formula:

[0150]

[0151] where m represents r ds(m) Element serial number of the sequence, L ds Indicates the length of the downsampling filter coefficient sequence, h ds (k) Represents the downsampling filter coefficient sequence, r(n) represents the time-domain received signal after phase compensation, 0 ≤ n < N rx , N rx Indicates the total number of sampling points of the time-domain signal for a single reception, and the value is related to the signal sampling rate. N rx Indicates the total number of sampling points of the time-domain signal for a single reception, and the value is related to the signal sampling rate, D ds Indicates the downsampling multiple, which needs to be divisible by N fft and the default value is 16.

[0152] Step 402: Perform PSS rough screening detection processing on multiple time-domain signals to obtain the first time-domain signals that pass the PSS rough screening detection, the total number of the first time-domain signals, as well as the PSS root sequence numbers, frequency offsets, time offsets, SINR, and noise powers corresponding to the first time-domain signals.

[0153] Further, Step 402 includes:

[0154] Initialize the starting point serial number of the time-domain signal used when calculating the first correlation sequence;

[0155] If the starting point serial number does not meet the termination condition, calculate the first correlation sequence corresponding to each group of local PSS sequences according to the local PSS sequences defined by the first preset number of NR standards;

[0156] Obtain the correlation weights of each group of the first correlation sequences at different coarse frequency offset compensation values according to each group of the first correlation sequences;

[0157] If the starting point serial number is less than the starting point serial number threshold, obtain the PSS rough screening existence weights and noise powers corresponding to each group of the first correlation sequences according to the correlation weights corresponding to each group of the first correlation sequences;

[0158] Perform PSS rough screening detection on multiple time-domain signals according to the PSS rough screening existence weights and noise powers corresponding to each group of the first correlation sequences to obtain the first time-domain signals that pass the PSS rough screening detection, the total number of the first time-domain signals, as well as the PSS root sequence numbers, frequency offsets, time offsets, SINR, and noise powers corresponding to the first time-domain signals.

[0159] Exemplarily, Figure 5 is a schematic diagram of the PSS rough screening detection process provided by the embodiment of the present application, as Figure 5 shown, the method includes:

[0160] Step 501: Initialize j = 0, l corse_pss= 0, where i represents the value of r used when calculating the first correlation sequence ds (m) is the starting point serial number, j represents the number of times of PSS rough screening detection, and l corse_pss represents the number of results passed the PSS rough screening detection;

[0161] Step 502: If then continue to execute Step 503; otherwise, jump to Step5010;

[0162] Step 503: Based on the local PSS sequences (denoted as s pss (m,x), x = 0, 1, 2) defined by 3 groups of NR standards, calculate the corresponding 3 groups of first correlation sequences, denoted as c pss (m,x), x = 0, 1, 2, as shown in the following formula:

[0163] c pss (m,x) = r ds (i + m)·s pss (m,x)

[0164] 0 ≤ m < M pss 0 ≤ x < 3

[0165] where m represents the element serial number of these 3 groups of first correlation sequences, and x represents the serial number of these 3 groups of local PSS sequences.

[0166] Step 504: Calculate the correlation weights of the 3 groups of first correlation sequences under different coarse frequency offset compensation values, denoted as C pss (k,i,x), x = 0, 1, 2, as shown in the following formula:

[0167]

[0168] 0 ≤ k ≤ 2K corse_pss 0 ≤ x < 3

[0169] where k represents the serial number of different coarse frequency offset compensation values, x represents the serial number of these 3 groups of first correlation sequences, and K corse_pss represents the number of single - direction frequency offset compensation values for PSS rough screening detection, with a default value of 3, N seg represents the total number of segments for segmented correlation, which needs to be divisible by M pss and M sss divisible, L seg represents the segment length of segmented correlation, L seg = M pss / N seg = M sss / N seg 。

[0170] Step 505: If i meets the condition:

[0171]

[0172] Then calculate the PSS rough screening existence weights corresponding to 3 groups of the first correlation sequences, denoted as W corse_pss (j, x), where x = 0, 1, 2, and the noise power (denoted as P noise (j)), as shown in the following formula. Otherwise, jump to Step 509.

