CN114124637B - Low-complexity method suitable for reducing peak-to-average ratio of OFDM (orthogonal frequency division multiplexing) system - Google Patents
Low-complexity method suitable for reducing peak-to-average ratio of OFDM (orthogonal frequency division multiplexing) system Download PDFInfo
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Abstract
The invention discloses a method suitable for reducing OFDM systemA low complexity method of peak to average ratio comprising the steps of: setting a system model, defining a subcarrier set and a signal vector d for data transmission n The method comprises the steps of carrying out a first treatment on the surface of the For signal vector d n Processing to obtain a frequency domain transmitting signal; acquiring a time domain transmitting signal through a frequency domain transmitting signal, inserting a CP into the time domain transmitting signal, and defining a baseband transmitting signal after OFDM modulation; the PAPR is used for measuring the fluctuation of the baseband transmitting signal, and the complementary cumulative distribution function is used for measuring the PAPR; and obtaining the power constraint combination of reducing the band interference, inhibiting the PAPR and inhibiting the signal by using the designed linearization alternating direction multiplier algorithm to form an optimization problem, and solving the optimization problem. The invention can reduce out-of-band radiated interference and effectively reduce PAPR value, and the PAPR of the transmitted signal can be reduced by using the LADMM method with little iteration times, thereby improving the system operation efficiency, not affecting the BER performance of the system, and being more suitable for practical application in engineering.
Description
Technical Field
The present invention relates to wireless communications, and more particularly to a low complexity method suitable for reducing peak-to-average ratio in an OFDM system.
Background
The human society enters an informatization age, and the requirements of people on communication are higher and higher, so that the development of wireless communication technology is continuously promoted. Orthogonal Frequency Division Multiplexing (OFDM) is used as an important multi-carrier modulation technology, and a wideband channel is decomposed into a plurality of independent narrowband channels, so that higher frequency spectrum utilization rate and frequency selective fading resistance are obtained, and the OFDM is widely applied to modern communication systems. However, the OFDM system includes a plurality of subcarriers, and during the modulation process, the plurality of subcarriers are superimposed together, so that a large instantaneous peak value may be generated when the phases are identical or adjacent, thereby resulting in an excessively high peak-to-average power ratio. And the more the number of carriers, the higher the peak-to-average ratio. The peak-to-average ratio signal can exceed the linear range of the amplifier, so that the signal is distorted, the frequency spectrum of the signal is changed, and the performance of the system is seriously deteriorated.
Many methods have been proposed by researchers to address this problem. Three main categories are: probability class, coding class and signal predistortion class techniques, but they all suffer from disadvantages such as signal distortion, high computational complexity. In recent years, many scholars have used mathematical optimization methods in PAPR suppression for OFDM, such as classical semi-positive programming (SDP) and Second Order Cone Programming (SOCP). While these algorithms can achieve a better result in reducing PAPR, the computational complexity is relatively high. This may lead to inefficiency and excessive time consumption when the data size is large. Therefore, a method for reducing the PAPR of the OFDM signal, improving the calculation efficiency and reducing the complexity of the algorithm is needed to be found.
Disclosure of Invention
The invention aims to: the invention aims to provide a low-complexity method suitable for reducing the peak-to-average ratio of an OFDM system, so that the PAPR of a transmitted signal is effectively restrained, and meanwhile, the BER of the system keeps a good state, and compared with other methods, the method can achieve a good effect with few iteration times.
The technical scheme is as follows: the invention relates to a low complexity method suitable for reducing peak-to-average ratio of an OFDM system, which comprises the following steps:
s1, setting various parameters of a system model, and defining a subcarrier set and a signal vector d for data transmission n ；
S2, designing a signal vector d n For signal vector d n Processing to obtain a frequency domain transmitting signal, and obtaining a time domain transmitting signal through the frequency domain transmitting signal;
s3, adopting an added suppression signal module to suppress spectrum sidelobes of a transmitting signal and reduce PAPR: adding a cyclic prefix to the time domain transmitting signal, and then adding a suppression signal module to the processed time domain transmitting signal to obtain a transmitting signal;
s4, reducing the PAPR: the PAPR is used for measuring the fluctuation of a transmitting signal, the PAPR represents the ratio of the peak value to the average value of the signal power, and the complementary cumulative distribution function is used for measuring the PAPR;
s5, combining the power constraint of reducing the band interference, inhibiting the PAPR and inhibiting the signal into an optimization problem, and solving the optimization problem by using a designed alternate direction multiplier algorithm.
