Aerial AM Communication System for Remote Post-Disaster Alerts Using Machine Learning
Aya EL BOUZID, Hajar SADOK, Kaoutar MESBAHI, Ilham NAITHIM, Nada AZOUAOU, Moulay EL HASSAN EL AZHARI
Abstract. This paper presents a low-power aerial AM communication system for delivering emergency alerts in disaster-affected areas. The system transmits voice or dual-tone signals using Double Sideband with Carrier (DSB-C) modulation through a bandwidth-limited LTI channel modeled with 10 dB free-space path loss, additive white Gaussian noise (AWGN), and low-pass filtering. At the receiver, an envelope detector recovers the message, which is evaluated by a lightweight signal classification algorithm trained on simulated data across four SNR levels (0, 10, 20, and 30 dB). The effectiveness of the suggested hybrid analog computational framework for real-time quality assessment in limited communication environments is confirmed by MATLAB and Simulink simulations, which demonstrate that the system consistently recognizes signals at 25 dB SNR as clean.
Keywords
Aerial Communication, Amplitude Modulation, Disaster Alerts, Signal Classification, Envelope Detection
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Aya EL BOUZID, Hajar SADOK, Kaoutar MESBAHI, Ilham NAITHIM, Nada AZOUAOU, Moulay EL HASSAN EL AZHARI, Aerial AM Communication System for Remote Post-Disaster Alerts Using Machine Learning, Materials Research Proceedings, Vol. 64, pp 477-484, 2026
DOI: https://doi.org/10.21741/9781644904091-60
The article was published as article 60 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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