68 citations · 372 across the 44 of their papers we have counts for
46 papers
Sum-Rate Maximization via Convex Optimization Using Subgradient Projections Onto Nonlinear Spectral Radius Constraint Sets
Hiroki Kuroda, Renato Luis Garrido Cavalcante
We solve the (weighted) sum-rate maximization problem over the set of achievable rates characterized by a nonlinear spectral radius function. This set has been recently shown to be…
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…
Positive concave deep equilibrium models
Mateusz Gabor, Tomasz Piotrowski, Renato L. G. Cavalcante
Deep equilibrium (DEQ) models are widely recognized as a memory efficient alternative to standard neural networks, achieving state-of-the-art performance in language modeling and c…
Distributed fixed-point algorithms for dynamic convex optimization over decentralized and unbalanced wireless networks
Navneet Agrawal, Renato L. G. Cavalcante, Sławomir Stańczak
We consider problems where agents in a network seek a common quantity, measured independently and periodically by each agent through a local time-varying process. Numerous solvers…
Distributed Convex Optimization "Over-the-Air" in Dynamic Environments
Navneet Agrawal, Renato L. G. Cavalcante, Masahiro Yukawa +1
This paper presents a decentralized algorithm for solving distributed convex optimization problems in dynamic networks with time-varying objectives. The unique feature of the algor…
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…
UL-DL duality for cell-free massive MIMO with per-AP power and information constraints
Lorenzo Miretti, Renato L. G. Cavalcante, Emil Björnson +1
We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information c…
Inverse Feasibility in Over-the-Air Federated Learning
Tomasz Piotrowski, Rafail Ismayilov, Matthias Frey +1
We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition…
Joint optimal beamforming and power control in cell-free massive MIMO
Lorenzo Miretti, Renato Luis Garrido Cavalcante, Slawomir Stanczak
We derive a fast and optimal algorithm for solving practical weighted max-min SINR problems in cell-free massive MIMO networks. For the first time, the optimization problem jointly…
Closed-form max-min power control for some cellular and cell-free massive MIMO networks
Lorenzo Miretti, Renato L. G. Cavalcante, Slawomir Stanczak +3
Many common instances of power control problems for cellular and cell-free massive MIMO networks can be interpreted as max-min utility optimization problems involving affine interf…
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…
The fixed point iteration of positive concave mappings converges geometrically if a fixed point exists: implications to wireless systems
Tomasz Piotrowski, Renato L. G. Cavalcante
We prove that the fixed point iteration of arbitrary positive concave mappings with nonempty fixed point set converges geometrically for any starting point. We also show that posit…
Deep Learning Beam Optimization in Millimeter-Wave Communication Systems
Rafail Ismayilov, Renato L. G. Cavalcante, Sławomir Stańczak
We propose a method that combines fixed point algorithms with a neural network to optimize jointly discrete and continuous variables in millimeter-wave communication systems, so th…
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…
Fixed points of nonnegative neural networks
Tomasz J. Piotrowski, Renato L. G. Cavalcante, Mateusz Gabor
We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors. We first show that nonnega…
Robust Cell-Load Learning with a Small Sample Set
Daniyal Amir Awan, Renato L. G. Cavalcante, Slawomir Stanczak
Learning of the cell-load in radio access networks (RANs) has to be performed within a short time period. Therefore, we propose a learning framework that is robust against uncertai…
Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems
Daniyal Amir Awan, Renato L. G. Cavalcante, Zoran Utkovski +1
Cloud-radio access network (C-RAN) can enable cell-less operation by connecting distributed remote radio heads (RRHs) via fronthaul links to a powerful central unit. In conventiona…
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…