The step S1 specifically comprises the following steps:
s1.1, setting a single-link OFDM system, wherein the system consists of a transmitter and a receiver for communication on a Rayleigh channel;
s1.2, for convenience of analysis and without loss of generality, assuming an adjacent user, adopting an OFDM technology to operate on bandwidths crossing K subcarriers in a transmission band of an OFDM system; thus, the OFDM transmitter/receiver correspondingly controls its transmission to minimize interference to the adjacent user;
s1.3, assuming that the total number of subcarriers is N, the adjacent users span { i+1,., i+k } subcarriers in the frequency band are inactive; residual N d Data subcarriers 1, i }, i + K +1, N, by inclusion in vectorsIs modulated by a set of QAM symbols.
The step S2 specifically comprises the following steps:
s2.1, remaining N d Serial-parallel conversion is carried out on the data subcarriers to become parallel data;
s2.2, mapping operation is carried out, and the spanned K data are processed to enable the frequency domain to transmit a signal d n Re-become to
S2.3, the obtained frequency domain signal d n And then IDFT (inverse discrete fourier transform) is performed on the signal to obtain a time domain transmission signal.
The step S3 specifically comprises the following steps:
s3.1, in order to avoid ISI (intersymbol interference), CP (cyclic prefix) is added to the time domain transmission signal obtained in step S2, and the obtained time domain OFDM signal is expressed as x= [ x ] in vector form 1 ,...,x N+L ] T ＝AF H Md; wherein the method comprises the steps ofIs to add CP matrix, F H Is an N-point inverse discrete Fourier transform matrix,>is a subcarrier mapping matrix;
s3.2, in order to control the PAPR of the transmitted signal, a time-domain suppression signal c is added to the transmitted signal, wherein
S3.3, the suppression signal is expressed as c=ps, so the signal at the transmitting end becomes t=x+c=af H Md+Ps, where
S3.4, the frequency domain signal vector received by the receiver is expressed as: r=ht+n, where r is the received signal vector, H is the channel matrix of OFDM, N is independently co-distributed complex gaussian noise with each component having a mean of 0 and a variance of N 0 ；
S3.5 for a given frequency domain signalThe baseband transmission signal after OFDM modulation is expressed as:
wherein,t is the OFDM symbol time, Δf is the subcarrier frequency spacing, and N is the number of subcarriers.
The expression of the PAPR in step S4 is as follows:
wherein I ∞ Representing an infinite norm of the sign of the sum, I.I 2 Representing 2 norms and E { · } representing the desired operation.
The complementary cumulative distribution function in step S4 is expressed as:
CCDF(α)＝Pr{PAPR＞α}＝1-(1-e -α ) N (3)
where α represents a threshold value of the PAPR.
The step S5 specifically comprises the following steps:
s5.1, designing an optimization model, and jointly executing power constraint of reducing band interference, suppressing PAPR and suppressing signals:
interference in adjacent bands is expressed as:
wherein,out-of-band power radiation indicative of information data, +.>Representing the out-of-band power radiation of the suppression signal.
S5.2, integrating the formulas (2), (4) and the suppression signal power constraint together, and describing the system as the following optimization problem:
s5.3 using mean square errorInstead of F d +F s s|| 2 The following optimization problems are obtained:
where α represents the peak-to-average ratio constraint, ε represents the power constraint of vector s, and α >1, ε > 0.