[0173]

[0174] 0 ≤ x < 3;

[0175] Among them, x represents the serial number of these 3 groups of existence weights, and M pss represents the length of the first correlation sequence of the calculated PSS, and the value must be N fft / D ds , N fft represents the number of points for FFT / IFFT performed by both the transmitter and the receiver, with a default value of 4096, and D ds represents the downsampling multiple, which needs to be divisible by N fft and has a default value of 16.

[0176] Step 506: Perform PSS rough screening detection according to W corse_pss (j, x). Record the corresponding detection results (u corse_pss (l corse_pss ), FO corse_pss (l corse_pss ), TO corse_pss (l corse_pss ), SINR corse_pss (l corse_pss ) and N corse_pss (l corse_pss )) when the following conditions are met, as shown in the following formula:

[0177]

[0178] Among them, N corse_pss_thr represents the decision threshold for PSS rough screening detection, l corse_pss represents the number of results passing the PSS rough screening detection, u corse_pss (l corse_pss ) represents the PSS root sequence serial number of the l corse_pss th detection result passing the PSS rough screening detection, FO corse_pss (l corse_pss ) represents the corresponding frequency offset of the l corse_pss th detection result passing the PSS rough screening detection, with the unit of subcarrier interval, and TO corse_pss (l corse_pss ) represents the lcorse_pss The corresponding time offset of the detection result detected by PSS rough screening, with the unit of D ds T s , T s represents the sampling interval, SINR corse_pss (l corse_pss ) represents the l corse_pss th corresponding SINR of the detection result detected by PSS rough screening, N corse_pss (l corse_pss ) represents the l corse_pss th corresponding noise power of the detection result detected by PSS rough screening, K corse_pss represents the number of unidirectional frequency offset compensation values detected by PSS rough screening, with the default value of 3, W corse_pssx (j) represents the correlation weight of the xth group of PSS sequences in the PSS rough screening process.

[0179] Step 507: Update the value of l according to the number of detection results (denoted as L corse_pss (j)) detected by this PSS rough screening, as shown in the following formula, corse_pss l

[0180] l corse_pss = l corse_pss + L corse_pss (j);

[0181] Step 508: Update j, j = j + 1;

[0182] Step 509: Update i, i = i + 1, and jump to Step 502;

[0183] Step 5010: End this processing procedure, and feedback all detection results, including {u corse_pss (l)}, {FO corse_pss (l)}, {TO corse_pss (l)}, {SINR corse_pss (l)}, {N corse_pss (l)} and l corse_pss , where l represents the serial number of the detection result detected by PSS rough screening.

[0184] Step 403: Perform PSS fine screening detection according to the first time-domain signal, the total number of the first time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signal, to obtain the second time-domain signal, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal.

[0185] Specifically, Step 403 includes:

[0186] Initialize the first detection sequence for PSS fine screening detection and the number of results passed by PSS fine screening detection;

[0187] If the first detection sequence is less than the total number of the first time-domain signals and the number of results meets the result number preset condition, then calculate the PSS coarse screening result index required for PSS fine screening processing corresponding to the first detection sequence;

[0188] Obtain a second correlation sequence according to the PSS coarse screening result index;

[0189] Obtain the correlation weights corresponding to the second correlation sequence under different fine frequency offset compensation values according to the second correlation sequence;

[0190] Obtain the PSS fine screening existence weights corresponding to the second correlation sequence according to the correlation weights corresponding to the second correlation sequence;

[0191] Perform PSS fine screening detection according to the PSS fine screening existence weights corresponding to the second correlation sequence, and obtain the second time-domain signal passed by PSS fine screening detection, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal.

[0192] Exemplarily, Figure 6 The schematic diagram of PSS fine screening detection processing proposed in the embodiment of the present application is as Figure 6 shown. The method includes:

[0193] Step 601: Initialize j = 0, l fine_pss = 0, where j represents the number of times of PSS fine screening detection, and l fine_pss represents the number of results passed by PSS fine screening detection;

[0194] Step 602: If j < l corse_pss and l fine_pss ≤ N max_fine_pss , N max_fine_pss represents the maximum number of output results in the PSS fine screening detection process, then continue to execute Step 603; otherwise, execute Step 6010;

[0195] Step 603: Calculate the PSS coarse screening result index required for the j-th PSS fine screening processing, denoted as l max_corse_pss , as shown in the following formula:

[0196]

[0197] where l represents the element number of {SINR corse_pss (l)}.

