CN106817334B

CN106817334B - Multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving observation interference

Info

Publication number: CN106817334B
Application number: CN201710028427.6A
Authority: CN
Inventors: 苗圃; 朱秉诚; 吴乐南; 宋康; 戚晨皓
Original assignee: Qingdao University
Current assignee: Qingdao University
Priority date: 2017-01-16
Filing date: 2017-01-16
Publication date: 2020-03-13
Anticipated expiration: 2037-01-16
Also published as: CN106817334A

Abstract

The invention provides a multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving observation interference, which comprises the following steps: s1, a channel observation interference suppression module is configured in advance before the target information is measured; s2, before measuring target information, estimating an observation interference component in advance by a channel observation interference suppression module, and performing denoising processing on an original frequency domain symbol of a receiving end to construct an alternative observation vector; and S3, in the process of measuring the target information, generating a corresponding row ladder measurement matrix according to the observation interference component estimation information, the measurement compression ratio and the measurement criterion, and acquiring data from a plurality of sub-channels as final observation vector information. The invention can still show robust communication performance under the condition of low-value measurement compression rate and deep clipping, and improves the system robustness.

Description

Multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving observation interference

Technical Field

The invention relates to the technical field of digital communication systems, in particular to a multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving observation interference.

Background

1. Multi-carrier modulation technique

The multi-carrier modulation adopts a plurality of carrier signals to divide data into a plurality of sub-data streams, reduces the sensitivity of the system to the time dispersion of a transmission channel, has the advantages of high frequency spectrum utilization rate and strong anti-multipath interference capability, and is widely applied to wireless communication and wired communication systems. However, the multi-carrier modulation signal has a high peak-to-average power ratio (PAPR), a large peak signal passes through a non-linear device (such as a power amplifier, HPA) in the system to cause signal distortion, and the generated non-linear noise seriously affects the communication performance of the system.

Orthogonal Frequency Division Multiplexing (OFDM) is one of the multicarrier transmission scheme implementations. Let the OFDM system have N subcarriers, and input OFDM data vector in one symbol period T is X ═ X₀,X₁,…,X_N-1]^TWherein X is_kAnd e x represents a modulation symbol of the kth subcarrier, and x is a QAM constellation point set. An OFDM baseband time domain discrete oversampled signal within T is:

where L is an oversampling factor, in this case,

that is, padding the original X with (L-1) N zeros produces an L times oversampled input data vector X'. When L is 1, (1) is the OFDM time domain signal x (n) at the nyquist sampling rate.

PAPR is defined as the ratio of the peak power to the average power of a signal in one OFDM symbol period:

where E { } represents a statistical expectation. For distortion-free signal transmission, the OFDM signal with large PAPR necessarily requires a large linear range of the digital device, but the proportion of large peak values in the whole signal is low, resulting in a reduced utilization rate of the digital device; in addition, for a nonlinear digital device, if the amplitude of a large peak exceeds the linear working range of the nonlinear digital device, obvious nonlinear distortion is generated, intermodulation interference and spectrum expansion are caused, and the performance of the whole system is reduced. Therefore, it is very important for the OFDM system to effectively reduce the signal PAPR. In the present invention, x is used for convenience_LDenotes x_L(n) use of x_nDenotes x (n).

2. Clipping distortion

Simple clipping operation on a multi-carrier modulation signal at a transmitting end can directly and effectively reduce the PAPR value of the signal, but clipping distortion is introduced, so that the communication performance of the system is degraded. Let A_thFor clipping threshold, for x_nAnd (3) carrying out clipping operation to obtain a clipped signal as follows:

wherein phi (x)_n) Is x_nThen the corresponding clipping ratio is:

wherein σ_xIs the mean power rms value of the OFDM signal. Then the corresponding clipping rate:

from the equation (3), the clipped signal

Can be expressed as an original OFDM signal x_nAnd clipping distortion signal c_nSum of (a):

c_nit is clipping distortion information to be estimated and suppressed in the present invention. As shown in fig. 1, in one OFDM symbol period, c_nIs a time domain sparse signal with c_nFor example, the larger the clipping rate, c_nThe smaller the number of medium and non-zero elements, c_nThe higher the level of sparsity.

