68 citations · 372 across the 44 of their papers we have counts for
20 papers · 1 filter
Beyond the Use-and-then-Forget (UatF) Bound: Fixed Point Algorithms for Statistical Max-Min Power Control
Renato Luis Garrido Cavalcante, Noor Ul Ain, Lorenzo Miretti +1
We introduce mathematical tools and fixed point algorithms for optimal statistical max-min power control in cellular and cell-less massive MIMO systems. Unlike previous studies tha…
Theoretical Validation of the Latent Optimally Partitioned- Penalty with Application to Angular Power Spectrum Estimation
Hiroki Kuroda, Renato Luis Garrido Cavalcante, Masahiro Yukawa
This paper demonstrates that, in both theory and practice, the latent optimally partitioned (LOP)- penalty is effective for exploiting block-sparsity without knowled…
Cellular, Cell-less, and Everything in Between: A Unified Framework for Utility Region Analysis in Wireless Networks
Renato Luis Garrido Cavalcante, Tomasz Piotrowski, Slawomir Stanczak
We introduce a unified framework for analyzing utility regions of wireless networks, with a focus on signal-to-interference-plus-noise-ratio (SINR) and achievable rate regions. The…
Localization in Dynamic Indoor MIMO-OFDM Wireless Systems using Domain Adaptation
Rafail Ismayilov, Renato L. G. Cavalcante, Slawomir Stanczak
We propose a method for predicting the location of user equipment (UE) using wireless fingerprints in dynamic indoor non-line-of-sight (NLoS) environments. In particular, our metho…
Characterization of the weak Pareto boundary of resource allocation problems in wireless networks -- Implications to cell-less systems
Renato Luis Garrido Cavalcante, Lorenzo Miretti, Slawomir Stanczak
We establish necessary and sufficient conditions for a network configuration to provide utilities that are both fair and efficient in a well-defined sense. To cover as many applica…
Superiorized Adaptive Projected Subgradient Method with Application to MIMO Detection
Jochen Fink, Renato L. G. Cavalcante, Slawomir Stanczak
In this paper, we show that the adaptive projected subgradient method (APSM) is bounded perturbation resilient. To illustrate a potential application of this result, we propose a s…
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
Matthias Mehlhose, Guillermo Marcus, Daniel Schäufele +6
In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated…
Deep Learning Based Hybrid Precoding in Dual-Band Communication Systems
Rafail Ismayilov, Renato L. G. Cavalcante, Sławomir Stańczak
We propose a deep learning-based method that uses spatial and temporal information extracted from the sub-6GHz band to predict/track beams in the millimeter-wave (mmWave) band. In…
Multi-Group Multicast Beamforming by Superiorized Projections onto Convex Sets
Jochen Fink, Renato L. G. Cavalcante, Slawomir Stanczak
In this paper, we propose an iterative algorithm to address the nonconvex multi-group multicast beamforming problem with quality-of-service constraints and per-antenna power constr…
Channel covariance estimation in multiuser massive MIMO systems with an approach based on infinite dimensional Hilbert spaces
Renato Luis Garrido Cavalcante, Slawomir Stanczak
We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems. The algorithm uses only knowledge of the array response…
Hybrid data and model driven algorithms for angular power spectrum estimation
Renato Luis Garrido Cavalcante, Slawomir Stanczak
We propose two algorithms that use both models and datasets to estimate angular power spectra from channel covariance matrices in massive MIMO systems. The first algorithm is an it…
Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
Matthias Mehlhose, Daniyal Amir Awan, Renato L. G. Cavalcante +2
Conventional multiuser detection techniques either require a large number of antennas at the receiver for a desired performance, or they are too complex for practical implementatio…
Tensor Completion for Radio Map Reconstruction using Low Rank and Smoothness
Daniel Schäufele, Renato L. G. Cavalcante, Slawomir Stanczak
Radio maps are important enablers for many applications in wireless networks, ranging from network planning and optimization to fingerprint based localization. Sampling the complet…
Connections between spectral properties of asymptotic mappings and solutions to wireless network problems
Renato Luís Garrido Cavalcante, Qi Liao, Slawomir Stańczak
In this study we establish connections between asymptotic functions and properties of solutions to important problems in wireless networks. We start by introducing a class of self-…
FDD massive MIMO channel spatial covariance conversion using projection methods
Lorenzo Miretti, Renato L. G. Cavalcante, Slawomir Stanczak
Knowledge of second-order statistics of channels (e.g. in the form of covariance matrices) is crucial for the acquisition of downlink channel state information (CSI) in massive MIM…
Error Bounds for FDD Massive MIMO Channel Covariance Conversion with Set-Theoretic Methods
Renato Luis Garrido Cavalcante, Lorenzo Miretti, Slawomir Stanczak
We derive novel bounds for the performance of algorithms that estimate the downlink covariance matrix from the uplink covariance matrix in frequency division duplex (FDD) massive m…
Downlink channel spatial covariance estimation in realistic FDD massive MIMO systems
Lorenzo Miretti, Renato L. G. Cavalcante, Slawomir Stanczak
The knowledge of the downlink (DL) channel spatial covariance matrix at the BS is of fundamental importance for large-scale array systems operating in frequency division duplexing…
Spectral radii of asymptotic mappings and the convergence speed of the standard fixed point algorithm
Renato L. G. Cavalcante, Slawomir Stanczak
Important problems in wireless networks can often be solved by computing fixed points of standard or contractive interference mappings, and the conventional fixed point algorithm i…
Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
Ban-Sok Shin, Masahiro Yukawa, Renato Luis Garrido Cavalcante +1
We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple…
Improving Resource Efficiency with Partial Resource Muting for Future Wireless Networks
Qi Liao, R. L. G. Cavalcante
We propose novel resource allocation algorithms that have the objective of finding a good tradeoff between resource reuse and interference avoidance in wireless networks. To this e…