[0198] Step 604: Calculate the second correlation sequence of PSS, denoted as c pss (m), as shown in the following formula: c pss (m) = r ds (TO corse_pss (l max_corse_pss ) + m) · s pss (m, u corse_pss (l max_corse_pss )) 0 ≤ m < M pss ;

[0199] Step 605: Calculate the correlation weights of the second correlation sequence c pss (m) under different fine frequency offset compensation values, denoted as C pss (k), as shown in the following formula:

[0200]

[0201] 0 ≤ k ≤ 2K fine_pss

[0202] where k represents the serial number of different fine frequency offset compensation values, and K fine_pss represents the number of single-direction frequency offset compensation values for PSS fine screening detection, and the default value is 1.

[0203] Step 606: Calculate the PSS fine screening existence weights corresponding to the second correlation sequence c pss (m), denoted as W fine_pss (j):

[0204]

[0205] Step 607: Based on W fine_pss (j) perform PSS fine screening detection. If the following conditions are met, record the corresponding detection results (u fine_pss (l fine_pss )), FO fine_pss (l fine_pss ), TO fine_pss (l fine_pss ), SINR fine_pss (l fine_pss ) and N fine_pss (l fine_pss )) as shown in the following formula:

[0206]

[0207] where, N fine_pss_thr represents the decision threshold for PSS fine screening detection.

[0208] Step 608: If the conditions in Step 607 are met and the PSS fine screening result is obtained, update lfine_pss , as shown in the following formula:

[0209] l fine_pss = l fine_pss + 1.

[0210] Step 609: Update j and reset SINR corse_pss (l max_corse_pss ): j = j + 1, SINR corse_pss (l max_corse_pss ) = 0, and jump to Step 602.

[0211] Step 6010: End the processing, and feedback all detection results, including {u fine_pss (l)}, {FO fine_pss (l)}, {TO fine_pss (l)}, {SINR fine_pss (l)}, {N fine_pss (l)} and l fine_pss , where l represents the serial number of the detection result refined by PSS.

[0212] Step 404: Perform SSS detection based on the second time-domain signal, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal, to obtain the third time-domain signal passing the SSS detection, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal.

[0213] Specifically, Step 404 includes:

[0214] Initialize the second detection sequence for SSS detection;

[0215] If the second detection sequence is less than the total number of the second time-domain signals, calculate the PSS detection result index required for the SSS detection processing corresponding to the second detection sequence;

[0216] Based on the local SSS sequence defined by the second preset quantity of NR standards, the time offset corresponding to the second time-domain signal, and the PSS detection result index, obtain the third correlation sequence corresponding to each local SSS sequence;

[0217] Obtain the correlation weight value of the third correlation sequence according to the third correlation sequence;

[0218] Obtain the SSS existence weight value corresponding to the third correlation sequence according to the correlation weight value of the third correlation sequence;

[0219] Perform SSS detection based on the SSS existence weight, to obtain the third time-domain signal that passes the SSS detection, the total number of the third time-domain signals, and the SSS root sequence numbers, frequency offsets, time offsets, SINR, and noise power corresponding to the third time-domain signals.

[0220] Wherein, the second preset quantity may be 336.

[0221] Exemplarily, Figure 7 The following is a schematic diagram of the SSS detection process proposed in the embodiment of this application. As Figure 7 shown, the method includes:

[0222] Step 701: Initialize j = 0, l sss = 0, where j represents the number of times of SSS detection, and l sss represents the number of results that pass the SSS detection;

[0223] Step 702: If j < l fine_pss , and l fine_pss represents the number of results that pass the PSS fine screening detection, then continue to execute Step 703; otherwise, execute Step 7010;

[0224] Step 703: Calculate the required PSS detection result index for the j-th SSS detection process, denoted as l max_fine_pss , as shown in the following formula:

[0225]

[0226] where l represents the element number of {SINR fine_pss (l)}, and SINR fine_pss (l) represents the corresponding SINR of the l-th detection result that passes the PSS fine screening detection.

