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Glauber Brante

Researcher at Federal University of Technology - Paraná

Publications -  115
Citations -  1040

Glauber Brante is an academic researcher from Federal University of Technology - Paraná. The author has contributed to research in topics: Efficient energy use & Energy consumption. The author has an hindex of 17, co-authored 106 publications receiving 847 citations. Previous affiliations of Glauber Brante include Universidade Federal de Santa Catarina.

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Analysis and Performance Optimization of LoRa Networks With Time and Antenna Diversity

TL;DR: Results show that LoRa networks are highly sensitive to the increase in user and traffic density, but both message replication and multiple antennas can enhance performance.
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Outage Probability and Energy Efficiency of Cooperative MIMO with Antenna Selection

TL;DR: The results show that selecting the antennas with respect to the destination is in general a better option when the energy consumption is accounted, and TAS+SSC outperforms the other schemes for high spectral efficiency when the number of antennas at each node is small.
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Short Channel Hopping Sequence Approach to Rendezvous for Cognitive Networks

TL;DR: A novel short-sequence-based (SSB) rendezvous algorithm which works under symmetric and asymmetric channel models and outperforms other recently developed rendezvous protocols in terms of expected TTR and maximum TTR.
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Optimizing the Code Rate of Energy-Constrained Wireless Communications With HARQ

TL;DR: This paper develops an energy consumption model that focuses on simple HARQ and Chase combining transmissions, and shows that transmission schemes with high diversity gain are the most efficient choice for long range transmissions and HARQ-CC can effectively extend the transmission range of a low-power communication device.
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Distributed Fuzzy Logic-Based Relay Selection Algorithm for Cooperative Wireless Sensor Networks

TL;DR: It is shown that the proposed scheme can transmit a larger amount of information during the network lifetime with respect to both the opportunistic and random selection algorithms, increasing the network utility.