Pedro Henrique Gomes

1.5k citations
43 papers · 1.1k indexed · 1 hit paper · h-index 13

Pedro Henrique Gomes

42 papers receiving 1.0k citations

Hit Papers

Deep Reinforcement Learning for Dynamic Multichannel Acce...3412018202620202023100200300

Peers

Pedro Henrique Gomes
Comparison fields: 5 of 94
  • Computer Networks and Communications 557
  • Electrical and Electronic Engineering 462
  • Computer Vision and Pattern Recognition 137
  • Aerospace Engineering 117
  • Signal Processing 43
Replace Jin‐Ghoo Choi with:
Jin‐Ghoo Choi South Korea
Bruno S. Faiçal Brazil
Miguel García Spain
Anis Ur Rahman Pakistan
Juan Pablo Garćıa-Vázquez Mexico
Mauro Tropea Italy
Gourab Sen Gupta New Zealand
Fazli Subhan Pakistan
Cong Zhao China
Makoto Ikeda Japan
Pedro Henrique Gomes relative to Jin‐Ghoo Choi South Korea Jin‐Ghoo Choi's profile →
Citations per field
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Jin‐Ghoo Choi · 1×
Citations per year

Countries citing papers authored by Pedro Henrique Gomes

Since Specialization
Citations

This map shows the geographic impact of Pedro Henrique Gomes's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Pedro Henrique Gomes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pedro Henrique Gomes more than expected).

Fields of papers citing papers by Pedro Henrique Gomes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pedro Henrique Gomes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Pedro Henrique Gomes. The network helps show where Pedro Henrique Gomes may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Pedro Henrique Gomes, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Pedro Henrique Gomes Line = papers co-authored together Pedro Henrique Gomes links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20238
2 20234
3 202216
4 202213
5 20204
6 202014
7 201910
8 201820
9
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networksbreakdown →
2018341
10 201816
11 20166
12 2016167
13 20165
14 201510
15 201511
16 201447
17 20136
18 20102
19 20104
20 20106

About Pedro Henrique Gomes

Pedro Henrique Gomes is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 43 papers that have together received 1.1k indexed citations. Recurring topics across this work include Software-Defined Networks and 5G (8 papers), Advanced Wireless Network Optimization (8 papers), Wireless Networks and Protocols (7 papers), Energy Efficient Wireless Sensor Networks (6 papers), Power Line Communications and Noise (5 papers), Network Traffic and Congestion Control (5 papers), Advanced MIMO Systems Optimization (5 papers) and IoT and Edge/Fog Computing (4 papers). The work is most often cited by research in Computer Networks and Communications (557 citations), Electrical and Electronic Engineering (462 citations) and Computer Vision and Pattern Recognition (137 citations). Pedro Henrique Gomes has collaborated with scholars based in Brazil, United States and Sweden. Frequent co-authors include Bhaskar Krishnamachari, Shangxing Wang, Hanpeng Liu, Jó Ueyama, Gustavo Pessin, Bruno S. Faiçal, Leandro Yukio Mano, Heitor Freitas, Geraldo P. Rocha Filho and Thomas Watteyne. Their work appears in journals such as IEEE Communications Surveys & Tutorials, IEEE Communications Magazine and Computers and Electronics in Agriculture.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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