[0227] Step 704: Based on 336 groups of locally defined SSS sequences in the NR standard (denoted as s sss (m, x), x = 0, 1,..., 335), calculate the corresponding 336 groups of third correlation sequences, denoted as c sss (m, x), x = 0, 1,..., 335, as shown in the following formula:

[0228] c sss (m, x) = r ds (TO fine_pss (l max_fine_pss ) + m) · s sss (m, x) 0 ≤ m < M sss ;

[0229] 0 ≤ x < 336

[0230] Here, m represents the element number of these 336 groups of third correlation sequences, and x represents the number of these 336 groups of local SSS sequences.

[0231] Step 705: Calculate the correlation weights of 336 groups of third correlation sequences c sss (m, x), denoted as C sss (k, x), as shown in the following formula:

[0232]

[0233] 0 ≤ x < 336;

[0234] where k represents the number of different fine frequency offset compensation values, x represents the number of these 336 groups of third correlation sequences, and M sss represents the length of the calculated PSS correlation sequence, and the value must be N fft / D ds .

[0235] Step 706: Calculate the SSS existence weights corresponding to 336 groups of third correlation sequences c sss (m, x), denoted as W sss (j, x), where x represents the number of these 336 groups of third correlation sequences,

[0236]

[0237] 0 ≤ x < 336.

[0238] Step 707: Perform SSS detection based on W sss (j, x), and record the corresponding detection results (u sss (l sss ), FO sss (l sss ), TO sss (l sss ), SINR sss (l sss ) and N sss (l sss )) when the following conditions are met. As shown in the following formula, it is necessary to perform the following detection and determination on 336 groups of W sss (j, x):

[0239]

[0240] 0 ≤ x < 336,

[0241] where, N sss_thr represents the decision threshold of SSS detection.

[0242] Step 708: According to the number of results passed in this SSS detection (denoted as Lsss (j)) Update l sss Obtain a value as shown in the following formula:

[0243] l sss = l sss + L sss (j).

[0244] Step 709: Update j and reset SINR fine_pss (l max_fine_pss ), j = j + 1, SINR fine_pss (l max_fine_pss ) = 0 and jump to Step 702.

[0245] Step 7010: End the processing procedure, and feedback all detection results, including {u sss (l)}, {FO sss (l)}, {TO sss (l)}, {SINR sss (l)}, {N sss (l)} and l sss , where l represents the serial number of the SSS detection result.

[0246] Step 405: Perform frequency offset calculation based on the third time-domain signal, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal to obtain the target frequency offset.

[0247] Specifically, the target frequency offset FO(l) can be:

[0248]

[0249] 0 ≤ l < l sss k ss_start ≤ k < k ss_start + 127

[0250] where N fft represents the number of points for the receiver to perform FFT, with the default value of 4096, N cp represents the CP length, with the default value of 288, TO pss (l) represents the corresponding time offset of the l-th detection result passing the PSS detection, FO pss (l) represents the corresponding frequency offset of the l-th detection result passing the PSS detection, lsss represents the number of results passing the SSS detection, and k ss_start represents the starting RB index occupied by the PSS and SSS in the frequency domain.

[0251] For easy viewing, the meanings of the symbols are summarized as follows:

[0252] r(n) represents the received time-domain signal after phase compensation, where 0 ≤ n < N rx , N rx represents the total number of sampling points of the time-domain signal for a single reception, and its value is related to the signal sampling rate;

[0253] r ds (m) represents the signal in the frequency band where PSS / SSS is separated after downsampling filtering;

[0254] N fft represents the number of points for FFT / IFFT performed by both the transmitter and receiver, with a default value of 4096;

[0255] D ds represents the downsampling factor, which must be divisible by N fft and has a default value of 16;

[0256] {h ds (k)} represents the sequence of downsampling filter coefficients, which is a sequence of length L ds ;

[0257] M pss represents the length of the PSS-related sequence to be calculated, and its value must be N fft / D ds ;

[0258] M sss represents the length of the PSS-related sequence to be calculated, and its value must be N fft / D ds ;

[0259] s pss (m, x) represents the x-th group of PSS time-domain signals generated locally after the same downsampling process, where x is the PSS root sequence number and 0 ≤ m < M pss , 0 ≤ x < 3;

[0260] s sss (m, x) represents the x-th group of SSS time-domain signals generated locally after the same downsampling process, where x is the SSS root sequence number and 0 ≤ m < M sss , 0 ≤ x < 336;

