CN101827059B - Digital signal transmission method and system based on multi-carrier pseudo-random sequence - Google Patents

Abstract

The present invention relates to a digital signal transmission method and system based on a multi-carrier pseudo-random sequence. The method includes the steps of: encoding and modulating the data to be transmitted to generate a data block to be transmitted; combining the data block to be transmitted with the selected Multi-carrier pseudo-random sequence framing to obtain a data frame to be transmitted; performing digital-to-analog conversion and radio frequency modulation processing on the data frame to be transmitted and sending it. The method and system of the invention can use the required multi-carrier pseudo-random sequence obtained in the least time as data assistance, and provide more accurate and reliable parameter estimation for digital signal transmission.

Description

Digital signal transmission method and system based on multi-carrier pseudo-random sequence

technical field

The invention relates to the technical field of digital information transmission, in particular to a digital signal transmission method and system based on a multi-carrier pseudo-random sequence. the

Background technique

In digital communication systems, accurate and reliable parameter estimation is critical for data recovery. Common parameter estimation methods include blind estimation and data-aided (Data Aided, DA) parameter estimation. Among them, data-aided parameter estimation has the advantages of accurate estimation, fast reliability, and simple implementation, and is widely used in digital communication systems. the

The training sequence (Training Sequence, TS) is a kind of auxiliary data. A known sequence is added to the transmitted signal, and various transmission parameters can be estimated at the receiving end with the help of the known sequence. Common training sequences include PN (Pseudo-Noise, pseudo-random noise) sequence, Golay sequence, Legendre sequence, CAZAC (Constant Amplitude Zero Auto-Correlation, constant modulus zero autocorrelation) sequence and so on. These sequences usually have good autocorrelation properties and are easy to generate, so they are widely used. Frequency-domain pilot (Pilot) is also a common auxiliary data, which sends known data according to a certain pattern in the frequency domain of the transmitted signal, and can also be used to estimate system parameters at the receiving end. the

The maximum linear feedback shift register sequence (abbreviated as m-sequence) is a kind of pseudo-random sequence, which has many excellent properties (see Cao Zhigang, Qian Yasheng, "Modern Communication Principles", Beijing, Tsinghua University Press, 1992). A typical application of m-sequence is TDS-OFDM (time-domain orthogonal frequency division multiplexing digital transmission) technology, which has been adopted by China's national terrestrial digital TV standard (GB20600-2006, digital TV terrestrial broadcast transmission system frame structure, channel coding and modulation). TDS-OFDM uses the training sequence to fill the guard interval, and the frame header is composed of the time-domain binary m-sequence and its cyclic extension, which can be used for fast synchronization and channel estimation. the

Compared with the time-domain binary sequence, the frequency-domain binary sequence (Chinese Patent: Frequency Domain Channel Estimation Method Based on Binary Allpass Sequence Guard Interval Filling, Tsinghua University, Publication No. CN101102114) is easier to use for channel estimation. Fang Yang et al. have proved that the frequency domain constant modulus sequence can obtain the optimal channel estimation result (see "TrainingSequence Design for Low Complexity Channel Estimation in TransmitDiversity TDS-OFDM System", IEICE Transactions on Communications, vol.E92-B , no.6, pp.230g-2311, June 2009). Since there are only +1 and -1 in the frequency domain, the receiving end can complete the channel estimation without division operation, thus greatly reducing the complexity of the channel estimation module. the

The sequence obtained by the discrete Fourier transform of the frequency domain sequence is called a multi-carrier pseudo-random sequence (Multi-Carrier Pseudo-Noise, PN-MC), and the multi-carrier pseudo-random sequence has excellent properties in the discrete Fourier transform domain. Binary sequence, while inheriting the advantages of multi-carrier communication. In practical applications, it is usually hoped that the multi-carrier pseudo-random sequence has better power peak-to-average ratio properties and autocorrelation properties. If searched by traversal method, there are 2 ^N possible sequences of multi-carrier pseudo-random sequences with length N, and the search amount is O(2 ^N ). Taking the sequence with a length of 256 as an example, the search amount reaches the order of 10 ⁷⁷ , which is impossible to realize with the current computer.

Contents of the invention

(1) Technical problems to be solved

The technical problem to be solved by the present invention is to obtain the required multi-carrier pseudo-random sequence as data assistance in the least time, so as to provide more accurate and reliable parameter estimation for digital signal transmission. the

(2) Technical plan

In order to achieve the above object, the present invention adopts the following technical solutions. the

The invention provides a digital signal transmission method based on a multi-carrier pseudo-random sequence, the method comprising steps:

S1. Perform encoding and modulation processing on the data to be transmitted, and generate data blocks to be transmitted;

S2. Framing the data block to be transmitted with the selected multi-carrier pseudo-random sequence to obtain a data frame to be transmitted;

S3. Perform digital-to-analog conversion and radio frequency modulation processing on the data frame to be transmitted, and send it. the

Wherein, the framing method in step S2 includes: filling the guard interval of the data block to be transmitted with at least one said multi-carrier pseudo-random sequence; or using at least one said multi-carrier pseudo-random sequence as said data block to be transmitted the leading sequence. the

Wherein, the multi-carrier pseudo-random sequence is a sequence obtained by inverse discrete Fourier transform of a binary sequence. the

Wherein, the selected method of the multi-carrier pseudo-random sequence comprises steps:

S2.1 Make the discrete Fourier transform of the multi-carrier pseudo-random sequence into a sequence C, divide the sequence C into K segments, and record them as C ₁ , C ₂ , ..., C _K in turn, and the values of the initialization sequence C are all 0, wherein the length of the multi-carrier pseudo-random sequence is N, and K is any positive integer less than N;

S2.2 Let i=1, C ₁ takes a value in {α ₁ , α ₂ } to obtain a new sequence C′, and perform N-point discrete Fourier inverse transform on the new sequence C′ to obtain a multi-carrier pseudo-random sequence, Calculate the parameters to be investigated of the multi-carrier pseudo-random sequence according to the optimal sequence selection criterion, traverse all possible C ₁ , obtain the multi-carrier pseudo-random sequence with the optimal parameter to be investigated, and record the corresponding sequence C ₁ , where 1 ≤i≤K, |α ₁ |=|α ₂ |;

S2.3 Let i=i+1, fixed sequence C ₁ , C ₂ , ..., C _i-1 , C _i take values in {α ₁ , α ₂ }, set C ₁ , C ₂ , .. ., C _i-1 , C _i is inserted into the sequence C to form a new sequence C", and the N-point discrete Fourier inverse transform is performed on the new sequence C" to obtain a multi-carrier pseudo-random sequence, and the multi-carrier pseudo-random sequence is calculated according to the optimal sequence selection criterion The parameter to be investigated of the carrier pseudo-random sequence, traversing all possible C _i , obtaining the optimal multi-carrier pseudo-random sequence of the parameter to be investigated, recording the corresponding C _i and the optimal parameter P ₀ to be investigated;

S2.4 Re-traverse the first i sequences in turn, fix all sequences in C ₁ -C _i except C _j , traverse all possible C _j , if the obtained optimal parameter to be investigated is better than P ₀ , then update The optimal parameters to be investigated and the corresponding sequence C _j , where 1≤j≤i;

S2.5 If i=K, execute step S2.6, otherwise return to step S2.3;

S2.6 Splice the currently selected C ₁ , C ₂ , ..., C _K into a binary sequence of length N, perform N-point discrete Fourier inverse transform on the binary sequence, and convert the obtained multi-carrier pseudo The random sequence is output as a selected multi-carrier pseudo-random sequence.

