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Rio de Janeiro, Brazil
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Zhang L.,Zhejiang University | Cai Y.,Zhejiang University | Li C.,Zhejiang University | de Lamare R.C.,CETUC PUC Rio
Eurasip Journal on Advances in Signal Processing | Year: 2017

In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We develop detailed analyses in terms of mean and mean square performance for the proposed algorithms and derive mathematical expressions for the mean square deviation (MSD) and the excess mean square error (EMSE). The simulation results show that the proposed low-complexity VFF-DRLS algorithms achieve superior performance to the existing DRLS algorithm with fixed forgetting factor when applied to scenarios of distributed parameter and spectrum estimation. Besides, the simulation results also demonstrate a good match for our proposed analytical expressions. © 2017, The Author(s).

Landau L.,CETUC PUC Rio | Dorpinghaus M.,TU Dresden | Fettweis G.P.,TU Dresden
IEEE Communications Letters | Year: 2017

A bandlimited additive white Gaussian noise channel is considered where the output is 1-bit quantized and oversampled with respect to the Nyquist rate. We consider root raised cosine filters at the transmitter and receiver. In particular, we focus on a roll-off factor equal to 1 and 0. Because of the oversampling the channel has infinite memory. An auxiliary channel law is proposed which describes the resulting received sequences based on a truncated waveform. The random distortion due to the residual sidelobes can be considered as an additional noise term in the auxiliary channel law. The auxiliary channel law is utilized for computing a lower bound on the achievable rate and in a further step for optimizing a Markov source model. Different signaling schemes have been considered, such as BPSK and ASK. Moreover, Nyquist signaling and faster-than-Nyquist signaling are considered. The resulting achievable rates are superior as compared with results from the literature on bandlimited channels with noise, 1-bit quantization, and oversampling at the receiver. © 1997-2012 IEEE.

Song N.,TU Ilmenau | Alokozai W.U.,TU Ilmenau | De Lamare R.C.,CETUC PUC Rio | De Lamare R.C.,University of York | Haardt M.,TU Ilmenau
IEEE Signal Processing Letters | Year: 2014

We propose a reduced-rank beamformer based on the rank-$D$ Joint Iterative Optimization (JIO) of the modified Widely Linear Constrained Minimum Variance (WLCMV) problem for non-circular signals. The novel WLCMV-JIO scheme takes advantage of both the Widely Linear (WL) processing and the reduced-rank concept, outperforming its linear counterpart as well as the full-rank WL beamformer. We develop an augmented recursive least squares algorithm and present an improved structured version with a much more efficient implementation. It is shown that the improved adaptive scheme achieves the best convergence performance among all the considered methods with a low computational complexity. © 1994-2012 IEEE.

De Lamare R.C.,University of York | Sampaio-Neto R.,CETUC PUC RIO | Haardt M.,TU Ilmenau
IEEE Transactions on Signal Processing | Year: 2011

This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications systems. The proposed scheme and algorithms are based on a two-stage processing framework that consists of a transformation matrix that performs dimensionality reduction followed by a reduced-rank estimator. The complex structure of the transformation matrix of existing methods motivates the development of a blind adaptive reduced-rank constrained (BARC) scheme along with a low-complexity reduced-rank decomposition. The proposed BARC scheme and a reduced-rank decomposition based on the concept of joint interpolation, switched decimation and reduced-rank estimation subject to a set of constraints are then detailed. The proposed set of constraints ensures that the multipath components of the channel are combined prior to dimensionality reduction. We develop low-complexity joint interpolation and decimation techniques, stochastic gradient, and recursive least squares reduced-rank estimation algorithms. A model-order selection algorithm for adjusting the length of the estimators is devised along with techniques for determining the required number of switching branches to attain a predefined performance. An analysis of the convergence properties and issues of the proposed optimization and algorithms is carried out, and the key features of the optimization problem are discussed. We consider the application of the proposed algorithms to interference suppression in DS-CDMA systems. The results show that the proposed algorithms outperform the best known reduced-rank schemes, while requiring lower complexity. © 2006 IEEE.