[0261] l corse_pss represents the number of results detected by PSS coarse screening;

[0262] l fine_pss represents the number of results detected by PSS fine screening;

[0263] l sss represents the number of results detected by SSS;

[0264] K corse_pssIndicates the number of unidirectional frequency offset compensation values for PSS coarse screening detection, with a default value of 3;

[0265] K fine_pss Indicates the number of unidirectional frequency offset compensation values for PSS fine screening detection, with a default value of 1;

[0266] N corse_pss_thr Indicates the decision threshold for PSS coarse screening detection;

[0267] N fine_pss_thr Indicates the decision threshold for PSS fine screening detection;

[0268] N sss_thr Indicates the decision threshold for SSS detection;

[0269] N seg Indicates the total number of segments for segment - related correlation, and it must be divisible by M pss and M sss exactly;

[0270] L seg Indicates the segment length for segment - related correlation, L seg = M pss / N seg = M sss / N seg ;

[0271] u corse_pss (l) represents the PSS root sequence number of the l - th detection result passing the PSS coarse screening detection, and the value range is {0, 1, 2};

[0272] u fine_pss (l) represents the PSS root sequence number of the l - th detection result passing the PSS fine screening detection, and the value range is {0, 1, 2};

[0273] u sss (l) represents the SSS root sequence number of the PSS detection result of the l - th detection passing the SSS detection, and the value range is {0, 1,..., 335};

[0274] FO corse_pss (l) represents the corresponding frequency offset of the l - th detection result passing the PSS coarse screening detection, with the unit of sub - carrier spacing;

[0275] TO corse_pss (l) represents the corresponding time offset of the l - th detection result passing the PSS coarse screening detection, with the unit of D ds T s ,T s represents the sampling interval;

[0276] TO fine_pss(l) represents the corresponding time offset of the detection result of the l-th detection through PSS fine screening, with the unit of D ds T s , T s represents the sampling interval;

[0277] TO fine_pss (l) represents the corresponding time offset of the detection result of the l-th detection through SSS detection, with the unit of D ds T s , T s represents the sampling interval;

[0278] SINR corse_pss (l) represents the corresponding SINR of the detection result of the l-th detection through PSS coarse screening;

[0279] SINR fine_pss (l) represents the corresponding SINR of the detection result of the l-th detection through PSS fine screening;

[0280] SINR sss (l) represents the corresponding SINR of the detection result of the l-th detection through SSS detection;

[0281] N corse_pss (l) represents the corresponding noise power of the detection result of the l-th detection through PSS coarse screening;

[0282] N fine_pss (l) represents the corresponding noise power of the detection result of the l-th detection through PSS fine screening;

[0283] N sss (l) represents the corresponding noise power of the detection result of the l-th detection through SSS detection;

[0284] N fft represents the number of points for the receiver to perform FFT, with the default value of 4096;

[0285] N cp represents the CP length, with the default value of 288;

[0286] k ss_start represents the starting RB index occupied by PSS and SSS in the frequency domain.

[0287] An anti-large frequency offset synchronization signal detection method applicable to the NR standard provided by this application can support the normal detection of PSS / SSS under the condition of a 2-fold subcarrier interval frequency offset, and the frequency offset measurement error can be guaranteed to be within the range of 0.03 times the subcarrier interval.

[0288] Two-step PSS detection processing including coarse screening detection and fine screening detection. During the coarse screening detection processing, candidate PSS detection results are scanned and detected with a frequency offset compensation granularity of 0.5 times the subcarrier spacing, and a frequency offset value in units of 0.5 times the subcarrier spacing is obtained; during the fine screening detection, candidate PSS detection results with high reliability are selected and scanned and detected with a smaller granularity of frequency offset compensation to obtain the final PSS detection results, and more accurate frequency offset measurement results are obtained. Such a design can effectively reduce the number of frequency offset compensation processes.

[0289] Frequency offset calculation is performed based on the frequency domain dimension correlation results of PSS and SSS signals with time domain frequency offset compensation. Based on the frequency offset results of PSS detection, frequency offset compensation is performed on PSS / SSS signals in the time domain dimension, and then the frequency domain dimension correlation results are obtained through FFT transformation, and then the frequency offset is calculated. The frequency offset error calculated in this way is significantly reduced compared with the traditional processing scheme, and the error can be controlled within the range of 0.03 times the subcarrier spacing under the condition of a frequency offset of 2 times the single subcarrier spacing.

