CN101572574A - Smart antenna self-adapting interference suppression method based on least square-lowest mean square - Google Patents
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Abstract
The invention relates to a smart antenna self-adapting interference suppression method based on least square-lowest mean square, which is an interference suppression algorithm based on training sequences; and by combining the least square algorithm with the lowest mean square algorithm, the method increases the velocity of convergence of the least mean square algorithm. The method includes the following steps: radio-frequency signals received by array antenna are converted into IF signals by a down converter; the IF signals are processed by the operations of analogue-to-digital conversion and digital down-conversion to obtain zero IF digital signals; the zero IF digital signals obtained in the step 2; and the local training sequence are utilized for carrying out corresponding processing to obtain local reference signals by computation delay; the low snapshot least square algorithm is utilized for calculating the initial weight vector of an aerial array; the weight vector calculated in the step 4 is used as the initial weight vector of the least mean square algorithm, and the least mean square algorithm is utilized for updating the aerial array weight vector; and the array weight vector calculated in the step 5 is adopted for the interference suppression of user data. The method achieves the purposes of increasing the availability of frequency spectrum of the system and reducing the complexity of the system.
Description
Technical Field
The invention relates to an intelligent antenna interference suppression algorithm. In particular to a least square-least mean square-based intelligent antenna self-adaptive interference suppression method based on training sequences.
Background
In the early 90 s of the last century, array signal processing technology was introduced into mobile communication, and a new research hotspot, namely a smart antenna, is rapidly formed. Smart antennas are defined as antenna arrays with direction finding and beam forming capabilities, generally classified into two main categories: multi-beam intelligent antenna and self-adaptive array intelligent antenna, which are called multi-beam antenna and self-adaptive antenna for short. The direction of each wave beam of the multi-beam antenna is fixed, the direction of arrival of signals is determined by a detection technology, and then the corresponding wave beam is selected by adjusting the weighting coefficient of each array element, so that the structure is simple. However, as the user moves, when the center of the beam direction is no longer fixed, the multi-beam antenna cannot receive a good reception effect. The adaptive antenna recognizes the direction of arrival of a user through an array signal processing technology, and then forms a main beam in the direction, and the main beam is continuously adjusted according to the change of the direction of arrival of a signal, so as to keep the direction of the signal required to be aligned, and simultaneously forms a null in the interference direction, thereby being widely applied to mobile communication systems.
Currently, the adaptive interference suppression technology for smart antennas can be roughly divided into three categories: (1) a typical beamforming technique based on known direction vectors of useful signals is Standard Capon Beamforming (SCB). (2) Based on known beamforming techniques for the reference signal, the method finds the appropriate weighting vector by minimizing the difference between the array output and the reference signal. In general, reference signals are difficult to obtain. In a communication system, to obtain this reference signal, a training sequence known to both the transmitter and the receiver must be periodically transmitted. Transmitting the training sequence occupies valuable frequency resources of the communication system. (3) Blind adaptive beamforming techniques do not require the transmission of training signals, nor the prior knowledge of the array direction vectors and the spatial autocorrelation matrices of interference and noise, but rather utilize the statistical properties of the signals or the deterministic properties of the signals themselves to perform beamforming.
The Least Mean Square (LMS) adaptive interference method based on the training sequence is widely used in communication systems, but because the LMS algorithm is sensitive to an initial value, the convergence rate is slow, and the convergence performance is related to the interference environment, a long training sequence is needed to achieve an ideal interference suppression effect, and precious frequency resources are seriously wasted. Although the Least square (LS for short) optimal beamforming has high interference suppression performance and the interference suppression performance is unrelated to the interference environment, the algorithm has a large computation amount and is not beneficial to the realization of an actual system.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent antenna self-adaptive interference suppression method based on Least square-Least Mean square (LS-LMS for short), which calculates the weighting vector of an array through an LS algorithm with small fast beat number, takes the weighting vector as the initial weighting vector of the LMS algorithm, effectively improves the convergence rate of the LMS algorithm, reduces the length of a training sequence, and thus achieves the purposes of improving the spectrum utilization rate of a system and reducing the complexity of the system.
