Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system

• DOI: 10.1371/journal.pone.0317355
• Publication Date: 2025-01-13T19:01:04Z
• Authors: Ting Xie; Xing Han
• Keywords: Benchmark (surveying); Modulation (music); SIGNAL (programming language); Feature (linguistics)

The aim of this study is to address the limitations convolutional networks in recognizing modulation patterns. These are unable utilize temporal information effectively for feature extraction and pattern recognition, resulting inefficient recognition. To issue, a signal recognition method based on two-way interactive attention network algorithm has been developed. A designed basis long short-term memory with objective enhancing contextual connection network. output attentively weighted using soft mechanism. proposed exhibited enhanced overall, average, maximum rates at varying signal-to-noise ratios, an increase 10.34%, 8.33%, 3.33%, respectively, comparison other algorithms within Radio Machine Learning (RML) 2016.10b dataset. Furthermore, modulated accuracy was as high 92.84%, average Kappa coefficient 12.28%. Communication Signal Processing Benchmark (CSPB.ML2018) 2018 dataset 0.62, representing 10.32% over algorithms. results demonstrate that can enhance network’s signals. Moreover, it potential applications automatic systems wireless communications.