Raghuram Ranganathan

Fast communication: Complex adaptive FIR digital filtering algorithm with time-varying independent convergence factors

The Complex Least Mean Square (Complex LMS) algorithm suffers from slow convergence and dependence on the choice of the convergence factor. In this paper, a novel Complex FIR Block Adaptive algorithm (Complex OBA-LMS) for digital filtering, which overcomes the inherent limitations of the Complex LMS, is presented. The proposed technique employs optimally derived convergence factors, updated at each block iteration, for independently adjusting the real and imaginary components of the Complex FIR adaptive filter coefficients. Simulation results confirm the performance improvement in terms of convergence speed and accuracy of the proposed method.

Optimal block adaptive I/Q mismatch compensation based on circularity

Wireless systems frequently employ I/Q modulation techniques to achieve spectral efficiency for high data rate applications. However, the main drawback of I/Q downconversion is the amplitude and phase imbalances between the analog components in the I and Q branches of the receiver. The resulting I/Q mismatch is unavoidable for practical quadrature receivers and can be frequency-dependent in nature. In this paper, a novel Optimal Block Adaptive algorithm based on the circularity property is presented for frequency-dependent I/Q imbalance compensation. The proposed technique, called OBA-C, is based on the assumption that the received baseband signal deviates from circularity in the presence of I/Q mismatch. OBA-C uses the complex Taylor series expansion to optimally update the adaptive filter coefficients at each iteration, until the circularity of the received signal is restored. Simulation results confirm the remarkable improvement in I/Q mismatch compensation and convergence speed of the proposed technique as compared to another recently proposed circularity based method.

Complex Adaptive ICA Employing the Conjugate Gradient Technique for Signal Separation in Time-Varying Flat Fading Channels

The conjugate gradient method is a prominent technique for solving systems of linear equations and unconstrained optimization problems, including adaptive filtering. Since it is an iterative method, it can be particularly applied to solve sparse systems which are too large to be handled by direct methods. The main advantage of the conjugate gradient method is that it employs orthogonal search directions with optimal steps along each direction to arrive at the solution. As a result, it has a much faster convergence speed than the steepest descent method, which often takes steps in the same direction as earlier steps. Furthermore, it has lower computational complexity than Newton’s iteration approach. This unique tradeoff between convergence speed and computational complexity gives the conjugate gradient method desirable properties for application in numerous mathematical optimization problems. In this paper, the conjugate gradient principle is applied to complex adaptive independent component analysis (ICA) for maximization of the kurtosis function, to achieve separation of complex-valued signals. The proposed technique is called the complex block conjugate independent component analysis (CBC-ICA) algorithm. The CBC-ICA derives independent conjugate gradient search directions for the real and imaginary components of the complex coefficients of the adaptive system employed for signal separation. In addition, along each conjugate direction an optimal update is generated separately for the real and imaginary components using the Taylor series approximation. Simulation results confirm that in dynamic flat fading channel conditions, the CBC-ICA demonstrates excellent convergence speed and accuracy, even for large processing block sizes.

Fast communication: A comparative study of complex gradient and fixed-point ICA algorithms for interference suppression in static and dynamic channels

Separation of complex signals using independent component analysis (ICA) is an area of extensive research. Several gradient and fixed-point complex ICA algorithms have been proposed in this regard. In this contribution, the performance of the recently developed complex ICA with individual adaptation (C-IA-ICA) is compared to the most recent gradient optimization KM algorithm (KM-G) and fixed-point complex fast-ICA (CF-ICA) algorithm. The algorithms are tested in interference suppression for QPSK based receivers, in both static and dynamic channel conditions. In addition, two simulation scenarios are presented. In the first case, the interferer is another QPSK signal, while in the second the interferer is a 16-QAM signal. In static conditions, the CF-ICA has the fastest convergence with high interference suppression. However, in dynamic scenarios frequently encountered in practice, its convergence speed is greatly affected. The complex IA-ICA achieves good interference suppression in both static and dynamic channels without a significant effect on its convergence speed. The KM-G, while not diverging, in both static and dynamic channel situations, is less effective in interference suppression, in contrast to the CF-ICA and C-IA-ICA which achieve acceptable interference suppression in both cases.

A Novel Interference Supression Technique employing Complex Adaptive ICA for Time-Varying Channels in Diversity Wireless QAM Receivers

This paper presents a novel complex adaptive ICA algorithm with individual adaptation of parameters. The algorithm employs optimal individual convergence factors for the real and imaginary components of the weight vector. The performance of this algorithm is tested and compared with the most recent complex fast-ICA in time-varying channel conditions frequently encountered in practice. Simulation results confirm the improved performance, in terms of convergence speed and accuracy, of the proposed technique, at the expense of a modest increase in computational complexity.

