arXiv subjects

Rodrigo C. de Lamare

Publications and source records attributed to Rodrigo C. de Lamare.

At least 91 records · Page 5Linked to original sources

arXiv2013

Distributed conjugate gradient strategies for parameter estimation over sensor networks

This paper presents distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks. Both incremental and diffusion adaptive solutions are all considered. The distributed conventional (CG) and modified CG (MCG) algorithms have an improved performance in terms of mean square error as compared with least-mean square (LMS)-based algorithms and a performance that is close to recursive least-squares (RLS) algorithms . The resulting algorithms are distributed, cooperative and able to respond in real time to changes in the environment.

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arXiv2013

Bidirectional MMSE Algorithms for Interference Mitigation in CDMA Systems over Fast Fading Channels

This paper presents adaptive bidirectional minimum mean-square error (MMSE) parameter estimation algorithms for fast-fading channels. The time correlation between successive channel gains is exploited to improve the estimation and tracking capabilities of adaptive algorithms and provide robustness against time-varying channels. Bidirectional normalized least mean-square (NLMS) and conjugate gradient (CG) algorithms are devised along with adaptive mixing parameters that adjust to the time-varying channel correlation properties. An analysis of the proposed algorithms is provided along with a discussion of their performance advantages. Simulations for an application to interference suppression in DS-CDMA systems show the advantages of the proposed algorithms.

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arXiv2013

Adaptive Reduced-Rank Processing Using a Projection Operator Based on Joint Iterative Optimization of Adaptive Filters For CDMA Interference Suppression

This paper proposes a novel adaptive reduced-rank filtering scheme based on the joint iterative optimization of adaptive filters. The proposed scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitutes the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe minimum mean-squared error (MMSE) expressions for the design of the projection matrix and the reduced-rank filter and simple least-mean squares (LMS) adaptive algorithms for its computationally efficient implementation. Simulation results for a CDMA interference suppression application reveals that the proposed scheme significantly outperforms the state-of-the-art reduced-rank schemes, while requiring a significantly lower computational complexity.

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arXiv2013

Joint Model-Order and Step-Size Adaptation using Convex Combinations of Adaptive Reduced-Rank Filters

In this work we propose schemes for joint model-order and step-size adaptation of reduced-rank adaptive filters. The proposed schemes employ reduced-rank adaptive filters in parallel operating with different orders and step sizes, which are exploited by convex combination strategies. The reduced-rank adaptive filters used in the proposed schemes are based on a joint and iterative decimation and interpolation (JIDF) method recently proposed. The unique feature of the JIDF method is that it can substantially reduce the number of coefficients for adaptation, thereby making feasible the use of multiple reduced-rank filters in parallel. We investigate the performance of the proposed schemes in an interference suppression application for CDMA systems. Simulation results show that the proposed schemes can significantly improve the performance of the existing reduced-rank adaptive filters based on the JIDF method.

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arXiv2013

Linear Reduced-Rank Interference Suppression for DS-UWB Systems Using Switched Approximations of Adaptive Basis Functions

In this work, we propose a novel low-complexity reduced-rank scheme and consider its application to linear interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. Firstly, we investigate a generic reduced-rank scheme that jointly optimizes a projection vector and a reduced-rank filter by using the minimum mean-squared error (MMSE) criterion. Then a low-complexity scheme, denoted switched approximation of adaptive basis functions (SAABF), is proposed. The SAABF scheme is an extension of the generic scheme, in which the complexity reduction is achieved by using a multi-branch framework to simplify the structure of the projection vector. Adaptive implementations for the SAABF scheme are developed by using least-mean squares (LMS) and recursive least-squares (RLS) algorithms. We also develop algorithms for selecting the branch number and the model order of the SAABF scheme. Simulations show that in the scenarios with severe inter-symbol interference (ISI) and multiple access interference (MAI), the proposed SAABF scheme has fast convergence and remarkable interference suppression performance with low complexity.

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arXiv2013

Blind Adaptive Reduced-Rank Detectors for DS-UWB Systems Based on Joint Iterative Optimization and the Constrained Constant Modulus Criterion

A novel linear blind adaptive receiver based on joint iterative optimization (JIO) and the constrained constant modulus (CCM) design criterion is proposed for interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. The proposed blind receiver consists of two parts, a transformation matrix that performs dimensionality reduction and a reduced-rank filter that produces the output. In the proposed receiver, the transformation matrix and the reduced-rank filter are updated jointly and iteratively to minimize the constant modulus (CM) cost function subject to a constraint. Adaptive implementations for the JIO receiver are developed by using the normalized stochastic gradient (NSG) and recursive least-squares (RLS) algorithms. In order to obtain a low-complexity scheme, the columns of the transformation matrix with the RLS algorithm are updated individually. Blind channel estimation algorithms for both versions (NSG and RLS) are implemented. Assuming the perfect timing, the JIO receiver only requires the spreading code of the desired user and the received data. Simulation results show that both versions of the proposed JIO receivers have excellent performance in suppressing the inter-symbol interference (ISI) and multiple access interference (MAI) with a low complexity.