The technical scheme adopted by the invention is as follows: a least square-least mean square-based intelligent antenna self-adaptive interference suppression method is an interference suppression algorithm based on a training sequence, improves the convergence rate of the least mean square algorithm by combining the least square algorithm with the least mean square algorithm, and comprises the following steps:
(1) converting radio frequency signals received by the array antenna into intermediate frequency signals through a down converter;
(2) carrying out A/D conversion and digital down-conversion on the intermediate frequency signal to obtain a zero intermediate frequency digital signal;
(3) performing correlation processing on the zero intermediate frequency digital signal obtained in the step (2) and a local training sequence to calculate delay to obtain a local reference signal;
(4) calculating an initial weighting vector of the antenna array by using a small snapshot least square algorithm;
(5) taking the weighted vector calculated in the step (4) as an initial weighted vector of a least mean square algorithm, and updating the antenna array weighted vector by using the least mean square algorithm;
(6) and (5) carrying out interference suppression on the user data by adopting the array weighting vector calculated in the step (5).
And (3) performing correlation processing on the intermediate frequency digital signal and a local training sequence to calculate delay to obtain a local reference signal, namely gradually delaying the local training sequence, performing correlation operation on each delayed training sequence and array output data in a training sequence period, comparing the output of each correlator, and selecting the maximum output from the correlation results, wherein the training sequence on a branch corresponding to the maximum output is the local reference signal.
And (4) calculating the initial weighting vector of the array by using a least square algorithm, wherein the minimum sum of squared errors is used as a cost function, and the weighting vector is calculated by using a small snapshot number.
And (5) updating the antenna array weighting vector by using the least mean square algorithm, namely updating the weight of the least mean square algorithm by using the mean square error as a cost function and using the random gradient algorithm as an optimization method, wherein the initial weighting vector of the least mean square algorithm is provided by the weighting vector calculated in the step (4).
The invention discloses an intelligent antenna self-adaptive interference suppression method based on least square-least mean square, which is provided for solving the problems that the calculation amount of an array weighting vector estimated by an LS algorithm is large and the convergence speed of an LMS algorithm is low in a communication system with a longer training sequence. The array weighting vector calculated by the LS algorithm is used as the initial weighting vector of the LMS algorithm, and when the weighting vector is calculated by the LS algorithm, the small snapshot number is used for calculation, so that the calculation amount of the LS algorithm can be reduced. The weighting vector calculated by the LS algorithm with small snapshot number is used as the initial weighting vector of the LMS algorithm, thereby greatly accelerating the convergence rate of the LMS algorithm, reducing the sensitivity of the LMS algorithm to the interference environment, and reducing the length of the training sequence, thereby achieving the purposes of improving the frequency spectrum utilization rate of the system and reducing the complexity of the system.
Drawings
Fig. 1 is a flow chart of a least squares-least mean squares based smart antenna adaptive interference suppression method;
FIG. 2 is a diagram of a training sequence based weighting vector update array structure;
fig. 3a is an array pattern of the LMS algorithm at fast beat 180;
fig. 3b is the array pattern of the LS-LMS algorithm when the fast beat number is 180;
fig. 3c is the array pattern of the LMS algorithm at fast beat 100;
fig. 3d is the array pattern of the LS-LMS algorithm when the fast beat number is 100;
fig. 3e is the array pattern of the LMS algorithm at fast beat 30;
fig. 3f is the array pattern of the LS-LMS algorithm at fast beat 30;
figure 4a shows the LMS algorithm with an initial weight vector of 0,0，0，0]Twhen the dry-to-noise Ratio (JNR) is 10dB, the convergence curve graphs of the signal-to-interference-noise Ratio are output by the LMS algorithm and the LS-LMS algorithm;
FIG. 4b shows the LMS algorithm with initial weighting vectors of [0, 0, 0, 0-]TThe convergence curve graphs of the signal-to-interference-and-noise ratios output by the LMS algorithm and the LS-LMS algorithm when JNR is 15 dB;
FIG. 4c shows the LMS algorithm with initial weighting vectors of [0, 0, 0, 0-]TThe convergence curve graphs of the signal-to-interference-and-noise ratios output by the LMS algorithm and the LS-LMS algorithm when JNR is 20 dB;
FIG. 5a shows the initial weight vectors of the LMS algorithm as [0.5+0.5i, 0.5+0.5i, 0.5+0.5i, 0.5+0.5i]TThe convergence curve graphs of the signal-to-interference-and-noise ratios are output by an LMS algorithm and an LS-LMS algorithm when JNR is 10 dB;
FIG. 5b shows the LMS algorithm initial weight vectors as [0.5+0.5i, 0.5+0.5i, 0.5+0.5i, 0.5+0.5i]TThe convergence curve graphs of the signal-to-interference-and-noise ratios output by the LMS algorithm and the LS-LMS algorithm when JNR is 15 dB;
FIG. 5c shows the LMS algorithm initial weight vectors as [0.5+0.5i, 0.5+0.5i, 0.5+0.5i, 0.5+0.5i]TThe convergence curve graphs of the signal-to-interference-and-noise ratios output by the LMS algorithm and the LS-LMS algorithm when JNR is 20 dB;
FIG. 6a is a comparison graph of the LS algorithm, LMS algorithm, and LS-LMS algorithm;
FIG. 6b is a comparison graph of LS algorithm, LMS algorithm, LS-LMS algorithm complex multiplication addition times with snapshot number variation;
FIG. 6c is a comparison graph of LS algorithm, LMS algorithm, LS-LMS algorithm complex multiplication and addition times with array element number.
