arXiv subjects

R. C. de Lamare

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

At least 55 records · Page 3Linked to original sources

arXiv2022

Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized Parameters for Impulsive Noise Environments

This paper proposes a unified sparsity-aware robust recursive least-squares RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive noise. The proposed algorithm generalizes multiple algorithms only by replacing the specified criterion of robustness and sparsity-aware penalty. Furthermore, by jointly optimizing the forgetting factor and the sparsity penalty parameter, we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only exhibits low misadjustment but also can track well sudden changes of a sparse system. Simulations in impulsive noise scenarios demonstrate that the proposed S-RRLS and JO-S-RRLS algorithms outperform existing techniques.

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arXiv2022

Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications

In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machine-type communications. By including the channel and the a priori activity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two message-scheduling strategies based on the residual belief propagation and the activity user detection in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.

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arXiv2022

Study of Robust Adaptive Power allocation for the Downlink of Multiple-Antenna Systems

Multiple-input multiple-output (MIMO) systems greatly increase the overall throughput of wireless systems since they are capable of transmitting multiple streams employing the same time-frequency resources. However, this gain requires an appropriate precoder design and a power allocation technique. In general, precoders and power allocation schemes are designed assuming perfect channel estate information (CSI). Nonetheless, this is an optimistic assumption since real systems only possess partial or imperfect CSI at the transmitter (CSIT). The imperfect CSIT originates residual inter-user interference, which is detrimental for wireless systems. In this paper, two adaptive power allocation algorithms are proposed, which are more robust against CSIT imperfections than conventional techniques. Both techniques employ the mean square error as the objective function. Simulation results show that the proposed techniques obtain a higher performance in terms of sum-rate than conventional approaches.

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arXiv2022

Study of filtered-x logarithmic recursive least $p$-power algorithm

For active impulsive noise control, a filtered-x recursive least $p$-power (FxRLP) algorithm is proposed by minimizing the weighted summation of the $p$-power of the \emph{a posteriori} errors. Since the characteristic of the target noise is investigated, the FxRLP algorithm achieves good performance and robustness. To obtain a better performance, we develop a filtered-x logarithmic recursive least $p$-power (FxlogRLP) algorithm which integrates the $p$-order moment with the logarithmic-order moment. Simulation results demonstrate that the FxlogRLP algorithm is superior to the existing algorithms in terms of convergence rate and noise reduction.

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arXiv2021

Study of Linear Precoding and Power Allocation for Large Multiple-Antenna Systems with Coarsely Quantized Signals

This work studies coarse quantization-aware BD (${\scriptstyle\mathrm{CQA-BD}}$) and coarse quantization-aware RBD (${\scriptstyle\mathrm{CQA-RBD}}$) precoding algorithms for large-scale MU-MIMO systems with coarsely quantized signals and proposes the coarse-quantization most advantageous allocation strategy (${\scriptstyle\mathrm{CQA-MAAS}}$) power allocation algorithm for linearly-precoded MU-MIMO systems. An analysis of the sum-rate along with studies of computational complexity is also carried out. Finally, comparisons between existing precoding and its power allocated version are followed by conclusions.

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arXiv2021

Study of Polar Codes Based on Piecewise Gaussian Approximation

In this paper, we investigate the construction of polar codes by Gaussian approximation (GA) and develop an approach based on piecewise Gaussian approximation (PGA). In particular, with the piecewise approach we obtain a function that replaces the original GA function with a more accurate approximation, which results in significant gain in performance. The proposed PGA construction of polar codes is presented in its integral form as well as an alternative approximation that does not rely on the integral form. Simulations results show that the proposed PGA construction outperforms the standard GA for several examples of polar codes and rates.

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arXiv2021

Decoding of Polar Codes Based on Q-Learning-Driven Belief Propagation

This paper presents an enhanced belief propagation (BP) decoding algorithm and a reinforcement learning-based BP decoding algorithm for polar codes. The enhanced BP algorithm weighs each Processing Element (PE) input based on their signals and Euclidean distances using a heuristic metric. The proposed reinforcement learning-based BP decoding strategy relies on reweighting the messages and consists of two steps: we first weight each PE input based on their signals and Euclidean distances using a heuristic metric, then a Q-learning algorithm (QLBP) is employed to figure out the best correction factor for successful decoding. Simulations show that the proposed enhanced BP and QLBP decoders outperform the successive cancellation (SC) and belief propagation (BP) decoders, and approach the SCL decoders.

