CN114070700A - Carrier synchronization method and device based on 5G high-order modulation signal clustering judgment - Google Patents
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Abstract
The invention provides a carrier synchronization method and a carrier synchronization device based on 5G high-order modulation signal clustering judgment, wherein the method comprises the following steps: setting basic parameters of the 5G communication signals; MATLAB-based baseband IQ two-path orthogonal signal x for generating band frequency phase offset under set parametersI(t) and xQ(t); sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method; the discriminated signal is subjected to frequency locking/phase locking loop correction frequency phase offset to obtain a signal xIC(t) and xQC(t); sampling decision xIC(t) and xQCAnd (t) calculating the bit error rate BER. The invention corrects the frequency deviation phase deviation of the 5G high-order modulation baseband signal data set based on a Frequency Locking Loop (FLL) and a Phase Locking Loop (PLL) of a clustering discrimination method, compares the constellation diagrams of the baseband signals before and after correction, realizes carrier synchronization, improves the calculation efficiency of carrier synchronization in the 5G high-order modulation signals on the premise of ensuring certain precision, and accelerates engineering application.
Description
Technical Field
The invention belongs to the technical field of signals, and particularly relates to a carrier synchronization method and a carrier synchronization device based on 5G high-order modulation signal clustering judgment.
Background
Carrier synchronization has become a hot topic in the field of signal processing, and in a digital communication system, accurate carrier frequency and phase estimation of a received signal is a very classic topic in a carrier synchronization technology. The carrier wave at the receiving end deviates from the carrier wave at the transmitting end due to fast fading in the channel, so that the center frequency of the signal deviates, and further, the frequency deviation is generated. In addition, the signal transmitted in the channel is affected by doppler effect, which causes phase jitter and phase offset of the signal. The frequency deviation and phase deviation generated in the signal transmission process can cause judgment errors, accurate demodulation cannot be carried out, and the system error rate cannot meet the communication requirement, so that the communication system cannot be normally used.
Because the existence of carrier frequency offset and phase offset directly affects the performance of a receiving system, accurately estimating the carrier frequency offset and the phase offset and then compensating the carrier frequency offset and the phase offset are very critical to the demodulation of signals. Therefore, in order to reduce the Bit Error Rate (BER) of the demodulated signal, many experts and scholars at home and abroad continuously explore new methods and new technologies for carrier synchronization.
Cui J et al disclose a system and method for blind frequency synchronization: first downconverting a received Orthogonal Frequency Division Multiplexed (OFDM) signal to baseband, then identifying a series of OFDM symbols in the time domain from the downconverted received signal, performing a Fast Fourier Transform (FFT), and finally calculating the cross-correlation between the in-phase and quadrature samples of each subcarrier and each frequency domain OFDM symbol to determine the frequency offset of the received OFDM signal; the Yang G et al thinks that under low signal-to-noise ratio, carrier frequency offset and phase offset will have a large impact on the LDPC code system, and a receiving end needs to estimate the frequency offset and phase offset before decoding. Based on statistics of soft information of the LDPC iterative decoder, coding-assisted iterative carrier estimation is proposed: the carrier coarse synchronization based on the cost function and the carrier synchronization based on the maximum likelihood iteration realize better carrier synchronization through the joint iteration of a synchronizer, a demodulator and a decoder; scholars such as Shi D, Yuan W and the like propose a cycle slip detection and correction method which is used for carrier phase synchronization in a coding system operating under an extremely low signal-to-noise ratio, particularly a detector adopts a log-likelihood ratio output by a demodulator to make a cycle slip decision and deduces the characteristics of the proposed detector in the aspects of detection and false alarm probability, and a numerical result shows that the error rate performance can be improved by adopting the proposed method under the condition of cycle slip; the Kumar M scholars have discussed a joint scheme of Orthogonal Frequency Division Multiplexing (OFDM) based CRN signal detection and non-data aided (blind) parameter estimation in the literature. Based on the binary hypothesis testing problem, the SU formulates a minimum cost signal detection scheme for the presence of OFDM-based PU signals in the CRN.
