CN106452652B - A kind of MPI suppression method based on chaos wireless communication system - Google Patents
A kind of MPI suppression method based on chaos wireless communication system Download PDFInfo
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Abstract
The invention discloses a kind of MPI suppression method based on chaos wireless communication system, step includes：1st, chaotically coding is carried out to source information bit sequence；2nd, receiver antenna receives high-frequency signal, and solution is transferred to base band；3rd, chaos matched filtering is carried out to baseband receiving signals；4th, the Hui-Hui calendar of chaos matched filtering output signal is calculated；5th, chaos matched filtering output signal Hui-Hui calendar is grouped again；6th, Hui-Hui calendar adjudicates line after calculating packet；7th, decision threshold is detected after calculating packet.The method of the present invention utilizes chaos self-characteristic, and multiple branches of chaotic maps under multipath channel are grouped again, and multi-path jamming is will be not present between Zu Nei branches, so as to achieve the purpose that multi-path jamming eliminates；Meanwhile the inventive method only needs the decision threshold in each sampling instant, calculating group, the computation complexity of multi-path jamming elimination is reduced.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a method for regrouping chaotic signal regression mapping branches under a multipath channel to reduce multipath interference in a wireless communication system, in particular to a multipath interference suppression method based on a chaotic wireless communication system.
Background
With the continuous development of informatization, wireless communication technology has been widely applied to various aspects of social life. Wireless channels are a more complex communication environment than wired communication. The channel characteristics of multipath propagation, limited bandwidth, doppler shift, etc. bring many difficulties to the design of wireless communication systems, wherein intersymbol interference caused by multipath propagation is a main cause of high error rate of wireless systems. In order to improve wireless communication performance, the existing systems often employ channel equalization techniques to eliminate inter-symbol interference. However, the low-complexity linear equalization algorithm is susceptible to noise and channel estimation errors, the performance of the nonlinear equalization algorithm is improved, and the calculation cost is high.
The chaotic signal has the advantages of high bandwidth, noise-like, easy hardware generation, etc., and has been widely applied to communication systems in recent years. For coherent reception communication systems under a white gaussian noise channel, a chaotic signal has been proven to be the best communication waveform with a linear matched filter that can be implemented in simple hardware to maximize the received signal-to-noise ratio. However, in a wireless channel, multipath propagation is still a main factor affecting the performance of the chaotic wireless communication system.
Disclosure of Invention
The invention aims to provide a multi-path interference suppression method based on a chaotic wireless communication system, which solves the problem that the performance of the chaotic wireless communication system is influenced by multi-path interference in the prior art.
The technical scheme adopted by the invention is that a multi-path interference suppression method based on a chaotic wireless communication system is implemented specifically according to the following steps:
step 1, carrying out chaotic coding on a source information bit sequence;
the encoding process is that the code is obtained by the following steps,
1.1 A unipolar binary bit sequence b) to the transmitting end n ∈[0,1]Converted into a bipolar binary sequence s n ∈[-1,1]；
1.2 For bipolar binary sequences s n Carrying out chaotic coding to generate a coding time sequence x (t);
step 2, a receiver antenna receives a high-frequency signal and demodulates the high-frequency signal to a baseband;
step 3, performing chaotic matched filtering on the baseband received signal;
step 4, calculating regression mapping of the chaos matched filtering output signal;
step 5, regrouping regression mapping of chaos matched filtering output signals;
step 6, calculating a regression mapping judgment line after grouping;
and 7, calculating a detection judgment threshold after grouping.
The method of the invention has the advantages that the intersymbol interference caused by multipath transmission can be reduced and the error rate can be reduced in the chaotic wireless communication system, and the method has the following specific advantages:
1) By using the piecewise linear chaotic system for information encoding, the receiving end can perform information decoding according to the regression mapping of the received signals.
2) By utilizing the one-to-one correspondence relationship between the regression mapping branches of the received signals in the multipath channel and the multipath time delay symbol combination, the multipath regression mapping branches can be grouped according to the decoded symbols, and the branches in each group do not have multipath interference, thereby achieving the purpose of eliminating the multipath interference.