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arXiv2021

Study of Joint Activity Detection and Channel Estimation Based on Message Passing with RBP Scheduling for MTC

In this work, based on the hybrid generalized approximate message passing (HyGAMP) algorithm, we propose the message-scheduling GAMP (MSGAMP) algorithm in order to address the problem of joint active device detection and channel estimation in an uplink grant-free massive MIMO system scenario. In MSGAMP, we apply three different scheduling techniques based on the Residual Belief Propagation (RBP) in which messages are generated using the latest available information. With a much lower computational cost than the state-of-the-art algorithms, MSGAMP-type schemes exhibits good performance in terms of activity error rate and normalized mean squared error, requiring a small number of iterations for convergence. %

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arXiv2021

Study of Multi-Branch Tomlinson-Harashima Precoding with Multiple-Antenna Systems and Rate Splitting

Rate splitting (RS) has emerged as a valuable technology for wireless communications systems due to its capability to deal with uncertainties in the channel state information at the transmitter (CSIT). RS with linear and non-linear precoders, such as the Tomlinson-Harashima (THP) precoder, have been explored in the downlink (DL) of multiuser multi antenna systems. In this work, we propose a multi-branch (MB) scheme for a RS-based multiple-antenna system, which creates patterns to order the transmitted symbols and enhances the overall sum rate performance compared to existing approaches. Closed-form expressions are derived for the sum rate through statistical analysis. Simulation results show that the proposed MB-THP for RS outperforms conventional THP and MB-THP schemes.

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arXiv2021

Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood Orthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with non-uniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimates when Khatri-Rao product-based models are used to generate difference coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP technique, for each iteration a set of candidates is generated based on the correlation-maximization between the dictionary and the residue vector. LBML-OMP then chooses the best candidate based on a reduced-complexity asymptotic maximum likelihood decision rule. Simulations show the improved results of EDCTM over existing approaches and that LBML-OMP outperforms existing sparse recovery algorithms as well as Spatial Smoothing Multiple Signal Classification with NLAs.

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arXiv2021

Robust MMSE Precoding and Power Allocation for Cell-Free Networks

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with \textcolor{red}{single}-antenna access points (APs) and single-antenna users. An iterative robust minimum mean-square error (RMMSE) precoder based on generalized loading is developed to mitigate interference in the presence of imperfect channel state information (CSI). An achievable rate analysis is carried out and optimal and uniform power allocation schemes are developed based on the signal-to-interference-plus-noise ratio. An analysis of the computational costs of the proposed RMMSE and existing schemes is also presented. Numerical results show the improvement provided by the proposed RMMSE precoder against linear minimum mean-square error, zero-forcing and conjugate beamforming precoders in the presence of imperfect CSI.

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arXiv2021

Iterative Access Point Selection, MMSE Precoding and Power Allocation for Cell-Free Networks

In this work, we propose iterative access point (AP) selection (APS), linear minimum mean-square error (MMSE) precoding and power allocation techniques for Cell-Free Massive multiple-input multiple-output (MIMO) systems. We consider the downlink channel with single-antenna users and multiple-antenna APs. We derive sum-rate expressions for the proposed iterative APS techniques followed by MMSE precoding and optimal, adaptive, and uniform power allocation schemes. Simulations show that the proposed approach outperforms existing conjugate beamforming (CB) and zero-forcing (ZF) schemes and that performance remains excellent with APS, in the presence of perfect and imperfect channel state information (CSI).

