PSS block cross-correlation detection method
阅读说明：本技术 一种pss分块互相关检测方法 (PSS block cross-correlation detection method ) 是由 王霄峻 郑瑞门 于 2020-12-30 设计创作，主要内容包括：本发明公开了一种PSS分块互相关检测方法,步骤一：在本地PSS时间序列生成时添加循环前缀CP,间接地增加进行互相关的序列长度；步骤二：使用非相干积累改善检测性能；步骤三：接收机在生成本地PSS时间序列后,对这些本地序列进行频偏预补偿,从而实现粗频偏估计；步骤四：对残留频偏进行估计和补偿；步骤五：考虑在低信噪比条件下,从降低粗频偏估计误差的影响、残留频偏估计抗噪声优化两方面对频偏估计算法进行优化。本发明可以提升在低信噪比环境下的检测性能和抗频偏性能,满足5G下行同步的需要。(The invention discloses a PSS block cross-correlation detection method, which comprises the following steps: adding a Cyclic Prefix (CP) when a local PSS time sequence is generated, and indirectly increasing the length of the sequence for performing cross-correlation; step two: improving detection performance using incoherent accumulation; step three: after the receiver generates the local PSS time sequences, the frequency offset precompensation is carried out on the local sequences, so that the coarse frequency offset estimation is realized; step four: estimating and compensating the residual frequency offset; step five: under the condition of low signal-to-noise ratio, the frequency offset estimation algorithm is optimized from two aspects of reducing the influence of a coarse frequency offset estimation error and optimizing residual frequency offset estimation anti-noise. The invention can improve the detection performance and the frequency deviation resistance performance under the environment of low signal-to-noise ratio and meet the requirement of 5G downlink synchronization.)
1. A PSS block cross-correlation detection method is characterized by comprising the following steps:
the method comprises the following steps: adding a Cyclic Prefix (CP) when a local PSS time sequence is generated, and indirectly increasing the length of the sequence for performing cross-correlation;
step two: detection performance is improved using incoherent accumulation: performing M times of accumulation on the square of the cross-correlation modulus value of each sliding window, and finally performing M times of accumulation to obtain a result setTo obtainCoarse timing position estimation and cell group internal identification estimation; setting accumulation ending conditions to make the accumulation times M adaptive, wherein the accumulation ending conditions are as follows: coarse timing position d and cell group mark obtained when ith accumulation valueFinishing accumulation when the result is the same as the result obtained by the i-1 st accumulation value;
step three: after the receiver generates a local PSS time sequence, the frequency offset pre-compensation is carried out on the local PSS time sequence, so that the coarse frequency offset estimation is realized;
the frequency offset detection granularity is set to 1/2 subcarrier spacings; the coarse frequency offset estimation uses a monte carlo detection method, and the PSS cross-correlation detection method fused with the coarse frequency offset estimation is described in detail as follows:
normalized frequency offset epsilon of Monte CarloestThe selected set is { -1, -0.5, 0, 0.5, 1}, after the receiver generates the local PSS time sequence, the receiver performs frequency offset pre-compensation on the local PSS time sequence, and 15 kinds of PSS local time sequences are generated in total;
the original local PSS time sequence is su(n), u is belonged to {0,1,2}, and the new sequence after coarse frequency offset compensation is as follows:
wherein the content of the first and second substances,representing a local PSS time sequence after coarse frequency offset compensation, j representing an imaginary unit, and n representing an independent variable of a discrete sequence;
the estimation formula of the coarse timing position, the cell group identifier and the coarse frequency offset estimation is as follows:
u∈{0,1,2},εest∈{-1,-0.5,0,0.5,1}
where M denotes the number of partitions, L denotes the length of each block sequence, r denotes the received sequence, d denotes the window start position,representing a conjugate form of a local PSS time sequence, respectively correlating r (n + d + k.L) by using three local PSS time sequences at each window position, and storing the modulus square of a correlation value to obtain three result sets C'u(d) Then, the result set is processed with approximate energy normalization to obtain Cu(d) P (d) represents the signal energy within the sliding window,respectively representing the estimated values of a coarse timing position, an intra-cell identifier and coarse frequency offset estimation;
