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20182021
most citedLIDAR Data for Deep Learning-Based mmWave Beam-Selection

180 citations · 191 across the 12 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.SP2019★ 1 cited

C-RAN Virtualization with OpenAirInterface

Igor Trindade, Cleverson Nahum, Camila Novaes +3

C-RAN virtualization is a research topic with great interest since it allows to share baseband processing resources.Therefore, in this work, we report the implementation of a virtu…

eess.SP2019

Evaluation of Simplified Methodology for Obtaining mmWave MIMO Channels from Ray-Tracing Simulations

Isabela Trindade, Brenda Vilas Boas, Aldebaro Klautau

The use of higher frequencies and MIMO is important in many 5G use cases. However, the available channel models for millimeter waves (mmWaves) currently demand investigation and th…

eess.SP2019

Development and Evaluation of Least Square Satellite Tracking in Real Antenna Control System

Thiago Sarmento, Andrey Nakamura, Itelo Filho +4

This work describes the implementation of a satellite tracking technique in a real antenna control system. The technique uses least squares estimation to search for the best antenn…

eess.SP2019

Tenant-Aware Slice Admission Control using Neural Networks-Based Policy Agent

Pedro Batista, Shah Nawaz Khan, Peter Öhlén +1

5G networks will provide the platform for deploying large number of tenant-associated management, control and end-user applications having different resource requirements at the in…

eess.SP2019★ 180 cited

LIDAR Data for Deep Learning-Based mmWave Beam-Selection

Aldebaro Klautau, Nuria González-Prelcic, Robert W. Heath

Millimeter wave communication systems can leverage information from sensors to reduce the overhead associated with link configuration. LIDAR (light detection and ranging) is one se…