Liu Quanji scholars have conducted some discussion on the digital demodulation method of MPSK, and in carrier synchronization, a method for performing phase locking based on phase difference detection by a frequency difference detection method based on power detection is analyzed; the influence of the basic principle degree amplitude and the spectrum width of the waveform estimation method of the clock synchronization on the timing error is introduced in detail; the scholars of Huang-Piao, Chen-Nai-equalling scholars aim at the problem of carrier synchronization of multi-system spread spectrum signals in a high dynamic environment, and researches the carrier signal tracking by adopting a mode of combining a frequency locking ring and a phase locking ring on the basis of analyzing the respective advantages of the phase locking ring and the frequency locking ring. The fast and accurate tracking of the dynamic signal is realized by utilizing the characteristics of stronger dynamic adaptive capacity of the FLL and better tracking accuracy of the PLL. In order to eliminate residual frequency offset generated by a statistical frequency offset estimation algorithm and improve the performance of a receiver, students such as Zhangli and the like propose a mean square error feedback algorithm for correcting the residual frequency offset, introduce a feedback link, specify a feedback step length, and feed back and correct the residual frequency offset on the basis of the existing frequency offset estimation. And finally, simulating the algorithm by using MATLAB, wherein the simulation result shows that the algorithm can effectively eliminate residual frequency offset, and the error rate of the system is greatly reduced. From the problem of carrier synchronization in coherent demodulation of a multilevel phase shift chain control (MPSK) signal, scholars such as guying and others propose a method for carrier extraction based on Zoom FFT and frequency domain interpolation. The method realizes the rough estimation of carrier frequency in a time-frequency domain through the nonlinear transformation of signals, and realizes the accurate extraction of the carrier wave through the interpolation correction in the frequency domain on the basis. Aiming at the problems that the traditional multi-system digital phase Modulation (MPSK) signal non-data-aided (NDA) frequency offset estimation algorithm is limited in estimation range, large in estimation variance, limited in estimation performance when the number of symbols is small and the like, researchers such as the Li and the Liao propose a frequency offset estimation method based on particle swarm optimization. The algorithm takes a likelihood function of frequency offset estimation as a target function and simulates population intelligent search for an optimal solution. Simulation results show that the algorithm has a large unbiased estimation range, the estimation variance is close to the CrLB lower limit when the number of symbols is small and the signal-to-noise ratio is low, and the performance is superior to that of a classical Discrete Fourier Transform (DFT) algorithm and a Kay algorithm; researchers such as the chengdi and the Chengdan have better estimation performance aiming at the traditional multiple power spectrum frequency offset estimation method and the secondary wavelet symbol rate estimation algorithm, but due to the fence effect of Discrete Fourier Transform (DFT) and the influence of Noise, the estimation precision and reliability are limited, and the like, a confidence criterion based on Signal-to-Noise Ratio (SNR) and spectral peak index is provided, and the reliability of frequency offset estimation is enhanced. By taking QPSK and 16QAM as examples, simulation analysis is carried out, and simulation results show that the complexity of the improved algorithm is slightly higher than that of the traditional algorithm, but the performance of the improved algorithm is superior to that of the traditional algorithm, so that the estimation accuracy and reliability of frequency offset and symbol rate can be effectively improved; in order to solve the problem that the demodulation performance of a low signal-to-noise ratio and short-lead code burst communication system deteriorates along with the increase of carrier frequency offset, researchers such as Tankao, Yangyou and the like design a hybrid frequency offset estimation scheme, frequency offset estimation is decomposed into a coarse estimation process and a fine estimation process, different estimation algorithms are adopted for each process, starting from three aspects of theoretical derivation, numerical simulation and experimental verification, and the problem of large frequency offset demodulation of burst communication under multi-system digital phase Modulation (MPSK) is effectively solved; the Hodgkin scholars analyze the influence of residual frequency offset and phase error on digital demodulation, introduce the maximum likelihood estimation theory of carrier synchronization parameters, then analyze some carrier frequency difference estimation calculation methods with representative significance, summarize an estimation algorithm with high performance and low complexity according to an improved algorithm in the existing literature, fully utilize frequency information of an input sequence by utilizing a phase expansion method, and computer simulation shows that the contradiction between estimation precision and estimation range can be solved well and the estimation performance is good; the yankee scholars have designed a joint carrier recovery algorithm of an automatic mode conversion control mechanism based on Var (Variance) for 256QAM modulated signals and have performed simulation verification. And the idea of keeping tracking by PFD (Phase and Frequency Detectors) is introduced into a combined carrier recovery loop, and a combined PFD carrier recovery algorithm based on Var mode conversion is provided.
