2019 MILCOM

12 Nov 19

2:00 PM - 5:00 PM

Tracks: Track 5: Big Data and ML for Tactical Networks, Unclassified Technical Paper Sessions

Chair: Brian Rivera (US Army Research Laboratory, USA)

Developing RFML Intuition: An Automatic Modulation Classification Architecture Case Study: William Clark, IV, Vanessa Arndorfer, Brook Tamir, Daniel Kim, Cristian Vives and Hunter Morris (Virginia Tech, USA); Lauren J Wong (Virginia Tech Hume Center, USA); William C Headley (Virginia Tech & Hume Center, USA)
Distributed Automatic Modulation Classification with Compressed Data: Lauren J Wong (Virginia Tech Hume Center, USA); Parker D White and Michael Fowler (Virginia Tech, USA); William C Headley (Virginia Tech & Hume Center, USA)
Data Augmentation for Blind Signal Classification: Peng Wang and Manuel Vindiola (Army Research Laboratory, USA)
Break
Practical Radio Frequency Learning for Future Wireless Communication Systems: Damilola Adesina (Prairie View A&M University, USA); Joshua Bassey (CREDIT Center, PVAMU, USA); Lijun Qian (Prairie View A&M University, USA)
Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio: Inna Valieva, Mats Björkman and Johan Åkerberg (Malardalen University, Sweden); Mikael Ekström (Mälardalen University, Sweden)
IoT: Smart City Parking Solutions with Metric-Chisini-Jensen-Shannon Divergence Based Kernels: Piyush K Sharma (Army Research Laboratory, USA); Adrienne Raglin (US Army Research Laboratory (ARL), USA)