Wherein, the multi-carrier pseudo-random sequence is a sequence obtained by extending or truncating the m-sequence, and then performing inverse discrete Fourier transform. the

Wherein, the extension method includes: cyclic extension, copying several bit symbols at the end of the m-sequence before the m-sequence; or zero-padding extension, adding several zero symbols at the front and end of the m-sequence respectively; or Inserts zero sign extensions into the sequence according to a known pattern. the

Wherein, the selected method of the multi-carrier pseudo-random sequence comprises steps:

或 

其中， 

和 

分别表示向下取整和向上取整；  S2.1' Select the length of the multi-carrier pseudo-random sequence to be N, determine the order K of the m-sequence, satisfy

or

in,

and

Indicates rounding down and rounding up, respectively;

S2.2' Select the generator polynomial and the initial phase of the m-sequence in turn to generate an m-sequence;

S2.3' maps the generated m-sequence to binary phase-shift keying symbols, and expands or truncates them to form a symbol sequence with a length of N;

S2.4' performs N-point discrete Fourier inverse transform on the symbol sequence to obtain a multi-carrier pseudo-random sequence with a length of N;

S2.5'according to the optimal sequence selection criterion, calculate the parameters to be investigated of the multi-carrier pseudo-random sequence obtained in step S2.4', and record the multi-carrier pseudo-random sequence and the value of the parameter to be investigated;

S2.6'judging whether all generator polynomials and all initial phases have been traversed, if so, execute step S2.7', otherwise, return to execute step S2.2';

S2.7' According to the optimal sequence selection criterion, select the optimal parameter to be investigated from all obtained parameters to be investigated, and output the corresponding multi-carrier pseudo-random sequence as the selected multi-carrier pseudo-random sequence. the

Wherein, the optimal sequence selection criteria include:

The power peak-to-average ratio minimum criterion, the parameter to be investigated is the power peak-to-average ratio PAPR of the sequence, and the power peak-to-average ratio PAPR of the sequence c(n) is:

$\mathrm{PAPR}=\underset{0\&le;\mathrm{no}<N}{\mathrm{<figure-callout\; id="0"\; label="max"\; filenames="HSA00000059525400031.png,HSA00000059525400032.png"\; state="\{\{state\}\}">max</figure-callout>}}\left(c^2\left(\mathrm{no}\right)\right)/\frac{1}{N}\underset{\mathrm{no}=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\left|c^2\left(\mathrm{no}\right)\right|;$ or

The maximum criterion of the partial quality factor of aperiodic autocorrelation, the parameter to be investigated is the partial quality factor F _part of the aperiodic autocorrelation of the sequence, and the partial quality factor F _part of the aperiodic autocorrelation of the sequence c(n), which is:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; part</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1,1,2,3</span>}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

where R(n) is the aperiodic autocorrelation of the sequence c(n), and

$R\left(\mathrm{no}\right)=\left\{\begin{array}{cc}\underset{i=0}{\overset{N-1-\mathrm{no}}{\mathrm{\&Sigma;}}}c\left(i\right)\&CenterDot;c^{*}(i+\mathrm{no}),& 0\&le;\mathrm{no}<N\\ R^{*}(-\mathrm{no}),& -N<\mathrm{no}<0\end{array}\right.;$ or

The maximum criterion of the aperiodic autocorrelation quality factor of the discrete Fourier transform, the parameter to be investigated is the quality factor of the aperiodic autocorrelation of the sequence, and the quality factor MF of the aperiodic autocorrelation of the sequence c(n) is:

$\mathrm{<span\; class="notranslate">\; MF</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}$

where R(n) is the aperiodic autocorrelation of the sequence c(n); or

The maximum criterion for the ratio of the quality factor of the aperiodic autocorrelation part to the power peak-to-average ratio, the parameter to be investigated is: P=F _part /PAPR; or

The maximum criterion for the ratio of aperiodic autocorrelation quality factor and power peak-to-average ratio of discrete Fourier transform, the parameter to be investigated is: P=MF/PAPR; or

The maximum criterion for the product of the non-periodic autocorrelation part quality factor and the non-periodic autocorrelation quality factor of the discrete Fourier transform, the parameter to be investigated is: P=F _part MF; or

Power peak-to-average ratio, aperiodic autocorrelation partial quality factor, and discrete Fourier transform aperiodic autocorrelation quality factor compromise criterion, the parameter to be investigated is P=F _part MF/PAPR, and the multi-carrier pseudo-random sequence with the largest P is selected .

The present invention also provides a digital signal transmission system based on a multi-carrier pseudo-random sequence, the system includes: a code modulation module, which encodes and modulates the data to be transmitted, and generates a data block to be transmitted; a sequence generation module, which generates the required Multi-carrier pseudo-random sequence; framing module, framing the data block to be transmitted and the selected multi-carrier pseudo-random sequence to obtain a data frame to be transmitted; the back-end processing module performs digital simulation on the data frame to be transmitted conversion, RF modulation processing and transmission. the

(3) Beneficial effects

The method and system of the present invention are based on a multi-carrier pseudo-random sequence, which has good peak-to-average ratio characteristics, and has good autocorrelation properties in the time domain and discrete Fourier transform domain, and can provide accurate and reliable channels for digital signal transmission Estimate; In addition, according to the optimal sequence selection method in the method of the present invention, the sequence with the optimal nature of actual needs can be obtained quickly with less search amount, and the search amount is only O(M 2 ^N/M ), M is the sequence is divided into the number of segments; according to the method of the present invention, the binary m-sequence can be extended or truncated and then through discrete Fourier inverse transform to construct a multi-carrier pseudo-random sequence, and the sequence with the best properties can be selected in all m-sequence sets, thereby greatly reducing the amount of search. It is also possible to obtain a multi-carrier pseudo-random sequence with better properties, while inheriting the multiple advantages of the m-sequence; the multi-carrier pseudo-random sequence obtained using the method of the present invention can be applied to various transmission systems as a training sequence, providing reliable and accurate parameters estimate.