The researchers at home and abroad realize carrier synchronization by adopting different algorithms, however, in a 5G communication scene, the information transmission rate reaches Gbit/s level, and under the condition of such high transmission rate, although the algorithm can improve certain precision, the algorithm has high complexity and large calculation amount, and cannot be realized in hardware. Therefore, in order to meet the engineering requirements, an algorithm for realizing carrier synchronization in hardware on the premise of meeting a certain precision is urgently needed.
Disclosure of Invention
The invention provides a carrier synchronization method and a carrier synchronization device based on 5G high-order modulation signal clustering judgment, which improve the calculation efficiency of carrier synchronization in 5G high-order modulation signals and accelerate engineering application on the premise of ensuring certain precision.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
the carrier synchronization method based on the 5G high-order modulation signal clustering judgment comprises the following steps:
a: setting basic parameters of the 5G communication signals;
b: based on MATLAB generates baseband IQ two-path orthogonal signal x with band frequency offset under set parametersI(t) and xQ(t)；
C: sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method;
d: the discriminated signal is subjected to frequency locking/phase locking loop correction frequency phase offset to obtain a signal xIC(t) and xQC(t)；
E: sampling decision xIC(t) and xQCAnd (t) calculating the bit error rate BER.
Further, the step a specifically includes:
a1: selecting a 5G high-order modulation mode;
a2: setting codeword length L, symbol rate fdSampling rate fsFrequency deviation feAnd a phase offset value pe。
Further, step B specifically includes:
b1: obtaining IQ two-way orthogonal signals x _ I (t) and x _ Q (t) based on the signals in the MATLAB platform simulation step A1; b2: setting the range of SNR, and performing Gaussian noise on the signals x _ I (t) and x _ Q (t) with frequency offset to obtain xI(t)
And xQ(t):
xI(t)＝[xI1(t),xI2(t),xI3(t),...xIN(t)]
xQ(t)＝[xQ1(t),xQ2(t),xQ3(t),...xQN(t)]
Wherein, N is the data length after the input code element is coded:
N＝L/log2and (M) is a modulation order.
Further, step C specifically includes:
c1: judging { xIi(t),xQi(t) whether it is in the first quadrant, if so, calculating { x }Ii(t),xQi(t) and the theoretical constellation point psi of the first quadrant1And determines the IQ data as the constellation point psi corresponding to the minimum distance1_opt：
ψ1＝[ψ1,1,ψ1,2,ψ1,3,...,ψ1,W]，
d1＝[d1,1,d1,2,d1,3,...,d1,W]，
Otherwise, go to step C2;
c2: judging { xIi(t),xQi(t) whether it is in the second quadrant, if so, calculating { x }Ii(t),xQi(t) and the theoretical constellation point psi of the second quadrant2And determines the IQ data as the constellation point psi corresponding to the minimum distance2_opt：
ψ2＝[ψ2,1,ψ2,2,ψ2,3,...,ψ2,X]，
d2＝[d2,1,d2,2,d2,3,...,d2,X]
Otherwise, go to step C3;
c3: judging { xIi(t),xQi(t) whether it is in the third quadrant, if so, calculating { x }Ii(t),xQi(t) and the theoretical constellation point psi of the third quadrant3And determines the IQ data as the constellation point psi corresponding to the minimum distance3_opt：
ψ3＝[ψ3,1,ψ3,2,ψ3,3,...,ψ3,Y],
d3＝[d3,1,d3,2,d3,3,...,d3,Y],
Otherwise, go to step C4;
C4：calculate { xIi(t),xQi(t) and the theoretical constellation point psi of the fourth quadrant4And determines the IQ data as the constellation point psi corresponding to the minimum distance4_opt：
