Blind identification and DOA estimation for array sources in presence of scattering

A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.According to the different characters of covariance matrix and general steering vector of the array received source,a second order blind identification method is used to separate the sources,the mixing matrix could be obtained.From the mixing matrix,the type of the source is identified by using an amplitude criterion.And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.Computer simulations validate the efficiency of the method.

Blind identification of space–time block codes based on deep learning

Deep Learning(DL)has important applications to both commercial and military communications,such as software-defined radio,cognitive radio and spectrum surveillance.While DL has been intensively studied for modulation recognition,there are very few investigations for blind identification of Space-Time Block Codes(STBCs).This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna,including the same length of coding matrix.In our work,we use the frequency-domain correlation function of a single time delay as the training data of DL model.Then,we explore the suitable RN structure for blind identification of STBCs.Finally,we compare the RN model with convolutional neural network and traditional method,and test the performance of RN model.Simulation results show that our RN-based model provides good performance with low sensitivity to decay of the dataset,such as sample length and data size.At the same time,better identification accuracy can be achieved under the condition of different modulation types and channel fading parameters at low Signal to Noise Ratio(SNR).

Blind Signal Processing in Telecommunication Systems Based on Polynomial Statistics

The tendencies of the contemporary communication systems development are characterized by the increasingly stringent requirements for maximum channel utilization. Considering discrete communication systems in channels with intersymbol interference identification with the use of training signal is the key technology to create various types of equalizers. However, the time (from 20% to 50%) spent on training signal is increasingly attractive resource for upgrading standards TDMA, especially in mobile systems. An alternative method to training signal is blind signal processing.

FOLMS-AMDCNet:an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code(STBC)for multi-antenna(MA)orthogonal frequencydivision multiplexing(OFDM)systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment.However,owing to the restrictions on the prior information and channel conditions,these existing algorithms cannot perform well under strong interference and noncooperative communication conditions.To overcome these defects,this study introduces deep learning into the STBCOFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum(FOLMS)and attention-guided multi-scale dilated convolution network(AMDCNet).The fourth-order lag moment vectors of the received signals are calculated,and vectors are stitched to form two-dimensional FOLMS,which is used as the input of the deep learning-based model.Then,the multi-scale dilated convolution is used to extract the details of images at different scales,and a convolutional block attention module(CBAM)is introduced to construct the attention-guided multi-scale dilated convolution module(AMDCM)to make the network be more focused on the target area and obtian the multi-scale guided features.Finally,the concatenate fusion,residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types.Simulation experiments show that the average recognition probability of the proposed method at−12 dB is higher than 98%.Compared with the existing algorithms,the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances.In addition,the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise,which is more suitable for non-cooperative communication systems than the existing algorithms.

基于频域变换的增强外积分解算法

The outer-product decomposition algorithm(OPDA)performs well at blindly identifying system function.However,the direct use of the OPDA in systems using bandpass source will lead to errors.This study proposes an approach to enhance the channel estimation quality of a bandpass source that uses OPDA.This approach performs frequency domain transformation on the received signal and obtains the optimal transformation parameter by minimizing the p-norm of an error matrix.Moreover,the proposed approach extends the application of OPDA from a white source to a bandpass white source or chirp signal.Theoretical formulas and simulation results show that the proposed approach not only reduces the estimation error but also accelerates the algorithm in a bandpass system,thus being highly feasible in practical blind system identification applications.

A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by using soft-decision data.Firstly,by using soft-decision data,the probability that a parity check equation holds is derived.Thus,solving the parity check equations is converted to maximize the joint probability that parity check equations hold.Secondly,based on the standard cuckoo search algorithm,the established cost function is optimized.According to the final solution of the optimization problem,the generator matrix of recursive systematic convolutional code is estimated.Compared with the existing methods,our proposed method does not need to search for the generator matrix exhaustively and has high robustness.Additionally,it does not require the prior knowledge of the constraint length and is applicable in any modulation type.

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output （SIMO） FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares （Bi-LS） subspace tracking method. The new algorithm requires only a computational complexity of O（md2） at each iteration, or even as low as O（md） if only equalization is necessary, where m is the dimension of the received data vector （or the row rank of channel matrix） and d is the dimension of the signal subspace （or the column rank of channel matrix）. To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.

A Specially Designed Modulation-Induced Cyclostationarity Scheme for Blind SISO Channel Identification and Equalization

A special Modulation-Induced Cyclostationarity(MIC)scheme is designed for the identification and equaliza-tion of FIR Single-Input-Single-Output(SISO)channel,with the property that the transmit power is constant and the re-ceiver needs only one antenna.The cyclic Wiener equalizer is presented based on the estimated channel.

Blind Multiuser Channel Identification Based on Rate Doubling and Modulation-Induced Cyclostationarity

In this paper, a novel approach is put forward to the multiuser channelidentification. The approach makes use of the modulation-induced Cyclostationarity to separate thesecond order cyclic statistics for every user, with the special features of one subspace for oneuser, so as to be able to identify individual channels of different users. In order to form aSingle-Input-Two-Output (SITO) system, the transmission rate is doubled by repeating at thetransmitters. The approach is rather simple, suitable for the multiuser uplink . And the channelidentifiability conditions with its proof are included in the paper, And finally the identificationalgorithm is proposed with simulation results.

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output （SIMO） systems of sensor networks （both time-invariant and time-varying networks）. At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.

Blind Multipath Identification: A Weighted Linear Prediction Approach

Blind channel identification exploits the measurable channel output signaland some prior knowledge of the statistics of the channel input signal. However, in many scenarios,more side information is available, In digital communication systems, the pulse-shaping filter inthe transmitter and the anti-aliasing filter in the receiver are often known to the receiver.Exploitation of this prior knowledge can simplify the channel identification problem. In this paper,we pose the multipath identification problem as solving a group of linear equations. While we solvethe linear equations in the least-square meaning, a weight matrix can be introduced to improve theperformance of the estimator. The optimal weight matrix is derived. Compared with the existingLinear Prediction (UP) based multipath identification approach, the proposed approach offers asubstantial performance gain.

Blind MIMO Channel Estimation Based on Modulation-Induced Two-Cycle Cyclostationarity

In this paper, a novel approach is presented to the multiuser channelidentification . The approach makes use of the modulation-induced Cyclostationarity, capable ofidentifying individual channels of different users. By means of the decomposition of the cyclicspectrum method, the blind estimation of the channel can be achieved . The approach is rathersimple, with considerable advantages over existing techniques, and suitable for the multiuser uplink. The identifiabilily condition and its proof are also concluded in the paper. And finally the.simulation of identification algorithm is given.

Joint Angle and Delay Estimation (JADE) in Antenna Array CDMA Systems

The estimate of signals parameters is very important in wireless communications. In this paper, we combine subspace based blind channel estimation algorithm with the extension of the JADE WSF algorithm to jointly estimate the Angles of Arrival ( AOAs ) and delays of multipath signals arriving at an antenna array in Code Division Multiple Access ( CDMA ) systems. Our approach uses a collection of estimates of a consistent chip sample of space time vector channel. The channel estimates are assumed to have constant path AOA and delay over a finite number of symbols. Unlike the traditional MUltiple SIgnal Classification ( MUSIC ) and Estimation of Signal Parameters via Rotational Invariance Techniques ( ESPRIT ) algorithms for the estimation of signals parameters, the proposed method can work when the number of paths exceeds the number of antennas. The Cramer Rao Bound ( CRB ) and simulations are provided.