The step S5.3 specifically comprises the following steps:
s5.3.1 directly applying the LADMM algorithm to the model (6) to obtain a LADMM model:
wherein L is ρ Is an augmented lagrangian function of the model described above,is the Lagrangian multiplier, k is the number of iterations; the augmented lagrangian function of model (7) can be expressed as:
wherein ρ >0 represents a penalty parameter;
s5.3.2, establishing a solving model of the signal s, and obtaining the solving model of the signal s according to the augmented Lagrange function of the model (7):
s5.3.3, establishing a solving model of the signal y, and obtaining the solving model of the signal y according to the augmented Lagrange function of the model (7):
s5.3.4, updating the Lagrange multiplier u, and updating and solving the signal s and the signal y by using the Lagrange multiplier;
the model solving process of the signal s in step S5.3.2 is as follows:
firstly, removing the constraint in the step (9), and taking unconstrained problems into consideration, and obtaining by using an approximation method:
the derivative is equal to zero, and the expression of s is simplified as follows:
wherein,returning to (9), projecting the resulting solution onto a feasible solution using projection to obtain s k+1 The update expression of (2) is:
the model solving process of the signal y in the step S5.3.3 is as follows:
introducing the auxiliary variables t and a represents y=ta, where t >0 andsubstituting it into (10) to obtain
Wherein b k ＝x+Ps k+1 -u k - ρ; since t is a real number, its value does not affect a k+1 The method comprises the steps of carrying out a first treatment on the surface of the So t is removed from (14) and a is solved by (15) k+1 ；
Further converting the constraint in (15) into an inequality constraint and establishing an equivalent optimization formula (16); because the optimal solution of (16) is taken when the constraint takes the equal sign, the two are equivalent;
since the complexity of the solution (16) is still high, the methodIntroducing constraintsLagrange y multiplier k And (16) is rewritten as:
because the problem and constraint in (17) can be handled separately, simplifying (17) into n+l sub-problems as follows:
setting the target gradient to zero, and then projecting the solution of the corresponding equation onto a feasible solution, so as to obtain the optimal solution of the model, wherein the optimal solution is as follows:
optimal Lagrangian multiplier y in equation (19) above k* Obtaining by a dichotomy; then utilize a k+1 Substituting into (14) to obtain t k+1 ＝max{0,Re(a k+1H b k ) -a }; finally, a is arranged k+1 And t k+1 Substituting y=ta to obtain y k+1 。
A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a low complexity method as described above adapted to reduce peak-to-average ratio of an OFDM system.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a low complexity method as described above adapted for reducing peak-to-average ratio of an OFDM system when the computer program is executed.
The beneficial effects are that: compared with the prior art, the invention has the following advantages: the method can reduce the frequency band interference and effectively reduce the PAPR value, and the PAPR of the transmitted signal can be reduced by using the LADMM method with fewer iteration times, so that the system operation efficiency is improved, the BER performance of the system is not affected, and the method is more suitable for practical application in engineering.
Drawings
Fig. 1 is a diagram of an OFDM system model;
FIG. 2 is a graph of time domain signal amplitude;
FIG. 3 is a graph of convergence characteristics of the LADMM algorithm;
fig. 4 is a comparison graph of different methods of reducing PAPR;
fig. 5 is a graph comparing BER of different methods.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a low complexity method for reducing peak-to-average ratio of an OFDM system, comprising the steps of:
s1, setting a single-link OFDM system, wherein the system consists of a transmitter and a receiver which communicate on a Rayleigh channel. For ease of analysis and without loss of generality, it is assumed that one neighboring user, using OFDM or any other technique, operates over a bandwidth spanning K subcarriers within the transmission band of an OFDM system. Thus, the OFDM transmitter/receiver correspondingly controls its transmission to minimize interference to the adjacent user. Assuming that the total number of subcarriers is N, the adjacent users span { i+1,... Residual N d Data subcarriers 1, i }, i + K +1, N, by inclusion in vectorsIs modulated by a set of QAM symbols.
S2, remaining N d Serial-parallel conversion is carried out on the data subcarriers to become parallel data; then mapping operation is carried out, and the spanned K data are processed to make the frequency domain transmit signal d n Re-become toObtained frequency domain signal d n And then IDFT (inverse discrete fourier transform) is performed on the signal to obtain a time domain transmission signal.
S3, adopting an added suppression signal module to suppress spectrum sidelobes of a transmitting signal and reduce PAPR:
to avoid ISI (intersymbol interference), CP (cyclic prefix) is added to the time-domain transmission signal obtained in step S2, and the obtained time-domain OFDM signal will be expressed in vector form as x= [ x ] 1 ,...,x N+L ] T ＝AF H Md. Wherein the method comprises the steps ofIs to add CP matrix, F H Is an N-point inverse discrete Fourier transform matrix,>is a subcarrier mapping matrix. But in order to control the PAPR of the transmission signal, a time domain suppression signal c is added to the transmission signal, wherein +.>The suppression signal is expressed as c=ps, so the signal at the transmitting end becomes t=x+c=af H Md+Ps, wherein->Accordingly, the frequency domain signal vector received by the receiver can be expressed as: r=ht+n, where r is the received signal vector, H is the channel matrix of OFDM, N is independently co-distributed complex gaussian noise with each component having a mean of 0 and a variance of N 0 The method comprises the steps of carrying out a first treatment on the surface of the For a given frequency domain signal +.>The baseband transmit signal after OFDM modulation may be expressed as:
wherein,t is the OFDM symbol time, Δf is the subcarrier frequency spacing, and N is the number of subcarriers.
