In this paper, we propose a Multi-layer Domain Adaptation Hybrid Network (MDAHN) tailored for modulation signal recognition scenarios.
Recent studies have indicated that deep neural networks can learn transferable features, which can generalize well to new tasks in domain-adaptive settings. In ...
Recently, deep learning models have been adopted in modulation recognition, which outperform traditional machine learning techniques based on hand-crafted ...
Oct 22, 2024 · This paper proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input ...
This paper presents an innovative AMR method known as the CVCNN-LSTM. Our study aims to leverage CNNs in conjunction with a long short-term memory network (LSTM) ...
This letter proposes a transfer learning model for automatic modulation recognition (AMR) with only a few modulated signal samples. The transfer model is ...
Nov 13, 2024 · This paper investigates the AMC technique for the signals with dynamic and varying SNRs, and a deep learning based noise reduction network is ...
People also ask
What are the methods of automatic modulation classification?
What is transfer learning techniques in deep learning?
Automatic modulation recognition (AMR) detects the modulation scheme of the received signals for further signal processing without needing prior information ...
In this paper, a hybrid model based on deep learning, which aims to quickly classify received modulated signals and help to plan spectrum resources, is proposed ...
Nov 13, 2024 · This paper investigates the AMC technique for the signals with dynamic and varying SNRs, and a deep learning based noise reduction network is ...