CN104518811A - Digital-weighted auto-correlation UWB (ultra wide band) receiving method and device based on channel characteristic sequences - Google Patents
Digital-weighted auto-correlation UWB (ultra wide band) receiving method and device based on channel characteristic sequences Download PDFInfo
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Abstract
The invention discloses a digital-weighted auto-correlation UWB (ultra wide band) receiving method and device based on channel characteristic sequences. The digital-weighted auto-correlation UWB receiving method includes employing the average superposition algorithm to estimate a characteristic vector reflecting multipath fading energy monotonous distribution and adopting the characteristic vector to rearrange auto-correlation sequence of a received signal into the channel characteristic sequence; subjecting the channel characteristic sequence to bin summation and assembly into a vector, and inputting the vector into an adaptive filter for optimizing training combining linear weighting to restrain noise interference; performing dynamic adaptive detection through restrained weighting coefficient after training. According to the arrangement, the character of the channel characteristic sequence that the closer the energy levels are, the closer the samples become is adopted, so that the most samples with close energy levels stay in the same bin, optimizing effect combining the linear weighting is improved, and BER (bit error rate) performance of the system is remarkably increased at the premise that computation complexity is acceptable.
Description
Technical Field
The invention belongs to the field of wireless communication, relates to an autocorrelation receiver in an incoherent ultra-wideband communication system, and particularly relates to a digital weighted autocorrelation ultra-wideband receiving method based on a channel characteristic sequence and a receiving device thereof.
Background
Ultra-wideband (UWB) technology has become a promising solution for short-range wireless communication with low cost, low power consumption, and good time-domain resolution. Especially in IEEE 802.15.4a, which is a modified standard of low bit rate Wireless Personal Area Networks (WPANs), ultra-wideband is adopted as one of alternative physical layer transmission technologies, and is a research hotspot in academia and industry. In an ultra-wideband system with a low code rate, a non-coherent (NC) receiver with low complexity, low requirement on a sampling rate, and no need for channel estimation is often used in consideration of requirements such as low cost and low power consumption. Currently popular detection techniques for incoherent ultra-wideband systems are Energy Detectors (EDs) and autocorrelation receivers (AcR). The autocorrelation receiver is generally used in an ultra-wideband communication system based on a Transmitted Reference (TR) signal due to its simple structure, robust performance, and other features. However, in the ultra-wideband dense multipath transmission environment, due to the cluster-like dispersion distribution characteristic of multipath energy, the optimization of the autocorrelation integration interval becomes difficult: even if the start point and the end point of the integration interval are determined, it is still difficult to avoid that a large amount of noise components are involved in the autocorrelation operation, resulting in a large error performance loss.
For this purpose, a weighted autocorrelation receiver (W-AcR) is proposed for suppressing the noise contribution, whose idea is: the whole integration interval is divided into a plurality of smaller segments (bins), autocorrelation signals are integrated on each segment, then the integration results of each segment are subjected to linear weighting combination, and the suppression of the segments with low signal-to-noise ratio can be realized by optimizing weighting coefficient vectors. Although weighted autocorrelation receivers improve the error performance to a large extent, it is still possible to have the high and low snr components in the same segment due to the limited time resolution of the segments. In this case, the piecewise integration also does not suppress the noise component, thereby reducing the overall optimization effect of the linear combination.
On the other hand, the above existing ultra-wideband receiver initially adopts the implementation scheme of an analog receiver due to the limitation of implementation complexity. However, recent research results in digital systems have shown that low complexity single bit (monobit) or limited resolution (fine-resolution) digital receivers can achieve bit error performance comparable to full-resolution digital receivers. These recent technological breakthroughs not only make it possible for digital receivers to be used in low-cost, low-power ultra-wideband applications on a large scale, but they also pave the way to fully exploit the flexible Digital Signal Processing (DSP) technology to improve the overall performance of the system.
Disclosure of Invention
In view of the above drawbacks or shortcomings, the present invention provides a method and apparatus for receiving a digital weighted autocorrelation ultra-wideband based on a channel signature sequence.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a digital weighted autocorrelation ultra-wideband receiving method based on a channel characteristic sequence is characterized by comprising the following steps:
1) filtering out-of-band noise of the received signal by a Low Pass Filter (LPF), and converting the out-of-band noise into a discrete sequence by an analog-to-digital converter (ADC); the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
2) performing N on discrete sequences of received signalsdDelaying unit, multiplying the delayed sequence after conjugation with the original discrete sequence to obtain an autocorrelation sequence, determining a starting point and an end point of a summation interval by a peak detection method, and intercepting the autocorrelation sequence by the starting point and the end point;
3) truncating auto-correlation sequencesAnd the truncated autocorrelation sequence is a multi-frame repeat sequence based on the frame repetition frequency N of the transmitted reference TR signalfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length, and dividing NfOverlapping the segment subsequences to obtain a noise reduction subsequence;
4) processing the noise reduction subsequence to obtain a channel characteristic sequence;
5) and carrying out sectional summation on the channel characteristic sequence, carrying out self-adaptive filtering processing, and finally outputting a decision symbol.
