arXiv subjects

R. C. de Lamare

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

At least 19 recordsLinked to original sources

arXiv2025

Study of AP Association and Users and Power Allocation for Cell-Free Massive MIMO Systems

This paper introduces an access point-user (AP-UE) association strategy combined with pilot power allocation to mitigate multiuser interference and enhance spectral efficiency (SE) in clustered cell-free massive MIMO (CCF-mMIMO) networks. We propose a dynamic channel-based clustering method that groups APs according to their channel correlation, ensuring users are associated with APs exhibiting similar channel characteristics. The proposed approach exploits hierarchical clustering, enabling flexible cluster sizing to improve interference management and overall SE. Moreover, we present a power control (PC) technique that is based on a weighted sum-rate maximization (WSRM) algorithm to ensure consistent service quality across users. Numerical results demonstrate that the proposed method achieves superior SE and robust performance in high-density multi-user environments as compared to competing approaches.

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arXiv2025

Study of Robust Resource Allocation in Cell-Free Multiple-Antenna Networks

Cell-free networks outperform cellular networks in many aspects, yet their efficiency is affected by imperfect channel state information (CSI). In order to address this issue, this work presents a robust resource allocation framework designed for the downlink of user-centric cell-free massive multi-input multi-output (CF-mMIMO) networks. This framework employs a sequential resource allocation strategy with a robust user scheduling algorithm designed to maximize the sum-rate of the network and two robust power allocation algorithms aimed at minimizing the mean square error, which are developed to mitigate the effects of imperfect CSI. An analysis of the proposed robust resource allocation problems is developed along with a study of their computational cost. Simulation results demonstrate the effectiveness of the proposed robust resource allocation algorithms, showing a performance improvement of up to 30\% compared to existing techniques.

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arXiv2025

Machine Learning-Based AP Selection in User-Centric Cell-free Multiple-Antenna Networks

User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, including reduced interference and consistent quality of service while maintaining manageable complexity. In this paper, we propose novel learning-based access point (AP) selection schemes tailored for UCCF massive MIMO systems. The learning model exploits the dataset generated from two distinct AP selection schemes, based on large-scale fading (LSF) coefficients and the sum-rate coefficients, respectively. The proposed learning-based AP selection schemes could be implemented centralized or distributed, with the aim of performing AP selection efficiently. We evaluate our model's performance against CF and two heuristic clustering schemes for UCCF networks. The results demonstrate that the learning-based approach achieves a comparable sum-rate performance to that of competing techniques for UCCF networks, while significantly reducing computational complexity.

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arXiv2025

Study of Robust Multiuser Scheduling and Power Allocation in Cell-Free MIMO Networks

This paper introduces a robust resource allocation framework for the downlink of cell-free massive multi-input multi-output (CF-mMIMO) networks to address the effects caused by imperfect channel state information (CSI). In particular, the proposed robust resource allocation framework includes a robust user scheduling algorithm to optimize the network's sum-rate and a robust power allocation technique aimed at minimizing the mean square error (MSE) for a network with a linear precoder. Unlike non-robust resource allocation techniques, the proposed robust strategies effectively counteract the effects of imperfect CSI, enhancing network efficiency and reliability. Simulation results show a significant improvement in network performance obtained by the proposed approaches, highlighting the impact of robust resource allocation in wireless networks.

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arXiv2024

Study of Iterative Detection and Decoding for Multiuser Systems and MMSE Refinements with Active or Passive RIS

An iterative detection and decoding (IDD) scheme is proposed for multiuser multiple-antenna systems assisted by an active or a passive Reconfigurable Intelligent Surface (RIS). The proposed approach features an IDD strategy that incorporates Low-Density Parity-Check (LDPC) codes, RIS processing with refinements of soft information in the form of log likelihood ratios (LLRs) and truncation. Specifically, a minimum mean square error (MMSE) receive filter is used for refinement of LLRs and truncation at the RIS, and for soft interference cancellation at the receiver. An analysis of the proposed MMSE refinement is also devised along with a study of the computational complexity of the proposed and existing schemes. Simulation results demonstrate significant improvements in system capacity and bit error rate in the presence of block-fading channels

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arXiv2024

Iterative Detection and Decoding for Clustered Cell-Free Massive MIMO Networks

In this letter, we propose an iterative soft interference cancellation scheme for intra-cluster (ICL) and out-of-cluster (OCL) interference mitigation in user-centric clustered cell-free massive multiple-antenna networks. We propose a minimum mean-square error receive filter with a novel modified parallel interference cancellation scheme to mitigate ICL and OCL interference. Unlike prior work, we model the OCL interference and devise a least squares estimator to perform OCL interference estimation. An iterative detection and decoding scheme that adopts low-density parity check codes and incorporates the OCL interference estimate is developed. Simulations assess the proposed scheme against existing techniques in terms of bit error rate performance.