3. Maximum likelihood criterion symbol decision device

Suppose H_kIs the frequency response, x, of the k sub-channel_nAfter channel transmission, an OFDM receiving signal y is obtained_nIn the frequency domain, it can be expressed as:

wherein Z is_kChannel noise for the k-th sub-channel, which can be considered as variance

White gaussian noise. Based on the pilot information, the receiver can accurately estimate under ideal channel estimation conditions

And acquiring channel state information, having

Assuming complete synchronization of the system, Y_kAfter a first-order zero-breaking equalizer, an equalized symbol Y is obtained_eq(k)Comprises the following steps:

is interference noise and contains clipping distortion information and channel interference information. Usually, X is derived from the maximum likelihood criterion_kIs determined by the frequency domain

Comprises the following steps:

wherein x_iBelongs to x, which is a QAM constellation point set,

representing a maximum likelihood decider.

4. Compressed sensing

Compressed Sensing (CS) is a new digital signal processing theory developed in the field of signal processing in recent years. The compressive sensing theory can recover an N-dimensional original signal from an M-dimensional sampling observation value, can solve the problem of signal reconstruction under the undersampling condition, mainly depends on the sparsity of the signal and the non-coherence between a measurement matrix and a measurement base, and is essentially a fitting process between a measured sampling signal and an estimated signal under the sparse constraint. Namely knowing a certain measurement matrix phi epsilon^M×N(M N), unknown signal c ∈^N×1Is a K sparse vector, and the linear measurement y under the action of phi is as follows:

y＝Φc (10)

defining a measured compression ratio of

Now consider that a signal c of length N is reconstructed from a measurement y of the compression ratio p, however equation (10) has an infinite number of solutions. According to the theory of compressed sensing correlation, if the measurement times M satisfy

And the measurement matrix phi meets the constraint equidistance condition

(RIP), i.e. the presence of an equidistant constant delta_KE (0,1) such that:

by weighting the matrix Φ to satisfy K-order RIP, the signal c can be passed through the optimum l by the measured value y₀And (3) accurately reconstructing a norm problem:

as can be seen from the foregoing, the clipping distortion information c is a sparse signal, and in order to be able to reconstruct c by using the CS algorithm, it is necessary to obtain a measurement value related to c first. In general, Y may be_eq(k)Minus

Then, an alternative measurement vector is obtained, written in a matrix form, and the measurement vector comprises:

where C is the frequency domain vector of the target signal C to be reconstructed, F ∈ N × N is the discrete Fourier transform matrix, and xi ═ N₀+n₁To observe interference noise, where n₀＝H^-1Z is the transmission channel equalization interference noise,

is in the structure

Decision noise introduced in the process of (a). In order to be able to use a compressed sensing algorithm to derive alternative measurement sequences

To accurately reconstruct the target signal c, ideally from what is desired

The more information quantity related to C is collected, the better the C is, namely the signal to noise ratio is measured

The larger the better. However, as can be seen from the formula (13),

besides the useful information C, the method also contains a large amount of observation interference noise xi, which further affects the performance of the compressive sensing algorithm significantly.

Assume the original modulation symbol X of the kth subchannel_k＝χ_aE x, modulation constellation point x_aIs valid decision interval

Wherein

Has a radius of μ, xi of different sub-channels_kPair of

The effects of (a) are different:

1) as shown in FIG. 2(a), if some sub-channels are subjected to

Has a small influence (wherein

) Satisfy the following requirements

Then there is

At this time n_1(k)＝0，Ξ_k＝n_0(k)，

2) As shown in fig. 2(b), some sub-channels are subjected to

The pollution of the waste water is serious,

so that Y is_eq(k)Stride over to get out of x_aIs valid decision interval

After passing through a maximum likelihood decision device

At this time

Ξ_k＝n_0(k)+n_1(k)，

Therefore, the measured signal-to-noise ratio MSNR within different sub-channels is different, and MSNR significantly affects the reconstruction performance of the compressed sensing algorithm on c.