Description of drawings

Fig. 1 is a flow chart of a digital signal transmission method based on a multi-carrier pseudo-random sequence according to an embodiment of the present invention;

Fig. 2 is according to the flow chart of the subsection optimal selection method of the multi-carrier pseudo-random sequence in the digital signal transmission method based on the multi-carrier pseudo-random sequence according to an embodiment of the present invention;

Fig. 3 is the flow chart of the multi-carrier pseudo-random sequence construction and selection method based on the m-sequence set in the digital signal transmission method based on the multi-carrier pseudo-random sequence according to an embodiment of the present invention;

Fig. 4 is a structural diagram of a digital signal transmission system based on a multi-carrier pseudo-random sequence according to an embodiment of the present invention;

Fig. 5 (a) is the PN-MC sequence time domain modulus value that length is 256 under the power peak-to-average ratio optimal criterion that subsection optimal selection method obtains in embodiment 1;

Fig. 5 (b) is the PN-MC sequence aperiodic autocorrelation result of 256 lengths under the power peak-to-average ratio optimal criterion obtained by subsection optimal selection method in embodiment 1;

Fig. 5 (c) is the aperiodic autocorrelation result of the PN-MC sequence discrete Fourier transform of 256 lengths under the power peak-to-average ratio optimal criterion obtained by subsection optimal selection method in embodiment 1;

Fig. 6 is the signal frame structure schematic diagram of embodiment 1;

Fig. 7 (a) is the PN-MC sequence time domain modulus value of length 512 under the optimal criterion of power peak-to-average ratio in embodiment 2;

Fig. 7 (b) is the aperiodic autocorrelation result of the PN-MC sequence that length is 512 under the optimum criterion of power peak-to-average ratio in embodiment 2;

Fig. 7 (c) is the aperiodic autocorrelation result of the PN-MC sequence discrete Fourier transform of length 512 under the power peak-to-average ratio optimal criterion in embodiment 2;

Fig. 8 (a) is the PN-MC sequence time domain modulus value that length is 128 under the autocorrelation quality partial factor optimal criterion in embodiment 2;

Fig. 8 (b) is the PN-MC sequence aperiodic autocorrelation result that length is 128 under the optimal criterion of autocorrelation quality partial factor in embodiment 2;

Fig. 8 (c) is the aperiodic autocorrelation result of the PN-MC sequence discrete Fourier transform that length is 128 under the optimum criterion of autocorrelation quality partial factor in embodiment 2;

Figure 8(d) is the result of local amplification near the correlation peak of the PN-MC sequence aperiodic autocorrelation result of Figure 8(b);

Fig. 9 is the signal frame structure schematic diagram of embodiment 2;

FIG. 10 is a schematic diagram of a signal frame structure in Embodiment 3. the

Detailed ways

The digital signal transmission method and system based on the multi-carrier pseudo-random sequence (PN-MC sequence) proposed by the present invention will be described in detail in conjunction with the accompanying drawings and embodiments as follows. the

As shown in Figure 1, according to a digital signal transmission method based on a multi-carrier pseudo-random sequence according to an embodiment of the present invention, the method includes steps:

S1. Perform encoding, modulation and other processing on the data to be transmitted to generate data blocks to be transmitted;

The data blocks to be transmitted can be single-carrier data blocks, multi-carrier data blocks, and generalized data blocks, that is, data blocks composed of single-carrier data blocks or multi-carrier data blocks and their guard intervals, or one or more data blocks The combination. the

S2. framing the data block to be transmitted and the selected PN-MC sequence to obtain a data frame to be transmitted;

The framing method includes but not limited to: using one or more PN-MC sequences to fill the guard interval of the data block to be transmitted; using one or more PN-MC sequences as the preamble sequence of the data block to be transmitted. the

S3. Perform back-end processing such as digital-to-analog conversion and radio frequency modulation on the data frame to be transmitted and send it. the

Wherein, the PN-MC sequence is a sequence obtained by inverse discrete Fourier transform of any binary sequence. In order to obtain the optimal PN-MC sequence with less search volume, as shown in Figure 2, the selection method of PN-MC sequence includes steps:

C_K长度为N-(K-1)L， 表示向下取整；  S2.1 Let the length of the selected PN-MC sequence be N, segment and initialize its discrete Fourier transform, specifically: record the discrete Fourier transform of the PN-MC sequence as sequence C, and divide the sequence C of length N into The K section is denoted as C ₁ , C ₂ , ..., C _K in turn, and the values of the initialized C sequence are all 0. Preferably, the lengths of C ₁ , C ₂ , ..., C _K-1 are all L ,

The length of C _K is N-(K-1)L, Indicates rounding down;

S2.2 Record i=1, traverse the first sequence, and obtain the first sequence with the best parameters to be investigated according to the selection criterion of the most sequence; specifically: let the sequence C ₁ be selected in {α ₁ , α ₂ } value, get a new sequence C′, and perform N-point discrete Fourier inverse transform on the new sequence C′ to obtain a PN-MC sequence, calculate the parameters to be investigated of the sequence according to the optimal sequence selection criterion, and traverse all possible C ₁ , Obtain the PN-MC sequence with the optimal parameter to be investigated, and record the corresponding sequence C ₁ , where, 1≤i≤K, |α ₁ |=|α ₂ |;

S2.3 Record i=i+1, for i>1, fix the previous i-1 segment sequence, traverse the i-th segment sequence, obtain the i-th end sequence with the optimal parameter to be investigated, and record the optimal parameter value to be investigated ; Specifically: for the i-th (i>1) segment sequence C _i , fixed sequence C ₁ , C ₂ , ..., C _i-1 , let C _i take values in {α ₁ , α ₂ }, set C ₁ , C ₂ ,..., C _i-1 , C _i are inserted into the sequence C to form a new sequence C″, and N-point discrete Fourier inverse transform is performed on the new sequence C″ to obtain the PN-MC sequence. According to the optimal sequence Select the criteria to calculate the parameters to be investigated of the sequence, traverse all possible C _i , obtain the PN-MC sequence with the optimal parameters to be investigated, and record the corresponding C _i and the optimal parameter P ₀ to be investigated;

S2.4 Re-traverse the first i sequences in turn and update the optimal parameters to be investigated and the corresponding sequences; specifically: fix all sequences in C ₁ -C _i except C _j , and traverse all possible C _j , if The obtained optimal parameter to be investigated is better than P ₀ , then update the corresponding sequence C _j and the optimal parameter to be investigated, where 1≤j≤i;

S2.5 If i=K, execute step S2.6, otherwise return to step S2.3;

S2.6 End the search, splice the currently selected C ₁ , C ₂ , ..., C _K into a binary sequence of length N, perform N-point discrete Fourier inverse transform on the binary sequence, and obtain the PN The -MC sequence is output as the selected PN-MC sequence.

