IEEE Military Communications Conference
28 October – 1 November 2024 // Washington, DC, USA
C3I Technologies for Military and Intelligence Operations Today and Tomorrow

Call for Unclassified Technical Papers

Unclassified Technical Program

The unclassified technical program provides a venue for papers and presentations that do not include ITAR-sensitive, CUI, classified, or proprietary information. EDAS will be used for all unclassified paper submissions. IEEE MILCOM 2024 welcomes submissions from researchers, inventors, and technology leaders from government, U.S. and coalition military services, industry, research laboratories, and universities. Papers with a strong emphasis on U.S. military operations should be submitted to the restricted or classified sessions. The following technical five tracks have been established for paper submission:

TRACK 1 – Waveforms and Signal Processing
Topics:
 Antennas & RF; Modulation & Coding; Software-Defined Radio; Cognitive Radio; Interference Management; Spectrum Coexistence; Emerging Concepts

TRACK 2 – Networking Protocols and Performance
Topics:
 Ad Hoc, Mesh, & Cooperative Networks; Software-Defined Networking; Information-Centric Networking; Network Virtualization; Network Science; Mobile Edge Networks; Emerging Concepts

TRACK 3 – Cyber Security and Trusted Computing
Topics: Advanced Persistent Threat Detection & Mitigation; Key Management; Tactical Cloud Security; Cryptography; Wireless Security; Multi-Level Security; Emerging Concepts

TRACK 4 – Integrated Network Architecture and Systems-of-Systems
Topics: SATCOM; Airborne Networks; Integrated UAV/Drone Networks; High Altitude Platform Systems; Space Networks; Multi-Domain Operations; Coalition Operations; Cross Domain Solutions

TRACK 5 – Machine Learning for Communications and Networking
Topics: Machine Learning (ML) for Channel Estimation, Decoding and Interference Mitigation; ML for Resource and Network optimization; Adversarial ML; ML at the Edge; Secure ML over Communication Networks; Robust Learning; Distributed and Federated Learning

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