{"ID":2833047,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04899","arxiv_id":"2512.04899","title":"Channel-Aware Multi-Domain Feature Extraction for Automatic Modulation Recognition in MIMO Systems","abstract":"Automatic modulation recognition (AMR) is a key technology in non-cooperative communication systems, aiming to identify the modulation scheme from signals without prior information. Deep learning (DL)-based methods have gained wide attention due to their excellent performance, but research mainly focuses on single-input single-output (SISO) systems, with limited exploration for multiple-input multiple-output (MIMO) systems. The confounding effects of multi-antenna channels can interfere with the statistical properties of MIMO signals, making identification particularly challenging. To overcome these limitations, we propose a Channel-Aware Multi-Domain feature extraction (CAMD) framework for AMR in MIMO systems. Our CAMD framework reconstructs the transmitted signal through an efficient channel compensation module and achieves a more robust representation capability against channel interference by extracting and integrating multi-domain features, including intra-antenna temporal correlations and inter-antenna channel correlations. We have verified our method on the widely-used dataset, MIMOSig-Ref, with complex mobile channel environments. Extensive experiments confirm the performance advantages of CAMD over previous state-of-the-art methods.","short_abstract":"Automatic modulation recognition (AMR) is a key technology in non-cooperative communication systems, aiming to identify the modulation scheme from signals without prior information. Deep learning (DL)-based methods have gained wide attention due to their excellent performance, but research mainly focuses on single-inpu...","url_abs":"https://arxiv.org/abs/2512.04899","url_pdf":"https://arxiv.org/pdf/2512.04899v1","authors":"[\"Yunpeng Qu\",\"Yazhou Sun\",\"Bingyu Hui\",\"Jintao Wang\",\"Jian Wang\"]","published":"2025-12-04T15:28:11Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"LoRA\"]","has_code":false}