Class Information Guided Reconstruction for Automatic Modulation Open-Set Recognition

Automatic Modulation Recognition (AMR) is vital for radar and communication systems. Traditional AMR operates under closed-set scenarios where all modulation types are predefined. However, in practical settings, unknown modulation types may emerge due to technological advancements. Closed-set training poses the risk of misclassifying unknown modulations into existing known classes, leading to serious implications for situation awareness and threat assessment. To tackle this challenge, this paper presents a Class Information guided Reconstruction (CIR) framework that can simultaneously achieve Known Class Classification (KCC) and Unknown Class Identification (UCI). The CIR leverages reconstruction losses to differentiate between known and unknown classes, utilizing Class Conditional Vectors (CCVs) and a Mutual Information (MI) loss function to fully exploit class information. The CCVs offer class-specific guidance for reconstruction process, ensuring accurate reconstruction for known samples while producing subpar results for unknown ones. Moreover, to enhance distinguishability, an MI loss function is introduced to capture class-discriminative semantics in latent space, enabling closer alignment with CCVs during reconstruction. The synergistic relationship between CCVs and MI facilitates optimal UCI performance without compromising KCC accuracy. The CIR is evaluated on simulated, public and real-world datasets, demonstrating its effectiveness and robustness, particularly in low SNR and high unknown class prevalence scenarios.