activity
20182021
most citedLow-Rank Tensor MMSE Equalization

5 citations · 6 across the 3 of their papers we have counts for

collaborators

6 papers

eess.SP20211 cited

Machine Learning Prediction of Time-Varying Rayleigh Channels

Joseph Kibugi, Lucas N. Ribeiro, Martin Haardt

Channel state information (CSI) rapidly becomes outdated in high mobility scenarios, degrading the performance of wireless communication systems. In these cases, time series predic…

cs.IT2021

Low-Complexity Zero-Forcing Precoding for XL-MIMO Transmissions

Lucas N. Ribeiro, Stefan Schwarz, Martin Haardt

Deploying antenna arrays with an asymptotically large aperture will be central to achieving the theoretical gains of massive MIMO in beyond-5G systems. Such extra-large MIMO (XL-MI…

eess.SP2020

Low-Complexity Massive MIMO Tensor Precoding

Lucas N. Ribeiro, Stefan Schwarz, André L. F. de Almeida +1

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading…

eess.SP20195 cited

Low-Rank Tensor MMSE Equalization

Lucas N. Ribeiro, André L. F. de Almeida, João C. M. Mota

New-generation wireless communication systems will employ large-scale antenna arrays to satisfy the increasing capacity demand. This massive scenario brings new challenges to the c…

eess.SP2019

Double-Sided Massive MIMO Transceivers for MmWave Communications

Lucas N. Ribeiro, Stefan Schwarz, André L. F. de Almeida

We propose practical transceiver structures for double-sided massive multiple-input-multiple-output (MIMO) systems. Unlike standard massive MIMO, both transmit and receive sides ar…

cs.IT2018

Low-complexity separable beamformers for massive antenna array systems

Lucas N. Ribeiro, André L. F. de Almeida, Josef A. Nossek +1

Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of lo…