The received signal in step 1) is a signal received at the antenna of the receiver after the TR signal transmitted from the transmitter is attenuated by the multipath channel, and the transmitted signal of the transmitter in the ith symbol period is represented as:
wherein E isbTransmit energy for a single symbol; w is atx(T) is energy normalization and pulse width is TpThe ultra-wideband pulse of (1);
Nfa frame repetition frequency for transmitting a reference TR signal, wherein each frame comprises a pulse pair comprising a reference pulse and a data pulse;
Tffor the repetition period of each pulse pair, biE { + -1} is a transmitted data symbol, TdIs the delay between the reference pulse and the data pulse; m is a non-negative integer, t is a continuous time variable;
the transmitted signal reaches the receiver through the multipath channel and is filtered by the low pass filter LPF, and the received signal is obtained as follows:
wherein, wrx(t) is the received ultra-wideband pulse waveform, i.e. wtx(t) convolution with the channel impulse response h (t), expressed as: <math>
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z (t) is the bilateral power spectral density PSD of N0Additive white Gaussian noise AWGN of/2, and Td≥Tp+τmax，Tf≥2Td，Ts≥NfTf(ii) a Wherein tau ismaxIndicating the maximum delay spread, T, of the multipath channelsIs the symbol period.
The received signal r (t) generates a corresponding discrete sequence after passing through the analog-to-digital converter, which can be expressed as:
wherein r [ l ]]＝r(lTsa)，wrx[l]＝wrx(lTsa)，z[l]＝z(lTsa)，Tsa＝1/fsaIs a sampling interval, fsaIs the sampling frequency;is equal to TdThe corresponding number of samples is delayed,andrespectively representing the number of samples corresponding to the symbol period and the frame period;
the AWGN noise sequence satisfies a complex Gaussian distribution of zero mean, i.e.Wherein <math>
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The autocorrelation sequence of the received signal in the step 2) refers to c [ n ]]＝r[n]r*[n-Nd],n＝0,1,…,Nd1, the summing interval of which can be determined roughly by a simple peak detection method;
roughly defining the autocorrelation integration interval of each frame as [ T1,T2]＝[Td,2Td]In each frame, the whole TdThe part after the moment is used as an autocorrelation integral interval; the starting point and the end point of the summation interval of the autocorrelation sequence in each frame are respectively taken as N1,m＝mNsr+NdAnd N2,m＝mNsr+2Nd,m＝0,1,…,Nf-1, on each frame, truncating the portion starting from the start to the end of the summation interval, and concatenating the truncated portions in the frames into the following sequence:
wherein,is defined as
zk,m[l]|k＝0,1Is a complex additive white gaussian noise sequence of independent equal distribution (i.i.d.), namely:
characterized in that the step 3) is to divide the sequence shown in the formula (4) intoNfEach length is NdAnd overlapping them to obtain a subsequence of length NdThe sequence of (a):
wherein the first term is the superposition of useful signal components of each frame in the formula (4), and the second term is Zm[n]Representing the overall effect of noise-noise and noise-signal cross terms, the statistical properties of which can be described by a zero-mean complex gaussian distribution.
The step 4) is to process the noise reduction subsequence to obtain a channel characteristic sequence specifically comprising;
inputting the noise reduction subsequence into a channel characteristic sequence-readjustment processing CCS-RAG module, wherein the CCS-RAG processing module works in three stages:
firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then follows a training phase and an adaptive detection phase, in both of which the RAG sub-module outputs a sequence of channel characteristics.
The working state of the CCS-RAG processing module of the step 4) in three phases can be described as follows: in the estimation stage, only the CCS sub-module is in a working state, namely a certain number of sub-sequences x [ n ] from different sending symbols are received, and superposition average operation is carried out to obtain a channel characteristic vector estimation value which is input into the RAG sub-module;
in the training phase and the adaptive detection phase, only the RAG submodule is in a working state, that is, the input subsequence x [ n ] is reordered according to the eigenvector estimation value input by the CCS submodule, so as to obtain a channel eigenvector v [ n ], and an ideal eigenvector can be described as:
wherein, for nonnegative integer l, m, N ∈ {0,1, …, Nd-1} and m ≠ n, each having qn≠qmAnd q isl∈{0,1,…,Nd-1}, channel signature sequence v [ n ]]As follows:
v[n]＝x[qn],n＝0,1,…,Nd-1. (9)
from equations (7) to (9), the feature vector q can be plottedI.e. for any nonnegative integer m, N ∈ {0,1, …, Nd-1} and n<m is all as follows:or <math>
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</math> This is true.