Detailed Description
The least square-least mean square-based intelligent antenna adaptive interference suppression method is described in detail below with reference to the accompanying drawings.
The invention relates to an intelligent antenna adaptive interference suppression method based on Least square-Least Mean square (LS-LMS for short), which is an adaptive interference suppression method based on a training sequence. As shown in fig. 1, the method comprises the following steps:
the first step is as follows: converting radio frequency signals received by the array antenna into intermediate frequency signals through a down converter;
the second step is that: carrying out A/D conversion and digital down-conversion on the intermediate frequency signal to obtain a zero intermediate frequency digital signal;
when there is interference, the array antenna received signal can be expressed as:
where x (L) denotes the L-th sample snapshot (L ═ 0, 1, …, L-1), L denotes the number of samples, sd(l) Representing the useful signal, sk(l) (K-1, …, K) represents the K-th interference signal, K represents the number of interference sources,
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the method comprises the steps of representing a guide vector of kth interference, representing signal wavelength by lambda, representing array receiving noise vector by e (l), representing a wave arrival direction of a signal by theta, representing an array element number by d, representing an array element interval by d, adopting a uniform linear array in the embodiment, representing the number of the array elements by 4, representing the interval by 1/2 wavelengths, representing a receiving signal by a useful signal and an interference signal, and representing the wave arrival directions by 30 degrees and 0 degrees respectively.
The third step: and performing correlation processing on the zero intermediate frequency digital signal obtained in the second step and a local training sequence to calculate delay to obtain a local reference signal, wherein in the self-adaptive algorithm based on the training sequence, the important step is to realize the synchronization of the training sequence. In this embodiment, the local training sequence is gradually delayed, each delayed training sequence is correlated with the array output data in a training sequence period, the outputs of the correlators are compared, the maximum output is selected, and the training sequence on the branch corresponding to the maximum output is the local reference signal.
The fourth step: calculating an initial weighting vector of the antenna array by using a small fast beat LS algorithm, wherein the weighting vector is calculated by using a small fast beat N (N < L) by taking the minimum sum of squared errors as a cost function;
the adaptive array structure based on the training sequence is shown in fig. 2.
Assuming N snapshot data vectors x (N), N is 0, 1, …, N-1, the cost function of the LS algorithm is:
wherein d (n) is the desired signal at time n,
the gradient is as follows:
let it be zero to obtain the optimal weight vector of the least squares method:
w＝(XXH)-1XdH (4)
wherein: x ═ X (0), X (1), …, X (N-1) ], d ═ d (0), d (1), …, d (N-1) ],
in this embodiment, the sampling fast beat number N is 8.
The fifth step: taking the weighted vector calculated in the fourth step as an initial weighted vector of an LMS algorithm, updating the weighted vector of the antenna array by using the LMS algorithm, updating the weight of the LMS algorithm by using a mean square error as a cost function and using a random gradient algorithm as an optimization method, and providing the initial weighted vector of the LMS algorithm by using the weighted vector calculated in the fourth step;
the LMS algorithm criterion is to minimize the mean square of the estimation error, i.e. the cost function is:
J(w)＝E{|e(l)|2} (5)
wherein E { } represents a statistical average, E (l) is an error, and E (l) ═ wHx(l)-d(l)。
Then:
J(w)＝E{e(l)e*(l)}＝E{|d(l)|2}-2Re[wHrxd]+wHRxxw (6)
wherein Re represents a real part, Rxx＝E{x(l)xH(l) Is the autocorrelation matrix of the input vector, rxd＝E{xl))d*(l) Is the cross-correlation matrix of the input vector x (l) with the desired signal d (l).