S4, measuring fluctuation of a transmitting signal by adopting a PAPR, wherein the PAPR represents the ratio of peak value to average value of signal power, and measuring the PAPR by adopting a complementary cumulative distribution function:
the expression of PAPR is as follows:
the complementary cumulative distribution function is expressed as:
CCDF(α)＝Pr{PAPR＞α}＝1-(1-e -α ) N (3)
wherein, alpha represents a threshold value of a designated PAPR, and N is the number of OFDM signal subcarriers.
S5, combining the power constraint of reducing the band interference, inhibiting the PAPR and inhibiting the signal into an optimization problem, and solving the optimization problem by using a designed linearization alternating direction multiplier algorithm:
s5.1, designing an optimization model, and jointly executing power constraint of reducing band interference, suppressing PAPR and suppressing signals:
interference in adjacent bands is expressed as:
wherein,out-of-band power radiation indicative of information data, +.>Representing the out-of-band power radiation of the suppression signal.
S5.2, integrating the formulas (2), (4) and the suppression signal power constraint together, and describing the system as the following optimization problem:
s5.3 using mean square errorInstead of F d +F s s|| 2 The following optimization problems are obtained:
the solving process of the optimization problem (6) in the step S5.3 is as follows:
s5.3.1 directly applying the LADMM algorithm to the model (6) to obtain a LADMM model:
wherein L is ρ Is an augmented lagrangian function of the model described above,is the Lagrangian multiplier, k is the number of iterations; the augmented lagrangian function of model (7) can be expressed as:
wherein ρ >0 represents a penalty parameter;
s5.3.2, establishing a solving model of the signal s, and obtaining the solving model of the signal s according to the augmented Lagrange function of the model (7):
s5.3.3, establishing a solving model of the signal y, and obtaining the solving model of the signal y according to the augmented Lagrange function of the model (7):
s5.3.4, updating the Lagrange multiplier u, and updating and solving the signal s and the signal y by using the Lagrange multiplier;
the detailed solving process of the signal s and the signal y is described as follows:
the model solving process of the signal s in step S5.3.2 is as follows:
firstly, removing the constraint in the step (9), and taking unconstrained problems into consideration, and obtaining by using an approximation method:
the derivative is equal to zero, and the expression of s is simplified as follows:
wherein,returning to (9), projecting the resulting solution onto a feasible solution using projection to obtain s k+1 The update expression of (2) is:
the model solving process of the signal y in the step S5.3.3 is as follows:
introducing the auxiliary variables t and a represents y=ta, where t >0 andsubstituting it into (10) to obtain
Wherein b k ＝x+Ps k+1 -u k And/. Rho.. Since t is a real number, its value does not affect a k+1 . So t is removed from (14) and a is solved by (15) k+1 。
The constraint in (15) is further converted into an inequality constraint and an equivalent optimization formula (16) is established. Because the optimal solution of (16) is taken when the constraint takes the equal sign, the two are equivalent.
Since the complexity of the solution (16) is still high, constraints are introducedLagrange y multiplier k And (16) is rewritten as:
because the problem and constraint in (17) can be handled separately, simplifying (17) into n+l sub-problems as follows:
setting the target gradient to zero, and then projecting the solution of the corresponding equation onto a feasible solution, so as to obtain the optimal solution of the model, wherein the optimal solution is as follows:
optimal Lagrangian multiplier y in equation (19) above k* Obtained by a dichotomy. Then utilize a k+1 Substituting into (14) to obtain t k+1 ＝max{0,Re(a k+1H b k ) }. Finally, a is arranged k+1 And t k+1 Substituting y=ta to obtain y k+1 。
Based on the above scheme, in order to verify the effect of the method of the present invention, the embodiment performs a simulation experiment of the algorithm, performs simulation by using software MATLAB, and verifies theoretical analysis. The specific simulation results and analysis are as follows:
as shown in fig. 2, which shows the signal amplitude curve, the amplitude curve of the time domain signal after being not optimized and being optimized by using the lammm algorithm is compared by using the data carrier modulation method of 16-QAM. Where "Original" represents the Original time domain signal that was not optimized and "lammm 1" represents the time domain signal that was obtained after 3 iterations using the lammm algorithm. From the figure, we can see that the amplitude value of the time domain signal obtained by using the LADMM algorithm is obviously reduced compared with that of the unprocessed time domain signal. Therefore, the lammm algorithm can effectively suppress the PAPR of the OFDM signal.