The step 5) of performing segmented summation on the channel feature sequence and performing adaptive filtering processing, and finally outputting a decision symbol specifically includes:
the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages:
firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
The step 5) specifically comprises the following steps:
for the channel characteristic sequence v [ n ]]By summing in segments, i.e. of length NdV [ n ] of-1]Is divided into NpThe segments are summed respectively to obtain a segment with a length of NpThe sequence of (a):
wherein,the number of samples contained for each segment. Then the sequence u [ n ]]Assembling into a vector:
u＝[u(0),u(1),…,u(Nd-1)]T. (11)
inputting u into Np-linear weighted combination of tapped adaptive filters, the output decision variable being
β＝wTRe{u}, (12)
Wherein Re {. is } represents an arithmetic unit, and w { [ w {. denotes0,w1,…,wNp-1]TRepresenting a weight coefficient vector of the adaptive filter, and optimizing w by adopting a Minimum Mean Square Error (MMSE) criterion:
the adaptive filter works in two stages: firstly, in the training stage, w is optimally trained in a self-adaptive iteration mode to be converged in woptNearby; then, an adaptive detection stage, linear combination of formula (12) is carried out by using converged w, and finally symbol decision is carried out:
wherein <math>
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A digitally weighted autocorrelation ultra-wideband receiving device based on a channel signature sequence, comprising: the device comprises a low-pass filter LPF, an analog-to-digital converter ADC, an autocorrelation sequence generation module, a noise reduction subsequence generation module, a CCS-RAG processing module, an adaptive filter AF and a symbol decision DEC unit;
the low pass filter LPF is used for filtering out the out-of-band noise of the received signal;
the analog-to-digital converter ADC is used for converting the low-pass filtered received signal into a discrete sequence, and the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
the autocorrelation sequence generation module is used for carrying out N on the discrete sequence of the received signaldDelaying unit, multiplying the delayed sequence after conjugation with the original discrete sequence to obtain an autocorrelation sequence, determining a starting point and an end point of a summation interval by a peak detection method, and intercepting the autocorrelation sequence by the starting point and the end point;
the noise reduction subsequence generating module is used for intercepting the autocorrelation sequence, the intercepted autocorrelation sequence is a multi-frame repetitive sequence, and the frame repetitive frequency N of the TR signal is usedfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length and willNfOverlapping the segment subsequences to obtain a noise reduction subsequence;
the CCS-RAG processing module is used for processing the noise reduction subsequence to obtain a channel characteristic sequence and carrying out segmented summation on the channel characteristic sequence;
the self-adaptive filter AF is used for carrying out self-adaptive filtering processing after carrying out segmented summation on the channel characteristic sequence;
the symbol decision DEC unit is used for outputting decision symbols after the adaptive filtering processing of the channel characteristic sequence;
processing the noise reduction subsequence to obtain a channel characteristic sequence specifically comprising; inputting the noise reduction subsequence into a CCS-RAG module, and dividing the CCS-RAG processing module into three stages: firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then, a training stage and a self-adaptive detection stage are carried out, and RAG sub-modules output channel characteristic sequences in the two stages;
the step of performing segmented summation and adaptive filtering processing on the channel feature sequence, and the step of outputting a decision symbol specifically comprises: the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages: firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a digital weighted autocorrelation ultra-wideband receiving method and a receiving device thereof based on a channel characteristic sequence, wherein, firstly, a simple and effective superposition average algorithm is adopted to estimate out the channel characteristic, namely a monotonous distribution vector of multipath fading energy, and the autocorrelation sequence of a received signal is rearranged into a channel characteristic sequence by using the monotonous distribution vector; then, the channel characteristic sequences are assembled into a vector input adaptive filter after being subjected to segmented summation, and optimization training of linear weighting and combination is carried out to suppress noise interference; and finally, carrying out dynamic self-adaptive weighting detection by using the weight vector after the training convergence, wherein the method utilizes the characteristic that the closer the energy levels in the channel characteristic sequence are, the closer the distance between the samples is, so that most of the samples with the similar energy levels are in the same segment, the optimization effect of linear weighting combination is improved, and the error code performance of the system is obviously improved under the condition of acceptable calculation complexity.