In addition, the PN-MC sequence can also be a sequence obtained by extending or truncating the m-sequence, and then undergoing inverse discrete Fourier transform. At this time, in order to obtain the PN-MC sequence with the best properties, as shown in Figure 3, the selection method of the PN-MC sequence includes steps:

或 其中， 

和 

分别表示向下取整和向上取整；  S2.1' Select the length of the PN-MC sequence as N, determine the order K of the m sequence, satisfy

or in,

and

Indicates rounding down and rounding up, respectively;

S2.2' Select the generator polynomial and the initial phase of the m-sequence in turn to generate an m-sequence;

S2.3' maps the generated m-sequence to binary phase-shift keying symbols, and expands or truncates them to form a symbol sequence with a length of N;

Extensions include, but are not limited to: cyclic extension, that is, copying a number of bit symbols at the end of the sequence to the front of the sequence; or zero-padding extension, that is, adding several zero symbols at the front and end of the sequence; or inserting zeros into the sequence according to a known pattern Symbol implementation extension. the

S2.4'does N-point discrete Fourier inverse transform to the symbol sequence to obtain a PN-MC sequence with a length of N;

S2.5'according to the optimal sequence selection criterion, calculate the parameters to be investigated of the PN-MC sequence obtained in step S2.4', record the PN-MC sequence and the parameter value to be investigated;

S2.6'judging whether all generator polynomials and all initial phases have been traversed, if so, execute step S2.7', otherwise, return to execute step S2.2';

S2.7'End the search, select the optimal parameter to be investigated from all obtained parameters to be investigated according to the optimal sequence selection criterion, and output the corresponding multi-carrier pseudo-random sequence as the selected multi-carrier pseudo-random sequence . the

The optimal sequence selection criteria mentioned in the above two PN-MC sequence selection methods include but are not limited to:

The power peak-to-average ratio minimum criterion, the parameter to be investigated is the peak-to-average power ratio (Peak-to-Average Power Ratio, PAPR) of the sequence, and the power peak-to-average ratio PAPR of the sequence c(n) is:

$\mathrm{<span\; class="notranslate">\; PAPR</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="0"\; label="max"\; filenames="HSA00000059525400031.png,HSA00000059525400032.png"\; state="\{\{state\}\}">max</figure-callout></span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; c</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; ;</span>$

The maximum criterion of the partial merit factor of aperiodic autocorrelation, the parameter to be investigated is the partial merit factor (Partial Merit Factor) F _part of the aperiodic autocorrelation of the sequence, the partial merit factor F _part of the aperiodic autocorrelation of the sequence c(n), for:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; part</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1,1,2,3</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

where R(n) is the aperiodic autocorrelation of the sequence c(n), and

$R\left(\mathrm{no}\right)=\left\{\begin{array}{cc}\underset{i=0}{\overset{N-1-\mathrm{no}}{\mathrm{\&Sigma;}}}c\left(i\right)\&Center\; Dot;c^{*}(i+\mathrm{no}),& 0\&le;\mathrm{no}<N\\ R^{*}(-\mathrm{no}),& -N<\mathrm{no}<0\end{array}\right.;$ or

The maximum criterion of the aperiodic autocorrelation quality factor of the discrete Fourier transform, the parameter to be investigated is the quality factor of the aperiodic autocorrelation of the sequence, and the quality factor MF of the aperiodic autocorrelation of the sequence c(n) is:

$\mathrm{<span\; class="notranslate">\; MF</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

Among them, R(n) is the aperiodic autocorrelation of sequence c(n);

Or take into account the above three criteria, and compromise among the three parameters of power peak-to-average ratio, autocorrelation part quality factor, and discrete Fourier transform autocorrelation quality factor:

Aperiodic autocorrelation partial quality factor and power peak-to-average ratio maximum criterion, the parameter to be investigated is: P=F _part /PAPR; or

The maximum criterion of the aperiodic autocorrelation quality factor and power peak-to-average ratio of discrete Fourier transform, the parameters to be investigated are: P=MF/PAPR; or

The maximum criterion for the product of the non-periodic autocorrelation part quality factor and the non-periodic autocorrelation quality factor of the discrete Fourier transform, the parameters to be investigated are: P=F _part MF; or

Power peak-to-average ratio, aperiodic autocorrelation partial quality factor, and discrete Fourier transform aperiodic autocorrelation quality factor compromise criterion, the parameter to be investigated is P=F _part MF/PAPR, and the multi-carrier pseudo-random sequence with the largest P is selected .

As shown in Figure 4, according to a digital signal transmission system based on a multi-carrier pseudo-random sequence according to an embodiment of the present invention, the system includes: a coding and modulation module, which performs coding, modulation and other processing on the data to be transmitted to generate a data block to be transmitted; The sequence generation module generates the required multi-carrier pseudo-random sequence; the framing module frames the data block to be transmitted with the selected PN-MC sequence to obtain the data frame to be transmitted; the post-processing module performs data processing on the data frame to be transmitted Analog conversion, radio frequency modulation and other back-end processing and transmission. the

Example 1

This embodiment takes the PN-MC with a length of 256 as an example to specifically describe the selection method of the PN-MC sequence in the method of the present invention and the digital signal transmission method based on the optimal PN-MC sequence. The PN-MC sequence is obtained by inverse discrete Fourier transform of any binary sequence whose value is {±1}, and the time-domain power peak-to-average ratio criterion is used as the optimal sequence selection criterion. The specific steps are as follows:

S101. Select PN-MC sequence length N=256, divide the discrete Fourier transform C of PN-MC into 16 equal segments, denote as C ₁ , C ₂ ,..., C ₁₆ , each segment Length L=16, initialize all elements of the C sequence to be 0;

S102. Note i=1, let the sequence C ₁ take a value in {±1}, obtain a new sequence C', perform 256-point discrete Fourier inverse transform on the sequence C', obtain a PN-MC sequence, and calculate the PN-MC The power peak-to-average ratio of the sequence, traverse all possible C ₁ , find the PN-MC sequence with the smallest power peak-to-average ratio, and record the corresponding sequence C ₁ ;