step four: estimating and compensating the residual frequency offset:
assuming that the cross-correlation detection is carried out by PSS, the estimated value of the mark in the cell group isCorresponding to a local PSS time sequence ofThe coarse frequency offset estimation value isThe received sequence after coarse frequency offset compensation is calculated as follows:
wherein the content of the first and second substances,represents the received sequence after coarse frequency offset compensation, r (n) represents the received sequence,representing a frequency offset;
receiving sequence after compensating coarse frequency deviationAnd local PSS sequencesAnd (5) carrying out point-by-point conjugate multiplication to obtain y (n):
in the formula (5), E (n) represents the energy of the sampling point of the PSS time sequence, I (n) represents the noise term, y (n) represents the result sequence of conjugate multiplication of the receiving sequence and the local sequence,denotes the conjugate form of the local PSS sequence, w (n) denotes the noise term;
when the signal-to-noise ratio is larger than 8dB, neglecting a noise term I (n), equally dividing the sequence y (n) into 2 subsequencesAndtwo subsequences are point-by-point conjugate multiplied and accumulated to obtainTo:
in the formula (6), E, (i) andall are real numbers, and the frequency deviation is obtained by calculating the phase of the complex number P according to the following formula
Wherein, angle (P) represents the phase angle of complex number P;
step five: under the condition of low signal-to-noise ratio, optimizing a frequency offset estimation algorithm from two aspects of reducing the influence of a coarse frequency offset estimation error and optimizing residual frequency offset estimation anti-noise:
when the signal-to-noise ratio is lower than 8dB, the noise term in equation (5) is not negligible, the residual frequency offset estimation using equations (6) and (7) will have a large error, and the RMSE of the cyclic prefix-based estimation algorithm will also rise, so the noise-resistant performance of the algorithm is optimized from the following two aspects:
A. reducing effects of coarse frequency offset estimation errors
Performing coarse frequency offset estimation once after each incoherent accumulation, and obtaining a coarse frequency offset estimation set after M times of incoherent accumulationAnd overlapping the coarse frequency deviation estimation after each accumulation according to the weight value to obtain a final estimation valueThe calculation formula is as follows:
in formula (8) { w1，w2，...，wMIs a normalized weight sequence, improved according to the signal-to-noise ratio due toAnd calculating the weight according to the following formula:
wherein, wiRepresenting the normalized weight after the ith incoherent integration;
B. residual frequency offset estimation anti-noise optimization
Improved method based on coarse frequency offset estimation, each calculationThen, residual frequency offset estimation is carried out to obtain M result sequences yi(n), i belongs to {1, 2.., M }, and M complex numbers P are obtained by calculation by using a formula (6)ii ∈ {1, 2., M }, the improved residual frequency offset estimation equation is:
wherein the content of the first and second substances,representing an improved residual frequency offset estimate.
2. The PSS blocking cross-correlation detection method of claim 1, wherein: the first step is as follows: method of adding CP when generating local PSS time sequences, indirectly increasing the length of sequences that are cross-correlated:
generating formula (1) from the PSS of the m sequence:
dPSS(n)＝1-2x(m)
0≤n＜127 (1)
wherein d isPSS(n) denotes the PSS frequency domain sequence, x (m) denotes an m-sequence of length 127, mod denotes the remainder function, n denotes the sequence argument,representing an intra-cell group identity;
generating three groups of m sequences with the length of 127, which respectively correspond toAndthen zero filling is carried out on both ends of the m sequence to LwindowPerforming IFFT on the m sequence after zero padding, performing power normalization, and finally adding CP and L according to the OFDM symbol lengthwindowIndicating the length of the sliding window.
3. The PSS blocking cross-correlation detection method of claim 2, wherein: normalized by a factor of
4. The PSS blocking cross-correlation detection method of claim 3, wherein: setting the maximum value of M as M in the second stepmaxThe maximum value is reached and accumulation ends at 10.
