Single-channel Blind Separation Research Articles

A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals

A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals

A novel end‐to‐end deep separation network based on attention mechanism for single channel blind separation in wireless communication

The traditional methods exhibit unstable performance and high complexity for separating co-frequency modulated wireless communication signals under single-channel conditions. In this study, an end-to-end deep separation network based on the attention mechanism is proposed, which employs the encoder-separator-decoder architecture to implement the separation. The encoder can convert the signal into a high-dimensional feature representation for the separator to achieve separation of the signal in a high-dimensional space. The core of the proposed deep separation network is the innovative separator, which is mainly composed of attention-based convolution units with residual connection. The attention-based convolution unit integrates the large kernel convolution and modified global context (GC) block to simultaneously capture the local and global information of the signal. Furthermore, we improve the GC block to implement channel weighting and location weighting for the given feature map, thus further enhancing the adaptability of the model. The experimental results show that the proposed method not only outperforms the traditional methods in separating mixed signals with the same modulation, but also enables the separation of mixed signals with different modulations.

Single-Channel Blind Separation of Unsynchronized Multiuser PSK Signals With Non-Identical Sampling Frequency Offsets

Single-channel blind source separation (SCBSS) of uncoordinated, non-spread, co-frequency interfering multi-user signals is a challenging task. When the multi-user signals have sampling frequency offset (SFO) due to the imperfection of the local oscillator (LO), this problem becomes even more difficult to be solved. In this letter, a select-stitch data-aided channel estimation (CE) initialized per-survivor processing (SS-DCE-PSP) algorithm is proposed for SCBSS of multi-user signals with SFOs caused by imperfect LOs. By dividing a long sequence into a number of segments and selecting the segments with highest detection reliability to stitch together, the time slip and symbol slip due to the non-identical SFOs can be resolved. The data-aided CE in SS-DCE-PSP further enhances the performance of PSP, especially for the initial symbols. Simulation results show that the proposed SS-DCE-PSP algorithm is robust to the normalized SFO up to 10−4, and the BERs obtained by the SS-DCE-PSP algorithm are close (within 0.5dB) to the BERs obtained for multi-user signals without SFOs.

Single-channel blind separation of co-frequency signals based on convolutional network

Single-channel blind separation (SCBS) of co-frequency signals has been a challenging task in the blind signal processing field. Most effective approaches were still formulated in signal space, triggering complicated signal processing and high computational cost. To reduce the complexity and improve the separation accuracy of SCBS, in this paper, we propose a new waveform separation-demodulation scheme. As the first quintessential step, we design a convolutional time-domain network with squeeze-and-excitation blocks (CNSE) to carry out waveform separation in latent feature space, then the separated results are demodulated by the low-complexity per-survivor processing (LC-PSP) method. Our network adopts the backbone structure of encoder, separator, and decoder. And, it is worth noting that we deepen the encoder/decoder of the network to emphasize the low-level features and further weigh these features by means of the channel attention module. The encoder-decoder structure helps to eliminate the influence of noise during the stage of signal feature extraction and signal reconstruction. In our experiment, CNSE surpasses long short-term memory network and convolutional time-domain audio separation network as evaluated by signal-to-distortion ratio, signal-to-interferences ratio, and symbol error rate. Additionally, to reduce the influence of the truncation effect in separation, we also propose a data pre-processing method called redundancy protection (RP), and our trials reveal that RP can compensate for the performance of CNSE in the whole signal-to-noise ratio range. It is also verified that, compared with the traditional PSP algorithm, our method has better noise robustness and higher separation efficiency, especially when processing more complex mixtures.

Method for the single-channel blind separation based on particle filtering

A method for single-channel blind separation of two digital signals is proposed. The method is based on particle filtering and state space model of the mixture of two signals. The effectiveness of method is verified and the advantage over the iterative Bayesian separation method is shown.

A blind separation algorithm for heterogeneous mixed signals

Aiming at the problem that the existing algorithms can only achieve single-channel blind separation (SCBS) for signals with co-frequency and co-modulated, the gravitational field resampling particle filter (GFR-PF) algorithm was proposed. The GFR-PF can realize SCBS of binary phase shift keying (BPSK) and binary frequency shift keying (2FSK) that overlap in both time and frequency domain and are mixed in a single-channel with environmental noise. The GFR-PF was used to jointly detect symbols and estimate unknown parameters of the BPSK and 2FSK signals. Besides, the blind separation of different frequencies and different modulations signals, which was called the mixed heterogeneous signal blind separation (HSBS). The proposed algorithm not only improved the global search performance and tracking accuracy of the blind separation of the same frequency and same modulation mixed signals but also realized the HSBS of BPSK and 2FSK. Thus, the blind separation of heterogeneous mixed signals containing BPSK and 2FSK with different amplitudes and bit rates were achieved by the proposed algorithm. Based on this heterogeneous mixed signal model, it was proposed by this paper that anti-interception capability was improved through mixed transmission of BPSK and 2FSK signals with different amplitudes.

Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals

In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems—based on the particle filter estimation algorithm—an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals.

Low-Complexity Single-Channel Blind Separation of Co-Frequency Coded Signals

Single-channel blind source separation (SCBSS) of uncoordinated, non-spread, co-frequency interfering signals with non-zero carrier frequency offset and timing offset, and without training sequence for channel estimation, is a challenging task. Iterative SCBSS of coded signals leads to good performance but is computationally expensive as it involves the turbo processing of multi-user per-survivor processing and soft-input soft-output channel decoding. In this letter, we propose a low-complexity SCBSS (LC-SCBSS) scheme, which reduces the computational complexity of the conventional iterative SCBSS by using interference-cancellation from the second iteration onward, and adaptive channel truncation for certain users. Simulation results show that the proposed LC-SCBSS reduces the computational complexity by more than 99%, with only a marginal degradation in performance.