Drawings
FIG. 1 is a diagram of a receiver model in a TR-UWB system of the present invention;
FIG. 2 is a flowchart illustrating an overall calculation of a digital weighted autocorrelation receiver based on a channel signature sequence in a TR-UWB system according to the present invention;
FIG. 3 is a flowchart illustrating an embodiment of three stages of channel eigen vector estimation, weight coefficient vector optimization training, and adaptive detection when implementing a digital weighted autocorrelation receiver based on a channel eigen sequence in a TR-UWB system according to the present invention;
FIG. 4 is an example of error performance when different length estimated sequences are used in the present invention;
FIG. 5 is an example of error performance when training sequences of different lengths are used in the present invention;
fig. 6 is a diagram comparing the error code performance of two digital weighted autocorrelation ultra-wideband receivers in the present invention and the prior art, wherein (a) is the indoor residential line-of-sight propagation environment, and (b) is the indoor residential non-line-of-sight propagation environment.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Example one
As shown in fig. 1.2, the present invention provides a digital weighted autocorrelation ultra-wideband receiving method based on a channel signature sequence, which includes the following steps:
1) filtering out-of-band noise of the received signal by a Low Pass Filter (LPF), and converting the out-of-band noise into a discrete sequence by an analog-to-digital converter (ADC); the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
wherein, the received signal is the signal received at the antenna of the receiver after the signal transmitted from the transmitter is attenuated by the multipath channel, and the transmitted signal of the transmitter in the ith symbol period is represented as:
wherein E isbTransmit energy for a single symbol; w is atx(T) is energy normalization and pulse width is TpThe ultra-wideband pulse of (1);
Nfa frame repetition frequency for transmitting a reference TR signal, wherein each frame comprises a pulse pair comprising a reference pulse and a data pulse;
Tffor the repetition period of each pulse pair, biE { + -1} is a transmitted data symbol, TdIs the delay between the reference pulse and the data pulse; m is a non-negative integer, t is a continuous time variable;
the transmitted signal reaches the receiver through a multipath channel and is filtered by an LPF, and the received signal is obtained as follows:
wherein, wrx(t) is the received ultra-wideband pulse waveform, i.e. wtx(t) convolution with the channel impulse response h (t), expressed as: <math>
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z (t) is the bilateral power spectral density PSD of N0Additive white Gaussian noise AWGN of/2, and Td≥Tp+τmax，Tf≥2Td，Ts≥NfTf(ii) a Wherein tau ismaxIndicating the maximum delay spread, T, of the multipath channelsIs the symbol period.
The received signal r (t) generates a corresponding discrete sequence after passing through the analog-to-digital converter, which can be expressed as:
wherein r [ l ]]＝r(lTsa)，wrx[l]＝wrx(lTsa)，z[l]＝z(lTsa)，Tsa＝1/fsaIs a sampling interval, fsaIs the sampling frequency;is equal to TdThe corresponding number of samples is delayed,andrespectively representing the number of samples corresponding to the symbol period and the frame period;
the AWGN noise sequence satisfies a complex Gaussian distribution of zero mean, i.e.Wherein <math>
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2) Performing N on discrete sequences of received signalsdDelaying unit, multiplying the delayed sequence after conjugation with the original discrete sequence to obtain an autocorrelation sequence, determining a starting point and an end point of a summation interval by a peak detection method, and intercepting the autocorrelation sequence by the starting point and the end point;
the autocorrelation sequence of the received signal in the step (2) is c [ n [ ]]＝r[n]r*[n-Nd],n＝0,1,…,Nd1, the summing interval of which can be determined roughly by a simple peak detection method;
roughly defining the autocorrelation integration interval of each frame as [ T1,T2]＝[Td,2Td]In each frame, the whole TdThe part after the moment is used as an autocorrelation integral interval; the starting point and the end point of the summation interval of the autocorrelation sequence in each frame are respectively taken as N1,m＝mNsr+NdAnd N2,m＝mNsr+2Nd,m＝0,1,…,Nf-1, on each frame, truncating the portion starting from the start to the end of the summation interval, and concatenating the truncated portions in the frames into the following sequence:
wherein,is defined as
zk,m[l]|k＝0,1Is independent ofA complex additive white gaussian noise sequence of the same distribution (i.i.d.), i.e.:
3) truncating the autocorrelation sequence, the truncated autocorrelation sequence being a multi-frame repeat sequence, based on the frame repetition frequency N of the TR signalfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length, and dividing NfOverlapping the segment subsequences to obtain a noise reduction subsequence;
the step 3) is to divide the sequence shown in the formula (4) into NfEach length is NdAnd overlapping them to obtain a subsequence of length NdThe sequence of (a):
wherein the first term is the superposition of useful signal components of each frame in the formula (4), and the second term is Zm[n]Representing the overall effect of noise-noise and noise-signal cross terms, the statistical properties of which can be described by a zero-mean complex gaussian distribution.
4) Processing the noise reduction subsequence to obtain a channel characteristic sequence;
specifically comprises the following steps of;
inputting the noise reduction subsequence into a CCS-RAG processing module, wherein the CCS-RAG processing module works in three stages:
firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then follows a training phase and an adaptive detection phase, in both of which the RAG sub-module outputs a sequence of channel characteristics.