Technical Field
The invention relates to a novel PSS block cross-correlation detection algorithm, belonging to the technical field of signal and information processing.
Background
Cell search is the first step in which the UE establishes communication with the base station, and under SA networking, the UE first attempts to access the last camped cell. If there is no last resident information, on the 5G operating band, the UE searches for SS/PBCH block (synchronization signal and PBCH block) by using GSCN (global synchronization channel number) according to the supported subcarrier spacing, and then completes detecting PSS (primary synchronization signal), SSs (secondary synchronization signal), and decoding PBCH (physical broadcast channel) to obtain MIB (primary system information block).
The PSS sequence detection is the first step in the analysis of the whole SS/PBCH block, and the marks in the cell group are obtained through the PSS sequence, so that the complexity of SSS sequence detection is reduced. In the LTE, in order to improve the PSS detection accuracy, the digital baseband signal after down-conversion is low-pass filtered, and then PSS detection is performed. Since the SS/PBCH block frequency domain location may not be centered in the channel bandwidth, low pass filtering of the received digital baseband signal may not be possible in 5G. Under the condition that the receiver does not know the channel bandwidth of the base station, the receiver samples by using the maximum sampling rate of 61.44MHz to obtain a 4096-point sequence, and performs 16 times of down-sampling on the sampled signal in order to reduce the computational complexity. And carrying out correlation detection on the down-sampled signals to finish coarse timing synchronization. Due to the down-sampling, the timing position resolution is 16 sampling points at this time, and fine timing synchronization is required to further determine the position of the timing point of the original sequence.
Time-domain correlation detection algorithms for PSS sequences may be classified into autocorrelation detection algorithms and cross-correlation detection algorithms. The autocorrelation detection algorithm performs correlation calculations using the received signal and a delayed version of the received signal, and the algorithm is less complex than the cross-correlation algorithm, but performs poorly at low signal-to-noise ratios. In 5G, the received signal cannot pass through a low-pass filter to remove subcarrier interference on both sides of the synchronous signal, so that the performance of the autocorrelation detection algorithm is affected.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a PSS block cross-correlation detection method, which aims at the performance reduction of the traditional algorithm under the conditions of time dispersion, frequency dispersion and poor signal to noise ratio and provides three improvement methods of improving a local PSS time sequence, introducing an incoherent accumulation method in a radar signal and optimizing a frequency offset estimation algorithm based on the traditional block cross-correlation algorithm.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a PSS block cross-correlation detection algorithm comprising the steps of:
the method comprises the following steps: adding a Cyclic Prefix (CP) when a local PSS time sequence is generated, and indirectly increasing the length of the sequence for performing cross-correlation; the method specifically comprises the following steps:
generating formula (1) from the PSS of the m sequence:
wherein d isPSS(n) denotes the PSS frequency domain sequence, x (m) denotes an m-sequence of length 127, mod denotes the remainder function, n denotes the sequence argument,indicating an intra-cell group identity.
Generating three groups of m sequences with the length of 127, which respectively correspond toAndthen zero filling is carried out on both ends of the m sequence to LwindowPoint, IFFT transform is carried out on the m sequence after zero padding, and power normalization is carried out, wherein the normalization factor isFinally, CP, L is added according to OFDM symbol lengthwindowIndicating the length of the sliding window.
Step two: improving detection performance using incoherent accumulation; the method specifically comprises the following steps:
on the basis of a block cross-correlation algorithm, performing M-time accumulation on the cross-correlation module value square of each sliding window, and finally performing M-time accumulation on the result setA coarse timing position estimate and an estimate of an identity within the cell group are obtained. In addition to this, in the non-coherent productIn the accumulation process, an accumulation ending condition is set so that the accumulation times M are self-adaptive, and the accumulation ending condition is as follows: coarse timing position d and cell group mark obtained when ith accumulation valueAnd finishing accumulation when the result is the same as the result obtained by the (i-1) th accumulation value. To avoid trapping in infinite accumulation due to some problems, M is set to a maximum value of MmaxThe maximum value is reached and accumulation ends at 10.