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arXiv2024

Study of Iterative Detection and Decoding for RIS-Aided Multiuser Multi-Antenna Systems

We present a novel iterative detection and decoding (IDD) scheme for Reconfigurable Intelligent Surface (RIS)-assisted multiuser multiple-antenna systems. The proposed approach introduces a joint iterative detection strategy that integrates Low-Density Parity-Check (LDPC) codes, RIS processing and iterative detection and decoding. In particular, we employ a minimum mean square error receive filter that performs truncation at the RIS and soft interference cancelation at the receiver. Simulation results evaluate the system's overall capacity and bit error rate, and demonstrate substantial improvements in bit error rate across block-fading channels.

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arXiv2024

Study of Weighted Residual Layered Belief Propagation for Decoding of LDPC Codes

In this work, we investigate the decoding of Low-Density Parity-Check (LDPC) codes using informed dynamic scheduling algorithms that require a reduced number of iterations. In particular, we devise the weighted residual layered belief propagation (WR-LBP) decoding algorithm, which exploits the residual within a structured layer framework to speed the number of required decoding iterations. The proposed WR-LBP algorithm is assessed against important LDPC decoding algorithms, in terms of the number of iterations required for convergence and the bit error rates.

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arXiv2024

Study of Tomlinson-Harashima Precoders for Rate-Splitting-Based Cell-Free MIMO Networks

Cell-free (CF) systems have the potential to fulfill the increasing performance demand of future wireless applications by employing distributed access points (APs) that transmit the information over the same time-frequency resources. Due to the simultaneous transmission, multiuser interference (MUI) degrades the overall performance. To cope with the MUI in the downlink several linear precoding techniques, which rely on perfect channel state information at the transmitter (CSIT), have been studied. However, perfect CSIT is hardly obtained in practical systems. In this context, rate-splitting (RS) has arisen as a potential solution to deal with CSIT imperfections. In contrast to existing works, we explore non-linear precoding techniques along with RS-CF systems. Furthermore, the multi-branch (MB) concept is included to further enhance the overall performance of the system. Simulations show that the proposed MB-THP for RS-based CF systems outperforms the conventional linear precoders.

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arXiv2024

Direction of Arrival Estimation with Sparse Subarrays

This paper proposes design techniques for partially-calibrated sparse linear subarrays and algorithms to perform direction-of-arrival (DOA) estimation. First, we introduce array architectures that incorporate two distinct array categories, namely type-I and type-II arrays. The former breaks down a known sparse linear geometry into as many pieces as we need, and the latter employs each subarray such as it fits a preplanned sparse linear geometry. Moreover, we devise two Direction of Arrival (DOA) estimation algorithms that are suitable for partially-calibrated array scenarios within the coarray domain. The algorithms are capable of estimating a greater number of sources than the number of available physical sensors, while maintaining the hardware and computational complexity within practical limits for real-time implementation. To this end, we exploit the intersection of projections onto affine spaces by devising the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) in conjunction with the estimation of a refined projection matrix related to the noise subspace, as proposed in the GCA root-MUSIC algorithm. An analysis is performed for the devised subarray configurations in terms of degrees of freedom, as well as the computation of the Cramèr-Rao Lower Bound for the utilized data model, in order to demonstrate the good performance of the proposed methods. Simulations assess the performance of the proposed design methods and algorithms against existing approaches.

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arXiv2024

Analysis of Partially-Calibrated Sparse Subarrays for Direction Finding with Extended Degrees of Freedom

This paper investigates the problem of direction-of-arrival (DOA) estimation using multiple partially-calibrated sparse subarrays. In particular, we present the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) DOA estimation algorithm to scenarios with partially-calibrated sparse subarrays. The proposed GCA-MUSIC algorithm exploits the difference coarray for each subarray, followed by a specific pseudo-spectrum merging rule that is based on the intersection of the signal subspaces associated to each subarray. This rule assumes that there is no a priori knowledge about the cross-covariance between subarrays. In that way, only the second-order statistics of each subarray are used to estimate the directions with increased degrees of freedom, i.e., the estimation procedure preserves the coarray Multiple Signal Classification and sparse arrays properties to estimate more sources than the number of physical sensors in each subarray. Numerical simulations show that the proposed GCA-MUSIC has better performance than other similar strategies.

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arXiv2024

Study of Robust Direction Finding Based on Joint Sparse Representation

Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers.