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arXiv2013

Adaptive Frequency Domain Detectors for SC-FDE in Multiuser DS-UWB Systems with Structured Channel Estimation and Direct Adaptation

In this paper, we propose two adaptive detection schemes based on single-carrier frequency domain equalization (SC-FDE) for multiuser direct-sequence ultra-wideband (DS-UWB) systems, which are termed structured channel estimation (SCE) and direct adaptation (DA). Both schemes use the minimum mean square error (MMSE) linear detection strategy and employ a cyclic prefix. In the SCE scheme, we perform the adaptive channel estimation in the frequency domain and implement the despreading in the time domain after the FDE. In this scheme, the MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, we propose simple algorithms for estimating these parameters. In the DA scheme, the interference suppression task is fulfilled with only one adaptive filter in the frequency domain and a new signal expression is adopted to simplify the design of such a filter. Least-mean squares (LMS), recursive least squares (RLS) and conjugate gradient (CG) adaptive algorithms are then developed for both schemes. A complexity analysis compares the computational complexity of the proposed algorithms and schemes, and simulation results for the downlink illustrate their performance.

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arXiv2013

Adaptive Reduced-Rank RLS Algorithms based on Joint Iterative Optimization of Filters for Space-Time Interference Suppression

This paper presents novel adaptive reduced-rank filtering algorithms based on joint iterative optimization of adaptive filters. The novel scheme consists of a joint iterative optimization of a bank of full-rank adaptive filters that constitute the projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank filter and recursive least squares (RLS) adaptive algorithms for its computationally efficient implementation. Simulations for a space-time interference suppression in a CDMA system application show that the proposed scheme outperforms in convergence and tracking the state-of-the-art reduced-rank schemes at about the same complexity.

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arXiv2013

Adaptive Decision Feedback Reduced-Rank Equalization Based on Joint Iterative Optimization of Adaptive Estimation Algorithms for Multi-Antenna Systems

This paper presents a novel adaptive reduced-rank multi-input-multi-output (MIMO) decision feedback equalization structure based on joint iterative optimization of adaptive estimators. The novel reduced-rank equalization structure consists of a joint iterative optimization of two equalization stages, namely, a projection matrix that performs dimensionality reduction and a reduced-rank estimator that retrieves the desired transmitted symbol. The proposed reduced-rank structure is followed by a decision feedback scheme that is responsible for cancelling the inter-antenna interference caused by the associated data streams. We describe least squares (LS) expressions for the design of the projection matrix and the reduced-rank estimator along with computationally efficient recursive least squares (RLS) adaptive estimation algorithms. Simulations for a MIMO equalization application show that the proposed scheme outperforms the state-of-the-art reduced-rank and the conventional estimation algorithms at about the same complexity.

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arXiv2013

Adaptive Switched Lattice Reduction-Aided Linear Detection Techniques for MIMO Systems

Lattice reduction (LR) aided multiple-input-multiple-out (MIMO) linear detection can achieve the maximum receive diversity of the maximum likelihood detection (MLD). By emloying the most commonly used Lenstra, Lenstra, and L. Lovasz (LLL) algorithm, an equivalent channel matrix which is shorter and nearly orthogonal is obtained. And thus the noise enhancement is greatly reduced by employing the LR-aided detection. One problem is that the LLL algorithm can not guarantee to find the optimal basis. The optimal lattice basis can be found by the Korkin and Zolotarev (KZ) reduction. However, the KZ reduction is infeasible in practice due to its high complexity. In this paper, a simple algorithm is proposed based on the complex LLL (CLLL) algorithm to approach the optimal performance while maintaining a reasonable complexity.

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arXiv2013

Low-Complexity Lattice Reduction-Aided Channel Inversion Methods for Large-Dimensional Multi-User MIMO Systems

Low-complexity precoding {algorithms} are proposed in this work to reduce the computational complexity and improve the performance of regularized block diagonalization (RBD) {based} precoding {schemes} for large multi-user {MIMO} (MU-MIMO) systems. The proposed algorithms are based on a channel inversion technique, QR decompositions{,} and lattice reductions to decouple the MU-MIMO channel into equivalent SU-MIMO channels. Simulation results show that the proposed precoding algorithms can achieve almost the same sum-rate performance as RBD precoding, substantial bit error rate (BER) performance gains{,} and a simplified receiver structure, while requiring a lower complexity.

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arXiv2013

Iterative Detection and Decoding for MIMO Systems with Knowledge-Aided Message Passing Algorithms

In this paper, we consider the problem of iterative detection and decoding (IDD) for multi-antenna systems using low-density parity-check (LDPC) codes. The proposed IDD system consists of a soft-input soft-output parallel interference (PIC) cancellation scheme with linear minimum mean-square error (MMSE) receive filters and two novel belief propagation (BP) decoding algorithms. The proposed BP algorithms exploit the knowledge of short cycles in the graph structure and the reweighting factors derived from the hypergraph's expansion. Simulation results show that when used to perform IDD for multi-antenna systems both proposed BP decoding algorithms can consistently outperform existing BP techniques with a small number of decoding iterations.

