Internal structure of DCNet.
<div><p>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 M...
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2025
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| _version_ | 1852020010132701184 |
|---|---|
| author | Yang Liu (4829) |
| author2 | Peng Liu (120506) Yu Shi (117923) Xue Hao (5699042) |
| author2_role | author author author |
| author_facet | Yang Liu (4829) Peng Liu (120506) Yu Shi (117923) Xue Hao (5699042) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yang Liu (4829) Peng Liu (120506) Yu Shi (117923) Xue Hao (5699042) |
| dc.date.none.fl_str_mv | 2025-05-27T17:40:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0324916.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Internal_structure_of_DCNet_/29157433 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified symbol error rate simulation training set simulation test set resource consumption assessment reducing resource consumption ofdm systems effectively modulation error rate broad application prospects 50 &# 8201 missed detection rates false detection rates moderate memory usage dnn cascade structure achieved significant results limited detection accuracy improving detection accuracy driven detection system 7 %, 18 enhanced signal detection manually designed features dcnet decoder based 4 &# 8201 signal detection accuracy model loading time 1 %, 81 detection speed system adopts results showed research model dnn method cpu usage 8 %, 3 %, 25 %, time performance suppress inter subcarrier interference optimizing real fully characterize essential characteristics deep learning channel estimation average latency 4 ghz 006 %. 004 %. |
| dc.title.none.fl_str_mv | Internal structure of DCNet. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>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.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_af0d59ea0470c1f8c878d1fe9fc6c19a |
| identifier_str_mv | 10.1371/journal.pone.0324916.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29157433 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Internal structure of DCNet.Yang Liu (4829)Peng Liu (120506)Yu Shi (117923)Xue Hao (5699042)Science PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsymbol error ratesimulation training setsimulation test setresource consumption assessmentreducing resource consumptionofdm systems effectivelymodulation error ratebroad application prospects50 &# 8201missed detection ratesfalse detection ratesmoderate memory usagednn cascade structureachieved significant resultslimited detection accuracyimproving detection accuracydriven detection system7 %, 18enhanced signal detectionmanually designed featuresdcnet decoder based4 &# 8201signal detection accuracymodel loading time1 %, 81detection speedsystem adoptsresults showedresearch modeldnn methodcpu usage8 %,3 %,25 %,time performancesuppress intersubcarrier interferenceoptimizing realfully characterizeessential characteristicsdeep learningchannel estimationaverage latency4 ghz006 %.004 %.<div><p>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.</p></div>2025-05-27T17:40:00ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0324916.g006https://figshare.com/articles/figure/Internal_structure_of_DCNet_/29157433CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291574332025-05-27T17:40:00Z |
| spellingShingle | Internal structure of DCNet. Yang Liu (4829) Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified symbol error rate simulation training set simulation test set resource consumption assessment reducing resource consumption ofdm systems effectively modulation error rate broad application prospects 50 &# 8201 missed detection rates false detection rates moderate memory usage dnn cascade structure achieved significant results limited detection accuracy improving detection accuracy driven detection system 7 %, 18 enhanced signal detection manually designed features dcnet decoder based 4 &# 8201 signal detection accuracy model loading time 1 %, 81 detection speed system adopts results showed research model dnn method cpu usage 8 %, 3 %, 25 %, time performance suppress inter subcarrier interference optimizing real fully characterize essential characteristics deep learning channel estimation average latency 4 ghz 006 %. 004 %. |
| status_str | publishedVersion |
| title | Internal structure of DCNet. |
| title_full | Internal structure of DCNet. |
| title_fullStr | Internal structure of DCNet. |
| title_full_unstemmed | Internal structure of DCNet. |
| title_short | Internal structure of DCNet. |
| title_sort | Internal structure of DCNet. |
| topic | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified symbol error rate simulation training set simulation test set resource consumption assessment reducing resource consumption ofdm systems effectively modulation error rate broad application prospects 50 &# 8201 missed detection rates false detection rates moderate memory usage dnn cascade structure achieved significant results limited detection accuracy improving detection accuracy driven detection system 7 %, 18 enhanced signal detection manually designed features dcnet decoder based 4 &# 8201 signal detection accuracy model loading time 1 %, 81 detection speed system adopts results showed research model dnn method cpu usage 8 %, 3 %, 25 %, time performance suppress inter subcarrier interference optimizing real fully characterize essential characteristics deep learning channel estimation average latency 4 ghz 006 %. 004 %. |