As shown in fig. 3, which shows a convergence characteristic diagram of the lammm algorithm, it can be seen that convergence can be achieved after several iterations of the lammm algorithm proposed by the present invention, especially after the first 4 iterations, the residual error is rapidly reduced, and the convergence speed is gradually slowed down and finally gradually flattened during the subsequent iterations.
A comparison of the different methods of reducing PAPR is shown in fig. 4, LADMM, AAC, AS and the solver algorithm. The solver solves the model by using optimization packages YALMIP and MOSEK integrated in MATLAB as solvers. At ccdf=pr (PAPR > PAPR 0 )＝10 -3 When the PAPR of the original OFDM signal which is not processed by the algorithm is 10.8dB, the LADMM algorithm can keep the PAPR at a constant value of 4dB, the PAPR is reduced by 6.8dB, and the PAPR reduction performances of the AAC, AS and the solver algorithm are respectively 2.4dB,0.6dB and 2.2dB, which are superior to the original OFDM signal. The lammm algorithm achieves PAPR reduction gains of about 4.4db,6.2db, and 4.6db, respectively, compared to the PAPR of AAC, AS, and solver algorithms. Therefore the invention is provided withThe LADMM algorithm can achieve a better effect in the aspect of reducing the PAPR, and the operation speed is greatly improved.
Fig. 5 shows BER effect graphs of different methods. It can be seen that the lammm is closest to the ideal value and the bit error rate performance is significantly better than other algorithms. Therefore, the invention can maintain better peak-to-average ratio performance, error code rate performance and operation efficiency.
In summary, the lammm algorithm for reducing the peak-to-average ratio of the OFDM system provided by the present invention has lower complexity and faster convergence speed, and can obtain better PAPR suppression effect only by a small number of iterations, so that the transmission quality of the communication system signal can be improved, and the present invention is more beneficial to practical engineering application.
Claims (7)
1. A low complexity method for reducing peak-to-average ratio in an OFDM system comprising the steps of:
s1, setting various parameters of a system model, and defining a subcarrier set and a signal vector d for data transmission n ；
S2, designing a signal vector d n For signal vector d n Processing to obtain a frequency domain transmitting signal, and obtaining a time domain transmitting signal through the frequency domain transmitting signal;
s3, adopting an added suppression signal module to suppress spectrum sidelobes of a transmitting signal and reduce PAPR: adding a cyclic prefix to the time domain transmitting signal, and then adding a suppression signal module to the processed time domain transmitting signal to obtain a transmitting signal;
s4, reducing the PAPR: the PAPR is used for measuring the fluctuation of a transmitting signal, the PAPR represents the ratio of the peak value to the average value of the signal power, and the complementary cumulative distribution function is used for measuring the PAPR;
the expression of the PAPR is as follows:
wherein I ∞ Representing an infinite norm of the sign of the sum, I.I 2 The number of 2 norms is indicated,e {. Cndot. } represents the desired operation;
s5, combining the power constraint of reducing the band interference, inhibiting the PAPR and inhibiting the signal into an optimization problem, and solving the optimization problem by using a designed alternate direction multiplier algorithm;
s5.1, designing an optimization model, and jointly executing power constraint of reducing band interference, suppressing PAPR and suppressing signals:
interference in adjacent bands is expressed as:
wherein,out-of-band power radiation indicative of information data, +.>Out-of-band power radiation representing the suppression signal;
s5.2, integrating the formulas (2), (4) and the suppression signal power constraint together, and describing the system as the following optimization problem:
s.t.PAPR(y)
y＝x+Ps (5)
s5.3 using mean square errorInstead of II F d +F s s‖ 2 The following optimization problems are obtained:
y＝x+Ps (6)
where α represents the peak-to-average ratio constraint, ε represents the power constraint of vector s, and α >1, ε >0;
s5.3.1 directly applying the LADMM algorithm to the model (6) to obtain a LADMM model:
wherein L is ε Is an augmented lagrangian function of the model described above,is the Lagrangian multiplier, k is the number of iterations; the augmented lagrangian function of model (7) can be expressed as:
wherein ε >0 represents a penalty parameter;
s5.3.2, establishing a solving model of the signal s, and obtaining the solving model of the signal s according to the augmented Lagrange function of the model (7):
s5.3.3, establishing a solving model of the signal y, and obtaining the solving model of the signal y according to the augmented Lagrange function of the model (7):
s5.3.4, updating the Lagrange multiplier u, and updating and solving the signal s and the signal y by using the Lagrange multiplier;
