P.S.R. Diniz

Set-membership affine projection algorithm

This letter presents a new data selective adaptive filtering algorithm, the set-membership affine projection (SM-AP) algorithm. The algorithm generalizes the idea of the set-membership NLMS (SM-NLMS) algorithm to include constraint sets constructed from the past input and desired signal pairs. The resulting algorithm can be seen as a set-membership version of the affine-projection (AP) algorithm with an optimized step size. Also, the SM-AP algorithm does not trade convergence speed with misadjustment and computational complexity as most adaptive filtering algorithms. Simulations show the good performance of the algorithm, especially for colored input signals, in terms of convergence, final misadjustment, and reduced computational complexity.

Partial-update NLMS algorithms with data-selective updating

In this paper, we present mean-squared convergence analysis for the partial-update normalized least-mean square (PU-NLMS) algorithm with closed-form expressions for the case of white input signals. The formulae presented here are more accurate than the ones found in the literature for the PU-NLMS algorithm. Thereafter, the ideas of the partial-update NLMS-type algorithms found in the literature are incorporated in the framework of set-membership filtering, from which data-selective NLMS-type algorithms with partial-update are derived. The new algorithms, referred to herein as the set-membership partial-update normalized least-mean square (SM-PU-NLMS) algorithms, combine the data-selective updating from set-membership filtering with the reduced computational complexity from partial updating. A thorough discussion of the SM-PU-NLMS algorithms follows, whereby we propose different update strategies and provide stability analysis and closed-form formulae for excess mean-squared error (MSE). Simulation results verify the analysis for the PU-NLMS algorithm and the good performance of the SM-PU-NLMS algorithms in terms of convergence speed, final misadjustment, and computational complexity.

Set-Membership Adaptive Algorithms Based on Time-Varying Error Bounds for CDMA Interference Suppression

This paper presents set-membership (SM) adaptive algorithms based on time-varying error bounds for code-division multiple-access (CDMA) interference suppression. We introduce a modified family of SM adaptive algorithms for parameter estimation with time-varying error bounds. The considered algorithms include modified versions of the SM normalized least mean square (SM-NLMS), the affine projection (SM-AP), and the bounding ellipsoidal adaptive constrained (BEACON) recursive least-square technique. The important issue of error-bound specification is addressed in a new framework that takes into account parameter estimation dependency, multiaccess, and intersymbol interference (ISI) for direct-sequence CDMA (DS-CDMA) communications. An algorithm for tracking and estimating the interference power is proposed and analyzed. This algorithm is then incorporated into the proposed time-varying error bound mechanisms. Computer simulations show that the proposed algorithms are capable of outperforming previously reported techniques with a significantly lower number of parameter updates and a reduced risk of overbounding or underbounding.

Low-complexity constrained affine-projection algorithms

This paper proposes low-complexity constrained affine-projection (CAP) algorithms. The algorithms are suitable for linearly constrained filtering problems often encountered in communications systems. The CAP algorithms derived in this paper trade convergence speed and computational complexity in the same way as the conventional affine-projection (AP) algorithm. In addition, data-selective versions of the CAP algorithm are derived based on the concept of set-membership filtering. The set-membership constrained affine-projection (SM-CAP) algorithms include several constraint sets in order to construct a space of feasible solutions for the coefficient updates. The SM-CAP algorithms include a data-dependent step size that provides fast convergence and low mean-squared error. The paper also discusses important aspects of convergence and stability of constrained normalized adaptation algorithms and shows that normalization may introduce bias in the final solution.

