Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

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Efficient Adaptive Filter Algorithms Using Variable Tap-length Scheme

Today the usage of digital signal processors has increased, where adaptive filter algorithms are now routinely employed in mostly all contemporary devices such as mobile phones, camcorders, digital cameras, and medical monitoring equipment, to name few. The filter tap-length, or the number of taps, is a significant structural parameter of adaptive filters that can influences both the complexity and steady-state performance characteristics of the filter. Traditional implementation of adaptive filtering algorithms presume some fixed filter-length and focus on estimating variable filter\u27s tap-weights parameters according to some pre-determined cost function. Although this approach can be adequate in some applications, it is not the case in more complicated ones as it does not answer the question of filter size (tap-length). This problem can be more apparent when the application involves a change in impulse response, making it hard for the adaptive filter algorithm to achieve best potential performance. A cost-effective approach is to come up with variable tap-length filtering scheme that can search for the optimal length while the filter is adapting its coefficients. In direct form structure filtering, commonly known as a transversal adaptive filter, several schemes were used to estimate the optimum tap-length. Among existing algorithms, pseudo fractional tap-length (FT) algorithm, is of particular interest because of its fast convergence rate and small steady-state error. Lattice structured adaptive filters, on the other hand, have attracted attention recently due to a number of desirable properties. The aim of this research is to develop efficient adaptive filter algorithms that fill the gap where optimal filter structures were not proposed by incorporating the concept of pseudo fractional tap-length (FT) in adaptive filtering algorithms. The contribution of this research include the development of variable length adaptive filter scheme and hence optimal filter structure for the following applications: (1) lattice prediction; (2) Least-Mean-Squares (LMS) lattice system identification; (3) Recursive Least-Squares (RLS) lattice system identification; (4) Constant Modulus Algorithm (CMA) blind equalization. To demonstrate the capability of proposed algorithms, simulations examples are implemented in different experimental conditions, where the results showed noticeable improvement in the context of mean square Error (MSE), as well as in the context of convergence rate of the proposed algorithms with their counterparts adaptive filter algorithms. Simulation results have also proven that with affordable extra computational complexity, an optimization for both of the adaptive filter coefficients and the filter tap-length can be attained

Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch

On issues of equalization with the decorrelation algorithm : fast converging structures and finite-precision

To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and compared to the widely used LMS algorithm

Least Squares Order-Recursive Lattice Smoothers

Conventional Least Squares Order-Recursive Lattice (LSORL) Filters Use Present and Past Data Values to Estimate the Present Value of a Signal. This Paper Introduces LSORL Smoothers Which Use Past, Present and Future Data for that Purpose. Except for an overall Delay Needed for Physical Realization, LSORL Smoothers Can Substantially Outperform LSORL Filters While Retaining All the Advantages of an Order-Recursive Structure. © 1995 IEE

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

MIMO equalization.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2005.In recent years, space-time block co'des (STBC) for multi-antenna wireless systems have emerged as attractive encoding schemes for wireless communications. These codes provide full diversity gain and achieve good performance with simple receiver structures without the additional increase in bandwidth or power requirements. When implemented over broadband channels, STBCs can be combined with orthogonal frequency division multiplexing (OFDM) or single carrier frequency domain (SC-FD) transmission schemes to achieve multi-path diversity and to decouple the broadband frequency selective channel into independent flat fading channels. This dissertation focuses on the SC-FD transmission schemes that exploit the STBC structure to provide computationally cost efficient receivers in terms of equalization and channel estimation. The main contributions in this dissertation are as follows: • The original SC-FD STBC receiver that bench marks STBC in a frequency selective channel is limited to coherent detection where the knowledge of the channel state information (CSI) is assumed at the receiver. We extend this receiver to a multiple access system. Through analysis and simulations we prove that the extended system does not incur any performance penalty. This key result implies that the SC-FD STBC scheme is suitable for multiple-user systems where higher data rates are possible. • The problem of channel estimation is considered in a time and frequency selective environment. The existing receiver is based on a recursive least squares (RLS) adaptive algorithm and provides joint equalization and interference suppression. We utilize a system with perfect channel state information (CSI) to show from simulations how various design parameters for the RLS algorithm can be selected in order to get near perfect CSI performance. • The RLS receiver has two modes of operation viz. training mode and direct decision mode. In training mode, a block of known symbols is used to make the initial estimate. To ensure convergence of the algorithm a re-training interval must be predefined. This results in an increase in the system overhead. A linear predictor that utilizes the knowled~e of the autocorrelation function for a Rayleigh fading channel is developed. The predictor is combined with. the adaptive receiver to provide a bandwidth efficient receiver by decreasing the training block size.· The simulation results show that the performance penalty for the new system is negligible. • Finally, a new Q-R based receiver is developed to provide a more robust solution to the RLS adaptive receiver. The simulation results clearly show that the new receiver outperforms the RLS based receiver at higher Doppler frequencies, where rapid channel variations result in numerical instability of the RLS algorithm. The linear predictor is also added to the new receiver which results in a more robust and bandwidth efficient receiver