Xu S.,University of York | De Lamare R.C.,CETUC PUC Rio | De Lamare R.C.,University of York | Poor H.V.,Princeton University
IET Signal Processing | Year: 2016

This study presents distributed conjugate gradient (CG) algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional CG (CCG) and modified CG (MCG) algorithms are developed with incremental and diffusion adaptive cooperation strategies. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least-mean square-based algorithms and a performance that is close to recursive least-squares algorithms. In comparison with existing centralised or distributed estimation strategies, key features of the proposed algorithms are: (i) more accurate estimates and faster convergence speed can be obtained and (ii) the design of preconditioners for CG algorithms, which can improve the performance of the proposed CG algorithms is presented. Simulations show the performance of the proposed CG algorithms against previously reported techniques for distributed parameter estimation and distributed spectrum estimation applications. © The Institution of Engineering and Technology.

Lu X.,University of York | Zu K.,Ericsson AB | De Lamare R.C.,CETUC PUC Rio
2014 Sensor Signal Processing for Defence, SSPD 2014 | Year: 2014

In this paper, we investigate precoding techniques for physical-layer security in multi-user MIMO systems under various conditions of channel state information (CSI). A Lattice Reduction (LR) aided non-linear precoding technique based on Successive Optimization Tomlinson-Harashima Precoding (SO-THP) and Simplified Generalized Matrix Inversion (S-GMI) technique is proposed along with a strategy for injecting artificial noise prior to transmission. Simulation results show that the proposed LR-SO-THP+S-GMI precoding technique outperforms existing non-linear and linear precoding algorithms in terms of BER and secrecy rate performances. © 2014 IEEE.

De Lamare R.C.,CETUC PUC Rio | De Lamare R.C.,University of York | Sampaio-Neto R.,CETUC PUC Rio
IEEE Signal Processing Letters | Year: 2014

This letter proposes a novel sparsity-aware adaptive filtering scheme and algorithms based on an alternating optimization strategy with shrinkage. The proposed scheme employs a two-stage structure that consists of an alternating optimization of a diagonally-structured matrix that speeds up the convergence and an adaptive filter with a shrinkage function that forces the coefficients with small magnitudes to zero. We devise alternating optimization least-mean square (LMS) algorithms for the proposed scheme and analyze its mean-square error. Simulations for a system identification application show that the proposed scheme and algorithms outperform in convergence and tracking existing sparsity-aware algorithms. © 1994-2012 IEEE.

Healy C.,CETUC PUC Rio | De Lamare R.C.,CETUC PUC Rio
2015 IEEE International Conference on Communication Workshop, ICCW 2015 | Year: 2015

Low-density parity-check (LDPC) codes offer excellent performance at competitive levels of complexity. Short block length LDPC codes avoid the large processing latency incurred by the large block lengths classically considered for this class of codes, making them a potential candidate for next generation wireless communications systems. In this paper, a novel informed dynamic scheduling (IDS) approach for decoding LDPC codes is developed based on the use of the current message reliabilities and the residuals of the potential updates to select the messages passed in the graph during iterative Sum Product decoding. An alternative measure of the iteration of the IDS schemes is also proposed which highlights the high cost of those algorithms in terms of processing complexity and motivates the development of the proposed approach. The proposed Rel.-RBP decoding algorithm offers very fast convergence at reduced complexity and gains in error rate performance when compared to the previous schemes. © 2015 IEEE.

De Lamare R.C.,University of York | Sampaio-Neto R.,CETUC PUC RIO
IEEE Transactions on Communications | Year: 2010

We propose blind adaptive multi-input multi-output (MIMO) linear receivers for DS-CDMA systems using multiple transmit antennas and space-time block codes (STBC) in multipath channels. A space-time code-constrained constant modulus (CCM) design criterion based on constrained optimization techniques is considered and recursive least squares (RLS) adaptive algorithms are developed for estimating the parameters of the linear receivers. A blind space-time channel estimation method for MIMO DS-CDMA systems with STBC based on a subspace approach is also proposed along with an efficient RLS algorithm. Simulations for a downlink scenario assess the proposed algorithms in several situations against existing methods. © 2010 IEEE.

De Marca R.,CETUC PUC Rio | Fettweis G.,TU Dresden
2011 IEEE Online Conference on Green Communications, GreenCom'11 | Year: 2011

It is our honor and pleasure to welcome you to the first all online IEEE Online Conference on Green Communications, September 26-29, 2011. © 2011 IEEE.

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