S103. For i>1, for the i-th sequence C _i , fix the sequence C ₁ , ..., C _i-1 , let the sequence C _i take a value in {±1}, set C ₁ , ..., C _i-1 , C _i is inserted into C to form a new sequence C″, and then the PN-MC sequence is obtained through 256-point discrete Fourier inverse transform, and the peak-to-average power ratio of the PN-MC sequence is calculated, and all possible C _i are traversed to search To the PN-MC sequence with the smallest power peak-to-average ratio, record the corresponding sequence C _i and the smallest power peak-to-average ratio PAPR ₀ ;

S104. Re-traverse and update the previous i sequences in sequence. Fix all sequences except C _j (1≤j≤i) in C ₁ ～C _i , traverse all possible values of C _j , if the obtained optimal parameter value is better than P ₀ , update the corresponding sequence C _j and the optimal parameter value P ₀ ;

S105. If i=16, then execute step S106, otherwise make i=i+1 return to step S103;

S106. End the search and output the best PN-MC sequence. Concatenate the currently searched C ₁ , C ₂ ,..., C _K into a binary sequence with a length of 256, perform 256-point discrete Fourier inverse transform, and output it as the optimal PN-MC sequence.

The PN-MC sequence with a length of 256 obtained by the above segmental optimal selection method is shown in Table 1, its time domain modulus is shown in Figure 5(a), and the aperiodic autocorrelation result is shown in Figure 5(b) , the aperiodic autocorrelation result of discrete Fourier transform is shown in Fig. 5(c). Using the same method, PN-MC sequences with lengths of 128 and 192 can be obtained, and the results are listed in Table 1 together. the

Table 1 The PN-MC sequence with the lowest power peak-to-average ratio obtained by the subsection optimal selection method

sequence length Power Peak-to-average ratio Discrete Fourier transform of PN-MC sequence (where 1 means +1, 0 means -1) 128 1.8664 101000010110100111011001110010101100110111010011100 000010011010111000001001011000110010010111010010011 11110011110100100100100 192 1.9409 110000011011111101100101100110011101110001101101011

[0118] the the 1101000101011000101110110001101111011100101110010111 1011111111001010001110111001100101000001000001000010 01101010101010110 256 2.0324   001101101010100110000110000111001000010000010001111  010010001000010010000010100101111111011010110101001  1111111101011100010001011101100001101001101101010100  0011100101101011111111010011110100010000011111000000  10011101100011111010100111000100111100100111000110

Apply the digital signal transmission method of the PN-MC sequence with the optimal power peak-to-average ratio above, and the specific steps are:

S1. Encode the data to be transmitted, map the constellation, and generate a single carrier data block;

S2. Use a PN-MC sequence with the optimal power peak-to-average ratio of 512 in length to insert the guard interval of the single-carrier data block to form a signal frame in which the training sequence fills the guard interval. The signal frame structure is shown in Figure 6;

S3. Post-processing the signal frame in step S2 and sending it out. the

Example 2

This embodiment takes the PN-MC sequence with a length of 256 as an example to specifically illustrate the construction and selection method of the PN-MC sequence under the optimal criterion of the autocorrelation quality factor of the discrete Fourier transform, and the autocorrelation quality based on the discrete Fourier transform A digital signal transmission method for factor-optimal PN-MC sequences. The specific steps of the PN-MC selection method under the optimal criterion of the autocorrelation quality factor of the discrete Fourier transform are as follows:

S201. The length of the selected PN-MC sequence is 256, and the order of the m sequence is 8;

S202. Select the generator polynomial and initial phase generated by the 8th-order m-sequence. There are 16 generator polynomials and 255 initial phases in the 8th-order m-sequence. According to the generator polynomial and the initial phase, construct an m-sequence {PN(k)} with a length of 255 ₀ ²⁵⁴ ;

S203.m sequence undergoes binary phase shift keying modulation (BPSK), and forms a sequence {C(k)} ₀ ²⁵⁵ with a length of 256 through 1-bit cyclic extension;

S204.{C(k)} ₀ ²⁵⁵ obtains the sequence PN-MC sequence {C(n)} ₀ ²⁵⁵ through 256 discrete Fourier inverse transforms;

c(n)=IDFT ₂₅₆ (C(k)), n=0,1,...,255

Among them, IDFT ₂₅₆ (·) represents the 256-point discrete inverse Fourier transform.

S105. Calculate the aperiodic autocorrelation of the discrete Fourier transform c (n) of the PN-MC sequence;

$\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 255</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; c</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 256</span>}\\ {\mathrm{<span\; class="notranslate">\; R</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 256</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; <</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

The autocorrelation quality factor of sequence c(n) can be calculated from R(n),

$\mathrm{<span\; class="notranslate">\; MF</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; R</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; |</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

S206. Judging whether all generating polynomials and all initial phases have been traversed currently, if not traversed, return to S202, if traversed, then skip to S207;

S207. Selecting the PN-MC sequence with the largest autocorrelation quality factor of the discrete Fourier transform as an optimal sequence under the optimal criterion of the autocorrelation quality factor of the discrete Fourier transform and outputting it. the

According to the above steps, there are two optimal PN-MC sequences with a length of 256 corresponding to m sequences, as shown in Table 2. Table 2 PN-MC sequence with optimal quality factor of discrete Fourier transform autocorrelation

the the x ⁹ +x ⁸ +x ⁷ +x ⁵ +x ³ +x ² 1100011100

Table 3 PN-MC sequence with optimal power peak-to-average ratio

sequence length Minimum Power Peak-to-Average Ratio m sequence generator polynomial initial phase 128 2.1250 x ⁶ +x ⁵ +x ² +1 1001001 256 2.3556 x ⁷ +x ⁵ +x ⁴ +x ² +1 11000111 512 2.4668 x ⁸ +x ⁶ +x ⁵ +x ² +x ¹ +1 010101111 1024 2.6318 x ⁹ +x ⁷ +x ⁵ +x ³ +x ¹ +1 0101000110

Table 4 PN-MC sequence with optimal quality factor of autocorrelation part

sequence length Maximum autocorrelation partial quality factor m sequence generator polynomial initial phase 128 2733 x ⁶ +1 0000010 256 9000 x ⁷ +x ³ +x ² +x ¹ +1 11000100 512 35056 x ⁸ +x ⁶ +x ⁵ +x ³ 001001000 1024 127710 x ⁹ +x ⁶ +x ² +1 0110000111

Comparing Table 1 and Table 3, it can be seen that the search results limited to m-sequences are worse than those obtained in all possible sequences, but the search volume is smaller, and some excellent properties of m-sequences can be inherited. the

