A blind separation algorithm for heterogeneous mixed signals

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

Conversion between multilevel modulation formats is one of the key functions for flexible networking aimed at high spectral efficiency in optical fiber transmission. The authors previously proposed an all-optical conversion system from quadrature phase shift keying signal to simultaneous two binary phase shift keying (BPSK) signals by using four-wave mixing and interference. Since the converted BPSK signals are spatially separated, there is an issue that they cannot be transmitted at the same time through a single fiber core when redirected to the same destination. In this paper, therefore, we consider to solve the issue by converting to a symbol rate doubled single BPSK signal using a pulse width compression technique. Numerical simulation reveals that the location of the pulse width compressor affects the system performances evaluated by bit error rate and constellation diagram.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

Upgrading Sparse NMF algorithm for blind source separation through Adaptive Parameterized Hybrid Kernel based approach

We propose a low-complexity degenerate entanglement-assisted free-space optical (FSO) communication system employing a two-pumps approach, a type-0 periodically poled lithium niobate (PPLN) waveguide, a multistage WDM demultiplexer, and a properly biased Mach-Zehnder modulator on transmitter side and, for binary phase-shift keying (BPSK) signaling, a single PIN photodetector or avalanche photodetector (APD) on a receiver side. Two-pumps-based approach utilizes two independent pump lasers operating at telecom wavelengths. The portion of degenerate entangled photons is used as the unmodulated carrier and the rest to transmit the modulated PSK/quadrature amplitude modulation (QAM) signal. The unmodulated portion of entangled photons is selected by properly choosing the bias voltage and phase representing the transmitted bits. In BPSK signaling, the unmodulated carrier photons are used on receiver side to reconstruct the transmitted symbol by beating them with modulated signal photons on either PIN photodetector or APD. We also describe how to implement the M-ary PSK and QAM transmitters for the degenerate entanglement assisted communications. Further, we propose a low-complexity PSK/QAM demodulator compatible with the proposed degenerate entanglement assisted communication concept. To demonstrate the quantum advantage, we conducted 1 Gb/s degenerate entanglement assisted BPSK experiments over a 1.5 km terrestrial FSO link we have developed previously at the University of Arizona, between ECE Dept. and Optical Sciences Meinel buildings. We demonstrate that in the strong atmospheric turbulence regime the proposed low-complexity degenerate entanglement assisted communication system performs significantly better compared to corresponding classical laser and thermal source-based communications counterparts.

In this article, kernelized-likelihood ratio tests (LRTs) for binary phase-shift keying (BPSK) signal detection based on the polynomial kernel function are proposed. Specifically, we kernelize the conventional LRT of BPSK signal detection using the so-called kernel trick, such that the inner product of the conventional LRT is replaced with proper polynomial kernel functions allowing for richer feature space to be deployed in the detection. We also derive computationally efficient recursive implementation structures for the proposed methods, resulting overall in six new detectors. With respect to the noise variance uncertainty (NVU), the proposed detectors can be divided into two general classes, namely i) constant false alarm rate (CFAR) and ii) semi-CFAR (S-CFAR) methods. To facilitate efficient operation under NVU, we also propose a new threshold-setting strategy to adjust the level of the proposed S-CFAR detectors. Additionally, we address the well-known energy detector (ED) under NVU and devise a new fixed-level ED formulation while also obtaining closed-form expressions for its false alarm and detection probabilities. Our extensive simulation results show that the proposed S-CFAR detectors outperform the state-of-the-art BPSK signal detectors with 2.4 dB signal-to-noise ratio (SNR) gain under practical worst-case NVU assumptions, while the performance gain is approximately 5.7 dB without NVU. In the case of the proposed CFAR detectors, the corresponding improvement in detection performance is approximately 1.8 dB.

Blind signal separation method is the method that the source signals are obtained by separating only the mixed signals. Generally, separation precision is lower when the basic method of blind signal separation is used for the sound signals. It is the cause that the power of the sound signals is concentrated in the low-frequency band. For the problem, we already considered that the method of the blind signal separation using QMF. Specifically, the mixed signals are divided into two frequency bands, and we perform suitable separation for each two bands. As the result, the conventional method has higher separation precision than the basic method. However, in the low-frequency band, power spectrum is not constant for frequency. Therefore, it is expected that the separation precision is higher than the conventional method by performing the suitable separation for each of signals based on the division to several narrow frequency bands on the low-frequency band. Then, in this paper, we propose the method that has higher separation precision and we evaluate the proposed method by computer simulation.