The working state of the CCS-RAG processing module of the step 4) in three phases can be described as follows: in the estimation stage, only the CCS sub-module is in a working state, namely a certain number of sub-sequences x [ n ] from different sending symbols are received, and superposition average operation is carried out to obtain a channel characteristic vector estimation value which is input into the RAG sub-module;
in the training phase and the adaptive detection phase, only the RAG submodule is in a working state, that is, the input subsequence x [ n ] is reordered according to the eigenvector estimation value input by the CCS submodule, so as to obtain a channel eigenvector v [ n ], and an ideal eigenvector can be described as:
wherein, for nonnegative integer l, m, N ∈ {0,1, …, Nd-1} and m ≠ n, each having qn≠qmAnd q isl∈{0,1,…,Nd-1}, channel signature sequence v [ n ]]As follows:
v[n]＝x[qn],n＝0,1,…,Nd-1. (9)
from equations (7) to (9), the feature vector q can be plottedI.e. for any nonnegative integer m, N ∈ {0,1, …, Nd-1} and n<m is all as follows:or <math>
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5) And carrying out sectional summation on the channel characteristic sequence, carrying out self-adaptive filtering processing, and finally outputting a decision symbol.
The method specifically comprises the following steps:
the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages:
firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
The step 5) specifically comprises the following steps:
for the channel characteristic sequence v [ n ]]By summing in segments, i.e. of length NdV [ n ] of-1]Is divided into NpThe segments are summed respectively to obtain a segment with a length of NpThe sequence of (a):
wherein,the number of samples contained for each segment. Then the sequence u [ n ]]Assembling into a vector:
u＝[u(0),u(1),…,u(Nd-1)]T. (11)
inputting u into Np-linear weighted combination of tapped adaptive filters, the output decision variable being
β＝wTRe{u}, (12)
Wherein Re {. is } represents an arithmetic unit, and w { [ w {. denotes0,w1,…,wNp-1]TRepresenting a weight coefficient vector of the adaptive filter, and optimizing w by adopting a Minimum Mean Square Error (MMSE) criterion:
therein, fromThe adaptive filter works in two stages: firstly, in the training stage, w is optimally trained in a self-adaptive iteration mode to be converged in woptNearby; then, an adaptive detection stage, linear combination of formula (12) is carried out by using converged w, and finally symbol decision is carried out:
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The invention also provides a digital weighted autocorrelation ultra-wide band receiving device based on the channel characteristic sequence, which comprises: the device comprises a low-pass filter LPF, an analog-to-digital converter ADC, an autocorrelation sequence generation module, a noise reduction subsequence generation module, a CCS-RAG processing module, an adaptive filter AF and a symbol decision DEC unit;
the low pass filter LPF is used for filtering out the out-of-band noise of the received signal;
the analog-to-digital converter ADC is used for converting the low-pass filtered received signal into a discrete sequence, and the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
the autocorrelation sequence generation module is used for carrying out N on the discrete sequence of the received signaldDelaying unit, multiplying the delayed sequence after conjugation with the original discrete sequence to obtain an autocorrelation sequence, determining a starting point and an end point of a summation interval by a peak detection method, and intercepting the autocorrelation sequence by the starting point and the end point;
the noise reduction subsequence generating module is used for intercepting the autocorrelation sequence, the intercepted autocorrelation sequence is a multi-frame repetitive sequence, and the frame repetitive frequency N of the TR signal is usedfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length, and dividing NfOverlapping the segment subsequences to obtain a noise reduction subsequence;
the CCS-RAG processing module is used for processing the noise reduction subsequence to obtain a channel characteristic sequence and carrying out segmented summation on the channel characteristic sequence;
the self-adaptive filter AF is used for carrying out self-adaptive filtering processing after carrying out segmented summation on the channel characteristic sequence;
the symbol decision DEC unit is used for outputting decision symbols after the adaptive filtering processing of the channel characteristic sequence;
processing the noise reduction subsequence to obtain a channel characteristic sequence specifically comprising; inputting the noise reduction subsequence into a CCS-RAG module, and dividing the CCS-RAG processing module into three stages: firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then, a training stage and a self-adaptive detection stage are carried out, and RAG sub-modules output channel characteristic sequences in the two stages;
the step of performing segmented summation and adaptive filtering processing on the channel feature sequence, and the step of outputting a decision symbol specifically comprises: the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages: firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
Example two
First, fig. 3 shows a flow chart of the three stages of channel eigenvector estimation, weight coefficient vector optimization training, and adaptive detection according to the present invention. The specific implementation route is described as follows:
the first phase, i.e. the transmitter-receiver link enters the channel eigenvector estimation phase, where only the working steps 1) -4) are carried out. Since a specific symbol sequence needs to be transmitted for estimating the channel eigenvector in step 4), the present invention adopts a length N for simplicity and practicalitycA sequence of consecutive "+ 1" symbols. I.e. continuously transmitting N at the transmitter endcThe +1 symbol is processed by the steps 1) to 3), and the following N can be obtained correspondinglycVector number:
x(i)＝[x[0](i),x[1](i),…,x[Nd-1](i)],i＝0,1,…,Nc-1. (15)
wherein, the formula (7) shows that:
in step 4), the CCS submodule converts N shown in formula (15)cAnd (3) carrying out superposition averaging on the vectors to obtain:
wherein,it can be seen from equations (15) to (17) that the noise components in the vector obtained after the superposition averaging are further cancelled, and the corresponding channel feature vector estimation values can be obtained by sorting the samples in ascending order or descending order of the amplitudes:
wherein, for nonnegative integer l, m, N ∈ {0,1, …, Nd-1} and m ≠ n, each has <math>
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</math> From the above analysis, equation (18) is a preferable estimate of equation (8).