[0290] A time-frequency synchronization device for an OFDM system based on ZC sequences provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar, and will not be elaborated here in this embodiment.

[0291] Figure 8 It is a schematic structural diagram of a large frequency offset resistant synchronization signal detection device applicable to the NR standard provided in an embodiment of the present application. As Figure 8 shown, the device 80 includes:

[0292] A downsampling processing module 801, configured to perform downsampling processing on a time domain received signal to separate multiple time domain signals in the frequency band where PSS / SSS is located;

[0293] A PSS coarse screening module 802, configured to perform PSS coarse screening detection processing on multiple time domain signals to obtain a first time domain signal that passes the PSS coarse screening detection, the total number of the first time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal;

[0294] A PSS fine screening module 803, configured to perform PSS fine screening detection according to the first time domain signal, the total number of the first time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal, to obtain a second time domain signal that passes the PSS fine screening detection, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal;

[0295] The SSS detection module 804 is configured to perform SSS detection based on the second time-domain signal, the total number of the second time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time-domain signal, so as to obtain the third time-domain signal that passes the SSS detection, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal;

[0296] The target frequency offset module 805 is configured to perform frequency offset calculation based on the third time-domain signal, the total number of the third time-domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time-domain signal, so as to obtain the target frequency offset.

[0297] In some embodiments, the PSS rough screening module 802 is further configured to:

[0298] Initialize the starting point number of the time-domain signal used when calculating the first correlation sequence;

[0299] If the starting point number does not meet the termination condition, calculate the first correlation sequence corresponding to each group of local PSS sequences according to the local PSS sequences defined by the first preset number of NR standards;

[0300] According to each group of first correlation sequences, obtain the correlation weights of each group of first correlation sequences under different coarse frequency offset compensation values;

[0301] If the starting point number is less than the starting point number threshold, obtain the PSS rough screening existence weight and noise power corresponding to each group of first correlation sequences according to the correlation weights corresponding to each group of first correlation sequences;

[0302] Perform PSS rough screening detection on multiple time-domain signals according to the PSS rough screening existence weight and noise power corresponding to each group of first correlation sequences, so as to obtain the first time-domain signal that passes the PSS rough screening detection, the total number of the first time-domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time-domain signal.

[0303] In some embodiments, the PSS fine screening module 803 is further configured to:

[0304] Initialize the first detection sequence for PSS fine screening detection and the number of results that pass the PSS fine screening detection;

[0305] If the first detection sequence is less than the total number of the first time-domain signals and the number of results meets the result number preset condition, calculate the PSS rough screening result index required for PSS fine screening processing corresponding to the first detection sequence;

[0306] Obtain the second correlation sequence according to the PSS rough screening result index;

[0307] According to the second correlation sequence, obtain the correlation weights corresponding to the second correlation sequence under different fine frequency offset compensation values;

[0308] According to the correlation weights corresponding to the second correlation sequence, obtain the PSS fine screening existence weights corresponding to the second correlation sequence;

[0309] Perform PSS fine screening detection according to the PSS fine screening existence weights corresponding to the second correlation sequence, and obtain the second time-domain signal passing the PSS fine screening detection, the total number of second time-domain signals, and the PSS root sequence numbers, frequency offsets, time offsets, SINR, and noise power corresponding to the second time-domain signals.

[0310] In some embodiments, the SSS detection module 804 is further configured to:

[0311] Initialize the second detection sequence for SSS detection;

[0312] If the second detection sequence is less than the total number of second time-domain signals, calculate the PSS detection result index required for SSS detection processing corresponding to the second detection sequence;

[0313] Based on the local SSS sequences defined by the second preset quantity, the time offset corresponding to the second time-domain signal, and the PSS detection result index, obtain the third correlation sequence corresponding to each local SSS sequence;

[0314] According to the third correlation sequence, obtain the correlation weights of the third correlation sequence;

[0315] According to the correlation weights of the third correlation sequence, obtain the SSS existence weights corresponding to the third correlation sequence;

[0316] Perform SSS detection based on the SSS existence weights, and obtain the third time-domain signal passing the SSS detection, the total number of third time-domain signals, and the SSS root sequence numbers, frequency offsets, time offsets, SINR, and noise power corresponding to the third time-domain signals.