3) Compared with the traditional multipath interference elimination method based on equalization, the method only needs to calculate a detection threshold at each sampling moment, does not need complex equalizer design, and has simple algorithm and easy realization.
Drawings
FIG. 1 is a schematic block diagram of a chaotic wireless communication system used in the method of the present invention;
FIG. 2 is a schematic diagram of a wireless multipath channel of the method of the present invention;
FIG. 3 is a time series waveform of a source sequence s (t) and a code sequence x (t) in the method of the present invention;
FIG. 4 is a regression map of a transmitted signal in the method of the present invention;
FIG. 5 is a regression map of the chaotic matched filtered output signal y (t) for a noise-free two-path channel;
FIG. 6 is a regression map of the chaotic matched filtered output signal y (t) for a noisy two-path channel;
FIG. 7 is a block diagram of regression mapping of chaos matched filter output signal y (t) obtained by the method of the present invention under a noisy two-path channel;
FIG. 8 is a plot of the variation of the bit error rate under the conditions of no grouping, MMSE equalization, the grouping optimal decision threshold of the present invention, the grouping suboptimal decision threshold of the present invention, and different SNR for the lower bound of the bit error rate under the condition of single path in the case of two-path ideal channel;
FIG. 9 is a graph showing the variation of the bit error rate under different SNR conditions for three methods of no-grouping, MMSE equalization, and grouping sub-decision threshold of the present invention under two-path estimation channel;
fig. 10 is a bit error rate variation curve of three methods of no-grouping, MMSE equalization, and grouping sub-decision threshold of the present invention under different snr conditions under a three-path estimation channel.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Fig. 1 is a schematic block diagram of an embodiment of a chaotic wireless communication system used in the method of the present invention. The chaotic wireless communication system comprises a chaotic transmitter, a wireless multi-path channel and a chaotic receiver. The chaos transmitter carries out chaos coding on the digital binary information, then modulates the coded signal to a high-frequency carrier wave through a radio frequency module, and transmits the coded signal by using an antenna. The transmitted signal is received by the chaos receiver antenna through a wireless multi-path channel, a high-frequency received signal is converted into a baseband through a radio frequency module, and the original transmitted information is restored through a chaos mixing matched filter and symbol detection after the baseband received signal, so that source information is obtained.
As shown in fig. 2, a wireless channel is illustrated that includes L multipaths, each multipath including a plurality of indistinguishable sub-paths. Suppose that the fading coefficient and the time delay of the channel of the first path are respectively expressed as alpha l And τ l Shown by a plurality of actual measurements and statistical results, alpha l Can be expressed as a function of τ l Models of exponential fading, i.e.Wherein the parameter gamma is a channel fading coefficient; supposing that the channel time delay satisfies 0 ≦ tau 0 <τ 1 <…<τ L-1 If the transmitted signal is a single impulse response delta (t), the channel impulse response is expressed asWhere t is a time variable, as shown in fig. 2, there are response values only at L multipath delay points.
Based on the wireless multipath channel condition, the multipath interference suppression method based on the chaotic wireless communication system is implemented according to the following steps:
step 1, chaos coding is carried out on source information bit sequence
The encoding process is as follows:
1.1 A unipolar binary ratio of a transmitting endCharacteristic sequence b n ∈[0,1]N is the serial number of binary data, and is converted into a bipolar binary sequence s n ∈[-1,1]；
1.2 For bipolar binary sequences s n Chaotic coding is carried out to generate a coded time sequence x (t),
the chaotic coding system adopted by chaotic coding is a hybrid dynamic model comprising a continuous differential equation and a discrete state switching condition, wherein the hybrid dynamic model comprises the continuous differential equation and the discrete state switching condition, and the chaotic coding system is shown as the following formula (1):
wherein s (t) is a bipolar binary sequence s n The time sequence is a corresponding time sequence, beta is a chaotic system parameter, omega =2 pi f, f is a base frequency, and a bipolar binary sequence s of information to be transmitted is realized by a perturbation method (Hai-Peng Ren, chao Bai, jian Liu, murilo Baitista, celso Grebogi, experimental variation of Wireless Communication with Chaos,2016,26 (8), 083117) n Encoding into a time series x (t);
solving the differential equation (1) to obtainWherein the symbolsExpressing the rounding up of variable t, the expression of the basis function p (t) is as follows:
the waveform at parameter β =0.65,f =2 is shown in fig. 3, fig. 3 being the time series x (t) and s (t), respectively;
sample at time t = n/f, let x n = x (n/f), regression map x obtained for time series x (t) n+1 ＝e β x n -(e β -1)s n As shown in FIG. 4, the point on the left branch in FIG. 4 corresponds to the source information s n = -1, point-to-point source information s on right branch n ＝1。
The coded signals are modulated onto a high-frequency carrier wave through a radio frequency module and then transmitted out through a transmitter antenna.