Step three: after the receiver generates the local PSS time sequences, the frequency offset precompensation is carried out on the local sequences, so that the coarse frequency offset estimation is realized; the method specifically comprises the following steps:
in order to improve the detection performance of the algorithm, the coarse frequency offset estimation is not equal to the integral multiple frequency offset estimation, and the frequency offset detection granularity is set as 1/2 subcarrier intervals; in order to reduce the influence of the computational complexity brought by the fusion of the coarse frequency offset estimation, the coarse frequency offset estimation uses a suboptimal signal detection algorithm, namely a Monte Carlo (MC) detection algorithm. The PSS cross-correlation detection algorithm fused with the coarse frequency offset estimation is described in detail as follows:
normalized frequency offset epsilon of Monte CarloestThe selected set is { -1, -0.5, 0, 0.5, 1}, and after the receiver generates the local PSS time sequences, the receiver performs frequency offset pre-compensation on the local sequences, and generates 15 kinds of PSS local time sequences in total.
The original local PSS time sequence is su(n) u is in the range of {0,1,2}, and the new sequence after coarse frequency offset compensation is as follows:
wherein the content of the first and second substances,and j represents an imaginary unit, and n represents an independent variable of the discrete sequence.
Based on the block cross-correlation detection algorithm, the estimation formula of the coarse timing position, the cell group identifier and the coarse frequency offset estimation is as follows:
where M denotes the number of partitions, L denotes the length of each block sequence, r denotes the received sequence, d denotes the window start position,representing a conjugate form of a local PSS time sequence, respectively correlating r (n + d + k.L) by using three local PSS time sequences at each window position, and storing the modulus square of a correlation value to obtain three result sets C'u(d) Then, the result set is processed with approximate energy normalization to obtain Cu(d) P (d) represents the signal energy within the sliding window,respectively representing the estimated values of a coarse timing position, an intra-cell identifier and coarse frequency offset estimation;
step four: estimating and compensating the residual frequency offset; the method specifically comprises the following steps:
assuming that the cross-correlation detection is carried out by PSS, the estimated value of the mark in the cell group isCorresponding to a local PSS time sequence ofThe coarse frequency offset estimation value isThe received sequence after coarse frequency offset compensation is calculated as follows:
wherein the content of the first and second substances,represents the received sequence after coarse frequency offset compensation, r (n) represents the received sequence,representing a frequency offset. Received sequence after compensating for coarse frequency offsetAnd local PSS sequencesAnd (5) carrying out point-by-point conjugate multiplication to obtain y (n):
in the formula (5), E (n) represents the energy of the sampling point of the PSS time sequence, I (n) represents the noise term, y (n) represents the result sequence of conjugate multiplication of the receiving sequence and the local sequence,denotes the conjugate form of the local PSS sequence, w (n) denotes the noise term.
When the signal-to-noise ratio is higher than 8dB, neglecting the noise term I (n), equally dividing the sequence y (n) into 2 subsequencesAndand performing point-by-point conjugate multiplication on the two subsequences and accumulating to obtain:
in the formula (6), E, (i) andall are real numbers, and the frequency deviation is obtained by calculating the phase of the complex number P according to the following formula
Wherein, angle (P) represents the phase angle of the complex number P.
Since the phase range is (-pi, pi), the frequency offsetThe detection range is (-1,1), the algorithm design requirement is met, and the maximum residual frequency offset of half of subcarrier interval can be covered.
Step five: optimizing a frequency offset estimation algorithm from two aspects of reducing the influence of a coarse frequency offset estimation error and optimizing residual frequency offset estimation anti-noise when the signal-to-noise ratio is lower than 8 dB; the method specifically comprises the following steps:
at low snr, the noise term in equation (5) is not negligible, the residual frequency offset estimation using equations (6) and (7) will have large error, and the RMSE of the cyclic prefix based estimation algorithm will also increase. The noise immunity of the algorithm is optimized from the following two aspects.