Analysis of LMS-Newton adaptive filtering algorithms with variable convergence factor

An analysis of two LMS-Newton adaptive filtering algorithms with variable convergence factor is presented. The relations of these algorithms with the conventional recursive least-squares algorithm are first addressed. Their performance in stationary and nonstationary environments is then studied and closed-form formulas for the excess mean-square error (MSE) are derived. The paper deals, in addition, with the effects of roundoff errors for the case of fixed-point arithmetic. Specifically, closed-form formulas for the excess MSE caused by quantization are obtained. The paper concludes with experimental results that demonstrate the validity of the analysis presented. >

Design of high-resolution cosine-modulated transmultiplexers with sharp transition band

Due to the growing importance of multichannel modulation, there has been great interest in the design of high-performance transmultiplex systems. In this paper, a new cosine-modulated transmultiplex structure is proposed based on a prototype filter designed with the frequency-response masking (FRM) approach. This new structure leads to substantial reduction in the computational complexity (number of multiplications per output sample) of the prototype filters having sharp transition band and equivalently small roll-off values. The relation between the interpolation factor used in the FRM prototype filter and the decimation factor in the subbands leads to distinct structures. Examples included indicate that the reduction in computational complexity can be higher than 50% of the current state-of-art designs, whereas the reduction on the number of distinct coefficients of the prototype filter can be reduced even further (over 75%). As a result, the proposed approach allows the design of very selective subfilters for transmultiplexes with a very large number of subchannels.

Jointly minimum BER transmitter and receiver FIR MIMO filters for binary signal vectors

A theory is developed for jointly minimizing the bit error rate (BER) between the desired and decoded signals with respect to the coefficients of transmitter and receiver finite impulse response (FIR) multiple-input multiple-output (MIMO) filters. The original signal is assumed to be a vector time-series with equally likely memoryless Bernoulli vector components. The channel model constitutes of a known FIR MIMO transfer function and Gaussian additive noise independent of the original signal. The channel input signal is assumed to be power constrained. Based on the formulas obtained, an iterative numerical optimization algorithm is proposed. When compared with other design methods available in the literature, the proposed method yields better results due to the generality of the model considered and the joint optimization of the transmitter-receiver pair.

Blind Adaptive Interference Suppression Based on Set-Membership Constrained Constant-Modulus Algorithms With Dynamic Bounds

This work presents blind constrained constant modulus (CCM) adaptive algorithms based on the set-membership filtering (SMF) concept and incorporates dynamic bounds for interference suppression applications. We develop stochastic gradient and recursive least squares type algorithms based on the CCM design criterion in accordance with the specifications of the SMF concept. We also propose a blind framework that includes channel and amplitude estimators that take into account parameter estimation dependency, multiple access interference (MAI) and inter-symbol interference (ISI) to address the important issue of bound specification in multiuser communications. A convergence and tracking analysis of the proposed algorithms is carried out along with the development of analytical expressions to predict their performance. Simulations for a number of scenarios of interest with a DS-CDMA system show that the proposed algorithms outperform previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding.

On the elimination of constant-input limit cycles in digital filters

A theorem is proved which establishes sufficient conditions that will ensure freedom from constant-input limit cycles in a general digital-filter structure in which zero-input limit cycles can be eliminated. The theorem is then applied to a specific digital biquad which realizes simultaneously all the standard second-order transfer functions.

Time-Varying FIR Transmultiplexers With Minimum Redundancy

In this paper, we derive conditions for existence of zero-forcing equalizers for FIR transmultiplexers. We extend theoretical results from the literature for zero-forcing equalization in transmultiplexer systems, and derive new conditions for a more general configuration that includes filter-bank based systems with long responses and time-varying filter banks. The time-varying filter banks can be used to model code division multiple access systems with long codes, as well as other practical problems. The results can be applied to both downlink and uplink scenarios, due to the general framework considered for the analysis. The derived relations allow the use of relatively simple equalizers, lead to transmitters using small amount of redundancy, and also allow the transmission through channels with long impulse responses. Experimental results obtained via computer simulations validate the derived expressions. The simulations were carried out for zero-forcing design and also for least-squares and minimum mean squared error designs that use the relations derived for existence of zero-forcing equalizers. The results show that the performance of all equalizers improve if the zero-forcing conditions derived in this paper are followed.