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arXiv2021

Study of Design of Rate-Compatible Polar Codes Based on Non-Uniform Channel Polarization

We propose a novel scheme for rate-compatible arbitrary-length polar code construction for the additive white Gaussian noise (AWGN) channel. The proposed scheme is based on the concept of non-uniform channel polarization. The original polar codes can only be designed with code lengths that are powers of two. Puncturing, shortening and extension are three strategies to obtain arbitrary code lengths and code rates for polar codes. There are other ways to design codes with arbitrary length but which have encoding and decoding with higher complexity such as multi-kernel, concatenated codes and specific constructions for Belief propagation (BP) or Successive Cancellation (SC) decoding. In general, the quality of the projected bit channels by these arbitrary-length techniques differs from that of the original bit channels, which can greatly affect the performance of the constructed polar codes. The proposed Non-Uniform Polarization based on Gaussian Approximation (NUPGA) is an efficient construction technique for rate-compatible arbitrary-length polar codes, which chooses the best channels (i.e., selects the positions of the information bits) by re-polarization of the codeword with desired length. A generalization of the Gaussian Approximation is devised for both polarization and re-polarization processes. We also present shortening and extension techniques for design polar codes. Simulations verify the effectiveness of the proposed NUPGA designs against existing rate-compatible techniques.

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arXiv2021

Tomlinson-Harashima Precoding with Stream Combiners for MU-MIMO with Rate-Splitting

This paper introduces multiuser multiple-input multiple-output (MU-MIMO) architectures based on non-linear precoding and stream combining techniques using rate-splitting (RS), where the transmitter often has only partial knowledge of the channel state information (CSI). In contrast to existing works, we consider deployments where the receivers may be equipped with multiple antennas. This allows us to employ linear combining techniques based on the Min-Max, the maximum ratio and the minimum mean-square error criteria along with Tomlinson-Harashima precoders (THP) for RS-based MU-MIMO systems to enhance the sum-rate performance. Moreover, we incorporate the Multi-Branch (MB) concept into the RS architecture to further improve the sum-rate performance. Closed-form expressions for the signal-to-interference-plus-noise ratio and the sum-rate at the receiver end are devised through statistical analysis. Simulation results show that the proposed RS-THP schemes achieve better performance than conventional linear and THP precoders.

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arXiv2021

Dynamic Message Scheduling With Activity-Aware Residual Belief Propagation for Asynchronous mMTC Systems

In this letter, we propose a joint active device detection and channel estimation framework based on factor graphs for asynchronous uplink grant-free massive multiple-antenna systems. We then develop the message-scheduling GAMP (MSGAMP) algorithm to perform joint active device detection and channel estimation. In MSGAMP we apply scheduling techniques based on the residual belief propagation (RBP) and the activity user detection (AUD) in which messages are generated using the latest available information. MSGAMP-type schemes show a good performance in terms of activity error rate and normalized mean squared error, requiring a smaller number of iterations for convergence and lower complexity than state-of-the-art techniques.

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arXiv2021

Robust Adaptive Filtering Based on Exponential Functional Link Network

The exponential functional link network (EFLN) has been recently investigated and applied to nonlinear filtering. This brief proposes an adaptive EFLN filtering algorithm based on a novel inverse square root (ISR) cost function, called the EFLN-ISR algorithm, whose learning capability is robust under impulsive interference. The steady-state performance of EFLN-ISR is rigorously derived and then confirmed by numerical simulations. Moreover, the validity of the proposed EFLN-ISR algorithm is justified by the actually experimental results with the application to hysteretic nonlinear system identification.

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arXiv2021

Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals

In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. We also carry out a statistical analysis of the proposed DQA-LMS algorithm that includes a stability condition. Simulations assess the DQA-LMS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode and demonstrate the effectiveness of the DQA-LMS algorithm.

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arXiv2020

Low-Cost Maximum Entropy Covariance Matrix Reconstruction Algorithm for Robust Adaptive Beamforming

In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions. The proposed method exploits algorithms based on the maximum entropy power spectrum (MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so that the beamforming weights are updated adaptively, thus greatly reducing the computational complexity. MEPS is further used to reconstruct the desired signal covariance matrix and to improve the estimate of the desired signals's steering vector (SV). Simulations show the superiority of the proposed MEPS-NPIC approach over previously proposed beamformers.

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