SPECTRUM HIVE: ARTIFICIAL INTELLIGENCE RADIO SPECTRUM SENSING ALGORITHMS FOR CONTESTED NETWORKS
SPECTRUM HIVE: ARTIFICIAL INTELLIGENCE RADIO SPECTRUM SENSING ALGORITHMS FOR CONTESTED NETWORKS
Speaker
Alexandru Lavric, PhD
is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Ștefan cel Mare University of Suceava, Romania, where he leads research on intelligent radio spectrum management and coordinates the Wireless Lab – Complex Wireless Communications Research Group.
His work is at the intersection of machine learning, AI, and wireless communications. Current research directions include deep learning models for real-time spectrum sensing, signal presence detection, radio transmission classification, bandwidth and SNR directly from IQ samples, as well as multi-protocol detection across LoRa, Sigfox, LTE and emerging 5G to 6G standards, jamming and interference detection with adaptive fallback strategies, and AI-native orchestration for dense IoT deployments.
More recent work addresses 6G enablers: numerology inference from IQ samples, dynamic OFDM/OTFS waveform switching for high mobility scenarios, and integrated sensing and communications. This line of research started with fundamental spectrum coexistence studies in 2016 and has evolved into an adaptive AI-based spectrum sensing orchestrator.
He has authored more than 125 peer-reviewed journal and conference publications, across wireless sensor networks, LPWAN and IoT architectures, spectrum coexistence, and applied machine learning for advanced wireless communications.
Also, he is the founder of Spectrum Hive (spectrumhive.com), a startup translating this research into deployable products for advanced radio spectrum analytics and drone RF detection. The company serves telecommunications operators (e.g., Orange), IoT network operators, smart-city programmers, and defense applications, providing real-time spectrum monitoring, interference mitigation, and resilience against jamming.
Spectrum Hive treats drone detection as a dual-use security problem in a radio environment that is not just congested but actively contested. The designed system fuses three layers: RF spectrum analysis, computer vision, and acoustic sensing with AI correlating the streams to detect, classify, and validate aerial targets in real time, improving both accuracy and range.
Event Details
Date:
Monday, August 3, 2026
Time:
9:30 AM
Location:
EC 3930
Join Online via Zoom:
https://fiu.zoom.us/j/3053485350
Meeting ID:
305 348 5350
Organizers
Hosted by:
- IEEE Miami Section
- IEEE Women in Engineering (WIE)
- IEEE Life Members
- IEEE Antennas and Propagation Society (AP-S)
Organized by:
IEEE Miami Section PES Chapter
In collaboration with:
IEEE WIE, IEEE Life Members & IEEE AP-S
Date and Time
Location
Hosts
Registration
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- 10555 west flagler st
- Miami, Florida
- United States 33174
- Building: EC
- Room Number: EC 3930
Speakers
SPECTRUM HIVE: ARTIFICIAL INTELLIGENCE RADIO SPECTRUM SENSING ALGORITHMS FOR CONTESTED NETWORKS
Speaker
Alexandru Lavric, PhD
is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Ștefan cel Mare University of Suceava, Romania, where he leads research on intelligent radio spectrum management and coordinates the Wireless Lab – Complex Wireless Communications Research Group.
His work is at the intersection of machine learning, AI, and wireless communications. Current research directions include deep learning models for real-time spectrum sensing, signal presence detection, radio transmission classification, bandwidth and SNR directly from IQ samples, as well as multi-protocol detection across LoRa, Sigfox, LTE and emerging 5G to 6G standards, jamming and interference detection with adaptive fallback strategies, and AI-native orchestration for dense IoT deployments.
More recent work addresses 6G enablers: numerology inference from IQ samples, dynamic OFDM/OTFS waveform switching for high mobility scenarios, and integrated sensing and communications. This line of research started with fundamental spectrum coexistence studies in 2016 and has evolved into an adaptive AI-based spectrum sensing orchestrator.
He has authored more than 125 peer-reviewed journal and conference publications, across wireless sensor networks, LPWAN and IoT architectures, spectrum coexistence, and applied machine learning for advanced wireless communications.
Also, he is the founder of Spectrum Hive (spectrumhive.com), a startup translating this research into deployable products for advanced radio spectrum analytics and drone RF detection. The company serves telecommunications operators (e.g., Orange), IoT network operators, smart-city programmers, and defense applications, providing real-time spectrum monitoring, interference mitigation, and resilience against jamming.
Spectrum Hive treats drone detection as a dual-use security problem in a radio environment that is not just congested but actively contested. The designed system fuses three layers: RF spectrum analysis, computer vision, and acoustic sensing with AI correlating the streams to detect, classify, and validate aerial targets in real time, improving both accuracy and range.
Address:United States