Disclosure of Invention

In view of this, the invention provides a multi-carrier nonlinear clipping distortion compressed sensing estimation method for alleviating observation interference.

A multi-carrier nonlinear clipping distortion compressed sensing estimation method for alleviating observation interference is characterized by comprising the following steps:

s1, a channel observation interference suppression module is configured in advance before the target information is measured; the channel observation interference suppression module comprises a channel equalization noise estimation submodule, a decision noise estimation submodule and a line ladder measurement matrix generation submodule;

s2, before measuring target information, estimating an observation interference component in advance by a channel observation interference suppression module, and performing denoising processing on an original frequency domain symbol of a receiving end to construct an alternative observation vector;

and S3, in the process of measuring the target information, generating a corresponding row ladder measurement matrix according to the observation interference component estimation information, the measurement compression ratio and the measurement criterion, and acquiring data from a plurality of sub-channels as final observation vector information.

In the multi-carrier non-linear clipping distortion compressed sensing estimation method for alleviating observed interference according to the present invention,

the step S2 includes:

s21, in the channel equalization noise estimation submodule, estimating a sub-channel equalization interference component according to the original frequency domain symbol and the clipping rate information of the receiving end;

and S22, according to S21, eliminating the equilibrium interference component of the sub-channel from the original frequency domain symbol of the receiving end, and processing the data information of the sub-channel of the specific type to obtain the processed receiving frequency domain symbol.

S23, carrying out maximum likelihood judgment on the received frequency domain symbol processed in the step S22 to obtain demapping constellation points of each sub-channel;

s24, constructing an alternative observation vector according to the demapping constellation points in the step S23;

s25, in the decision noise estimation submodule, estimating the decision noise introduced in the step S23 according to the demapping constellation points, the original frequency domain symbols of the receiving end and the clipping rate information in the step S23;

the step S3 includes:

s31, in the row ladder measurement matrix generation submodule, according to the judgment noise estimation information obtained in S25, and by combining the measurement target compression ratio and the target information measurement criterion, generating a corresponding row ladder measurement matrix;

and S32, in the process of measuring the target information, acquiring data in a sub-channel with relatively small ratio of the judgment noise interference according to the alternative observation vector obtained in S24, the judgment noise estimation information obtained in S25 and the line ladder measurement matrix obtained in S31, and determining a final observation vector.

the step S3 is followed by:

and S4, performing iterative search and matching on the observation vector obtained by the processing of the step S3 by adopting a greedy type compressive sensing algorithm to obtain distortion signal estimation information.

the channel observation interference suppression module is applicable to a scene including a transmission environment for low value measurement compression rate and deep clipping.

The multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving observation interference provided by the invention can overcome the defect of larger interference noise of an observation sequence aiming at the problem of performance reconstruction of a multi-carrier nonlinear clipping distortion signal degraded by observation interference noise, and improve the signal-to-noise ratio of target information measurement, thereby improving the sensing efficiency and reconstruction precision of the target signal. On the basis of the invention, a multi-carrier clipping distortion suppression scheme which is more effective and reliable under the conditions of low measurement compression ratio and deep clipping and has smaller data processing capacity is further designed, the nonlinear distortion of a system can be effectively compensated while the peak value is extremely limited, and the nonlinear interference resistance and the communication performance of a multi-carrier transmission system are improved.