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arXiv2024

Iterative Detection and Decoding Schemes with LLR Refinements in Cell-Free Massive MIMO Networks

In this paper, we propose low-complexity local detectors and log-likelihood ratio (LLR) refinement techniques for a coded cell-free massive multiple input multiple output (CF- mMIMO) systems, where an iterative detection and decoding (IDD) scheme is applied using parallel interference cancellation (PIC) and access point (AP) selection. In particular, we propose three LLR processing schemes based on the individual processing of the LLRs of each AP, LLR censoring, and a linear combination of LLRs by assuming statistical independence. We derive new closed-form expressions for the local soft minimum mean square error (MMSE)-PIC detector and receive matched filter (RMF). We also examine the system performance as the number of iterations increases. Simulations assess the performance of the proposed techniques against existing approaches.

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arXiv2024

Study of Clustering Techniques and Scheduling Algorithms with Fairness for Cell-Free MIMO Networks

In this work, we propose a clustering technique based on information rates for cell-free massive multiple-input multiple-output (MIMO) networks. Unlike existing clustering approaches that rely on the large scale fading coefficients of the channels and user-centric techniques, we develop an approach that is based on the information rates of cell-free massive MIMO networks. We also devise a resource allocation technique to incorporate the proposed clustering and schedule users with fairness. An analysis of the proposed clustering approach based on information rates is carried out along with an assessment of its benefits for scheduling. Numerical results show that the proposed techniques outperform existing approaches.

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arXiv2024

Study of Adaptive Reweighted Sparse Belief Propagation Decoders for Polar Codes

In this paper, we present an adaptive reweighted sparse belief propagation (AR-SBP) decoder for polar codes. The AR-SBP technique is inspired by decoders that employ the sum-product algorithm for low-density parity-check codes. In particular, the AR-SBP decoding strategy introduces reweighting of the exchanged log-likelihood-ratio in order to refine the message passing, improving the performance of the decoder and reducing the number of required iterations. An analysis of the convergence of AR-SBP is carried out along with a study of the complexity of the analyzed decoders. Numerical examples show that the AR-SBP decoder outperforms existing decoding algorithms for a reduced number of iterations, enabling low-latency applications.

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arXiv2024

Study of Noncoherent Sparse Subarrays for Direction Finding Based on Low-Rank and Sparse Recovery

This paper investigates the problem of noncoherent direction-of-arrival (DOA) estimation using different sparse subarrays. In particular, we present a Multiple Measurements Vector (MMV) model for noncoherent DOA estimation based on a low-rank and sparse recovery optimization problem. Moreover, we develop two different practical strategies to obtain sparse arrays and subarrays: i) the subarrays are generated from a main sparse array geometry (Type-I sparse array), and ii) the sparse subarrays that are directly designed and grouped together to generate the whole sparse array (Type-II sparse array). Numerical results demonstrate that the proposed MMV model can benefit from multiple data records and that Type-II sparse noncoherent arrays are superior in performance for DOA estimation

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arXiv2023

Study of Adaptive LLR-based AP selection for Grant-Free Random Access in Cell-Free Networks

This paper presents an iterative detection and decoding scheme along with an adaptive strategy to improve the selection of access points (APs) in a grant-free uplink cell-free scenario. With the requirement for the APs to have low-computational power in mind, we introduce a low-complexity scheme for local activity and data detection. At the central processing unit (CPU) level, we propose an adaptive technique based on local log-likelihood ratios (LLRs) to select the list of APs that should be considered for each device. Simulation results show that the proposed LLRs-based APs selection scheme outperforms the existing techniques in the literature in terms of bit error rate (BER) while requiring comparable fronthaul load.

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arXiv2023

Tomlinson-Harashima Cluster-Based Precoders for Cell-Free MU-MIMO Networks

Cell-free (CF) multiple-input multiple-output (MIMO) systems generally employ linear precoding techniques to mitigate the effects of multiuser interference. However, the power loss, efficiency, and precoding accuracy of linear precoders are usually improved by replacing them with nonlinear precoders that employ perturbation and modulo operation. In this work, we propose nonlinear user-centric precoders for CF MIMO, wherein different clusters of access points (APs) serve different users in CF multiple-antenna networks. Each cluster of APs is selected based on large-scale fading coefficients. The clustering procedure results in a sparse nonlinear precoder. We further devise a reduced-dimension nonlinear precoder, where clusters of users are created to reduce the complexity of the nonlinear precoder, the amount of required signaling, and the number of users. Numerical experiments show that the proposed nonlinear techniques for CF systems lead to an enhanced performance when compared to their linear counterparts.

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