{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:54:30Z","timestamp":1791914070521,"version":"4.3.4"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"52nd Research Institute of China Electronics Technology Group Corporation","id":[{"id":"https:\/\/ror.org\/0098hst83","id-type":"ROR","asserted-by":"crossref"}]},{"name":"52nd Research Institute of China Electronics Technology Group Corporation","id":[{"id":"https:\/\/ror.org\/0098hst83","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Detecting and precisely localizing multiple radio signals in wideband power spectral density (PSD) vectors under low signal-to-noise ratio (SNR) conditions remains a fundamental challenge for spectrum monitoring and cognitive radio. Traditional energy detection and threshold-based methods suffer from the SNR-wall effect and poor performance in dense, non-cooperative environments. While deep-learning approaches have advanced the field, most treat spectrum sensing as a binary classification task, discarding the fine-grained frequency-bin information needed for accurate signal boundary and parameter estimation. In this paper, we propose\n                    <jats:bold>SpecUNeSt-MLP<\/jats:bold>\n                    , an one-dimensional semantic segmentation framework built upon the U-Net encoder-decoder architecture. The model directly predicts per-frequency-bin occupancy masks from normalized PSD vectors, thereby simultaneously achieving signal detection and parameter estimation (center frequency and bandwidth). Three key enhancements are introduced: (1) a 1D-adapted Split-Attention mechanism in the encoder that dynamically recalibrates cross-channel features to enhance weak-signal extraction under heavy noise; (2) a Bidirectional Rolling MLP (RMLP) module in the bottleneck that efficiently captures long-range spectral dependencies with linear complexity; and (3) Feature Incentive Blocks that refine feature representations through lightweight channel projection. Extensive experiments on 67,200 synthetic broadband PSDs and 820 real-world UAV PSD samples demonstrate the effectiveness of SpecUNeSt-MLP. Across all tested SNR levels (from \u22124 dB to 8 dB), SpecUNeSt-MLP consistently outperforms LAD, FCN, SS-CNN, and SS-LSTM in terms of detection probability, false alarm rate, and average localization error. Compared with the recent RMP baseline, SpecUNeSt-MLP achieves better performance under low-SNR conditions while maintaining comparable performance at higher SNR levels, with only one-sixth of the parameter count of RMP. These results demonstrate that SpecUNeSt-MLP provides an effective and computationally efficient solution for fine-grained spectrum sensing in complex electromagnetic environments.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0359267","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:34:52Z","timestamp":1791912892000},"page":"e0359267","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["One dimensional detection for radio signals based on SpecUNeSt-MLP"],"prefix":"10.1371","volume":"21","author":[{"given":"Chongxiao","family":"Qu","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Cai","sequence":"additional","affiliation":[{"name":"52nd Research Institute of China Electronics Technology Group Corporation","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohua","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Nanhu Academy of Electronics and Information Technology","place":["Jiaxing, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhizhou","family":"Liao","sequence":"additional","affiliation":[{"name":"52nd Research Institute of China Electronics Technology Group Corporation","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Chen","sequence":"additional","affiliation":[{"name":"China Nanhu Academy of Electronics and Information Technology","place":["Jiaxing, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2377-5840","authenticated-orcid":true,"given":"Baoqing","family":"Zeng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["Chengdu, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"issue":"1","key":"pone.0359267.ref001","doi-asserted-by":"crossref","first-page":"138","DOI":"10.3390\/electronics12010138","article-title":"Analysis of Hybrid Spectrum Sensing for 5G and 6G Waveforms","volume":"12","author":"A Kumar","year":"2022","journal-title":"Electronics"},{"issue":"11","key":"pone.0359267.ref002","doi-asserted-by":"crossref","first-page":"17222","DOI":"10.1109\/TVT.2024.3427664","article-title":"Average Energy Detection With Adaptive Threshold for Spectrum Sensing in Cognitive Radio Systems","volume":"73","author":"C Vl\u0103deanu","year":"2024","journal-title":"IEEE Trans Veh Technol"},{"issue":"2","key":"pone.0359267.ref003","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s11235-023-01079-1","article-title":"A review of spectrum sensing in modern cognitive radio networks","volume":"85","author":"MU Muzaffar","year":"2023","journal-title":"Telecommun Syst"},{"issue":"5","key":"pone.0359267.ref004","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1109\/TCCN.2024.3392261","article-title":"Influence of Noise Uncertainty Source and Model on the SNR Wall of Energy Detection","volume":"10","author":"S Costa L dos","year":"2024","journal-title":"IEEE Trans Cogn Commun Netw"},{"key":"pone.0359267.ref005","doi-asserted-by":"crossref","unstructured":"Vartiainen J, Lehtomaeki J, Vuohtoniemi R. 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