Drawings

Fig. 1(a) is clipping distortion information at a clipping rate of 2.5 dB; fig. 1(b) is clipping distortion information at a clipping rate of 4.5 dB; fig. 1(c) is clipping distortion information at a clipping rate of 6.5 dB;

FIG. 2(a) is a situation where an OFDM frequency domain symbol is lightly observed to be interference contaminated; FIG. 2(b) is a case where an OFDM frequency domain symbol is heavily observed interference contaminated;

FIG. 3 is a block diagram of the overall structure of the modules in a multi-carrier nonlinear clipping distortion compressive sensing estimation method for mitigating observation interference according to the present invention;

FIG. 4 is a block diagram of a conventional compressed perceptual signal reconstruction technique;

fig. 5 is a comparison of demodulation performance for an OFDM system in clipping-distortion-free transmission, 2.5dB clipping transmission with CR set, and the proposed scheme of the present invention;

FIG. 6 is a comparison of the demodulation performance of the present invention versus a conventional compressed sensing scheme at a measured compression ratio of 0.5;

fig. 7 is a comparison of the demodulation performance of the present invention with a conventional compressed sensing scheme with deep clipping at CR-1.5 dB;

fig. 8 is an overall block diagram of an OFDM receiver including a clipping distortion suppression module according to an embodiment of the present invention;

fig. 9 is a decision noise estimate information constellation representation.

Fig. 10(a) shows an original OFDM frequency domain symbol at the receiving end, and fig. 10(b) shows an OFDM frequency domain received symbol after clipping distortion suppression.

FIG. 11 is a comparison of transmission performance at different measured compression ratios for one embodiment of the present invention;

fig. 12(a) comparison of transmission performance at different clipping rates for one embodiment of the present invention; fig. 12(b) is a comparison of transmission performance of the conventional scheme of fig. 4 at different clipping rates.

Detailed Description

The technical solution of the embodiment of the present invention is further explained below with reference to the drawings.

The idea of the embodiment of the invention is as follows:

the fact that the structure influence of observation interference noise on alternative observation vectors disclosed in the background art is that the relation between the position of the original frequency domain symbol of the receiving end in the constellation space and the effective decision area of the constellation point determines the size of the measurement signal-to-noise ratio MSNR, and in order to improve the measurement signal-to-noise ratio MSNR, breakthroughs can be made from the following two aspects:

1) trying to move the original frequency domain symbols in fig. 2 away from

Is pulled back to

Near the boundary or

In the region, the pollution of channel equalization noise and decision noise to alternative observation vectors can be reduced;

2) and constructing a proper measurement matrix, collecting data from a plurality of 'good' sub-channels with low judgment noise as effective observation information, avoiding the 'bad' sub-channels, and improving the perception efficiency of target information.

At the moment, the observation interference component can be restrained in the target information measurement process, and the measurement signal-to-noise ratio is improved. The improvement of the measurement signal to noise ratio ensures the reconstruction performance of the compressed sensing algorithm, and then the real clipping distortion information can be approximately fitted. And finally, removing clipping distortion estimation information from the original receiving frequency domain symbol, thereby improving the nonlinear interference resistance of the system.

Under the guidance of the theory, the design idea of the multi-carrier nonlinear clipping distortion compressive sensing estimation method for alleviating observation interference in the embodiment of the invention can be analyzed.

The technical scheme is as follows:

the overall structure of the modules in the multi-carrier nonlinear clipping distortion compressed sensing estimation method for relieving the observation interference is shown in fig. 3, and compared with the conventional compressed sensing signal reconstruction technology in fig. 4, the embodiment of the invention introduces a channel observation interference suppression module to relieve the influence of the observation noise on the performance of a compressed sensing algorithm so as to improve the measurement signal-to-noise ratio of target information. And after the original frequency domain symbol of the receiving end passes through the channel observation interference suppression module, obtaining a measurement sequence for observing noise mitigation, and performing iterative matching search on the measurement sequence by using a compressed sensing algorithm in the distorted signal estimation module to estimate a clipped distorted target signal. The channel observation interference suppression module is divided into three sub-modules:

1) a channel equalization noise estimation submodule;

2) decision noise estimation submodule

3) And generating a submodule by the line ladder measurement matrix.

