SER of each model on the dataset.

<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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Bibliographic Details
Main Author:	Yang Liu (4829) (author)
Other Authors:	Peng Liu (120506) (author), Yu Shi (117923) (author), Xue Hao (5699042) (author)
Published:	2025
Subjects:	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 %.
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SER of each model on the dataset.

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