Blind separation of PCMA signals based on neural network

For paired carrier multiple access (PCMA) signals, a new single-channel blind separation on neural network was proposed. Firstly, the sample waveforms (three symbols) which contains different bit information are constructed, secondly, the time-frequency spectrum of each sample under the different influences of the trailing symbols is Intercepted, finally, the characteristic data of the spectrum as the input data, and the two-bit sequence in each sample as the output data to be trained, network trains these data repeatedly to complete the construction of separation model. The receiver carries on window truncation to the time-frequency spectrum of PCMA signal, neural network recognize the characteristic data of these spectrums to realizes the separation of bit sequences. Experimental results show that this algorithm has lower complexity than PSP algorithm, and the accuracy of it is close to PSP algorithm (L=5).

More general performance evaluation for single‐channel PCMA signals blind separation

The single-channel blind separation performance of the mixed signals with parameters estimation error in paired carrier multiple access (PCMA) satellite communication systems is analysed in this study. Current literatures have theoretically analysed the blind separation performance using the symbol detection error probability of PCMA signals. However, the parameters of PCMA signals are assumed to be accurate during their derivations, which are not practical in blind separation scenario. In this study, the parameters' estimation error is considered during the derivation of the blind separation performance of PCMA signals. Thus, the derived performance is more general. The simulation separation result is obtained by per-survivor processing method. It is in accordance with the derived performance.

Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images.

In this paper, we propose a novel method for single-channel blind separation of nonoverlapped sources and, to the best of our knowledge, apply it for the first time to automatic segmentation of lung tumors in positron emission tomography (PET) images. Our approach first converts a 3-D PET image into a pseudo-multichannel image. Afterward, regularization free sparseness constrained non-negative matrix factorization is used to separate tumor from other tissues. By using complexity based criterion, we select tumor component as the one with minimal complexity. We have compared the proposed method with threshold based on 40% and 50% maximum standardized uptake value (SUV), graph cuts (GC), random walks (RW), and affinity propagation (AP) algorithms on 18 nonsmall cell lung cancer datasets with respect to ground truth (GT) provided by two radiologists. Dice similarity coefficient averaged with respect to two GTs is: 0.78 ± 0.12 by the proposed algorithm, 0.78 ± 0.1 by GC, 0.77 ± 0.13 by AP, 0.77 ± 0.07 by RW, and 0.75 ± 0.13 by 50% maximum SUV threshold. Since the proposed method achieved performance comparable with interactive methods, considering the unique challenges of lung tumor segmentation from PET images, our findings support possibility of using our fully automated method in routine clinics. The source codes will be available at www.mipav.net/English/research/research.html.

The DFF‐PSP Iterative Separation and Theoretical Bound for PCMA with Long Memory

This paper proposes the Decision feedbackfeedforward per-survivor processing (DFF-PSP) iterative separation algorithm and the Monte Carlo integrationbased method to compute the theoretical bound for Paired carrier multiple access (PCMA) Single channel blind separation (SCBS) which is highly complex with long memory. The truncated PSP is employed while the pre- and post- cursors are disregarded to control the complexity. The delayed decisions feedforward and local decisions feedback filters are designed to process the pre- and post- cursors. The Bit-interleaved code modulation iterative decoder (BICM-ID) is combined with the second soft decision of the truncated PSP to obtain a better performance. The mixture signal model is regarded as a Multiple-access channel (MAC) with memory, and the theoretical performance bound is obtained via entropy inequality, Monte Carlo integration and Fano inequality. Simulation results show that the DFF-PSP iterative separation provides the good tradeoff between complexity and performance.

Single-channel blind separation using L₁-sparse complex non-negative matrix factorization for acoustic signals.

An innovative method of single-channel blind source separation is proposed. The proposed method is a complex-valued non-negative matrix factorization with probabilistically optimal L1-norm sparsity. This preserves the phase information of the source signals and enforces the inherent structures of the temporal codes to be optimally sparse, thus resulting in more meaningful parts factorization. An efficient algorithm with closed-form expression to compute the parameters of the model including the sparsity has been developed. Real-time acoustic mixtures recorded from a single-channel are used to verify the effectiveness of the proposed method.

Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram

A novel single-channel blind source separation (SCBSS) algorithm is presented. The proposed algorithm yields at least three benefits of the SCBSS solution: 1) resemblance of a stereo signal concept given by one microphone; 2) independent of initialization and a priori knowledge of the sources; and 3) it does not require iterative optimization. The separation process consists of two steps: 1) estimation of source characteristics, where the source signals are modeled by the autoregressive process and 2) construction of masks using only the single-channel mixture. A new pseudo-stereo mixture is formulated by weighting and time-shifting the original single-channel mixture. This creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2-D histogram. In this paper, we derive the separability of the proposed mixture model. Conditions required for unique mask construction based on maximum likelihood are also identified. Finally, experimental testing on both synthetic and real-audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods.

Blind-restoration-based blind separation method for permuted motion blurred images

A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function (PSF) are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.

Single-channel blind separation of overlapped multicomponents based on energy operator

A method utilizing single channel recordings to blind separate the multicomponents overlapped in both time and frequency domains is proposed in this paper. The main idea of the method is based on using the energy operator and differential energy operator to estimate instantaneous frequencies and amplitudes of each component and then to reconstruct these components. This method, due to the good time resolution of the energy operator and differential energy operator, as well as the very low computational complexity, is very useful in real time processing and practical applications. Simulation results demonstrated the efficiency of this method.