1. Channel equalization noise estimation submodule Md1

As is known in the art, as shown in figure 2,

from C and n₀C is a frequency domain form of target observation information, and the value size and the sparsity of C are directly determined by the clipping rate; in addition, C is an inherent distortion introduced by a clipping operation of a transmitting end, which is unavoidable, and n₀It is the equalized interference noise within the sub-channel that can be "cleaned up" by a corresponding method. Therefore, if it is desired to reduce

Make the receiving end original frequency domain symbol Y_eqFrom far away

Is pulled back to

Near the boundary or

Internally, n can only be reduced from₀Starting the process. Thus, according to Y_eqAnd clipping rate auxiliary information, designing a channel equalization interference noise estimation submodule Md1, by which the equalization interference noise n of each subchannel can be approximately estimated₀Is a reaction of Y_eqSubtracting n₀Then Y is obtained_eq0And then fed into a maximum likelihood decision device to obtainTo the first decision symbol

The clipping rate is consistent with the value of the sending end, and the clipping rate can be stored in a Read Only Memory (ROM) and can be directly called in design. Obviously, the module Md1 can effectively slow down the equalized interference components of some sub-channels to a certain extent, and on one hand, can improve the correct decision probability of symbols; on the other hand, the observation noise component can be reduced simultaneously, so that clipping information in some sub-channels meets | C_k|＞|n_0(k)And I, primarily improving the signal-to-noise ratio of target information measurement. The Md1 module purifies the sub-channel transmission environment, and is the first 'barrier' for alleviating the observation interference.

2. Decision noise estimation submodule Md2

When receiving the original frequency domain symbols in some sub-channels

When the contamination is serious, Y is shown in FIG. 2(b)_eqIs far away from

Region, when Y is obtained even after denoising by submodule Md1_eq0Also, Y still cannot be taken out_eq0Is pulled back to

Near the region such that

An erroneous decision occurs. Then at this point an alternative observation sequence is constructed

A large decision error n is introduced₁. And n is₁The existence of the target information can degrade the measurement signal-to-noise ratio of the target information, and further influences the reconstruction performance of a subsequent compressed sensing algorithm. Therefore, as shown in FIG. 3, the decision noise estimation submodule Md2 is designed by

Y_eqObtaining sub-channel decision noise estimation information from clipping rate assistance information

The Md2 submodule is designed to provide a priori information for the generation of subsequent measurement matrices.

3. Line ladder measurement matrix generation submodule Md3

In the embodiment of the present invention, a second "barrier" for alleviating observation interference is provided, that is, the row ladder measurement matrix generation submodule Md3 is shown in fig. 3, and the working manner thereof is as follows: based on Y_eq、

And measuring compression ratio auxiliary information, and designing a proper row step measurement matrix phi to acquire alternative vectors under the constraint of a certain measurement matrix generation criterion

Observing to obtain the final observation vector

Further improve the useful information C in

The specific gravity of the Chinese medicinal materials. The design of the sub-module Md3 aims to collect clipping distortion related information from the sub-channel with smaller noise, to exclude the sub-channel with larger noise, and to improve the perception efficiency of useful information. Therefore, according to different channel environments, a special line ladder measurement matrix is formulated to observe alternative measurement information, so that the judgment noise ratio in an observation vector is far smaller than that of target observation information.

4. Distortion signal estimation module

The greedy algorithm in the compressive sensing theory is to perform sparse approximation through iteration, calculate a margin, calculate a support set of a target signal, and further gradually approximate the target signal. Book (I)The invention adopts a compressed sensing greedy algorithm as a signal reconstruction algorithm in a distorted signal estimation module. The final observation vector is obtained after the receiving end original frequency domain symbol passes through the submodules Md1, Md2 and Md3

Has less observed interference components, and can be processed by a compressed sensing algorithm

