A BiLSTM and self-attention deep learning method for enhancing OFDM signal detection capability in unmanned swarms

Abstract

Unmanned swarm systems—including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous surface vehicles—critically depend on highly reliable and secure communication networks for cooperative perception, collaborative decision-making, and coordinated control. Orthogonal Frequency Division Multiplexing (OFDM) technology is widely adopted in swarm communication due to its high spectral efficiency and strong resistance to multipath fading. However, traditional OFDM signal recognition methods heavily rely on manually extracted features (e.g., cyclic prefix, cyclic spectrum), and their recognition performance deteriorates sharply under low signal-to-noise ratio (SNR) and complex channel conditions—conditions commonly encountered in dynamic swarm environments with fast-varying topologies and electromagnetic interference. This paper proposes a deep learning-based OFDM signal detection method specifically designed for unmanned swarm communication scenarios. The proposed approach achieves higher recognition accuracy and stronger robustness under low SNR conditions compared to conventional techniques. By reducing dependence on prior knowledge and manual feature engineering, our method provides an effective solution for intelligent signal recognition in complex electromagnetic environments. Simulation results demonstrate that under low SNR conditions, across different subcarrier modulation schemes, the proposed model achieves lower channel estimation mean squared error (MSE) and superior bit error rate (BER) performance relative to traditional channel estimation techniques. These capabilities directly support the highly safe and reliable communication networks required for mission-critical swarm applications, including multi-UAV cooperative search, anti-terrorism operations, and disaster relief coordination.