Among them, the optimal PN-MC sequence time-domain modulus with a length of 512 in Table 3, the aperiodic autocorrelation and the aperiodic autocorrelation of the discrete Fourier transform are shown in Figure 7(a)-7(c) respectively. the

The best PN-MC sequence time-domain modulus, aperiodic autocorrelation, discrete Fourier transform aperiodic autocorrelation, and aperiodic autocorrelation results of local amplification near the peak of the aperiodic autocorrelation results are shown in Figure 8 ( a)-8(c). the

The specific steps of the digital signal transmission method of the PN-MC with the optimal autocorrelation quality factor of the above-mentioned discrete Fourier transform are as follows:

S1. Coding the data to be transmitted, constellation mapping, OFDM modulation, and generating OFDM data blocks;

S2. use 2 identical lengths to insert the guard interval of the OFDM data block into the best PN-MC sequence of 256, form the signal frame that the training sequence fills the guard interval, and the signal frame structure is as shown in Figure 9;

S3. Post-processing the signal frame in S2 and sending it out. the

Example 3

In this embodiment, the PN-MC with a length of 420 is taken as an example to specifically illustrate the optimal sequence selection method when multiple criteria are jointly considered. For example, it is hoped that the time-domain power peak-to-average ratio of the PN-MC sequence is low and the autocorrelation of the discrete Fourier transform is good. You can define the parameter to be investigated A=MF/PAPR, and select the PN-MC with the largest A as the optimal sequence; or hope that the PN- The MC sequence has good autocorrelation in both the time domain and the discrete Fourier transform domain. The parameter to be investigated can be defined as B=MF·F _part , and the PN-MC sequence with the largest B is selected as the optimal sequence. In the following, low peak-to-average ratio of time-domain power and good autocorrelation of discrete Fourier transform are used as the optimal sequence selection criterion, and a PN-MC sequence with a length of 420 is constructed by m-sequence zero-padded expansion. The steps are as follows:

S301. The length of the selected PN-MC sequence is 420, and the order of the selected m sequence is 8;

S302. select generator polynomial and initial phase of m sequence successively; According to generator polynomial and initial phase construction length is the m sequence {PN(k)} ₀ ²⁵⁴ of 255;

The S303.m sequence is modulated by BPSK, and then 82 and 83 0 symbols are supplemented at the front end and the end respectively to obtain a sequence {C(k)} ₀ ⁴¹⁹ with a length of 420;

S304. {C(k)} ₀ ⁴¹⁹ obtains the sequence {C(n)} ₀ ⁴¹⁹ through 420 inverse discrete Fourier transforms;

c(n)=IDFT ₄₂₀ (C(k)), n=0, 1, . . . , 419

Wherein IDFT ₄₂₀ (·) represents 420-point discrete Fourier inverse transform.

S305. Calculate the power peak-to-average ratio PAPR of sequence c (n) and the quality factor MF of the aperiodic autocorrelation of sequence C (k), define A=MF/PAPR, calculate the A value of PN-MC;

S306. Judging whether all generator polynomials and all initial phases of the 8th-order m-sequence have been traversed at present, if traversed, then jump to step 307, otherwise return to step 302;

S307. Select the PN-MC sequence with the largest parameter A as the optimal sequence under the optimal criterion of this implementation and output it. the

According to the above steps, there are two optimal PN-MC sequences with a length of 420, and the generator polynomials and initial phases corresponding to the m sequences are x ⁷ +x ⁴ +x ² +1, 11101001, x ⁷ +x ⁶ + x ⁴ +x ² , 11111000, its power peak-to-average ratio is 2.6797, and the autocorrelation quality factor of discrete Fourier transform is 3.5685.

The specific steps of the digital signal transmission method using the above-mentioned optimal PN-MC are as follows:

S1. The data to be transmitted is encoded, constellation mapped, and OFDM modulated to generate an OFDM data block, and the last L symbols of the OFDM data block are copied before the data block to form a CP-OFDM (cyclic prefix OFDM) data block;

S2. Use 4 identical PN-MC sequences with the best quality of the autocorrelation part whose length is 420 to be inserted before M CP-OFDM data blocks, as the preamble sequence of the signal frame, and the signal frame structure is as shown in Figure 10;

S3. Post-processing the signal frame in step S2 and sending it out. the

Example 4

In this embodiment, a PN-MC with a length of 420 is constructed by m-sequence truncation, and the time-domain power peak-to-average ratio is low, the quality factor of the aperiodic autocorrelation part is large, and the aperiodic autocorrelation quality factor of the discrete Fourier transform is large as the optimal The best sequence selection criteria, the steps are as follows:

S401. The length of the selected PN-MC sequence is 420, and the order of the selected m sequence is 9;

S402. select generator polynomial and initial phase of m sequence successively; According to generator polynomial and initial phase construction length is the m sequence {PN(k)} ₀ ⁵¹⁰ of 511;

S403.m sequence undergoes BPSK modulation, intercepts the first 420 symbols, and obtains a sequence {C(k)} ₀ ⁴¹⁹ with a length of 420;

S404. {C(k)} ₀ ⁴¹⁹ obtains the sequence {C(n)} ₀ ⁴¹⁹ through 420 inverse discrete Fourier transforms;

c(n)=IDFT ₄₂₀ (C(k)), n=0, 1, . . . , 419

Wherein IDFT ₄₂₀ (·) represents 420-point discrete Fourier inverse transform.

S405. Calculate the power peak-to-average ratio PAPR of the sequence c(n), the partial quality factor F _part of the aperiodic autocorrelation, and the quality factor MF of the aperiodic autocorrelation of C(k), define the parameter A=MF·F _part / PAPR, calculate the parameter A value of PN-MC;

S406. Judging whether all generator polynomials and all initial phases of the 9th-order m-sequence have been traversed at present, if traversed, then skip to S407, otherwise return to S402;

S407. Select the PN-MC sequence with the largest parameter A as the optimal sequence under the optimal criterion of this implementation and output it. the

The generator polynomial corresponding to the m-sequence of the optimal PN-MC sequence with a length of 420 searched according to the above steps is x ⁸ +x ⁷ +x ⁵ +x ⁴ +x ² +x ¹ , the initial phase is 100100001, and its peak-average power The ratio is 2.3525, the quality factor of autocorrelation part is 996.2265, and the quality factor of autocorrelation of discrete Fourier transform is 2.6164.