Estimating time domain clipping distortion signal with high probability

After FFT, the frequency domain component is obtained

Then from Y_eqMinus

Then, the data symbol with the clipping distortion suppressed is obtained

The advantages and the beneficial effects are that:

compared with the prior art, the method has the following advantages and beneficial effects:

(1) good demodulation performance and strong nonlinear interference resistance

The transmission capability of the multi-carrier system in the nonlinear environment is obviously improved, and the anti-noise performance of the system is improved. The adoption of the clipping technology in the transmitter can effectively control the transmission signal below a preset threshold, directly restrain the PAPR of the signal, but the signal distortion introduced by the clipping itself deteriorates the signal transmission quality and degrades the system BER performance. The scheme of the invention can be adopted at the receiving end to offset clipping distortion interference, thereby improving the transmission performance of the system. Fig. 5 shows a comparison of the demodulation performance of an OFDM system in clipping distortion free transmission, CR 2.5dB clipping transmission, and the proposed scheme of the present invention, which uses a measured compression ratio of ρ 0.8 in the measurement matrix generation submodule Md 3. It can be seen that the original OFDM system is greatly affected by clipping distortion, and BER performance deteriorates seriously, and the proposed scheme can effectively alleviate the nonlinear effect of the system in the receiver, and improve the nonlinear interference resistance of the system. For example, when the SNR is 22dB, the Bit Error Rate (BER) of the receiving end of the original OFDM system is 1.2592 × 1e-3, but the BER of the scheme of the present invention is reduced to 7.2311 × 1e-5, and the performance is improved by nearly two orders of magnitude. The scheme can improve the communication performance of the nonlinear OFDM system, and shows the effectiveness and feasibility of the method in relieving the nonlinear distortion of the system.

(2) The system has good stability and strong robust performance

a) The method still has good communication performance under a lower measurement compression ratio, the compression ratio is flexible to change, and the robustness of a signal reconstruction algorithm to an application scene can be improved. Although the reduction of the sampling rate can effectively reduce the data storage capacity, reduce the data processing capacity of the device and improve the operation speed of the algorithm, the acquisition of effective information can be reduced, and the reconstruction efficiency of the target signal is influenced. However, because the scheme provided by the invention introduces the channel observation interference suppression module, part of interference noise is removed in advance before the target information is measured and perceived, the signal-to-noise ratio of the target information measurement is correspondingly improved, and the reconstruction performance of the signal can be further ensured. Similarly, different application scenarios have different requirements on the communication quality of the system, and the proposed scheme can also adaptively change the measurement compression ratio according to the service requirements, that is, the length of the measurement matrix (the number of column vectors) can be adaptively changed, thereby improving the robustness of the system to the transmission environment. The length of the measurement matrix is mainly determined by a preset measurement compression ratio, the measurement compression ratio of the Md3 submodule is set to be ρ 0.5, other parameters are kept unchanged, the BER at the receiving end of the proposed scheme is counted, and in order to highlight the advantages of the scheme, the BER is compared with the BER of the conventional scheme in fig. 4 under the same system parameters, and the result is shown in fig. 6. It can be seen, however, that at a compression ratio of 0.5The performance of the conventional compressed sensing scheme is fragile, the conventional compressed sensing scheme has no reconstruction effect on clipping distortion, and the performance of the conventional compressed sensing scheme is even inferior to the BER performance containing the clipping distortion; however, the proposed new scheme is robust, and at a signal-to-noise ratio of 20dB, the BER can still remain less than 1 × 10^-3And higher working efficiency is shown.