In the process of obtaining channel characteristic vector estimated valueThereafter, the CCS submodule inputs it to the RAG submodule.
The second phase, i.e. the transmitter-receiver link enters the optimal training phase of the weighting coefficient vector, in which the working steps 1) -5) are carried out. Wherein the training sequence for optimizing the weighting coefficient vector is known at the receiver end and has a length of NtIs a bipolar pseudo-random sequence ofThe whole optimization process is to repeat the following operation flow by NtSecondly:
1) working steps 1) to 3) are carried out to obtain x [ n ] represented by formula (7)],n＝0,1,…,Nd-1；
2) Working step 4) is implemented, namely the RAG submodule estimates the channel characteristic vectorSubstituting into equation (9) to rearrange x [ n ]]Obtaining the channel characteristic sequence v [ n ]]And outputs it;
3) working step 5) is carried out by first converting v [ n ] according to the formulae (10) to (11)]The vector u is obtained by sectional summation and assembly, after the u is input into the self-adaptive filter, beta can be calculated according to the formula (12), and then residual error is obtainedAnd finally updating the weighting coefficient vector w by adopting a Recursive Least Square (RLS) adaptive filtering algorithm according to the residual error e.
The third phase, the transmitter-receiver link, enters the adaptive detection phase. At which stage the transmitter transmits unknown data symbolsThe receiver performs the following operation procedure in the detection period of each symbol:
1) working steps 1) to 3) are carried out to obtain x [ n ] represented by formula (7)],n＝0,1,…,Nd-1；
2) Working step 4) is implemented, namely the RAG submodule estimates the channel characteristic vectorSubstituting into equation (9) to rearrange x [ n ]]Obtaining the channel characteristic sequence v [ n ]]And outputs it;
3) working step 5) is carried out by first converting v [ n ] according to the formulae (10) to (11)]The vector u is obtained by sectional summation and assembly, after the u is input into the adaptive filter, beta can be calculated according to the formula (12), and then symbol decision is carried out according to the formula (14) to obtain an estimated symbolThereby calculating a residual errorAnd finally updating the weighting coefficient vector w by adopting an RLS adaptive filtering algorithm according to the residual error e.
As can be seen from fig. 4 to 6, the present invention estimates the channel feature vector by using the superposition average algorithm, and the number N of training symbols is the same as the number of training symbols in the superposition averagecAfter 512, the error performance similar to the ideal channel feature vector can be obtained. In addition, when the adaptive filter taps NpIncreasing from 2 to 16, when BER is 10-6The relative power gain at (a) is reduced from 1.5dB to 0.1 dB. Therefore, considering the cost performance factor comprehensively, N can be takenc512 and Np16, the error code performance of the system can be obviously improved at the cost of certain implementation complexity, and compared with the existing two weighted autocorrelation receivers, the invention has the BER of 10-6The power gain is more than 1.2dB, only N needs to be increasedc512 bitsAn overhead is estimated.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A digital weighted autocorrelation ultra-wideband receiving method based on a channel characteristic sequence is characterized by comprising the following steps:
1) filtering out-of-band noise of the received signal by a Low Pass Filter (LPF), and converting the out-of-band noise into a discrete sequence by an analog-to-digital converter (ADC); the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
2) performing N on discrete sequences of received signalsdUnit delay, conjugate delay sequence and original discrete sequence multiplication to obtain self-correlation sequenceDetermining a starting point and an end point of a summation interval by a peak detection method, and intercepting an autocorrelation sequence by the starting point and the end point;
3) intercepting the autocorrelation sequence as a multi-frame repeat sequence according to a frame repetition frequency N of the transmitted reference TR signalfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length, and dividing NfOverlapping the segment subsequences to obtain a noise reduction subsequence;
4) processing the noise reduction subsequence to obtain a channel characteristic sequence;
5) and carrying out sectional summation on the channel characteristic sequence, carrying out self-adaptive filtering processing, and finally outputting a decision symbol.