Complex FIR block adaptive digital filtering algorithm with independent adaptation of real and imaginary filter parameters

Complex signal representations are being frequently employed in various adaptive filtering applications such as wireless communications, beamforming, etc. In this paper, a novel complex optimum block adaptive algorithm with individual adaptation of parameters (Complex OBAI-LMS) is presented. The proposed technique effectively utilizes the degrees of freedom of the adaptive filter by individually adapting the real and imaginary components of the complex adaptive finite impulse response (FIR) filter coefficients employing optimally derived convergence factors. In addition, the convergence factors are updated at each block iteration. The formulation of the complex OBAI-LMS shows that the update vectors for the real and imaginary components of the adaptive filter coefficients are estimates of the Wiener solution at each iteration. Furthermore, the matrix inversion operation in the formulation is eliminated by processing the input signal in overlapping blocks and applying a matrix inversion lemma. The convergence properties of the complex OBAI-LMS are compared to the block implementation of the complex LMS algorithm in the estimation of a complex FIR filter. Simulation results show that the complex OBAI-LMS yields a significant improvement in convergence speed over the block complex LMS for different input training signals.

A novel digital beamforming technique based on homogeneous adaptation employing time-varying convergence factors

In this paper, a novel complex homogeneous adaptation least mean square algorithm (complex HA-LMS) for digital beamforming is presented. The proposed technique independently adjusts the real and imaginary components of the complex adaptive filter coefficients using optimally derived convergence factors. In addition, the convergence factors are updated at very sample iteration. The complex HA-LMS is applied to adaptive beamforming in a multi-antenna receiver processing Quadrature Amplitude Modulation (QAM) signals. Extensive simulation results show that the complex HA-LMS exhibits improved and consistent performance, in terms of the Symbol Error Rate (SER) and convergence speed, for different flat fading channel conditions and varied number of antenna elements, in contrast to the complex Least Mean Square algorithm (complex LMS) which uses a fixed convergence factor.

Intercarrier interference mitigation and multi-user detection employing adaptive ICA for MIMO-OFDM systems in time variant channels

Orthogonal Frequency Division Multiplexing (OFDM) is a widely applied scheme in modern wireless communication systems that effectively operate in frequency selective fading channels. The combination of OFDM and the Multiple-Input-Multiple-Output (MIMO) technique represents a promising candidate for future broadband wireless systems. This paper addresses the InterCarrier Interference (ICI) issue in multi-user MIMO-OFDM systems operating in time-varying frequency selective channel environments. ICI, which is caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver, can lead to severe system performance degradation. In our proposed method, a recently presented Independent Component Analysis (ICA) technique called Complex Optimum Block Adaptive ICA (Complex OBA-ICA) is employed to recover user signals in the presence of ICI and channel induced mixing. Simulation results indicate that the new technique significantly reduces Inter Symbol Interference (ISI) in multi-user MIMO-OFDM systems in dynamic channel environments.

Separation of complex signals with known source distributions in time-varying channels using optimum complex block adaptive ICA

This paper presents a novel realization of the complex block adaptive independent component analysis algorithm. The algorithm optimally updates the real and imaginary components of the weight vector independently. The new implementation is employed for the separation of complex signals with known source distributions, a scenario frequently encountered in practice. Under time-varying channel conditions, the performance of the proposed method is compared with the widely known Complex Fast-ICA. Simulation results show that this new technique exhibits superior performance in time varying channel conditions in terms of convergence speed. In addition, the performance of the proposed method is independent of the processing block length and is achieved without any additional cost in computational complexity.

Novel conjugate-gradient based complex adaptive ICA for diversity QPSK receivers in time-varying channel applications

In this paper, a novel conjugate-gradient based complex independent component analysis algorithm (CBC- ICA) for processing complex signals is presented, which employs independent conjugate-gradient based search directions, rather than the gradient directions, for the real and imaginary components of the weight vector. The proposed technique is used to perform interference suppression for diversity QPSK receivers in time-varying channels. In contrast to the well known Complex FastICA algorithm, the CBC-ICA demonstrates fast convergence in dynamic environments, while maintaining satisfactory symbol error rate (SER) performance. In addition, the performance of the CBC-ICA is preserved for large processing block sizes.