b) Under the condition of deep clipping, the method still keeps good communication performance. As can be seen from fig. 1, the smaller the clipping rate, the deeper the clipping degree, the greater the number of nonzero elements in the clipping distortion signal, and the greater the sparsity K. For a conventional reconstruction scheme, as the clipping depth is gradually increased, a distorted signal approaches to an under-sparse state, and the condition of recovering a target signal by adopting a compressed sensing algorithm is gradually damaged. However, the Md3 submodule designed in the method can search and screen out the measurement sequence meeting the observation condition from the alternative measurement information as much as possible according to the observation requirement; in addition, different measurement compression ratios can be set according to different transmission environments in different symbol periods, so that target data with different lengths can be sensed, and adaptive compression sensing can be realized. Therefore, the BER performance of the conventional scheme drops sharply for the deep clipping case, and the proposed scheme can slow down the degradation rate of the signal reconstruction performance of the compressed sensing algorithm to some extent. Setting CR to 1.5dB, the BER of the proposed scheme is shown in fig. 7. It can be seen that the performance deterioration of the conventional compressed sensing scheme is very serious, but the novel scheme of the present invention can select and preferably select observation elements, significantly slow down the performance degradation step of the signal reconstruction algorithm, and still maintain less than 1 × 10 at a signal-to-noise ratio of 20dB^-3The decoding performance of the method shows better self-adaptive capability, thereby having excellent working performance under the condition of deep clipping.

(3) Simple implementation and low cost

a) In a channel observation interference suppression module introduced by the method, the adopted line ladder measurement matrix hardware is simpler to realize, and the element needing to be sensed is determined to be in the channel observation interference suppression module according to the measurement matrix generation rule

And (5) selecting corresponding rows from the unit matrix to form a required row ladder measurement matrix phi. Thus greatly reducing the cost, complexity and power consumption of the receiver hardware implementation.

b) In addition, the measurement matrix is flexible to use and has multiple alternatives. Different measurement rules can be set in the Md3 sub-module, so as to generate measurement matrixes with different performances to meet different target reconstruction requirements.

The above is an overview of the process of the present invention. The following detailed description of the preferred embodiments of the present invention is provided in connection with the accompanying drawings.

DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION

Fig. 8 is an overall block diagram of an OFDM receiver including a clipping distortion suppression module according to an embodiment of the present invention. Wherein channel estimation and sub-channel demapping are basic steps of a conventional OFDM receiver and are not listed in the invention of this patent application. Fig. 3 is a block diagram of a specific implementation of an OFDM clipping distortion compressed sensing reconstruction and suppression scheme for mitigating observation interference according to the present invention. The invention will be described in detail below with reference to other figures and embodiments. The parameters for designing an OFDM system are shown in table 1.

TABLE 1

Item	Parameter(s)
		Sampling rate (GHz)	1
IFFT/FFT Length	2048
		Subcarrier spacing (MHz)	0.488
Number of subcarriers N	512
		Modulation system	16-QAM
Clipping rate (dB)	1.5/2/2.5
		Measuring compression ratio	0.4/0.6/0.8

1. Estimation of channel equalization noise

The channel equalization noise estimation submodule approximately estimates the equalization noise of each subchannel

From Y_eqMinus

Then Y is obtained_eq0：

Then the Y is put_eq0Sending the data into a maximum likelihood decision device to obtain:

then from Y_eqMinus

Constructing an alternative observation vector for C:

the observation signal-to-noise ratio of the target information is preliminarily improved.

2. Estimation of decision noise

By using

And Y_eqConstructed by

Containing a large number of decision errors n₁A large number of observation interferences xi are introduced, and the signal-to-noise ratio of the target information measurement is further reduced. The decision noise of each sub-channel is estimated by the decision noise sub-module Md2

As shown in fig. 9.

3. Generation of line ladder measurement matrix and observation vector

From

In which appropriate elements are collected as observation vectors, based first on

And measuring the compression ratio to determine a measurement matrix. Setting the measured compression ratio ρ in the Md3 submodule, since

Is 512 (equal to the number of OFDM active subcarriers), the length M of the observation matrix can be calculated as 512 × ρ, which is rounded down.

The measurement rules are set as: get

The smallest number of sub-channels are observed. Therefore, will

According to

Is arranged from small to large to obtain a new sequence

Get

The sequence index numbers of the first M elements form a set Λ, and corresponding rows of Λ are found out from the unit matrix of 512 × 512 to form a row ladder measurement matrix Φ of M × 512. Measuring the row step matrix phi and

after multiplication, an observation vector is obtained

Where a ═ Φ F is the perceptual matrix.

