基于双曲状态空间模型的无线电信号调制识别OA

In wireless communication systems,automatic modulation recognition(AMR)leveraging the intrinsic characteristics of received signals serves as a crucial prerequisite for intelligent electromagnetic spectrum monitoring and management.In recent years,deep learning technology has been widely studied due to its powerful implicit feature represen⁃tation capabilities.Many scholars have explored the potential of deep learning technology in signal modulation recognition tasks and have proposed a series of AMR methods,which can be roughly divided into three types based on their network ar⁃chitecture:convolutional neural networks-based(CNN),recurrent neural networks-based(RNN),and Transformer-based methods.However,in dynamic and complex electromagnetic environments,existing AMR methods face two common chal⁃lenges:existing models typically lack adaptive perception capabilities for time-varying channel noise,leading to confusion between different modulation types under varying signal-to-noise ratio(SNR)conditions;existing models struggle to bal⁃ance computational efficiency and representation capabilities in long-term signal modeling,limiting the accuracy of discrim⁃ination for long-sequence signals.Considering existing modulation recognition methods typically lack the capabilities of electromagnetic environment perception and struggle to efficiently model long-term time sequences,this paper proposes a novel hyperbolic state space model(H-Mamba)that integrates the long-sequence modeling capability of state space models(SSMs)with the SNR awareness inherent in hyperbolic geometry.Specifically,we first develop a Mamba-based time-fre⁃quency feature mining(MTFM)mechanism to jointly extract discriminative representations from both time and frequency domains,thereby enhancing inter-class separability among different modulation types.Next,we introduce a novel signal quality perception method from the perspective of hyperbolic geometry that correlates the hyperbolic radius of a received signal with its SNR distribution.Building upon this insight,we design a hyperbolic SNR-aware feature modulation(HSFM)module that dynamically adjusts signal representations under hyperbolic geometric guidance,improving model robustness across varying SNR conditions.Furthermore,we propose a hyperbolic SNR-aware curriculum learning(HSCL)strategy that leverages hyperbolic distance to perceive sample quality differences,enabling adaptive training dynamics that mitigate the adverse impact of low-quality data.Extensive experiments on multiple public AMR benchmarks,including Ra⁃dioML2016.10A(RML2016A),RadioML2016.10B(RML2016B),RadioML2018(RML2018),demonstrate that the pro⁃posed H-Mamba achieves state-of-the-art performance,outperforming current best baselines by 4.09％,1.58％,and 1.21％,respectively,thereby validating its efficacy.