Sridhar Gollamudi

BEACON: an adaptive set-membership filtering technique with sparse updates

This paper deals with adaptive solutions to the so-called set-membership filtering (SMF) problem. The SMF methodology involves designing filters by imposing a deterministic constraint on the output error sequence. A set-membership decision feedback equalizer (SM-DFE) for equalization of a communications channel is derived, and connections with the minimum mean square error (MMSE) DFE are established. Further, an adaptive solution to the general SMF problem via a novel optimal bounding ellipsoid (OBE) algorithm called BEACON is presented. This algorithm features sparse updating, wherein it uses about 5-10% of the data to update the parameter estimates without any loss in mean-squared error performance, in comparison with the conventional recursive least-squares (RLS) algorithm. It is shown that the BEACON algorithm can also be derived as a solution to a certain constrained least-squares problem. Simulation results are presented for various adaptive signal processing examples, including estimation of a real communication channel. Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. This improvement is a result of incorporating an explicit test to check if an update is needed at every time instant as well as an optimal data-dependent assignment to the updating weights whenever an update is required.

Adaptive multiuser detection and beamforming for interference suppression in CDMA mobile radio systems

This paper considers the problem of interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems over fading channels. An adaptive array receiver is presented which integrates multiuser detection, beamforming, and RAKE reception to mitigate cochannel interference and fading. The adaptive multiuser detector is formulated using a blind constrained energy minimization criterion and adaptation is carried out using a novel algorithm based on set-membership parameter estimation theory. The proposed detector overcomes the shortcomings of conventional LMS- and RLS-type algorithms, namely, that of slow convergence and large computational load, respectively. This is especially the case when strong interferers are present or when the number of adaptive weights is relatively large. DS-CDMA systems can have a relatively large number of spatially distributed interferers. Thus beamforming is based on direction-of-arrival (DOA) estimates provided by an approximate maximum-likelihood estimator (DOA-MLE). Unlike previous approaches, the DOA-MLE exploits the structure of the DS-CDMA signaling scheme resulting in robust performance and simple implementation in the presence of angle spreading. The overall method is suitable for real-time implementation and can substantially improve the interference suppression capabilities of a CDMA system.

Intelligent antenna solutions for UMTS: algorithms and simulation results

This article outlines the development of intelligent antenna (IA) solutions for UMTS, a third-generation W-CDMA system. Since the selection of an antenna configuration paired with realizable uplink/downlink algorithms that can satisfy all operating environments is a broad task, this article focus is on cost-effective antenna arrays for macrocells. Algorithms that exploit the antenna configurations and act at both the physical and MAC layers are highlighted and supported by simulation results. Two solutions stand out for UMTS: a universal beamforming algorithm that unifies user-specific and fixed beamforming under one framework, and multibeam scheduling (MBS) that significantly increases downlink packet data throughput using the concept of code reuse in conjunction with beamforming. The article summarizes the critical issues that were faced in the development of an IA solution capable of delivering the theoretically promised benefits to end users.

A minimax approach to open-loop downlink beamforming

A new method for downlink beamforming is analyzed in this paper. In this open-loop solution, a minimax algorithm based on optimal set membership filtering is utilized to synthesize a wide and power efficient beam. It is shown that the robustness of this method against angle spread and error of DOA estimation is greater than that of the classical methods.

Adaptive blind equalization with bounded-error specification

A novel blind equalization algorithm derived with an l/sub /spl infin// specification, featuring data-dependent selective updates, is presented.

Optimal Set-Membership Filtering Using an Adaptive Minimax Algorithm with Automatic Bound Tuning

Abstract This paper shows that optimal set-membership filtering (SMF) is identical to minimax (l∞) filtering, if the optimal SMF corresponds to the smallest error bound specification that leads to a non-empty feasible solution. An adaptive minimax filtering algorithm that uses optimal quadratic approximations to the minimax cost function is proposed for adaptive implementation of optimal SMF. The proposed algorithm automatically tunes the error bound in an optimal manner, and can also be used for set-membership identification (SMI) when the noise bounds are not known. Simulation results verify the convergence of the proposed algorithm to the minimax solution.