Taking the derivative of equation (6) and making it zero yields:
considering the stochastic gradient algorithm, the general formula for weight vector update is:
wherein,
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μ is a step factor, which can be obtained from equation (6):
in the LMS algorithm, the instantaneous value is used for replacing mathematical expectation, and a weight value updating formula is obtained:
and a sixth step: and performing interference suppression on the user data by adopting the array weighting vector calculated in the fifth step.
FIG. 3 shows the array patterns obtained by performing 100 Mont Carlo (Mont Carlo) experiments on LMS and LS-LMS algorithms under the conditions of Signal-to-noise Ratio (SNR) of 10dB and SIR of-20 dB, wherein the initial values of the LMS algorithms in the simulation experiment are all [0.5+0.5i, 0.5+0.5i, 0.5+0.5i, 0.5+0.5 i%]TThe LS-LMS calculates initial weighting vectors by using 8 snapshots, the step lengths of both the LMS and the LS-LMS are 0.00005, fig. 3a and 3b are array patterns of the LMS and the LM-LMS algorithms when the snapshot number is 180, fig. 3c and 3d are array patterns of the LMS and the LM-LMS algorithms when the snapshot number is 100, and fig. 3e and 3f are array patterns of the LMS and the LM-LMS algorithms when the snapshot number is 30. As can be seen from fig. 3, the LMS algorithm has been unable to form a null in the interference direction as the number of fast beats decreases, but the new method still aligns the main beam to the desired signal and the null to the interfering signal.
Fig. 4 shows convergence curves of the output Signal to interference plus Noise Ratio (SINR) of the LMS algorithm and the LS-LMS algorithm when the SNR is 10dB, where JNR in fig. 4a, 4b, and 4c is 10dB, 15dB, and 20dB, respectively. The initial value of the LMS algorithm is taken as [0, 0, 0, 0%]TIn LS-LMS, 8 snapshots are used to calculate initial weighting vectors, the step lengths of both algorithms are 0.00005, and 100 Mont Carlo simulation experiments are performed to obtain the transformation curve of the average SINR output by the beam former along with the sampling number of the snapshots, which is shown in FIG. 4. It can be seen that both algorithms converge to the theoretical value (SCB method corresponding to infinite snapshot number) with the increase of snapshot number) However, the LMS algorithm is significantly slower than the LS-LMS, and the convergence rate of the LMS algorithm decreases with the increase of JNR, but has little effect on the LS-LMS algorithm. The simulation experiment contents and conditions in FIG. 5 and FIG. 4 are the same, except that the initial values of the LMS algorithm are [0.5+0.5i, 0.5+0.5i, 0.5+0.5i, 0.5+0.5i [ ]]T. Comparing fig. 4 and fig. 5, it can be seen that the LMS algorithm is sensitive to the initial weighting vector, and different initial weighting vectors have a large influence on the convergence rate of the LMS algorithm, but have a small influence on the convergence rate of the LS-LMS algorithm.
Fig. 6a shows a comparison graph of the computation amounts of the LS algorithm, the LMS algorithm, and the LS-LMS algorithm, where M is the number of array elements, L is the number of fast beats, N is the number of fast beats in the LS-LMS algorithm to compute the initial weighting vector, and the complexity of matrix inversion operation is approximated to the computation amount of matrix inversion in the graph. Fig. 6b shows a comparison graph of the complex multiplication and addition times of the LS algorithm, the LMS algorithm and the LS-LMS algorithm with the variation of the snapshot number when M is 4 and N is 8. Fig. 6c shows comparison graphs of the complex multiplication and addition times of the LS algorithm, the LMS algorithm and the LS-LMS algorithm with the array element number when L is 120 and N is 8. The LS algorithm operation amount is increased sharply with the increase of the fast beat number and the array element number through comparison of FIG. 6, but the LS-LMS algorithm operation amount is increased relatively slightly.