Example 5

In this embodiment, a PN-MC with a length of 420 is constructed by filling zero symbols in the m-sequence according to a known pattern, and the minimum peak-to-average power ratio in the time domain is used as the optimal sequence selection criterion. The steps are as follows:

S501. The length of the selected PN-MC sequence is 420, and the order of the selected m sequence is 8;

S502. Select the generator polynomial and initial phase of m sequence successively, according to generator polynomial and initial phase construction length be the m sequence {PN (k)} ₀ ²⁵⁴ of 255;

S503. After the m sequence is BPSK modulated, a known pattern is selected, and the m sequence after BPSK modulation is inserted into the empty symbol with a length of 420 to obtain a sequence {C(k)} ₀ ⁴¹⁹ with a length of 420, namely

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; PN</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; <figure-callout\; id="0"\; label="S"\; filenames="HSA00000059525400031.png,HSA00000059525400032.png"\; state="\{\{state\}\}">S</figure-callout></span>}\\ \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& \mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&NotElement;</span>\mathrm{<span\; class="notranslate">\; S</span>}\end{array}\right.<span\; class="notranslate">\; ;</span>$

Wherein S is a set of subscripts inserted into an empty symbol by the m sequence, and S ⁻¹ (k)=n satisfies k=S(n).

S504. {C(k)} ₀ ⁴¹⁹ obtains the sequence {c(n)} ₀ ⁴¹⁹ through 420 inverse discrete Fourier transforms;

c(n)=IDFT ₄₂₀ (C(k)), n=0, 1, . . . , 419

Wherein IDFT ₄₂₀ (·) represents 420-point discrete Fourier inverse transform.

S505. Calculate the power peak-to-average ratio PAPR of the sequence c(n), record c(n) and the corresponding PAPR;

S506. Judging whether all generator polynomials and all initial phases of the 8th-order m-sequence have been traversed at present, if traversed, then jump to step 607, otherwise return to step 602;

S507. Select the PN-MC sequence with the smallest PAPR as the optimal sequence under the optimal criterion of this implementation and output it. the

The above embodiments are only used to illustrate the present invention, but not to limit the present invention. Those of ordinary skill in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all Equivalent technical solutions also belong to the category of the present invention, and the scope of patent protection of the present invention should be defined by the claims. the

Claims (5)

1. digital signal transmission method based on multi-carrier pseudorandom sequence, the method comprising the steps of:

S1. to data to be transmitted encode, modulation treatment, generate the data to be transmitted piece;

S2. with described data to be transmitted piece and the multi-carrier pseudorandom sequence framing of selecting, obtain the data to be transmitted frame;

S3. described data to be transmitted frame is carried out digital-to-analogue conversion, rf modulations processing and transmission;

The sequence of described multi-carrier pseudorandom sequence for being obtained through inverse discrete Fourier transformer inverse-discrete by binary sequence, the method for selecting of described multi-carrier pseudorandom sequence comprises step:

S2.1 makes that the discrete Fourier transform (DFT) of multi-carrier pseudorandom sequence is sequence C, and described sequence C is divided into the K section, and note is C successively ₁, C ₂..., C _K, the value of initialization sequence C is 0 entirely, and wherein, the length of multi-carrier pseudorandom sequence is N, and K is any positive integer less than N;

S2.2 makes i=1, C ₁At { α ₁, α ₂In value, obtain new sequence C ', with new sequence C ' do the inverse transformation of N point discrete Fourier, obtain multi-carrier pseudorandom sequence, calculate the parameter to be investigated of this multi-carrier pseudorandom sequence according to the optimal sequence Criterion of Selecting, travel through all possible C ₁, obtain the multi-carrier pseudorandom sequence of parameter optimum described to be investigated, the sequence C that record is corresponding ₁, wherein, 1≤i≤K, | α ₁|=| α ₂|;

S2.3 makes i=i+1, fixed sequence program C ₁, C ₂..., C _I-1, C _iAt { α ₁, α ₂Middle value, with C ₁, C ₂..., C _I-1, C _iInsetion sequence C consists of new sequence C ", to described new sequence C " does the inverse transformation of N point discrete Fourier and obtains multi-carrier pseudorandom sequence, calculates the parameter to be investigated of this multi-carrier pseudorandom sequence according to the optimal sequence Criterion of Selecting, travels through all possible C _i, obtain the multi-carrier pseudorandom sequence of parameter optimum described to be investigated, the C that record is corresponding _iAnd optimum parameter P to be investigated ₀

I sequence before S2.4 travels through successively again, fixation of C ₁-C _iIn except C _jOutside all sequences, travel through all possible C _jIf the parameter to be investigated of the optimum that obtains is better than P ₀, then upgrade optimum parameter to be investigated and corresponding sequence C _j, wherein, 1≤j≤i;

If S2.5 is i=K, execution in step S2.6 then, otherwise return step S2.3;

S2.6 is with the C of current selected ₁, C ₂..., C _KBe spliced into the binary sequence that length is N, described binary sequence is done the inverse transformation of N point discrete Fourier, the multi-carrier pseudorandom sequence that obtains is exported as selected multi-carrier pseudorandom sequence;

Perhaps

Described multi-carrier pseudorandom sequence is through expanding or blocking the sequence that obtains through inverse discrete Fourier transformer inverse-discrete again by the m sequence; The method for selecting of described multi-carrier pseudorandom sequence comprises step:

The length of the selected multi-carrier pseudorandom sequence of S2.1 ' is N, determines the exponent number K of m sequence, satisfies

Or

Wherein,

With

Expression rounds downwards and rounds up respectively;

S2.2 ' is generator polynomial and the initial phase of selected m sequence successively, generates a m sequence;

S2.3 ' is the two-phase PSK symbol with the m sequence mapping that generates, and through expanding or blocking, consisting of length is the symbol sebolic addressing of N;

S2.4 ' does the inverse transformation of N point discrete Fourier to described symbol sebolic addressing, obtains the multi-carrier pseudorandom sequence that length is N;

S2.5 ' is according to the optimal sequence Criterion of Selecting, and the parameter to be investigated of the multi-carrier pseudorandom sequence that calculation procedure S2.4 ' obtains records described multi-carrier pseudorandom sequence and parameter value described to be investigated;

S2.6 ' judges whether to travel through all generator polynomials and all initial phases, if travel through, and execution in step S 2.7 ' then, otherwise, return execution in step S2.2 ';

S2.7 ' chooses optimum parameter to be investigated according to described optimal sequence Criterion of Selecting from the parameter to be investigated that all obtain, and the multi-carrier pseudorandom sequence that it is corresponding is as selected multi-carrier pseudorandom sequence output.

2. the digital signal transmission method based on multi-carrier pseudorandom sequence as claimed in claim 1 is characterized in that, the framing method among the step S2 comprises:

Fill the protection interval of described data to be transmitted piece with at least one described multi-carrier pseudorandom sequence; Or

With the targeting sequencing of at least one described multi-carrier pseudorandom sequence as described data to be transmitted piece.

