Deep learning-enhanced signal detection for communication systems.
Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0324916 |
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| author | Yang Liu Peng Liu Yu Shi Xue Hao |
| author_facet | Yang Liu Peng Liu Yu Shi Xue Hao |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO) with orthogonal frequency division multiplexing technology to construct a data-driven detection system. The system adopts a Multi-DNN method with a dual-DNN cascade structure and mixed activation function design to optimize the channel estimation and signal detection coordination process of the MIMO part. At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. The results showed that on the simulation training set, the accuracy of the research model was 93.8%, the symbol error rate was 17.6%, the throughput was 81.3%, and the modulation error rate was 0.004%. On the simulation test set, its accuracy, symbol error rate, throughput, and modulation error rate were 90.7%, 18.1%, 81.2%, and 0.006%. In both 2.4 GHz and 5 GHz WiFi signals, the signal detection accuracy of the research model reached 91.5% and 91.6%, with false detection rates of 1.9% and 1.5%, and missed detection rates of 1.6% and 4.2%. In resource consumption assessment, the detection speed of this model reached 120 signals/s, with an average latency of 50 ms. The model loading time was only 2.4 s, and the CPU usage was as low as 25%, with moderate memory usage. Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. It has broad application prospects in the field of communication signal detection. |
| format | Article |
| id | doaj-art-32850dddb97e4ff4a7a44b8d33f7bab4 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-32850dddb97e4ff4a7a44b8d33f7bab42025-08-20T03:25:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032491610.1371/journal.pone.0324916Deep learning-enhanced signal detection for communication systems.Yang LiuPeng LiuYu ShiXue HaoTraditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovatively combines Multiple Input Multiple Output (MIMO) with orthogonal frequency division multiplexing technology to construct a data-driven detection system. The system adopts a Multi-DNN method with a dual-DNN cascade structure and mixed activation function design to optimize the channel estimation and signal detection coordination process of the MIMO part. At the same time, a DCNet decoder based on a convolutional neural network batch normalization mechanism is designed to suppress inter-subcarrier interference in OFDM systems effectively. The results showed that on the simulation training set, the accuracy of the research model was 93.8%, the symbol error rate was 17.6%, the throughput was 81.3%, and the modulation error rate was 0.004%. On the simulation test set, its accuracy, symbol error rate, throughput, and modulation error rate were 90.7%, 18.1%, 81.2%, and 0.006%. In both 2.4 GHz and 5 GHz WiFi signals, the signal detection accuracy of the research model reached 91.5% and 91.6%, with false detection rates of 1.9% and 1.5%, and missed detection rates of 1.6% and 4.2%. In resource consumption assessment, the detection speed of this model reached 120 signals/s, with an average latency of 50 ms. The model loading time was only 2.4 s, and the CPU usage was as low as 25%, with moderate memory usage. Overall, the research model has achieved significant results in improving detection accuracy, optimizing real-time performance, and reducing resource consumption. It has broad application prospects in the field of communication signal detection.https://doi.org/10.1371/journal.pone.0324916 |
| spellingShingle | Yang Liu Peng Liu Yu Shi Xue Hao Deep learning-enhanced signal detection for communication systems. PLoS ONE |
| title | Deep learning-enhanced signal detection for communication systems. |
| title_full | Deep learning-enhanced signal detection for communication systems. |
| title_fullStr | Deep learning-enhanced signal detection for communication systems. |
| title_full_unstemmed | Deep learning-enhanced signal detection for communication systems. |
| title_short | Deep learning-enhanced signal detection for communication systems. |
| title_sort | deep learning enhanced signal detection for communication systems |
| url | https://doi.org/10.1371/journal.pone.0324916 |
| work_keys_str_mv | AT yangliu deeplearningenhancedsignaldetectionforcommunicationsystems AT pengliu deeplearningenhancedsignaldetectionforcommunicationsystems AT yushi deeplearningenhancedsignaldetectionforcommunicationsystems AT xuehao deeplearningenhancedsignaldetectionforcommunicationsystems |