{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:00:29Z","timestamp":1760148029455,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The passive optical network (PON) is widely used in optical fiber communication thanks to its low cost and low resource consumption. However, the passiveness brings about a critical problem that it requires manual work to identify the topology structure, which is costly and prone to bringing noise to the topology logs. In this paper, we provide a base solution firstly introducing neural networks for such problems, and based on that solution we propose a complete methodology (PT-Predictor) for predicting PON topology through representation learning on its optical power data. Specifically, we design useful model ensembles (GCE-Scorer) to extract the features of optical power with noise-tolerant training techniques integrated. We further implement a data-based aggregation algorithm (MaxMeanVoter) and a novel Transformer-based voter (TransVoter) to predict the topology. Compared with previous model-free methods, PT-Predictor is able to improve prediction accuracy by 23.1% in scenarios where data provided by telecom operators is sufficient, and by 14.8% in scenarios where data is temporarily insufficient. Besides, we identify a class of scenarios where PON topology does not follow a strict tree structure, and thus topology prediction cannot be effectively performed by relying on optical power data alone, which will be studied in our future work.<\/jats:p>","DOI":"10.3390\/s23063345","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T07:09:31Z","timestamp":1679468971000},"page":"3345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time-Series Representation Learning in Topology Prediction for Passive Optical Network of Telecom Operators"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5467-5412","authenticated-orcid":false,"given":"Haoran","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yuchen","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yuxiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Innovation Research Co., Ltd., Hangzhou 310016, China"}]},{"given":"Zheng","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Creativity and Art, ShanghaiTech University, Shanghai 201210, China"},{"name":"Digital Brain Laboratory, Shanghai 200072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0127-2425","authenticated-orcid":false,"given":"Weinan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Xidong","family":"Feng","sequence":"additional","affiliation":[{"name":"Computer Science Department, University College London, London WC1E 6BT, UK"}]},{"given":"Li","family":"Yu","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Innovation Research Co., Ltd., Hangzhou 310016, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Innovation Research Co., Ltd., Hangzhou 310016, China"}]},{"given":"Hulei","family":"Fan","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Innovation Research Co., Ltd., Hangzhou 310016, China"}]},{"given":"Tiema","family":"Mu","sequence":"additional","affiliation":[{"name":"China Mobile (Zhejiang) Innovation Research Co., Ltd., Hangzhou 310016, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","unstructured":"Lam, C.F. 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