Step 2, receiving the high-frequency signal by the antenna of the chaotic receiver and demodulating the high-frequency signal to a baseband
After receiving the high-frequency signal, the antenna of the chaotic receiver demodulates the high-frequency signal to a baseband through another radio frequency module, and a baseband receiving signal r (t) mixed with additive white Gaussian noise is expressed as follows:
wherein, w (t) is an additive white Gaussian noise time sequence, and L is the multipath number of the channel;
step 3, chaos matched filtering is carried out on the baseband received signal
And (3) processing the baseband receiving signal r (t) obtained in the step (2) through a chaotic matched filter g (t) = p (-t), and obtaining an expression of a chaotic matched filtering output signal y (t) of the chaotic matched filtering output signal as follows:
similarly, y (t) is sampled at time t = n/f, and the sampled output y n The expression of (a) is as follows:
in equation (5), the first term on the right of the equal sign is the expected received signal, the second term is the filtered intersymbol interference, and the third term W is the filtered noise;is a variable related to the multipath parameter, i is the multipath sequence number, and i is the variable of the symbol loop.
Step 4, calculating regression mapping of chaos matched filtering output signal
And (3) performing regression mapping expression on the chaos matched filtering output signal y (t) obtained in the step 3 as follows:
therein containing 2 L A plurality of branches, L is the number of channel multipaths;
as shown in fig. 5, the regression map of the chaos matched filter output signal y (t) in the two-path channel (L = 2) is shown, the result in fig. 5 does not include noise, β =0.65, and the time delays of the two paths are τ respectively 0 =0 and τ 1 =1, fading coefficient γ =0.9. Using decision line y n+1 ＝e β y n Symbol detection is performed, and the thin dotted line in fig. 5 is a decision line, and when the sampling point is located on the regression mapping branch on the left of the decision line (thick solid line), it corresponds to s n = -1; when the sample point is located on the regression-mapped branch to the left of the decision line (bold dashed line), it corresponds to s n ＝1。
In the presence of noise, the regression mapping point fluctuates, as shown by the black circle in fig. 6, due to the influence of noise, the point originally located on the left side (right side) of the decision line may fluctuate to the right side (left side) of the decision line, as shown by the solid black circle point in fig. 6, which makes the symbol detection erroneous, resulting in an increase in the system error rate. The horizontal distance between the different symbol branches of the regression mapping is defined as the decision distance, which in fig. 6 is equal to 0.5673.
Step 5, regrouping the regression mapping of the chaos matched filtering output signal
The grouping principle is as follows: s n+i For transmitted sequences, s will be the same n+i (i&And lt 0) grouping the branches corresponding to the values into a group.
As shown in FIG. 7, the two paths of time delay are respectively tau 0 =0 and τ 1 And =1, and when the fading coefficient γ =0.9, the grouped chaotic matched filtering output signal y (t) is regressed to a map, and the four branches are respectively marked as 1, 2, 3 and 4. Branch 1 corresponds to s n ,s n-1 } = { -1, -1}, branch 2 corresponds to s n ,s n-1 } = { -1,1}, branch 3 corresponds to { s { n ,s n-1 } = {1, -1}, branch 4 corresponds to { s } n ,s n-1 } = {1,1}. It can be seen that branch 1 and branch 3 correspond to the case when s n-1 When =1, s n Two branches, -1 and 1 respectively, and branch 2 and branch 4 correspond to the case s n-1 When =1, s n Two branches of-1 and 1, respectively. Thus, in FIG. 7, branch 1 and branch 3 are grouped as group one, corresponding to s n-1 = -1; branch 2 and branch 4 are grouped into group two, corresponding to s n-1 And =1. The decision distance is now the horizontal distance between the two branches in the group, which in fig. 7 is 0.9559.