ψ4＝[ψ4,1,ψ4,2,ψ4,3,...,ψ4,Z]
d4＝[d4,1,d4,2,d4,3,...,d4,Z]
Wherein i is 1,2,3, …, N;
w, X, Y and Z are the numbers of constellation points of the first, second, third and fourth quadrants in the selected modulation mode, djvIs { xIi(t),xQi(t) } and the v-th theoretical constellation point ψ in the j-th quadrantjvThe euclidean distance between j 1,2,3, 4; v ═ W, X, Y, Z; psijv＝(ψjv_I,ψjv_Q)。
Further, step D specifically includes:
d1: sending the discriminated signal to a frequency/phase discriminator to calculate a difference dif (i)
Wherein the sign function sign (x) is expressed as:
d2: designing gains G1 and G2 of a second-order loop filter, and sending signals passing through the frequency/phase detector into the loop filter for filtering to obtain UC (r):
UC(r+1)＝G1·dif(r+1)+G2·dif(r)+UC(r)
wherein UC (1) ═ 0, r ═ 1,2,3, … N-1;
d3: the signal after loop filtering is processed by a digital voltage controlled oscillator to obtain a VCO (r):
VCO(r+1)＝VCO(r)+UC(r)，
wherein VCO (1) ═ 0;
d4: feeding back the signal to the step D1 for frequency/phase discrimination to obtain the signal x with corrected frequency/phase deviationIC(t) and xQC(t)：
xIC(t)＝[xIC_1(t),xIC_2(t),...,xIC_r(t),...xIC_N-1(t)],
xQC(t)＝[xQC_1(t),xQC_2(t),...,xQC_r(t),...xQC_N-1(t)]
Wherein:
xIC_r(t)＝xIr(t)·cos(VCO(r))+xQr(t)·sin(VCO(r)),
xQC_r(t)＝xQr(t)·cos(VCO(r))-xIr(t)·sin(VCO(r))。
the invention also provides a carrier synchronization device based on 5G high-order modulation signal clustering judgment, which comprises the following components:
a parameter setting module: setting basic parameters of the 5G communication signals;
a signal generation module: MATLAB-based baseband IQ two-path orthogonal signal x for generating band frequency phase offset under set parametersI(t) and xQ(t)；
A judging module: sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method;
a deviation rectifying module: the discriminated signal is subjected to frequency locking/phase locking loop correction frequency phase offset to obtain a signal xIC(t) and xQC(t)；
An error rate calculation module: sampling decision xIC(t) and xQCAnd (t) calculating the bit error rate BER.
Compared with the prior art, the invention has the following beneficial effects:
the invention corrects the frequency deviation phase deviation of a 5G high-order modulation baseband signal data set based on a frequency-locked loop (FLL) and a phase-locked loop (PLL) of a clustering discrimination method, compares the constellation diagrams of baseband signals before and after correction, realizes carrier synchronization, improves the calculation efficiency of carrier synchronization in the 5G high-order modulation signals on the premise of ensuring certain precision, and accelerates engineering application;
in the military aspect, the invention can accurately capture the modulation signal sent by the enemy, improve the decoding rate and provide sufficient information quantity for electronic reconnaissance, electronic countermeasure and the like; in the aspect of national defense application, the carrier synchronization is quickly and accurately realized, so that the national defense communication construction is enhanced; in the civil aspect, the efficient carrier synchronization can realize better communication among people, people and objects and between objects; in the aspect of future artificial intelligence, the method is favorable for accelerating implementation of communication scene application of the advanced intelligent interconnection.
