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)

Adaptive Methods Employing Optimal Convergence Factors For Processing Complex Signals and Systems

Complex adaptive methods for complex information processing employ optimal individual convergence factors for real and imaginary components of the weight vector. For wireless receivers operating on QPSK, a Complex IA-ICA performs better than existing Complex Fast-ICA methods in terms of accuracy and convergence speed, can process such complex signals in time-varying channels, and employs time-varying and time-invariant convergence factors, independent for the real and imaginary components of the system parameters, and provide individual or group system parameter adjustments. Such systems employ the within complex adaptive ICA with individual element adaptation (Complex IA-ICA). In adaptive beamforming, system identification and other adaptive systems based on the Least Squares method, complex least mean squares methods, with optimally and automatically derived convergence factors, are employed and which perform much better in terms of convergence speed and accuracy, when compared to the traditional Complex LMS and Block Complex LMS methods

Method of adaptive solar tracking using variable step size

A method for controlling a photovoltaic (PV) panel in a PV system including a computing device that provides motor control signals and implements an iterative adaprtive control (IAC) algorithm for adjusting an angle of the PV panel. The IAC algorithm relates P at a current time k (P(k)), an elevation angle of the PV panel at k (0s(k)), P after a next step (P(k+1)) and an elevation angle of the PV panel at k+1 (0s(k+1)). The algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates 0s(k+1) using P(k+1). The motor control signals cause the motor to position the PV panel to achieve 0s(k+1). A change in P resulting from the positioning is compared to a predetermined change limit, and only if the change in P is greater than/equal to the change limit, again sensing P, and repeating the generating, calculating and positioning

Iterative Adaptive Solar Tracking Having Variable Step Size

A system controller for position controlling a photovoltaic (PV) panel in a PV system including a power sensor sensing output power (P), and a motor for positioning the PV panel. The system controller includes a computing device having memory that provides motor control signal and implements an iterative adaptive control (IAC) algorithm stored in the memory for adjusting an angle of the PV panel. The IAC algorithm includes an iterative relation that relates P at current time k (P(k)), its elevation angle at k (?s(k)), P after a next step (P(k+1)) and its elevation angle at k+1(?s(k+1)). The IAV algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates a position angle ?s(k+1) of the PV panel using the perturbed power value. The motor control signals from the computing device cause the motor to oposition the PV panel to achieve ?s(k+1)

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. © 2008 Elsevier B.V. All rights reserved

Mismatch Cancellation For Low-If Wireless Receivers Using Obai-Ica

I/Q Signal Processing is widely utilized in today\u27s communication receivers. However, all I/Q processing receiver structures, such as the low-IF receiver, face a common problem of matching the amplitudes and phases of the I and Q branches. In practice, imbalances are unavoidable in the analog front end, which results in finite and usually insufficient rejection of the image frequency band. This causes the image signal to appear as interference on top of the desired signal. A brief overview of the low-IF receiver architecture is presented and a novel method based on combining an adaptive DSP algorithm and Independent Component analysis is proposed for the I/Q imbalance compensation. Some simulation results are provided in order to evaluate the performance of the proposed method. The results indicate that the I/ Q imbalance can be effectively compensated during the normal operation of the receiver without the need of a training signal for calibration

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. © 2007 Elsevier B.V. All rights reserved

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 multiantenna 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. ©2009 IEEE

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. © 2007 IEEE

Complex Fir Block Adaptive Algorithm Employing Optimal Time-Varying Convergence Factors

The Complex Least Mean Square algorithm (Complex LMS) has been widely used in various adaptive filtering applications, e.g. in the wireless communications and biomedical fields, due to its computational simplicity. However, the main drawback of the Complex LMS algorithm is its slow convergence. In addition, the performance is dependent on the choice of the convergence factor or learning rate. In this paper, a novel complex block adaptive algorithm is presented that overcomes the performance limitation of the Complex LMS. The proposed algorithm (Complex OBA-LMS) derives independent time-varying convergence factors for the real and imaginary components of the FIR complex adaptive filter coefficients. Furthermore, the convergence factors are updated at each block iteration. The convergence speed and accuracy of the Complex OBA-LMS algorithm are investigated and compared with the Complex LMS algorithm. Simulation results show that the proposed technique exhibits superior performance at the expense of a modest increase in computational complexity for different training inputs. © 2008 IEEE