A Spatio-Temporal Liquid Learning Network for Robust Electromagnetic Signal Identification in 6G RIS-Aided UAV Cognitive Communications With Composite Channel Impairments

In sixth-generation (6G) cognitive communications, deep learning (DL)-driven electromagnetic signal identification (ESI) emerges as a pivotal sensing paradigm to fortify physical layer security in reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) networks through intelligent waveform learning while preserving spectral efficiency. This paper proposes a robust RIS-aided UAV ESI framework under multiple channel impairments, addressing key challenges in practical deployment scenarios. Specifically, the methodology integrates a real-world RIS signal acquisition system comprising a Rohde & Schwarz vector signal generator (R&S-SMBV100B) emulating UAV transmissions, an FPGA-controled 16×16 programmable RIS metasurface for signal reflection, and an R&S-FSW 67 GHz spectrum analyzer for receiving data capture, augmented by UAV 3D modeling and emulation to construct composite channel fading impairments. The proposed Spatio-Temporal Liquid Learning ESI Network (STL-ESINet) innovatively integrates spatial convolutional operations with temporal liquid learning mechanisms to extract discriminative high-dimensional spatiotemporal features from electromagnetic signals. A hierarchical classification module then synthesizes these spatio-temporal features for precise decision-making, while inter-class and intraclass feature statistics techniques rigorously validate the model interpretability. Experimental validation demonstrates that STL-RSINet outperforms existing DL-based approaches by achieving 97.38% and 100% recognition accuracy at SNR=2dB and 12dB, respectively, while exhibiting robustness against composite channel impairments including path loss, multipath fading, and Doppler shifts. These results establish a new paradigm for secure RIS-UAV communications, offering both theoretical insights into waveform-learning security mechanisms and practical guidelines for hardware implementation in 6G cognitive communications.