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FIG. 1 is a schematic flow diagram of an embodiment of the present invention;
FIG. 2 is a schematic flow chart of step A of an embodiment of the present invention;
FIG. 3 is a schematic flow chart of step B of the present invention;
FIG. 4 is a schematic flow chart of step C of the present invention;
FIG. 5 is a schematic flow chart of step D of an embodiment of the present invention;
FIG. 6 is a schematic diagram of a transmitted symbol time domain portion waveform according to an embodiment of the present invention;
FIG. 7 is a comparison of a 64QAM theoretical constellation with a constellation with frequency offset according to an embodiment of the present invention;
FIG. 8 is a comparison of a 64QAM theoretical constellation with a phase-shifted constellation according to an embodiment of the present invention;
FIG. 9 is a comparison of a 64QAM theoretical constellation with a frequency-offset constellation according to an embodiment of the present invention;
fig. 10 is a constellation diagram after frequency correction by 64QAM according to an embodiment of the present invention;
fig. 11 is a constellation diagram after phase correction by 64QAM according to an embodiment of the present invention;
FIG. 12 is a constellation diagram of a 64QAM constellation diagram after frequency offset correction according to an embodiment of the present invention;
fig. 13 is a comparison graph of BER before and after frequency offset correction based on the clustering discrimination method according to the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention provides a carrier synchronization method and a carrier synchronization device based on 5G high-order modulation signal clustering judgment, which are used for improving the calculation efficiency of carrier synchronization in 5G high-order modulation signals and accelerating engineering application.
The invention is further described with reference to the following figures and specific embodiments.
The process of the invention is shown in fig. 1,2,3,4 and 5, and specifically comprises the following steps:
step A: and setting basic parameters of the 5G communication signal.
A1, in the case of the scheme, a 5G high-order modulation mode-64 QAM is selected, and the modulation order M is 64;
a2, setting code word length L to 30000, and symbol rate fd250k Baud, sample rate fs2MHz, frequency offset fe0.25kHz, phase offset pe＝π/6。
And B: MATLAB-based baseband IQ two-path orthogonal signal x with frequency offset under preset parametersI(t) and xQ(t)；
B1, obtaining IQ two-path orthogonal signals x _ I (t) and x _ Q (t) based on the signals in the MATLAB platform simulation step A1; FIG. 6 shows a simulated transmitted symbol time domain partial waveform;
b2, setting the range of signal-to-noise ratio to be 0 dB-20 dB, and carrying out Gaussian noise on the signals x _ I (t) and x _ Q (t) with frequency offset to obtain xI(t) and xQ(t):
N＝30000/log2(64)＝5000，
xI(t)＝[xI1(t),xI2(t),xI3(t),...xI5000(t)]，
xQ(t)＝[xQ1(t),xQ2(t),xQ3(t),...xQ5000(t)]；
Comparing the 64QAM theoretical constellation with the frequency offset constellation (SNR 20dB) as shown in fig. 7, comparing the 64QAM theoretical constellation with the frequency offset constellation (SNR 20dB) as shown in fig. 8, and comparing the 64QAM theoretical constellation with the frequency offset constellation (SNR 20dB) as shown in fig. 9;
and C: sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method;
c1, judgment { xIi(t)，xQi(t) whether it is in the first quadrant, if so, calculating { x }Ii(t)，xQi(t) and the theoretical constellation point psi of the first quadrant1And determines the IQ data as the constellation point psi corresponding to the minimum distance1_opt：
ψ1＝[ψ1,1,ψ1,2,ψ1,3,...,ψ1,16]，
d1＝[d1,1,d1,2,d1,3,...,d1,16]，
Otherwise, go to step C2;
c2, judgment { xIi(t)，xQi(t) whether it is in the second quadrant, if so, calculating { x }Ii(t)，xQi(t) and the theoretical constellation point psi of the second quadrant2And determines the IQ data as the constellation point psi corresponding to the minimum distance2_opt：
ψ2＝[ψ2,1,ψ2,2,ψ2,3,...,ψ2,16]，
d2＝[d2,1,d2,2,d2,3,...,d2,16]
Otherwise, go to step C3;
c3, judgment { xIi(t)，xQi(t) whether it is in the third quadrant, if so, calculating { x }Ii(t)，xQi(t) and the theoretical constellation point psi of the third quadrant3And determines the IQ data as the star corresponding to the minimum distanceSeating point psi3_opt：
ψ3＝[ψ3,1,ψ3,2,ψ3,3,...,ψ3,16]，
d3＝[d3,1,d3,2,d3,3,...,d3,16]，
Otherwise, go to step C4;
c4, calculation { xIi(t)，xQi(t) and the theoretical constellation point psi of the fourth quadrant4And determines the IQ data as the constellation point psi corresponding to the minimum distance4_opt：
ψ4＝[ψ4,1,ψ4,2,ψ4,3,...,ψ4,16]
d4＝[d4,1,d4,2,d4,3,...,d4,16]
Wherein i is 1,2,3, …, 5000.