the model solving process of the signal s in step S5.3.2 is as follows:
firstly, removing the constraint in the step (9), and taking unconstrained problems into consideration, and obtaining by using an approximation method:
the derivative is equal to zero, and the expression of s is simplified as follows:
wherein,returning to (9), projecting the resulting solution onto a feasible solution using projection to obtain s k+1 The update expression of (2) is:
the model solving process of the signal y in the step S5.3.3 is as follows:
introducing the auxiliary variables t and a represents y=ta, whereint>0 and 0Substituting it into (10) to obtain
Wherein b k ＝x+Ps k+1 -u k - ρ; since t is a real number, its value does not affect a k+1 The method comprises the steps of carrying out a first treatment on the surface of the So t is removed from (14) and a is solved by (15) k+1 ；
Further converting the constraint in (15) into an inequality constraint and establishing an equivalent optimization formula (16); because the optimal solution of (16) is taken when the constraint takes the equal sign, the two are equivalent;
since the complexity of the solution (16) is still high, constraints are introducedLagrange y multiplier k >0, rewriting (16) to:
because the problem and constraint in (17) can be handled separately, simplifying (17) into n+l sub-problems as follows:
setting the target gradient to zero, and then projecting the solution of the corresponding equation onto a feasible solution, so as to obtain the optimal solution of the model, wherein the optimal solution is as follows:
optimal Lagrangian multiplier y in equation (19) above k* Obtaining by a dichotomy; then utilize a k+1 Substituting into (14) to obtain t k +1 ＝max{0,Re(a k+1H b k ) -a }; finally, a is arranged k+1 And t k+1 Substituting y=ta to obtain y k+1 。
2. A low complexity method for reducing peak-to-average ratio of an OFDM system according to claim 1, wherein said step S1 is specifically:
s1.1, setting a single-link OFDM system, wherein the system consists of a transmitter and a receiver for communication on a Rayleigh channel;
s1.2, for convenience of analysis and without loss of generality, assuming an adjacent user, operating on a bandwidth spanning K subcarriers within a transmission band of the OFDM system; thus, the OFDM transmitter/receiver correspondingly controls its transmission to minimize interference to the adjacent user;
s1.3, assuming that the total number of subcarriers is N, the adjacent users span { i+1,., i+k } subcarriers in the frequency band are inactive; residual N d Data subcarriers 1, i }, i + K +1, N, by inclusion in vectorsIs modulated by a set of QAM symbols.
3. A low complexity method for reducing peak-to-average ratio in an OFDM system according to claim 1 or 2, wherein said step S2 is specifically:
s2.1, remaining N d Serial-parallel conversion is carried out on the data subcarriers to become parallel data;
s2.2, mapping operation is carried out, and the spanned K data are processed to enable the frequency domain to transmit a signal d n Re-become to
S2.3, the obtained frequency domain signal d n And performing IDFT on the signal to obtain a time domain transmitting signal.
4. A low complexity method for reducing peak-to-average ratio of an OFDM system according to claim 1 or 2, wherein said step S3 is specifically:
s3.1, adding CP to the time domain transmission signal obtained in the step S2, wherein the obtained time domain OFDM signal is expressed as x= [ x ] in vector form 1 ,...,x N+L ] T ＝AF H Md; wherein the method comprises the steps ofIs to add CP matrix, F H Is an N-point inverse discrete Fourier transform matrix,>is a subcarrier mapping matrix;
s3.2 adding a time domain suppression signal c to the transmitted signal, wherein
S3.3, representing the suppression signal as c=ps, the signal at the transmitting end becomes t=x+c=af H Md+Ps, where
S3.4, the frequency domain signal vector received by the receiver is expressed as: r=ht+n, where r is the received signal vector, H is the channel matrix of OFDM, N is independently co-distributed complex gaussian noise with each component having a mean of 0 and a variance of N 0 ；
S3.5 for a given frequency domain signalThe baseband transmission signal after OFDM modulation is expressed as:
wherein,t is the OFDM symbol time, Δf is the subcarrier frequency spacing, and N is the number of subcarriers.
5. A low complexity method for reducing peak-to-average ratio in an OFDM system according to claim 1, wherein the complementary cumulative distribution function in step S4 is expressed as:
CCDF(α)＝Pr{PAPR>α}＝1-(1-e -α ) N (3)
where α represents a threshold value of the PAPR.
6. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a low complexity method according to any one of claims 1-5 adapted to reduce peak-to-average ratio of an OFDM system.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a low complexity method according to any one of claims 1-5 adapted to reduce peak-to-average ratio of an OFDM system when executing the computer program.
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