The performance of an adaptive technique for the generation of decision functions used in classification of several types of digitally modulated signals-binary amplitude-shift keying (BASK), binary phase-shift keying (BPSK), quaternary phase-shift keying (QPSK), and binary frequency-shift keying (BFSK)-is discussed. A computer simulation is performed to measure the properties of the classifier which is trained with one signal from each class. The method correctly identifies all the signals considered during this experiment. The most interesting result from a small number of trials is considered to be that a novel feature, useful for the identification of the number of phase states of BPSK and QPSK signals, is seen to provide adequate information identification at SNRs down to 5 dB. This feature is the existence of narrowband energy near M times the carrier frequency for M-ary PSK signals. >

A novel binary phase-shift keying (BPSK) demodulator architecture is presented. By using fully digital and trigger receiving method, the proposed demodulator can work properly at a wide speed range and hence can be applied in different applications such as wireless communications, biological implants and portable facilities, etc. The proposed demodulator was implemented by a 0.35-μm complementary metal-oxide-semiconductor (CMOS) process and the chip area is about 0.5 mm2. Measurement results reveal that the proposed demodulator can successfully demodulate the 100 MHz/10 Mbps BPSK signal which is the highest performance. While when it is used to demodulate the lowest speed BPSK signal of 10 MHz/1.0 Mbps, the power consumption is only 319 μw, and the bit error rate (BER) is less than the low test bond (10–9). Measurement results also show that the designed demodulator can work at 100% data-rate-to-carrier-frequency (DRCF) ratio with the frequency up to 25 MHz.

Most of the existing algorithms for blind sources separation have a limitation that sources are statistically independent. However, in many practical applications, the source signals are nonnegative and mutual statistically dependent signals. When the observations are nonnegative linear combinations of nonnegative sources, the correlation coefficients of the observations are larger than these of source signals. In this letter, a novel Nonnegative Matrix Factorization (NMF) algorithm with least correlated component constraints to blind separation of convolutive mixed sources is proposed. The algorithm relaxes the source independence assumption and has low-complexity algebraic computations. Simulation results on blind source separation including real face image data indicate that the sources can be successfully recovered with the algorithm.

Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices

The problem of blind separation of signals in a post-nonlinear mixture is addressed in this paper. A post-nonlinear mixture is formed by a component-wise nonlinear distortion of a linear mixture. Hence, a nonlinear adjusting part, placed in front of the linear separation structure, is needed to compensate for the distortion in separating such signals. The learning rules for the post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for the blind separation of post-nonlinearly mixed suband super-Gaussian signals is proposed, based on some previous works. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub- and super-Gaussian probability models during learning according to a stability condition, and it operates in a block-adaptive manner. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

Modulation classification (MC) of a signal is one of the major tasks of an intelligent receiver in various civilian and military applications. For mixed signals, it is a big challenge to recognize their modulation types. In this paper, a novel MC algorithm based on independent component analysis (ICA) is proposed. The proposed algorithm can separate mixed signals and identify modulation types for demodulation simultaneously under block fading channel. The design of the algorithm essentially involves two steps, namely, the separation of mixed signals through ICA algorithm and MC for the separated signals. Depending on ICA algorithm chosen in the first step, statistically independent signals are separated in block fading channel. Regarding the second step, higher-order cumulants are chosen as a promising approach for MC of the separated signals. Through the algorithm, the problem of the MC of the mixed signals can be solved and the mixed signals can be demodulated. Furthermore, simulation results are provided to verify the performance and robustness of the algorithm.

This paper presents a new method and circuit for the conversion of binary phase-shift keying (BPSK) signals into amplitude shift keying signals. The basic principles of the conversion method are the superharmonic injection and locking of oscillator circuits, and interference phenomena. The first one is used to synchronize the oscillators, while the second is used to generate an amplitude interference pattern that reproduces the original phase modulation. When combined with an envelope detector, the proposed converter circuit allows the coherent demodulation of BPSK signals without need of any explicit carrier recovery system. The time response of the converter circuit to phase changes of the input signal, as well as the conversion limits, are discussed in detail.

The spectral or band occupancy of an RF signal is often defined as the bandwidth that contains a specified fraction (usually 99 percent) of the modulated RF power. The band occupancy of binary and quaternary PSK signals with and without RF filtering and with modulation pulses of several shapes has been evaluated and the results presented in graphical and tabular form. For a binary FSK signal with phase deviation of ± π/2, sometimes called an FM-PSK signal, numerical values of the spectral occupancy with rectangular and raised-cosine signaling have been obtained and the results given in graphical form. For a binary PSK signal with signaling rate 1/T and with arbitrary baseband pulse shaping, we have derived a lower bound on the fraction of the continuous power contained outside any given band, but have not been able to get a bound on the total band occupancy. However, for an FM-PSK signal, a lower bound on the total band occupancy has been derived, and it is shown that the value of this lower bound for 99-percent power occupancy is 1.117/T. The 99-percent power occupancy bandwidth of an FM-PSK signal is 1.170/T with rectangular signaling and 2.20/T with raised-cosine signaling.