Combined with compressed sensing algorithm

Recovering a time domain clipping signal C, obtaining frequency domain information C after discrete Fourier transform, and then converting C from Y_eqAfter the intermediate elimination, the data symbol with the clipping distortion suppressed is obtained

The original OFDM frequency domain symbol and the frequency domain symbol after nonlinear clipping distortion suppression at the receiving end are shown in fig. 10.

4. Performance simulation

Take a multipath channel transmission environment as an example. After the first 2 steps, the estimation is respectively carried out

And

setting the measurement compression ratios rho to be 0.4, 0.6 and 0.8 respectively, and obtaining corresponding line ladder measurement matrix phi and observation vector through the 3 rd step according to the measurement rule

The BER statistics at the receiving end are shown in fig. 11. It is clear that at ρ ═ 0.6 and ρ ═ 0.8, the proposed scheme exhibits excellent BER performance with strong nonlinear suppression capability, but at ρ ═ 0.4,

less clipping distortion information is contained in the sequence, resulting in degraded performance of compressed perceptual reconstruction. Therefore, in order to achieve excellent transmission performance, the setting of the measurement compression ratio needs to satisfy a value of not less than 0.5. The invention has smaller data processing capacity and can accurately recover clipping distortion information with lower complexity.

Taking ρ as 0.8, clipping operations with CR of 1.5dB, 2dB and 2.5dB are performed at the transmitting end, and BER performance pairs of the method of the present invention and the conventional method of fig. 4 are counted at the receiving end, as shown in fig. 12(a) and fig. 12 (b). It can be seen that the conventional method has lost the effect of suppressing clipping distortion in the case of less than 2.5dB clipping, and the applicable range is very limited. However, in the case of extremely limited transmission signal peak, i.e. deep clipping, for example, CR is 1.5dB, the communication performance of the method of the present invention is still robust and effective. Therefore, the invention enhances the signal receiving quality and improves the system robustness.

The embodiment shows that the introduced channel observation interference suppression module can effectively alleviate the reduction of the observation interference on the performance of the compressed sensing algorithm, the reconstruction accuracy of clipping distortion information is improved, the transmission performance of the system under the conditions of low compression ratio and low clipping rate is greatly improved, and the robustness of the system is improved. The method is particularly suitable for OFDM transmission systems with low sampling rate and deep clipping.

The methods or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in random access memory, read only memory, electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

It is understood that various other changes and modifications may be made by those skilled in the art based on the technical idea of the present invention, and all such changes and modifications should fall within the protective scope of the claims of the present invention.

Claims

1. A multi-carrier nonlinear clipping distortion compressed sensing estimation method for alleviating observation interference is characterized by comprising the following steps:

s3, in the process of measuring target information, generating a corresponding row ladder measurement matrix according to the observation interference component estimation information, the measurement compression ratio and the measurement criterion, and collecting data from a plurality of sub-channels as final observation vector information;

the step S2 includes:

s22, according to S21, eliminating the sub-channel balanced interference component from the original frequency domain symbol of the receiving end, and processing the data information of the sub-channel of the specific type to obtain the processed receiving frequency domain symbol;

s25, in the decision noise estimation submodule, estimating the decision noise introduced in the step S23 according to the demapping constellation points in the step S23, the original frequency domain symbols of the receiving end and the clipping rate information;

the step S3 includes:

2. The multi-carrier non-linear clipping distortion compressed perceptual estimation method for mitigating observed interference of claim 1, wherein the step S3 is followed by further comprising:

3. The multi-carrier non-linear clipping distortion compressed perceptual estimation method for mitigating observed interference of any of the claims 1 to 2, wherein the channel observed interference suppression module applicability scenario includes a transmission environment for low value measurement compression rate and deep clipping.