Step 6, calculating a regression mapping judgment line after grouping
And (5) mapping each group of regression obtained in the step (5), taking the central lines of the two branches in the group as a group of judgment lines, wherein the expression of the judgment lines is as follows:
for the two-path situation in step 5, the decision line 1 in fig. 7 is a decision line grouped into one, and the decision line 2 is a decision line grouped into two; within each group, the point on the left branch of the decision line corresponds to s n = -1, point on the right branch of decision line corresponds to s n ＝1。
Step 7, calculating the grouped decision threshold
And (6) calculating the judgment threshold of the sampling time n according to the judgment line expression obtained in the step (6). When all transmitted sequences s are known n+i When the prior information is obtained, the optimal judgment threshold expression of the method is obtained asWhen s is n+i (i>, 0) unknown, the method of the invention is obtained after transformationThe sub-optimal decision threshold expression of the method is
The multi-path interference suppression method described in the above process of the invention utilizes the self characteristics of the chaotic sequence to regroup the multiple branches chaotically mapped under the multi-path channel, and the multi-path interference does not exist between the branches in the group, thereby achieving the purpose of multi-path interference elimination; meanwhile, the method only needs to recalculate the decision threshold in the group at each sampling moment, thereby reducing the calculation complexity of the multipath interference elimination.
The method of the invention is verified
Carrying out comparison simulation in a wireless multipath channel environment, wherein simulation parameters are set as follows: the source information bits having a unit energy E b =1, the channel fading coefficient γ =0.6, the chaotic system parameter β =0.65, and the state switching frequency f =1, and the simulation result is obtained by averaging 50000 times of algorithm implementation.
Experiment one
Under two-path channel, τ 0 ＝0，τ 1 =1, the exact channel information is known to the receiving end. The performance of the algorithms is compared under the condition of different signal-to-noise ratios, and the algorithms are respectively as follows: no packet, traditional multipath interference cancellation (MMSE equalization), a packet optimal decision threshold, a packet suboptimal decision threshold, and a lower bound on bit error rate under single path. The bit error rates of the lower bound of the bit error rate under the conditions of the grouped optimal decision threshold and the single path are obtained by theoretical calculation, the bit error rates of the other three algorithms are obtained by simulation, and the result is shown in fig. 8.
In fig. 8, the non-packet algorithm cannot eliminate multipath interference, so the performance is worst, the conventional MMSE equalization can eliminate part of intersymbol interference, the performance is improved to some extent, the performance of the packet suboptimum decision threshold of the present invention is obviously improved compared with the two algorithms, when the SNR is greater than 11dB, the simulation bit error rate is 0, and the packet optimal decision threshold performance of the present invention is very close to the single-path optimal bit error performance. Compared with the traditional method, the method can more effectively eliminate the multipath interference and reduce the error rate of the system.
Both MMSE equalization and the algorithm of the present invention require known channel parameters to be performed. In practice, the channel parameters are unknown and must be obtained by channel estimation. And there may be errors in estimating the channel due to channel time-variability or imperfections in the channel estimation algorithm.
The method comprises the following steps of estimating channel parameters by adopting a compressed sensing algorithm under unknown channel parameters, comparing the error rate performances of different algorithms by utilizing simulation, and analyzing the influence of channel errors on various algorithms.
Experiment two
Under two-path channel, τ 0 ＝0，τ 1 =1, the receiving end knows the channel information, and compares the performances of three algorithms under different signal-to-noise ratios, where the three algorithms are: no packet, MMSE equalization, packet suboptimal decision threshold, and the comparison result is shown in fig. 9. The non-packet algorithm does not need channel parameters, so the performance of the algorithm is the same as that of the first experiment, both MMSE equalization and the algorithm of the invention can be influenced by channel errors, but from the simulation result, the influence is about 0.5 dB.
