Optimum Block Adaptive Ica For Separation Of Real And Complex Signals With Known Source Distributions In Dynamic Flat Fading Environments

Efficient co-channel and adjacent channel interference rejection is often one of the most demanding requirements for wireless receivers. Independent Component Analysis (ICA) has been previously applied to realize interference suppression. In particular, the fixed-point FastICA and complex FastICA algorithms can successfully perform blind signal extraction for real and complex valued communication signals in stationary or slow fading environments. Both algorithms exhibit fast convergence speed and impressive accuracy due to their Newton type fixed-point iteration. However, under dynamic channel conditions often encountered in practice, the fixed-point algorithms\u27 performance is significantly degraded. In this contribution, a novel complex block adaptive ICA algorithm and its simplified real version is proposed, that overcome this limitation for the separation of complex valued and real signals with known source distributions. The new methods exploit prior information about the modulation scheme of the communication signals of interest, and achieve improved interference suppression performance in dynamic channel environments. The proposed complex ICA algorithm is called Complex Optimum Block Adaptive ICA (Complex OBA-ICA), and its abridged version for separating real signals is called General Optimum Block Adaptation ICA (GOBA-ICA). The proposed methods are applied to interference rejection in linearly and abruptly flat fading dynamic environments for diversity QPSK and BPSK wireless receivers. Simulation results show that the presented techniques demonstrate better convergence properties and accuracy as compared to the complex FastICA and FastICA algorithms. © 2010 World Scientific Publishing Company

OPTIMUM BLOCK ADAPTIVE ICA FOR SEPARATION OF REAL AND COMPLEX SIGNALS WITH KNOWN SOURCE DISTRIBUTIONS IN DYNAMIC FLAT FADING ENVIRONMENTS

Efficient co-channel and adjacent channel interference rejection is often one of the most demanding requirements for wireless receivers. Independent Component Analysis (ICA) has been previously applied to realize interference suppression. In particular, the fixed-point FastICA and complex FastICA algorithms can successfully perform blind signal extraction for real and complex valued communication signals in stationary or slow fading environments. Both algorithms exhibit fast convergence speed and impressive accuracy due to their Newton type fixed-point iteration. However, under dynamic channel conditions often encountered in practice, the fixed-point algorithms\u27 performance is significantly degraded. In this contribution, a novel complex block adaptive ICA algorithm and its simplified real version is proposed, that overcome this limitation for the separation of complex valued and real signals with known source distributions. The new methods exploit prior information about the modulation scheme of the communication signals of interest, and achieve improved interference suppression performance in dynamic channel environments. The proposed complex ICA algorithm is called Complex Optimum Block Adaptive ICA (Complex OBA-ICA), and its abridged version for separating real signals is called General Optimum Block Adaptation ICA (GOBA-ICA). The proposed methods are applied to interference rejection in linearly and abruptly at fading dynamic environments for diversity QPSK and BPSK wireless receivers. Simulation results show that the presented techniques demonstrate better convergence properties and accuracy as compared to the complex FastICA and FastICA algorithms

Novel Complex Adaptive Signal Processing Techniques Employing Optimally Derived Time-varying Convergence Factors With Applicatio