A. Reducing effects of coarse frequency offset estimation errors
Performing coarse frequency offset estimation once after each incoherent accumulation, and obtaining a coarse frequency offset estimation set after M times of incoherent accumulationAnd overlapping the coarse frequency deviation estimation after each accumulation according to the weight value to obtain a final estimation valueThe calculation formula is as follows:
in formula (8) { w1,w2,...,wMThe normalized weight sequence is improved by a factor according to the signal-to-noise ratioAnd calculating the weight according to the following formula:
wherein, wiRepresenting the normalized weight after the ith non-coherent accumulation.
B. Residual frequency offset estimation anti-noise optimization
Improved algorithm based on coarse frequency offset estimation, each calculationThen, residual frequency offset estimation is carried out to obtain M result sequences yi(n) i belongs to {1, 2.. multidot.M }, and M complex numbers P are obtained by calculation by using a formula (6)ii ∈ {1, 2., M }, the improved residual frequency offset estimation equation is:
wherein the content of the first and second substances,representing an improved residual frequency offset estimate.
Compared with the prior art, the invention has the following beneficial effects:
the invention starts from the traditional LTE downlink synchronization scheme, analyzes the advantages and disadvantages of the traditional PSS time domain cross-correlation detection algorithm, and provides three improvement methods of improving the local PSS time sequence, introducing the incoherent accumulation method in the radar signal and optimizing the frequency offset estimation algorithm aiming at the performance degradation of the traditional algorithm under the conditions of time dispersion, frequency dispersion and bad signal to noise ratio. The methods improve the local PSS time sequence, reduce the influence of down-sampling on the autocorrelation characteristic of the m sequence, enable the PSS cross-correlation detection algorithm to have stable performance under the condition of poor signal-to-noise ratio, and solve the defect that the RMSE of the traditional algorithm is higher under the condition of low signal-to-noise ratio.
Drawings
FIG. 1 is a flow chart of a method in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of local PSS time sequence generation in an embodiment of the present invention;
FIG. 3 is a flow chart of a non-coherent accumulation algorithm in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of the pre-compensation of the frequency offset of the local PSS time sequence in the embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following description in conjunction with the accompanying drawings and the specific embodiments, it is to be understood that these examples are given solely for the purpose of illustration and are not intended as a definition of the limits of the invention, since various equivalent modifications will occur to those skilled in the art upon reading the present invention and fall within the limits of the appended claims.
A PSS blocking cross-correlation detection method, as shown in fig. 1, comprising the steps of:
the method comprises the following steps: adding a CP when a local PSS time sequence is generated, and indirectly increasing the length of the sequence which carries out cross correlation;
fig. 2 is a flow chart of local PSS time sequence generation as used by the present invention. In the first step, generating a formula (1) according to the PSS of the m sequence:
three sets of m-sequences of length 127 are generated,respectively correspond toAndthen zero filling is carried out on both ends of the m sequence to LwindowPoint, IFFT transform is carried out on the m sequence after zero padding, and power normalization is carried out, wherein the normalization factor isFinally, the CP is added according to the OFDM symbol length.
Step two: improving detection performance using incoherent accumulation;
fig. 3 is a flow chart of a non-coherent accumulation algorithm used by the present invention. In the second step, on the basis of the block cross-correlation algorithm, the cross-correlation module value square of each sliding window is accumulated for M times, and finally, a result set obtained after M times of accumulation is passedA coarse timing position estimate and an estimate of an identity within the cell group are obtained. In addition, in the incoherent accumulation process, accumulation ending conditions are set so that the accumulation times M are self-adaptive, wherein the accumulation ending conditions are as follows: coarse timing position d and cell group mark obtained when ith accumulation valueAnd finishing accumulation when the result is the same as the result obtained by the (i-1) th accumulation value. To avoid trapping in infinite accumulation due to some problems, M is set to a maximum value of MmaxThe maximum value is reached and accumulation ends at 10.
