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This paper presents one efficient blind separation method for non-orthogonal cochannel communication signals received by a single channel receiver. Unlike most existing works dealing with lower order modulation such as binary phase shift keying and quadrature phase shift keying in the context of single channel blind source separation, this method focuses on higher order modulation. This method is a blind alternative channel and sequence estimation method. The key for this method is the conversion of the separation problem in a multiple input single output system to a joint sequence estimation problem in a virtual multiple input multiple output system and the efficient implementation of semidefinite relaxation technique in a decision feedback and feedforward mode that is able to solve the joint sequence estimation problem in polynomial time. Simulation studies are used to demonstrate the effectiveness of the proposed methods.

This paper presents a new approach to automatic modulation classification of cochannel signals based on cumulants. Unlike the case of single signal modulation classification, the presence of cochannel interference may render the computation of cumulant inaccurate, which therefore leads to poor modulation classification performance if the conventional cumulant based approaches are used. The main concept of this new approach is the finding that cumulant of communication signals can be frequency offset dependent. In particular, cumulant can be zero when frequency offset is non-zero. Based on this property of cumulant, a new way of feature vector computation and automatic modulation classification is proposed based on properly chosen cumulants. Simulation results are given to illustrate the effectiveness of the proposed method.

Blind estimation of code and interleaver parameters plays a vital role in various applications such as non-cooperative systems, adaptive modulation and coding, signal intelligence, etc. The present paper proposes novel algorithms to jointly estimate code and interleaver parameters from Reed-Solomon (RS) coded and convolutionally interleaved data stream based on the rank ratio and non-zero-mean-ratio values for noiseless and noisy environments, respectively. Simulation results validating the proposed algorithms are given for various test cases and the accuracy of estimation of convolutional interleaver and RS code parameters is investigated for different values of interleaver width and modulation schemes. It is inferred that the accuracy of parameter estimation improves with decrease in modulation order and interleaver width of convolutional interleaver. Accepted version

Considering groups of parameters rather than individual parameters can be beneficial for estimation accuracy if structural relationships between parameters exist (e.g., spatial, hierarchical, or related to the physics of the problem). Group sparsity-estimators are typical examples that benefit from such prior information. Building on this principle, we show that the diffusion LMS algorithm used for distributed inference over adaptive networks can be extended to deal with structured criteria built upon groups of variables, leading to a flexible framework that can encode various relationships in the parameters to estimate. We also introduce online strategies to group the parameters to estimate in an unsupervised manner, and to promote or inhibit collaborations between nodes depending on whether these groups are locally or globally applicable. Simulations illustrate the theoretical findings and the estimation strategies.

This paper proposes an improved blind channel equalization method based on the generalized eigenvector algorithm (GEA). The new method can extract the desired signal in the presence of strong co-channel interference. The basic idea underlying the improved GEA method is that by exploiting higher order statistics that is sensitive to frequency offset, blind equalizer can be designed to equalize the desired signal with known frequency offset while suppressing the interference with a different frequency offset. Simulation results are given to demonstrate the effectiveness of the proposed method.

Blind reconstruction of code parameters plays a vital role in non-cooperative applications. It also provides additional advantages in applications such as adaptive modulation and coding, reconfigurable receivers, etc. In this paper, blind estimation algorithm is proposed to identify Reed-Solomon (RS) code parameters at the receiver over noisy channels. Simulation results validating the proposed algorithm are given for various test cases. Further, the accuracy of estimation of RS code parameters using the proposed algorithm is also evaluated for various M-ary quadrature amplitude modulation schemes. From the simulation results, it is observed that the accuracy of estimation improves with a decrease in code dimension and modulation order and the proposed algorithm outperforms other algorithms in the prior works. Accepted version

Clock timing jitter refers to random perturbations in the sampling time in analog-to-digital converters (ADCs). The perturbations are caused by circuit imperfections in the sampling clock. This paper analyzes the effect of sampling clock jitter on the acquired samples in the midst of random noise. We propose low-complexity digital signal processing methods for estimating the jitter in real-time for direct downconversion receivers at high sampling rates. We also propose adaptive compensation methods for the jitter and analyze the performance of the proposed techniques in some detail as well as through simulations.

Considering groups of variables, rather than variables individually, can be beneficial for estimation accuracy if structural relationships between variables exist (e.g., spatial, hierarchical or related to the physics of the problem). Group-sparsity inducing estimators are typical examples that benefit from such type of prior knowledge. Building on this principle, we show that the diffusion LMS algorithm for distributed inference over networks can be extended to deal with structured criteria built upon groups of variables, leading to a flexible framework that can encode various structures in the parameters to estimate. We also propose an unsupervised online strategy to differentially promote or inhibit collaborations between nodes depending on the group of variables at hand.

In a non-ideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used in an analog-to-digital converter (ADC), it creates spurious tones in the sampled data as well. In spectrum sensing applications, the presence of spurious tones can lead to false detection of signals in otherwise empty channels. In a typical spectrum sensing application, there usually exists a Fourier transform block. We propose an algorithm to use this block to estimate the jitter errors from the spurious sidebands and to compensate the distorted samples in the digital domain. Index Terms-PLL, sideband suppression, spurious tones.

In a non-ideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used in an analog-to-digital converter (ADC), it creates spurious tones in the sampled data as well. This paper analyzes this effect and proposes a solution to remove the leakage effects. The algorithm estimates the jitter errors from the spurious sidebands and provides a way to compensate the distorted sampled data in the digital domain.

In a non-ideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used as a clock signal, it creates spurious tones in the sampled data. Our prior work used a training signal to estimate the distortions and then correct the samples. In this work, we propose an alternative approach that estimates and removes the distortions directly from the sampled data without a training signal. Simulations indicate that the proposed solution is able to reduce the root-mean-square (RMS) sampling errors to about 15% of the original values.

Clock timing jitters refer to random perturbations in the sampling time in analog-to-digital converters (ADCs). The perturbations are caused by circuit imperfections in the sampling clock. This paper analyzes the effect of sampling clock jitter on the acquired samples. The paper proposes two methods to estimate the jitter for superheterodyne receiver architectures and cognitive radio architectures at high sampling rates. The paper also proposes a method to compensate for the jitter. The methods are tested and validated via computer simulations and theoretical analysis.

One major source of nonlinear distortion in analog-to-digital converters (ADCs) is clipping. The problem introduces spurious noise across the bandwidth of the sampled data. Prior works recover the signal from the acquired samples by relying on oversampling or on the assumption of vacant frequency bands and on the use of sparse signal representations. In this work, we propose a different approach, which uses two streams of data to mitigate the clipping distortion. Simulation results show an SNR improvement of 9dB, while the conventional approaches may even degrade the SNR in some situations.

In a nonideal PLL circuit, leakage of the reference signal into the control line produces spurious tones. When the distorted PLL signal is used in an analog-to-digital converter (ADC), it injects the spurious tones into the sampled data. These distortions are particularly harmful for wideband applications, such as spectrum sensing, since they affect the detection of vacant frequency bands. This paper analyzes this distortion effect in some detail and proposes estimation and filtering techniques in the digital domain to clean the data and remove the PLL leakage effects. Rather than remove the PLL sidebands by perfecting the circuitry, the proposed approach focuses on compensating their effect on the sampled data by using signal processing compensation algorithms. We study the performance of the proposed estimation algorithms and compare it with the corresponding Cramer-Rao bound (CRB). Such digitally based approaches are cost effective since the cost of perfecting the analog circuits can be prohibitive due to regular variations and imperfections in circuit fabrication processes.