3. the digital signal transmission method based on multi-carrier pseudorandom sequence as claimed in claim 1 is characterized in that, described extended method comprises:

Cyclic extensions copies to the some bit signs in the end of described m sequence before the described m sequence; Or

The zero padding expansion replenishes respectively some nil symbols at front end and the end of described m sequence; Or

In sequence, insert the nil symbol expansion according to known pattern.

4. the digital signal transmission method based on multi-carrier pseudorandom sequence as claimed in claim 1 is characterized in that, described optimal sequence Criterion of Selecting comprises:

The power PAR minimum criteria, parameter P to be investigated is the power PAR PAPR of sequence, the described power PAR PAPR of sequence c (n) is:

Or

Aperiodic autocorrelative part quality factor maximal criterion, parameter P to be investigated is the autocorrelative part quality factor F aperiodic of sequence _Part, autocorrelative part quality factor F aperiodic of sequence c (n) _Part, for:

Wherein, R (n) is auto-correlation aperiodic of sequence c (n), and

Or

Auto-correlation quality factor maximal criterion aperiodic of discrete Fourier transform (DFT), parameter P to be investigated is the autocorrelative quality factor aperiodic of sequence, autocorrelative quality factor MF aperiodic of sequence c (n) is:

Wherein, R (n) is auto-correlation aperiodic of sequence c (n); Or

Aperiodic auto-correlation part quality factor and power PAR ratio maximal criterion, parameter to be investigated is: P=F _Part/ PAPR; Or

The auto-correlation quality factor and power PAR ratio maximal criterion aperiodic of discrete Fourier transform (DFT), parameter to be investigated is: P=MF/PAPR; Or

Aperiodic auto-correlation part quality factor and discrete Fourier transform (DFT) auto-correlation quality factor product maximal criterion aperiodic, parameter to be investigated is: P=F _PartMF; Or

Power PAR, aperiodic auto-correlation part quality factor and discrete Fourier transform (DFT) auto-correlation quality factor compromise aperiodic criterion, parameter to be investigated is P=F _PartMF/PAPR chooses the multi-carrier pseudorandom sequence of P maximum.

5. digital signal transmission system based on multi-carrier pseudorandom sequence, this system comprises:

Code modulation module, to data to be transmitted encode, modulation treatment, generate the data to be transmitted piece;

The sequence generation module generates needed multi-carrier pseudorandom sequence;

The framing module with described data to be transmitted piece and the multi-carrier pseudorandom sequence framing of selecting, obtains the data to be transmitted frame;

Back end processing module carries out digital-to-analogue conversion, rf modulations processing and transmission to described data to be transmitted frame;

The sequence of described multi-carrier pseudorandom sequence for being obtained through inverse discrete Fourier transformer inverse-discrete by binary sequence, described framing module be selected described multi-carrier pseudorandom sequence by the following method:

S2.1 makes that the discrete Fourier transform (DFT) of multi-carrier pseudorandom sequence is sequence C, and described sequence C is divided into the K section, and note is C successively ₁, C ₂..., C _K, the value of initialization sequence C is 0 entirely, and wherein, the length of multi-carrier pseudorandom sequence is N, and K is any positive integer less than N;

S2.2 makes i=1, C ₁At { α ₁, α ₂In value, obtain new sequence C ', with new sequence C ' do the inverse transformation of N point discrete Fourier, obtain multi-carrier pseudorandom sequence, calculate the parameter to be investigated of this multi-carrier pseudorandom sequence according to the optimal sequence Criterion of Selecting, travel through all possible C ₁, obtain the multi-carrier pseudorandom sequence of parameter optimum described to be investigated, the sequence C that record is corresponding ₁, wherein, 1≤i≤K, | α ₁|=| α ₂|;

S2.3 makes i=i+1, fixed sequence program C ₁, C ₂.., C _I-1, C _iAt { α ₁, α ₂Middle value, with C ₁, C ₂..., C _I-1, C _iInsetion sequence C consists of new sequence C ", to described new sequence C " does the inverse transformation of N point discrete Fourier and obtains multi-carrier pseudorandom sequence, calculates the parameter to be investigated of this multi-carrier pseudorandom sequence according to the optimal sequence Criterion of Selecting, travels through all possible C _i, obtain the multi-carrier pseudorandom sequence of parameter optimum described to be investigated, the C that record is corresponding _iAnd optimum parameter P to be investigated ₀

I sequence before S2.4 travels through successively again, fixation of C ₁-C _iIn except C _jOutside all sequences, travel through all possible C _jIf the parameter to be investigated of the optimum that obtains is better than P ₀, then upgrade optimum parameter to be investigated and corresponding sequence C _j, wherein, 1≤j≤i;

If S2.5 is i=K, execution in step S2.6 then, otherwise return step S2.3;

S2.6 is with the C of current selected ₁, C ₂..., C _KBe spliced into the binary sequence that length is N, described binary sequence is done the inverse transformation of N point discrete Fourier, the multi-carrier pseudorandom sequence that obtains is exported as selected multi-carrier pseudorandom sequence;

Perhaps

Described multi-carrier pseudorandom sequence is through expanding or blocking the sequence that obtains through inverse discrete Fourier transformer inverse-discrete again by the m sequence; Described framing module is selected described multi-carrier pseudorandom sequence by the following method:

The length of the selected multi-carrier pseudorandom sequence of S2.1 ' is N, determines the exponent number K of m sequence, satisfies

Or

Wherein,

With

Expression rounds downwards and rounds up respectively;

S2.2 ' is generator polynomial and the initial phase of selected m sequence successively, generates a m sequence;

S2.3 ' is the two-phase PSK symbol with the m sequence mapping that generates, and through expanding or blocking, consisting of length is the symbol sebolic addressing of N;

S2.4 ' does the inverse transformation of N point discrete Fourier to described symbol sebolic addressing, obtains the multi-carrier pseudorandom sequence that length is N;

S2.5 ' is according to the optimal sequence Criterion of Selecting, and the parameter to be investigated of the multi-carrier pseudorandom sequence that calculation procedure S2.4 ' obtains records described multi-carrier pseudorandom sequence and parameter value described to be investigated;

S2.6 ' judges whether to travel through all generator polynomials and all initial phases, if travel through, and execution in step S 2.7 ' then, otherwise, return execution in step S2.2 ';

S2.7 ' chooses optimum parameter to be investigated according to described optimal sequence Criterion of Selecting from the parameter to be investigated that all obtain, and the multi-carrier pseudorandom sequence that it is corresponding is as selected multi-carrier pseudorandom sequence output.

Images