Step three: after the receiver generates the local PSS time sequences, the frequency offset precompensation is carried out on the local sequences, so that the coarse frequency offset estimation is realized;
figure 4 is a flow chart of the local PSS time sequence frequency offset pre-compensation used in the present invention. In step three, in order to improve the algorithm detection performance, the coarse frequency offset estimation is not equal to the integral multiple frequency offset estimation, and the frequency offset detection granularity is set as 1/2 subcarrier intervals; in order to reduce the influence of the computational complexity brought by the fusion of the coarse frequency offset estimation, the coarse frequency offset estimation uses a suboptimal signal detection algorithm, namely a Monte Carlo (MC) detection algorithm. The PSS cross-correlation detection algorithm fused with the coarse frequency offset estimation is described in detail as follows:
normalized frequency offset epsilon of Monte CarloestThe selected set is { -1, -0.5, 0, 0.5, 1}, and after the receiver generates the local PSS time sequences, the receiver performs frequency offset pre-compensation on the local sequences, and generates 15 kinds of PSS local time sequences in total.
The original local PSS time sequence is su(n) u is in the range of {0,1,2}, and the new sequence after coarse frequency offset compensation is as follows:
based on the block cross-correlation detection algorithm, the estimation formula of the coarse frequency offset estimation, the coarse timing position and the cell group internal identification is as follows:
step four: estimating and compensating the residual frequency offset;
in the fourth step, the PSS cross-correlation detection is assumed, and the estimated value of the mark in the cell group isCorresponding to a local PSS time sequence ofCoarse frequency offset estimationEvaluated asThe received sequence after coarse frequency offset compensation is calculated as follows:
for received sequenceAnd local PSS sequencesAnd (5) carrying out point-by-point conjugate multiplication to obtain y (n):
in the formula (5), e (n) represents the PSS time series sample point energy, and i (n) represents the noise term.
When the signal-to-noise ratio is high, neglecting the noise term I (n), equally dividing the sequence y (n) into 2 subsequencesAndand performing point-by-point conjugate multiplication on the two subsequences and accumulating to obtain:
in the formula (6), E, (i) andall are real numbers, and the frequency deviation is obtained by calculating the phase of the complex number P according to the following formula
Since the phase range is (-pi, pi), the frequency offsetThe detection range is (-1,1), the algorithm design requirement is met, and the maximum residual frequency offset of half of subcarrier interval can be covered.
Step five: under the condition of low signal-to-noise ratio, the frequency offset estimation algorithm is optimized from two aspects of reducing the influence of a coarse frequency offset estimation error and optimizing residual frequency offset estimation anti-noise.
In step five, under a low snr, the noise term in equation (5) is not negligible, and the residual frequency offset estimation using equations (6) and (7) will have a large error, and the RMSE of the cyclic prefix-based estimation algorithm will also increase. The noise immunity of the algorithm is optimized from the following two aspects.
A. Reducing effects of coarse frequency offset estimation errors
Performing coarse frequency offset estimation once after each incoherent accumulation, and obtaining a coarse frequency offset estimation set after M times of incoherent accumulationAnd overlapping the coarse frequency deviation estimation after each accumulation according to the weight value to obtain a final estimation valueThe calculation formula is as follows:
in formula (8) { w1,w2,...,wMThe normalized weight sequence is improved by a factor according to the signal-to-noise ratioAnd calculating the weight according to the following formula:
B. residual frequency offset estimation anti-noise optimization
Improved algorithm based on coarse frequency offset estimation, each calculationThen, residual frequency offset estimation is carried out to obtain M result sequences yi(n) i belongs to {1, 2.. multidot.M }, and M complex numbers P are obtained by calculation by using a formula (6)ii ∈ {1, 2., M }, the improved residual frequency offset estimation equation is:
the invention can improve the detection performance and the frequency deviation resistance performance under the environment of low signal-to-noise ratio and meet the requirement of 5G downlink synchronization.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.