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arXiv2013

Adaptive Iterative Decision Feedback Detection Algorithms for Multi-User MIMO Systems

An adaptive iterative decision multi-feedback detection algorithm with constellation constraints is proposed for multiuser multi-antenna systems. An enhanced detection and interference cancellation is performed by introducing multiple constellation points as decision candidates. A complexity reduction strategy is developed to avoid redundant processing with reliable decisions along with an adaptive recursive least squares algorithm for time-varying channels. An iterative detection and decoding scheme is also considered with the proposed detection algorithm. Simulations show that the proposed technique has a complexity as low as the conventional decision feedback detector while it obtains a performance close to the maximum likelihood detector.

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arXiv2013

A Unified Approach to Joint and Iterative Adaptive Interference Cancellation and Parameter Estimation for CDMA Systems in Multipath Channels

This paper proposes a unified approach to joint adaptive parameter estimation and interference cancellation (IC) for direct sequence code-division-multiple-access (DS-CDMA) systems in multipath channels. A unified framework is presented in which the IC problem is formulated as an optimization problem with extra degrees of freedom of an IC parameter vector for each stage and user. We propose a joint optimization method for estimating the IC parameter vector, the linear receiver filter front-end, and the channel along with minimum mean squared error (MMSE) expressions for the estimators. Based on the proposed joint optimization approach, we derive low-complexity stochastic gradient (SG) algorithms for estimating the desired parameters. Simulation results for the uplink of a synchronous DS-CDMA system show that the proposed methods significantly outperform the best known IC receivers.

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arXiv2013

Frequency-Domain Group-based Shrinkage Estimators for UWB Systems

In this work, we propose low-complexity adaptive biased estimation algorithms, called group-based shrinkage estimators (GSEs), for parameter estimation and interference suppression scenarios with mechanisms to automatically adjust the shrinkage factors. The proposed estimation algorithms divide the target parameter vector into a number of groups and adaptively calculate one shrinkage factor for each group. GSE schemes improve the performance of the conventional least squares (LS) estimator in terms of the mean-squared error (MSE), while requiring a very modest increase in complexity. An MSE analysis is presented which indicates the lower bounds of the GSE schemes with different group sizes. We prove that our proposed schemes outperform the biased estimation with only one shrinkage factor and the best performance of GSE can be obtained with the maximum number of groups. Then, we consider an application of the proposed algorithms to single-carrier frequency-domain equalization (SC-FDE) of direct-sequence ultra-wideband (DS-UWB) systems, in which the structured channel estimation (SCE) algorithm and the frequency domain receiver employ the GSE. The simulation results show that the proposed algorithms significantly outperform the conventional unbiased estimator in the analyzed scenarios.

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arXiv2013

Blind Interference Suppression and Power Adjustment with Alternating Optimization for Cooperative DS-CDMA Networks

This work presents blind joint interference suppression and power allocation algorithms for DS-CDMA networks with multiple relays and decode and forward protocols. A scheme for joint allocation of power levels across the relays subject to group-based power constraints and the design of linear receivers for interference suppression is proposed. A code-constrained constant modulus (CCM) design for the receive filters and the power allocation vectors is devised along with a blind channel estimator. In order to solve the proposed optimization efficiently, an alternating optimization strategy is presented with recursive least squares (RLS)-type algorithms for estimating the parameters of the receiver, the power allocation and the channels. Simulations show that the proposed algorithms obtain significant gains in capacity and performance over existing schemes.

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arXiv2013

Interference Suppression and Group-Based Power Adjustment via Alternating Optimization for DS-CDMA Networks with Multihop Relaying

This work presents joint interference suppression and power allocation algorithms for DS-CDMA networks with multiple hops and decode-and-forward (DF) protocols. A scheme for joint allocation of power levels across the relays subject to group-based power constraints and the design of linear receivers for interference suppression is proposed. A constrained minimum mean-squared error (MMSE) design for the receive filters and the power allocation vectors is devised along with an MMSE channel estimator. In order to solve the proposed optimization efficiently, a method to form an effective group of users and an alternating optimization strategy are devised with recursive alternating least squares (RALS) algorithms for estimating the parameters of the receiver, the power allocation and the channels. Simulations show that the proposed algorithms obtain significant gains in capacity and performance over existing schemes.

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arXiv2013

Minimum BER Power Adjustment and Receiver Design for Distributed Space-Time Coded Cooperative MIMO Relaying Systems

An adaptive joint power allocation (JPA) and linear receiver design algorithm using the minimum bit error rate (MBER) criterion for a cooperative Multiple-Input Multiple-Output (MIMO) network is proposed. The system employs multiple relays with Distributed Space-Time Coding (DSTC) schemes and an Amplify-and-Forward (AF) strategy. It is designed according to a joint constrained optimization algorithm to determine the MBER power allocation parameters and the receive filter parameters for each transmitted symbol. The simulation results indicate that the proposed algorithm obtains performance gains compared to the equal power allocation systems and the minimum mean square error (MMSE) designs.

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