Step D: the discriminated signal is subjected to frequency locking/phase locking loop correction frequency phase offset to obtain a signal xIC(t) and xQC(t)；
D1 sending the discriminated signal to the frequency/phase discriminator to calculate the difference dif (i)
D2, designing second-order loop filter gain G1 to be 2-2、G2＝2-11Sending the signal passing through the frequency/phase detector to a loop filter for filtering to obtain UC (r):
UC(r+1)＝G1·dif(r+1)+G2·dif(r)+UC(r)
d3, the signal after loop filtering is processed by a digital voltage controlled oscillator to obtain a VCO (r):
VCO(r+1)＝VCO(r)+UC(r)，
d4, feeding back to step D1 in a closed loop manner to carry out frequency/phase discrimination, and finally obtaining a signal x after correcting frequency/phase deviationIC(t) and xQC(t)：
xIC(t)＝[xIC_1(t),xIC_2(t),...,xIC_r(t),...xIC_4999(t)]，
xQC(t)＝[xQC_1(t),xQC_2(t),...,xQC_r(t),...xQC_4999(t)]
xIC_r(t)＝xIr(t)·cos(VCO(r))+xQr(t)·sin(VCO(r))，
xQC_r(t)＝xQr(t)·cos(VCO(r))-xIr(t)·sin(VCO(r))。
Fig. 10 shows a constellation diagram after frequency offset correction by 64QAM (SNR is 20dB), fig. 11 shows a constellation diagram after phase offset correction by 64QAM (SNR is 20dB), and fig. 12 shows a constellation diagram after frequency offset correction by 64QAM (SNR is 20 dB).
Step E: sampling decision xIC(t) and xQC(t), calculating the bit error rate BER;
in order to make the experimental result closer to the true value, the demodulation system and FLL/PLL adopt 500 Monte Carlo simulations, the hard decision is selected as the decision mode, and the BER (bit error rate) before and after frequency deviation correction based on the clustering discrimination method is shown in FIG. 13
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The carrier synchronization method based on 5G high-order modulation signal clustering judgment is characterized by comprising the following steps:
a: setting basic parameters of the 5G communication signals;
b: MATLAB-based baseband IQ two-path orthogonal signal x for generating band frequency phase offset under set parametersI(t)And xQ(t)；
C: sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method;
d: the discriminated signal is subjected to frequency locking/phase locking loop correction frequency phase offset to obtain a signal xIC(t) and xQC(t)；
E: sampling decision xIC(t) and xQCAnd (t) calculating the bit error rate BER.
2. The carrier synchronization method based on 5G high-order modulation signal clustering discrimination as claimed in claim 1, wherein the step A specifically comprises:
a1: selecting a 5G high-order modulation mode;
a2: setting codeword length L, symbol rate fdSampling rate fsFrequency deviation feAnd a phase offset value pe。
3. The carrier synchronization method based on 5G high-order modulation signal clustering discrimination as claimed in claim 1, wherein the step B specifically comprises:
b1: obtaining IQ two-way orthogonal signals x _ I (t) and x _ Q (t) based on the signals in the MATLAB platform simulation step A1;
b2: setting the range of SNR, and performing Gaussian noise on the signals x _ I (t) and x _ Q (t) with frequency offset to obtain xI(t) and xQ(t):
xI(t)＝[xI1(t),xI2(t),xI3(t),...xIN(t)]
xQ(t)＝[xQ1(t),xQ2(t),xQ3(t),...xQN(t)]
Wherein, N is the data length after the input code element is coded, and N is L/log2And (M) is a modulation order.
