Machine Learning, Communication and Networks

A student at SMU Lyle inspects a drone he is working on at the Electrical and Computer Engineering Department of SMU

Research in Machine Learning, Communication and Networks encompasses wireless systems, signal processing, and adaptive networking. Projects range from massive MIMO architectures and federated vehicular swarms to machine learning-based signal analysis for environmental and acoustic sensing.

Wireless Networks, Swarm Teaming, and Intelligent Infrastructure

Faculty Contacts: J. Camp, D. Rajan

This group area explores scalable, adaptive communication platforms with an emphasis on multi-antenna systems, drone-based and swarm networks, and topology-aware infrastructure for both rural and urban settings. These efforts have been supported by NSF and DHS and include experimental testbeds advancing swarm coordination, autonomous learning, and next-generation wireless capabilities. In addition to foundational wireless systems, the group is deeply engaged in integrating emerging technologies such as digital twins, federated learning, smart contracts, and fully homomorphic encryption. Applications span agriculture, spectrum management, transportation, and cyber-physical systems, emphasizing secure, privacy-preserving, and intelligent communication architectures.

Next-Generation Massive Multi-Antenna Towers and Federated Learning Based Vehicular Swarms Image

Sensor Signal Processing and Machine Learning

Faculty Contacts: S. Douglas, D. Rajan

This group applies adaptive algorithms and information theory to extract content from environmental sensor data. Applications include remote air quality sensing, acoustic monitoring, and vehicular emissions tracking.

Sensor Processing, Remote Sensing & ML for Vehicular Emissions Detection Image