2. The digital weighted autocorrelation ultra-wideband receiving method based on the channel signature sequence as claimed in claim 1, wherein the received signal in step 1) is a signal received at the receiver antenna after the transmission reference TR signal transmitted from the transmitter undergoes multipath channel fading, and the transmitted signal of the transmitter in the ith symbol period is represented as:
wherein E isbTransmit energy for a single symbol; w is atx(T) is energy normalization and pulse width is TpThe ultra-wideband pulse of (1);
Nfa frame repetition frequency for transmitting a reference TR signal, wherein each frame comprises a pulse pair comprising a reference pulse and a data pulse;
Tffor the repetition period of each pulse pair, biE { + -1} is a transmitted data symbol, TdIs the delay between the reference pulse and the data pulse; m is a non-negative integer, t is a continuous time variable;
the transmitted signal reaches the receiver through the multipath channel and is filtered by the low pass filter LPF, and the received signal is obtained as follows:
wherein, wrx(t) is the received ultra-wideband pulse waveform, i.e. wtx(t) convolution with the channel impulse response h (t), expressed as:
z (t) is the bilateral power spectral density PSD of N0Additive white Gaussian noise AWGN of/2, and Td≥Tp+τmax，Tf≥2Td，Ts≥NfTf(ii) a Wherein tau ismaxIndicating the maximum delay spread, T, of the multipath channelsIs the symbol period.
3. The method according to claim 2, wherein the received signal r (t) is processed by an analog-to-digital converter to generate a corresponding discrete sequence, which is represented by:
wherein r [ l ]]＝r(lTsa)，wrx[l]＝wrx(lTsa)，z[l]＝z(lTsa)，Tsa＝1/fsaIs a sampling interval, fsaIs the sampling frequency;is equal to TdThe corresponding number of samples is delayed,andrespectively representing symbol periods and frame periodsThe corresponding number of samples;
the AWGN noise sequence satisfies a complex Gaussian distribution of zero mean, i.e.Wherein
4. The method as claimed in claim 3, wherein the autocorrelation sequence of the received signal in step (2) is c [ n ]]＝r[n]r*[n-Nd],n＝0,1,…,Nd1, the summing interval of which can be determined roughly by a simple peak detection method;
roughly defining the autocorrelation integration interval of each frame as [ T1,T2]＝[Td,2Td]In each frame, the whole TdThe part after the moment is used as an autocorrelation integral interval; the starting point and the end point of the summation interval of the autocorrelation sequence in each frame are respectively taken as N1,m＝mNsr+NdAnd N2,m＝mNsr+2Nd,m＝0,1,…,Nf-1, on each frame, truncating the portion starting from the start to the end of the summation interval, and concatenating the truncated portions in the frames into the following sequence:
wherein,is defined as
zk,m[l]|k＝0,1Are independently distributed at the same time(i.i.d.) a complex additive white gaussian noise sequence, namely:
。
5. the method as claimed in claim 4, wherein the step 3) is to divide the sequence shown in formula (4) into NfEach length is NdAnd overlapping them to obtain a subsequence of length NdThe sequence of (a):
wherein the first term is the superposition of useful signal components of each frame in the formula (4), and the second term is Zm[n]Representing the overall effect of noise-noise and noise-signal cross terms, the statistical properties of which can be described by a zero-mean complex gaussian distribution.
6. The method for receiving the channel signature sequence-based digital weighted autocorrelation ultra-wideband according to claim 5, wherein the step 4) processes the noise reduction subsequence to obtain the channel signature sequence specifically includes;
inputting the noise reduction subsequence into a channel characteristic sequence-readjusting CCS-RAG module, wherein the CCS-RAG processing module works in three stages:
firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then follows a training phase and an adaptive detection phase, in both of which the RAG sub-module outputs a sequence of channel characteristics.
7. The digital weighted autocorrelation ultra-wideband receiving method based on channel characteristic sequence according to claim 6, wherein the operating status of the CCS-RAG processing module of step 4) in three phases can be described as: in the estimation stage, only the CCS sub-module is in a working state, namely a certain number of sub-sequences x [ n ] from different sending symbols are received, and superposition average operation is carried out to obtain a channel characteristic vector estimation value which is input into the RAG sub-module;
in the training phase and the adaptive detection phase, only the RAG submodule is in a working state, that is, the input subsequence x [ n ] is reordered according to the eigenvector estimation value input by the CCS submodule, so as to obtain a channel eigenvector v [ n ], and an ideal eigenvector can be described as:
wherein, for nonnegative integer l, m, N ∈ {0,1, …, Nd-1} and m ≠ n, each having qn≠qmAnd q isl∈{0,1,…,Nd-1}, channel signature sequence v [ n ]]As follows:
v[n]＝x[qn],n＝0,1,…,Nd-1. (9)
from equations (7) to (9), the feature vector q can be plottedI.e. for any nonnegative integer m, N ∈ {0,1, …, Nd-1} and n<m is all as follows:orThis is true.