In digital signal processing in general, and wireless communications in particular, the increased usage of complex signal representations, and spectrally efficient complex modulation schemes such as QPSK and QAM has necessitated the need for efficient and fast-converging complex digital signal processing techniques. In this research, novel complex adaptive digital signal processing techniques are presented, which derive optimal convergence factors or step sizes for adjusting the adaptive system coefficients at each iteration. In addition, the real and imaginary components of the complex signal and complex adaptive filter coefficients are treated as separate entities, and are independently updated. As a result, the developed methods efficiently utilize the degrees of freedom of the adaptive system, thereby exhibiting improved convergence characteristics, even in dynamic environments. In wireless communications, acceptable co-channel, adjacent channel, and image interference rejection is often one of the most critical requirements for a receiver. In this regard, the fixed-point complex Independent Component Analysis (ICA) algorithm, called Complex FastICA, has been previously applied to realize digital blind interference suppression in stationary or slow fading environments. However, under dynamic flat fading channel conditions frequently encountered in practice, the performance of the Complex FastICA is significantly degraded. In this dissertation, novel complex block adaptive ICA algorithms employing optimal convergence factors are presented, which exhibit superior convergence speed and accuracy in time-varying flat fading channels, as compared to the Complex FastICA algorithm. The proposed algorithms are called Complex IA-ICA, Complex OBA-ICA, and Complex CBC-ICA. For adaptive filtering applications, the Complex Least Mean Square algorithm (Complex LMS) has been widely used in both block and sequential form, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence and dependence on the choice of the convergence factor. In this research, novel block and sequential based algorithms for complex adaptive digital filtering are presented, which overcome the inherent limitations of the existing Complex LMS. The block adaptive algorithms are called Complex OBA-LMS and Complex OBAI-LMS, and their sequential versions are named Complex HA-LMS and Complex IA-LMS, respectively. The performance of the developed techniques is tested in various adaptive filtering applications, such as channel estimation, and adaptive beamforming. The combination of Orthogonal Frequency Division Multiplexing (OFDM) and the Multiple-Input-Multiple-Output (MIMO) technique is being increasingly employed for broadband wireless systems operating in frequency selective channels. However, MIMO-OFDM systems are extremely sensitive to Intercarrier Interference (ICI), caused by Carrier Frequency Offset (CFO) between local oscillators in the transmitter and the receiver. This results in crosstalk between the various OFDM subcarriers resulting in severe deterioration in performance. In order to mitigate this problem, the previously proposed Complex OBA-ICA algorithm is employed to recover user signals in the presence of ICI and channel induced mixing. The effectiveness of the Complex OBA-ICA method in performing ICI mitigation and signal separation is tested for various values of CFO, rate of channel variation, and Signal to Noise Ratio (SNR)

Complex-valued Adaptive Digital Signal Enhancement For Applications In Wireless Communication Systems

In recent decades, the wireless communication industry has attracted a great deal of research efforts to satisfy rigorous performance requirements and preserve high spectral efficiency. Along with this trend, I/Q modulation is frequently applied in modern wireless communications to develop high performance and high data rate systems. This has necessitated the need for applying efficient complex-valued signal processing techniques to highly-integrated, multi-standard receiver devices. In this dissertation, novel techniques for complex-valued digital signal enhancement are presented and analyzed for various applications in wireless communications. The first technique is a unified block processing approach to generate the complex-valued conjugate gradient Least Mean Square (LMS) techniques with optimal adaptations. The proposed algorithms exploit the concept of the complex conjugate gradients to find the orthogonal directions for updating the adaptive filter coefficients at each iteration. Along each orthogonal direction, the presented algorithms employ the complex Taylor series expansion to calculate time-varying convergence factors tailored for the adaptive filter coefficients. The performance of the developed technique is tested in the applications of channel estimation, channel equalization, and adaptive array beamforming. Comparing with the state of the art methods, the proposed techniques demonstrate improved performance and exhibit desirable characteristics for practical use. The second complex-valued signal processing technique is a novel Optimal Block Adaptive algorithm based on Circularity, OBA-C. The proposed OBA-C method compensates for a complex imbalanced signal by restoring its circularity. In addition, by utilizing the complex iv Taylor series expansion, the OBA-C method optimally updates the adaptive filter coefficients at each iteration. This algorithm can be applied to mitigate the frequency-dependent I/Q mismatch effects in analog front-end. Simulation results indicate that comparing with the existing methods, OBA-C exhibits superior convergence speed while maintaining excellent accuracy. The third technique is regarding interference rejection in communication systems. The research on both LMS and Independent Component Analysis (ICA) based techniques continues to receive significant attention in the area of interference cancellation. The performance of the LMS and ICA based approaches is studied for signals with different probabilistic distributions. Our research indicates that the ICA-based approach works better for super-Gaussian signals, while the LMS-based method is preferable for sub-Gaussian signals. Therefore, an appropriate choice of interference suppression algorithms can be made to satisfy the ever-increasing demand for better performance in modern receiver design