Research on Radio Signal Modulation Recognition Based on Hyperbolic State Space Model

WANG Guanchun; LIU Chun; ZHANG Xiangrong; CHEN Yifan; ZHANG Tianyang; TANG Xu

doi:10.12263/DZXB.20251225

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Research on Radio Signal Modulation Recognition Based on Hyperbolic State Space Model

PAPERS | 更新时间：2026-05-11

- Research on Radio Signal Modulation Recognition Based on Hyperbolic State Space Model
- ACTA ELECTRONICA SINICA Vol. 54, Issue 2, Pages: 517-531(2026)
- 作者机构：
  
  西安电子科技大学人工智能学院，陕西西安 710126
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62506285;62501433;62571387;62276197);Natural Science Basic Research Program of Shaanxi(2025JC-YBQN-795);China Postdoctoral Science Foundation(2025T180431;2025M771550)
- DOI：10.12263/DZXB.20251225
  CLC： TN911.72;TP183
- Received：18 January 2026，
  
  Accepted：13 February 2026，
  
  Published：25 February 2026
- 稿件说明：
移动端阅览
王冠淳, 刘淳, 张向荣, 等. 基于双曲状态空间模型的无线电信号调制识别[J]. 电子学报, 2026, 54(02): 517-531.

WANG Guanchun, LIU Chun, ZHANG Xiangrong, et al. Research on Radio Signal Modulation Recognition Based on Hyperbolic State Space Model[J]. Acta Electronica Sinica, 2026, 54(02): 517-531.
王冠淳, 刘淳, 张向荣, 等. 基于双曲状态空间模型的无线电信号调制识别[J]. 电子学报, 2026, 54(02): 517-531. DOI：10.12263/DZXB.20251225

WANG Guanchun, LIU Chun, ZHANG Xiangrong, et al. Research on Radio Signal Modulation Recognition Based on Hyperbolic State Space Model[J]. Acta Electronica Sinica, 2026, 54(02): 517-531. DOI：10.12263/DZXB.20251225

摘要

无线通信系统利用接收信号的数据特性进行自动调制识别（Automatic Modulation Recognition，AMR）是确保电磁频谱智能监测与管控的重要先导步骤。近年来，深度学习技术凭借其强大的隐式特征提取能力被广泛研究，国内外学者致力于探索深度学习技术在信号调制识别任务中的潜力，并已提出一系列AMR方法，依据其采用的网络架构可粗略划分为以下三种类型：基于卷积神经网络（Convolutional Neural Networks，CNN）、基于循环神经网络（Recurrent Neural Networks，RNN）以及基于Transformer网络的方法。然而，在动态复杂电磁环境中，现有AMR方法面临两大共性挑战：现有深度学习模型对时变信道噪声缺乏自适应感知能力，导致不同信噪比（Signal-to-Noise Ratio，SNR）条件下的不同调制类型之间易产生混淆；现有模型在长时序信号建模中的计算效率与特征提取能力难以兼顾，对长序列信号数据的判别精度受限。因此，针对现有调制识别方法缺乏电磁环境感知能力、长时序高效建模能力不足等问题，本文将状态空间模型（State Space Models，SSMs）的长序列建模能力与双曲几何空间的SNR感知特性相结合，提出一种基于双曲状态空间模型（Hyperbolic state space model，H-Mamba）的无线电信号调制识别方法。具体地，本文首先建立了一种基于状态空间模型的时频特征挖掘机制（Mamba-based Time-frequency Feature Mining，MTFM），联合提取时域与频域判别性表征以增强不同调制类型信号间可分性；其次，从双曲几何空间的角度提出了一种新颖的信号质量感知方法，依据所接收信号的双曲几何半径判断其SNR分布情况，并基于此设计了一种基于双曲SNR提示的特征调制模块（Hyperbolic SNR-aware Feature Modulation，HSFM），通过双曲几何引导动态调整信号表征，提升模型对不同SNR条件的适应能力；最后，提出了一种基于双曲SNR感知的课程学习策略（Hyperbolic SNR-aware Curriculum Learning，HSCL），通过双曲距离度量实现对样本质量差异的感知，从而动态调整模型训练过程以缓解低质数据干扰。实验结果表明：本文方法在RML2016A（RadioML2016.10A）、RML2016B（RadioML2016.10B）以及RML2018（RadioML2018）等多个公开信号调制识别数据集上均取得最优性能，较现有最优方法分别提高了4.09%、1.58%、1.21%，证明了其有效性。

Abstract

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 representation 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 architecture: 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 challenges: 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 balance computational efficiency and representation capabilities in long-term signal modeling

limiting the accuracy of discrimination 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-frequency 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 RadioML2016.10A (RML2016A)

RadioML2016.10B (RML2016B)

RadioML2018 (RML2018)

demonstrate that the proposed 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.

关键词

Keywords

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