Experiment three
Under three-path channel, τ 0 ＝0，τ 1 ＝1，τ 2 =2, the receiving end knows the channel information, and compares the performances of three algorithms under different signal-to-noise ratios, and the three algorithms are respectively: no packet, MMSE equalization, packet suboptimal decision threshold, the comparison result is shown in fig. 10. When the number of the multipath increases, the performances of the three algorithms are all deteriorated, the influence of the non-grouping algorithm is maximum, the MMSE equalization is second, and the influence of the algorithm is minimum.
Comparing the above experimental results, the method of the present invention has better multi-path elimination capability and stronger robustness compared with the traditional mode.
Claims (2)
1. A multi-path interference suppression method based on a chaotic wireless communication system is characterized by comprising the following steps:
step 1, performing chaotic coding on a source information bit sequence;
the encoding process is that the code is obtained by the following steps,
1.1 A unipolar binary bit sequence b) to the transmitting end n ∈[0,1]Converted into a bipolar binary sequence s n ∈[-1,1]；
1.2 For bipolar binary sequences s n Carrying out chaotic coding to generate a coding time sequence x (t), wherein a chaotic coding system adopted by the chaotic coding is a mixed dynamics model containing a continuous differential equation and a discrete state switching condition, and the regression mapping of the chaotic coding meets Bernoulli shift mapping, wherein the chaotic coding system contains a continuous differential equation and a discrete state switching condition, and the chaotic coding system is shown in the following formula (1):
wherein, the first and the second end of the pipe are connected with each other,representing the second order differential of the variable x,representing the first differential of a variable x, sgn (·) is a sign function, t is a time variable, s (t) is a bipolar binary sequence s n Beta is a chaotic system parameter, omega =2 pi f, f is a base frequency, and a bipolar binary sequence s of information to be transmitted is realized by a perturbation method n Encoding into a time series x (t);
solving the differential equation (1) to obtainWherein the symbolsExpressing the rounding up of variable t, the expression of the basis function p (t) is as follows:
step 2, receiving the high-frequency signal by a receiver antenna, and demodulating the high-frequency signal to a baseband;
step 3, chaotic matched filtering is carried out on the baseband receiving signal,
the chaotic matched filter corresponds to the chaotic coding system adopted in the step 1.2), if the basis function of the chaotic coding system is p (t), the time domain response of the chaotic matched filter is g (t) = p (-t), and the expression of the output signal y (t) of the chaotic matched filter is obtained as follows:
sampling y (t) at time t = n/f, sampling output y n The expression of (a) is as follows:
wherein the content of the first and second substances,is a variable related to multipath parameters, i is the multipath number, i is the variable of symbol circulation, L is the multipath number of the channel, W is the noise after matched filtering, alpha l Fading of the channel of the first path;
step 4, calculating regression mapping of the chaos matched filtering output signal,
the regression mapping expression of the chaos matched filtering output signal is as follows:
wherein, beta is a parameter of the chaotic system, f is a base frequency, y (t) is a chaotic matched filtering output signal, and the chaotic matched filtering output signal comprises 2 L Each branch, n is the serial number of binary data, L is the multipath number of the channel, and L is the multipath serial number;
step 5, regrouping the regression mapping of the chaos matched filtering output signals,
the grouping principle is as follows:
s n+i for all transmitted sequences, s will be the same n+i The branches corresponding to the values are grouped, i is less than 0;
step 6, calculating a regression mapping judgment line after grouping,
and (5) mapping each group of regression obtained in the step (5), taking the central lines of the two branches in the group as a group of judgment lines, wherein the expression of the judgment lines is as follows:
and 7, calculating a detection judgment threshold after grouping.
2. The method for suppressing multipath interference based on chaotic wireless communication system as claimed in claim 1, wherein in step 7, the decision threshold of the sampling time n is calculated according to the decision line expression obtained in step 6,
when the transmitted sequence s is known n+i When the prior information is obtained, the optimal judgment threshold expression is obtained as
When i > 0, future information s n+i Unknown, the expression of suboptimal decision threshold obtained by calculation is
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