[0317] In some embodiments, the target frequency offset module 805 is further configured to satisfy:

[0318]

[0319] 0 ≤ l < l sss k ss_start ≤ k < k ss_start +127

[0320] where FO(l) represents the target frequency offset, N fft represents the number of points for the receiver to perform FFT, N cp represents the CP length, TO pss (l) represents the time offset corresponding to the l-th detection result passing the PSS detection, FOpss (l) represents the corresponding frequency offset of the detection result of the l-th detection passed by PSS, l sss represents the number of results detected by SSS, k ss_start represents the starting RB index occupied by PSS and SSS in the frequency domain.

[0321] Figure 9 This is a schematic structural diagram of an electronic device provided by the present application. As Figure 9 shown, the electronic device 90 provided in this embodiment includes: at least one processor 901 and a memory 902. Optionally, the device 90 further includes a communication component 903. Among them, the processor 901, the memory 902, and the communication component 903 are connected through a bus 904.

[0322] In the specific implementation process, at least one processor 901 executes the computer execution instructions stored in the memory 902, so that at least one processor 901 executes the above method.

[0323] For the specific implementation process of the processor 901, reference can be made to the above method embodiment, and its implementation principle and technical effect are similar, so details are not described here in this embodiment.

[0324] In the above embodiment, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, CPU), or may also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, DSP), application specific integrated circuits (English: Application Specific Integrated Circuit, ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the application can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0325] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0326] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the buses in the drawings of this application are not limited to only one bus or one type of bus.

[0327] This application also provides a computer program product, including a computer program which, when executed by a processor, implements the above method.

[0328] This application also provides a computer-readable storage medium storing computer-executable instructions, which when executed by a processor, implement the above method.

[0329] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disc. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0330] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0331] The division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0332] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units can be selected according to actual needs to achieve the objectives of the solution of this embodiment.

[0333] In addition, in each embodiment of this application, the various functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0334] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of this application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0335] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps included in the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, and other various media that can store program codes.

[0336] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard, characterized in that: include: Down-sampling the time domain received signal to separate multiple time domain signals in the frequency band where the PSS / SSS is located; Perform PSS coarse screening detection processing on the multiple time domain signals to obtain a first time domain signal detected by PSS coarse screening and the total number of the first time domain signals, and a PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal; Perform PSS fine screening detection according to the first time domain signal, the total number of the first time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal, to obtain a second time domain signal that passes the PSS fine screening detection, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal; Perform SSS detection according to the second time domain signal, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal, to obtain a third time domain signal detected by SSS, the total number of the third time domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time domain signal; Frequency offset calculation is performed according to the third time domain signal, the total number of the third time domain signals, and the SSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the third time domain signal to obtain a target frequency offset.

2. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 1, characterized in that: The performing PSS coarse screening detection processing on the multiple time domain signals to obtain the first time domain signal detected by the PSS coarse screening and the total number of the first time domain signal, and the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the first time domain signal, includes: Initialize the starting point number of the time domain signal used in calculating the first correlation sequence; If the starting point sequence number does not meet the termination condition, calculating the first correlation sequence corresponding to each group of local PSS sequences respectively according to the local PSS sequences defined by the NR standard of the first preset number; According to each group of first correlation sequences, a correlation weight of each group of first correlation sequences under different coarse frequency offset compensation values ​​is obtained; If the starting point sequence number is less than the starting point sequence number threshold, then according to the correlation weight corresponding to each group of first correlation sequences, the PSS coarse screening existence weight and noise power corresponding to each group of first correlation sequences are obtained; According to the PSS coarse screening existence weights and noise power corresponding to each group of first related sequences, PSS coarse screening detection is performed on the multiple time domain signals to obtain the first time domain signals detected by PSS coarse screening and the total number of the first time domain signals, as well as the PSS root sequence number, frequency deviation, time deviation, SINR and noise power corresponding to the first time domain signal.

3. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 2, characterized in that: The termination conditions include: Where i indicates that r is used when calculating the first correlation sequence. ds (m) starting point number, N rx Indicates the total number of sampling points of the time domain signal received in a single time, D ds Indicates the downsampling multiple, M pss Indicates the calculated PSS correlation sequence length, the value is N fft / D ds , N fft Indicates the number of points used by the sender and receiver to perform FFT / IFFT.

4. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 1, characterized in that: According to the first time domain signal, the total number of the first time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal, PSS fine screening detection is performed to obtain a second time domain signal detected by PSS fine screening, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal, including: Initialize the first test sequence of PSS fine screening test and the number of results that pass PSS fine screening test; If the first detection sequence is less than the total number of the first time domain signals, and the number of results meets the preset condition of the number of results, then calculating the PSS coarse screening result index required for the PSS fine screening processing corresponding to the first detection sequence; Obtaining a second correlation sequence according to the PSS coarse screening result index; obtaining, according to the second correlation sequence, correlation weights corresponding to the second correlation sequence under different precise frequency offset compensation values; Obtaining a PSS fine screening existence weight corresponding to the second correlation sequence according to the correlation weight corresponding to the second correlation sequence; PSS fine screening detection is performed according to the PSS fine screening existence weight corresponding to the second related sequence to obtain the second time domain signal that passes the PSS fine screening detection, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the second time domain signal.

5. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 4, characterized in that: The preset conditions for the number of results are: l fine_pss ≤N max_fine_pss ； l fine_pss Indicates the number of results, N max_fine_pss Indicates the maximum number of output results of the PSS fine screening test process.

6. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 1, characterized in that: Performing SSS detection according to the second time domain signal, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal, to obtain a third time domain signal detected by SSS, the total number of the third time domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time domain signal, including: Initialize the second detection sequence of SSS detection; If the second detection sequence is less than the total number of the second time domain signals, calculating a PSS detection result index required for SSS detection processing corresponding to the second detection sequence; Obtaining a third correlation sequence corresponding to each local SSS sequence based on a second preset number of local SSS sequences defined by the NR standard, a time offset corresponding to the second time domain signal, and the PSS detection result index; Obtaining a correlation weight of the third correlation sequence according to the third correlation sequence; Obtaining, according to the correlation weight of the third correlation sequence, an SSS existence weight corresponding to the third correlation sequence; SSS detection is performed based on the SSS existence weight to obtain a third time domain signal detected by SSS, the total number of the third time domain signals, and an SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time domain signal.

7. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 6, characterized in that: The second preset number is 336.

8. The method for detecting a synchronization signal with high frequency deviation resistance applicable to the NR standard according to claim 1, characterized in that: A frequency offset calculation is performed according to the third time domain signal, the total number of the third time domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time domain signal to obtain a target frequency offset, satisfying: Where FO(l) represents the target frequency deviation, N fft Indicates the number of points used by the receiver to perform FFT, N cp Indicates CP length, TO pss (l) represents the corresponding time offset of the lth detection result detected by PSS, FO pss (l) represents the corresponding frequency deviation of the lth detection result through PSS detection, l sss Indicates the number of results that pass SSS detection, k ss_start Indicates the starting RB index occupied by PSS and SSS in the frequency domain.

9. A large frequency deviation resistant synchronization signal detection device applicable to the NR standard, characterized in that: include: A down-sampling processing module is used to perform down-sampling processing on the time domain received signal to separate multiple time domain signals in the frequency band where the PSS / SSS is located; A PSS coarse screening module, configured to perform PSS coarse screening detection processing on the multiple time domain signals, to obtain a first time domain signal detected by PSS coarse screening and the total number of the first time domain signals, and a PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the first time domain signal; A PSS fine screening module, configured to perform PSS fine screening detection according to the first time domain signal, the total number of the first time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the first time domain signal, to obtain a second time domain signal detected by PSS fine screening, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR and noise power corresponding to the second time domain signal; An SSS detection module, configured to perform SSS detection according to the second time domain signal, the total number of the second time domain signals, and the PSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the second time domain signal, to obtain a third time domain signal detected by SSS, the total number of the third time domain signals, and the SSS root sequence number, frequency offset, time offset, SINR, and noise power corresponding to the third time domain signal; The target frequency deviation module is used to calculate the frequency deviation according to the third time domain signal, the total number of the third time domain signals, and the SSS root sequence number, frequency deviation, time deviation, SINR and noise power corresponding to the third time domain signal to obtain the target frequency deviation.

10. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

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