Claims (4)
1. A least square-least mean square-based intelligent antenna self-adaptive interference suppression method is characterized in that the method is an interference suppression algorithm based on a training sequence, the convergence speed of the least mean square algorithm is improved by combining the least square algorithm with the least mean square algorithm, and the method comprises the following steps:
(1) converting radio frequency signals received by the array antenna into intermediate frequency signals through a down converter;
(2) carrying out A/D conversion and digital down-conversion on the intermediate frequency signal to obtain a zero intermediate frequency digital signal;
(3) performing correlation processing on the zero intermediate frequency digital signal obtained in the step (2) and a local training sequence to calculate delay to obtain a local reference signal;
(4) calculating an initial weighting vector of the antenna array by using a small snapshot least square algorithm;
(5) taking the weighted vector calculated in the step (4) as an initial weighted vector of a least mean square algorithm, and updating the antenna array weighted vector by using the least mean square algorithm;
(6) and (5) carrying out interference suppression on the user data by adopting the array weighting vector calculated in the step (5).
2. The method for suppressing adaptive interference of an intelligent antenna based on least square-least mean square according to claim 1, wherein the step (3) of correlating the intermediate frequency digital signal with the local training sequence to calculate the delay to obtain the local reference signal is to delay the local training sequence step by step, perform correlation operation on each delayed training sequence and the array output data in a training sequence period, compare the outputs of each correlator, and select the maximum output, wherein the training sequence on the branch corresponding to the maximum output is the local reference signal.
3. A method for suppressing interference adaptive to smart antenna based on least square-least mean square according to claim 1, wherein the step (4) of calculating the initial weighting vector of the array by using least square algorithm is to calculate the weighting vector by using small snapshot number by using the least square sum of errors as the cost function.
4. A method for suppressing interference of an intelligent antenna adaptive based on least square-least mean square according to claim 1, wherein the updating of the antenna array weighting vector by the least mean square algorithm in step (5) is to update the weight of the least mean square algorithm by using the mean square error as a cost function and using the random gradient algorithm as an optimization method, and the initial weighting vector of the least mean square algorithm is provided by the weighting vector calculated in step (4).
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| CN109831236A (en) * | 2018-11-13 | 2019-05-31 | 电子科技大学 | A kind of beam selection method based on Monte Carlo tree search auxiliary |
| CN112003645A (en) * | 2020-10-29 | 2020-11-27 | 南昌大学 | Synchronous reference signal extraction method and device |
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| JP4299323B2 (en) * | 2006-08-04 | 2009-07-22 | 株式会社日立国際電気 | Communications system |
| CN101013916B (en) * | 2007-02-05 | 2010-05-19 | 华南理工大学 | Method and apparatus for implementing reception of diversity with intelligent antenna |
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| CN103095357A (en) * | 2013-01-18 | 2013-05-08 | 成都九华圆通科技发展有限公司 | Intelligence antenna self-adaptive beam forming method |
| CN104679976A (en) * | 2014-10-31 | 2015-06-03 | 哈尔滨工业大学深圳研究生院 | Contractive linear and contractive generalized linear complex-valued least squares algorithm for signal processing |
| CN104679976B (en) * | 2014-10-31 | 2017-11-28 | 哈尔滨工业大学深圳研究生院 | Contraction for signal transacting is linear and shrinks the multiple least-squares algorithm of generalized linear |
| CN107135022B (en) * | 2017-05-04 | 2020-06-09 | 成都航天通信设备有限责任公司 | Rapid multi-antenna weighting combination method |
| CN107135022A (en) * | 2017-05-04 | 2017-09-05 | 成都航天通信设备有限责任公司 | A kind of quick multiple antennas weight merging approach |
| CN107332601B (en) * | 2017-06-18 | 2020-10-30 | 南京理工大学 | Adaptive interference suppression method based on beamforming |
| CN107332601A (en) * | 2017-06-18 | 2017-11-07 | 南京理工大学 | A kind of self-adapting interference suppression method based on wave beam forming |
| CN109217844A (en) * | 2018-10-30 | 2019-01-15 | 哈尔滨理工大学 | Hyperparameter optimization method based on the random Fourier's feature core LMS of pre-training |
| CN109217844B (en) * | 2018-10-30 | 2022-02-25 | 哈尔滨理工大学 | Hyper-parameter optimization method based on pre-training random Fourier feature kernel LMS |
| CN109831236A (en) * | 2018-11-13 | 2019-05-31 | 电子科技大学 | A kind of beam selection method based on Monte Carlo tree search auxiliary |
| CN109831236B (en) * | 2018-11-13 | 2021-06-01 | 电子科技大学 | Beam selection method based on Monte Carlo tree search assistance |
| CN112003645A (en) * | 2020-10-29 | 2020-11-27 | 南昌大学 | Synchronous reference signal extraction method and device |
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