8. The method for receiving the channel signature sequence-based digital weighted autocorrelation ultra-wideband as claimed in claim 5, wherein the step 5) performs the step of performing the:
the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages:
firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
9. The method according to claim 8, wherein the step 5) specifically includes:
for the channel characteristic sequence v [ n ]]By summing in segments, i.e. of length NdV [ n ] of-1]Is divided into NpThe segments are summed respectively to obtain a segment with a length of NpThe sequence of (a):
wherein,for the number of samples contained in each segment, the sequence u n is then repeated]Assembling into a vector:
u＝[u(0),u(1),…,u(Nd-1)]T. (11)
inputting u into Np-linear weighted combining of the tapped adaptive filters, the output decision variable being:
β＝wTRe{u}, (12)
wherein Re {. is } represents an arithmetic unit, and w { [ w {. denotes0,w1,…,wNp-1]TRepresenting the weight coefficient vector of the adaptive filter, using Minimum Mean Square Error (MMSE) criterion to perform on wOptimizing:
the adaptive filter works in two stages: firstly, in the training stage, w is optimally trained in a self-adaptive iteration mode to be converged in woptNearby; then, an adaptive detection stage, linear combination of formula (12) is carried out by using converged w, and finally symbol decision is carried out:
whereinIs a symbolic function.
10. A digitally weighted autocorrelation ultra-wideband receiving device based on a channel signature sequence, comprising: the device comprises a low-pass filter LPF, an analog-to-digital converter ADC, an autocorrelation sequence generation module, a noise reduction subsequence generation module, a CCS-RAG processing module, an adaptive filter AF and a symbol decision DEC unit;
the low pass filter LPF is used for filtering out the out-of-band noise of the received signal;
the analog-to-digital converter ADC is used for converting the low-pass filtered received signal into a discrete sequence, and the analog-to-digital converter is a low-complexity single-bit or limited-resolution analog-to-digital converter;
the autocorrelation sequence generation module is used for carrying out N on the discrete sequence of the received signaldDelaying unit, multiplying the delayed sequence after conjugation with the original discrete sequence to obtain an autocorrelation sequence, determining a starting point and an end point of a summation interval by a peak detection method, and intercepting the autocorrelation sequence by the starting point and the end point;
the noise reduction subsequence generating module is used for intercepting the autocorrelation sequence, the intercepted autocorrelation sequence is a multiframe repetition sequence, and the signal is sent according to TRFrame repetition frequency N of numbersfDividing the multi-frame repeat sequence into NfSubsequences of the same segment length, and dividing NfOverlapping the segment subsequences to obtain a noise reduction subsequence;
the CCS-RAG processing module is used for processing the noise reduction subsequence to obtain a channel characteristic sequence and carrying out segmented summation on the channel characteristic sequence;
the self-adaptive filter AF is used for carrying out self-adaptive filtering processing after carrying out segmented summation on the channel characteristic sequence;
the symbol decision DEC unit is used for outputting decision symbols after the adaptive filtering processing of the channel characteristic sequence;
processing the noise reduction subsequence to obtain a channel characteristic sequence specifically comprising; inputting the noise reduction subsequence into a CCS-RAG module, and dividing the CCS-RAG processing module into three stages: firstly, in an estimation stage, a CCS submodule estimates a characteristic vector and outputs a characteristic vector estimation value to a RAG submodule, and the RAG submodule does not output data in the stage; then, a training stage and a self-adaptive detection stage are carried out, and RAG sub-modules output channel characteristic sequences in the two stages;
the step of performing segmented summation and adaptive filtering processing on the channel feature sequence, and the step of outputting a decision symbol specifically comprises: the channel characteristic sequence output by the CCS-RAG processing module is subjected to segmented summation, and the segmented summation results are assembled into a vector input adaptive filter AF, wherein the adaptive filter works according to two stages: firstly, in a training stage, self-adaptive optimization training is carried out on a weighting coefficient vector until the weighting coefficient vector converges, and no output exists in a symbol decision DEC unit in the training stage; then, in the adaptive detection stage, the adaptive detection of the data symbols is carried out through the converged weighting coefficient vector, and the DEC unit outputs decision symbols.
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