4. The carrier synchronization method based on 5G high-order modulation signal clustering discrimination as claimed in claim 1, wherein the step C specifically comprises:
c1: judging { xIi(t),xQi(t) whether it is in the first quadrant, if soThen calculate { xIi(t),xQi(t) and the theoretical constellation point psi of the first quadrant1And determines the IQ data as the constellation point psi corresponding to the minimum distance1_opt：
ψ1＝[ψ1,1,ψ1,2,ψ1,3,...,ψ1,W]，
d1＝[d1,1,d1,2,d1,3,...,d1,W]，
Otherwise, go to step C2;
c2: judging { xIi(t),xQi(t) whether it is in the second quadrant, if so, calculating { x }Ii(t),xQi(t) and the theoretical constellation point psi of the second quadrant2And determines the IQ data as the constellation point psi corresponding to the minimum distance2_opt：
ψ2＝[ψ2,1,ψ2,2,ψ2,3,...,ψ2,X]，
d2＝[d2,1,d2,2,d2,3,...,d2,X]
Otherwise, go to step C3;
c3: judging { xIi(t),xQi(t) whether it is in the third quadrant, if so, calculating { x }Ii(t),xQi(t) and the theoretical constellation point psi of the third quadrant3And determines the IQ data as the constellation point psi corresponding to the minimum distance3_opt：
ψ3＝[ψ3,1,ψ3,2,ψ3,3,...,ψ3,Y],
d3＝[d3,1,d3,2,d3,3,...,d3,Y],
Otherwise, go to step C4;
c4: calculate { xIi(t),xQi(t) and the theoretical constellation point psi of the fourth quadrant4And determines the IQ data as the constellation point psi corresponding to the minimum distance4_opt：
ψ4＝[ψ4,1,ψ4,2,ψ4,3,...,ψ4,Z]
d4＝[d4,1,d4,2,d4,3,...,d4,Z]
Wherein i is 1,2,3, …, N;
w, X, Y and Z are the numbers of constellation points of the first, second, third and fourth quadrants in the selected modulation mode, djvIs { xIi(t),xQi(t) } and the v-th theoretical constellation point ψ in the j-th quadrantjvThe euclidean distance between j 1,2,3, 4; v ═ W, X, Y, Z; psijv＝(ψjv_I,ψjv_Q)。
5. The carrier synchronization method based on 5G high-order modulation signal clustering discrimination as claimed in claim 1, wherein the step D specifically comprises:
d1: sending the discriminated signal to a frequency/phase discriminator to calculate a difference dif (i)
Wherein the sign function sign (x) is expressed as:
d2: designing gains G1 and G2 of a second-order loop filter, and sending signals passing through the frequency/phase detector into the loop filter for filtering to obtain UC (r):
UC(r+1)＝G1·dif(r+1)+G2·dif(r)+UC(r)
wherein UC (1) ═ 0, r ═ 1,2,3, … N-1;
d3: the signal after loop filtering is processed by a digital voltage controlled oscillator to obtain a VCO (r):
VCO(r+1)＝VCO(r)+UC(r)，
wherein VCO (1) ═ 0;
d4: feeding back the signal to the step D1 for frequency/phase discrimination to obtain the signal x with corrected frequency/phase deviationIC(t) and xQC(t)：
xIC(t)＝[xIC_1(t),xIC_2(t),...,xIC_r(t),...xIC_N-1(t)],
xQC(t)＝[xQC_1(t),xQC_2(t),...,xQC_r(t),...xQC_N-1(t)]
Wherein:
xIC_r(t)＝xIr(t)·cos(VCO(r))+xQr(t)·sin(VCO(r)),
xQC_r(t)＝xQr(t)·cos(VCO(r))-xIr(t)·sin(VCO(r))。
6. carrier synchronization device based on 5G high order modulation signal clustering is distinguished, its characterized in that includes:
a parameter setting module: setting basic parameters of the 5G communication signals;
a signal generation module: MATLAB-based baseband IQ two-path orthogonal signal x for generating band frequency phase offset under set parametersI(t) and xQ(t)；
A judging module: sequentially judging the input IQ two paths of orthogonal signals based on a clustering judgment method;
a deviation rectifying module: the discriminated signal is subjected to frequency locking/phase locking loop to correct frequency phase offset to obtain a signalNumber xIC(t) and xQC(t)；
An error rate calculation module: sampling decision xIC(t) and xQCAnd (t) calculating the bit error rate BER.
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