arXiv subjects

S. Pinto

Publications and source records attributed to S. Pinto.

4 recordsLinked to original sources

arXiv2023

Study of Multiuser Multiple-Antenna Wireless Communications Systems Based on Super-Resolution Arrays

This work studies multiple-antenna wireless communication systems based on super-resolution arrays (SRAs). We consider the uplink of a multiple-antenna system in which users communicate with a multiple-antenna base station equipped with SRAs. In particular, we develop linear minimum mean-square error (MMSE) receive filters along with linear and successive interference cancellation receivers for processing signals with the difference co-array originating from the SRAs. We then derive analytical expressions to assess the achievable sum-rates associated with the proposed multiple-antenna systems with SRAs. Simulations show that the proposed multiple-antenna systems with SRAs outperform existing systems with standard arrays that have a larger number of antenna elements.

cs.IT↗

arXiv2021

Study of Block Diagonalization Precoding and Power Allocation for Multiple-Antenna Systems with Coarsely Quantized Signals

In this work, we present block diagonalization and power allocation algorithms for large-scale multiple-antenna systems with coarsely quantized signals. In particular, we develop Coarse Quantization-Aware Block Diagonalization ${\scriptstyle\mathrm{\left(CQA-BD\right)}}$ and Coarse Quantization-Aware Regularized Block Diagonalization ${\scriptstyle\mathrm{\left(CQA-RBD\right)}}$ precoding algorithms that employ the Bussgang decomposition and can mitigate the effects of low-resolution signals and interference. Moreover, we also devise the Coarse Quantization-Aware Most Advantageous Allocation Strategy ${\scriptstyle\mathrm{\left(CQA-MAAS\right)}}$ power allocation algorithm to improve the sum rate of precoders that operate with low-resolution signals. An analysis of the sum-rate performance is carried out along with computational complexity and power consumption studies of the proposed and existing techniques. Simulation results illustrate the performance of the proposed ${\scriptstyle\mathrm{CQA-BD}}$ and ${\scriptstyle\mathrm{CQA-RBD}}$ precoding algorithms, and the proposed ${\scriptstyle\mathrm{CQA-MAAS}}$ power allocation strategy against existing approaches.

cs.IT↗

arXiv2018

Direction Finding Based on Multi-Step Knowledge-Aided Iterative Conjugate Gradient Algorithms

In this work, we present direction-of-arrival (DoA) estimation algorithms based on the Krylov subspace that effectively exploit prior knowledge of the signals that impinge on a sensor array. The proposed multi-step knowledge-aided iterative conjugate gradient (CG) (MS-KAI-CG) algorithms perform subtraction of the unwanted terms found in the estimated covariance matrix of the sensor data. Furthermore, we develop a version of MS-KAI-CG equipped with forward-backward averaging, called MS-KAI-CG-FB, which is appropriate for scenarios with correlated signals. Unlike current knowledge-aided methods, which take advantage of known DoAs to enhance the estimation of the covariance matrix of the input data, the MS-KAI-CG algorithms take advantage of the knowledge of the structure of the forward-backward smoothed covariance matrix and its disturbance terms. Simulations with both uncorrelated and correlated signals show that the MS-KAI-CG algorithms outperform existing techniques.

eess.SP↗

arXiv2018

Study of Multi-Step Knowledge-Aided Iterative Nested MUSIC for Direction Finding

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation applied to the signals impinging on a two-level nested array, referred to as multi-step knowledge-aided iterative nested MUSIC method (MS-KAI-Nested-MUSIC), which significantly improves the accuracy of the original Nested-MUSIC. Differently from existing knowledge-aided methods applied to uniform linear arrays (ULAs), which make use of available known DOAs to improve the estimation of the covariance matrix of the input data, the proposed Multi-Step KAI-Nested-MU employs knowledge of the structure of the augmented sample covariance matrix, which is obtained by exploiting the difference co-array structure covariance matrix, and its perturbation terms and the gradual incorporation of prior knowledge, which is obtained on line. The effectiveness of the proposed technique can be noticed by simulations focusing on uncorrelated closely-spaced sources.

eess.SP↗