{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:37:10Z","timestamp":1760060230309,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Ministry of Science and Technology","award":["D20011"],"award-info":[{"award-number":["D20011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>This paper proposes a low-complexity signal detection method for orthogonal time frequency space (OTFS) communication systems, based on a separable convolutional neural network (SeCNN), termed SeCNN-OTFS. A novel SeparableBlock architecture is introduced, which integrates residual connections and a channel attention mechanism to enhance feature discrimination and training stability under high Doppler conditions. By decomposing standard convolutions into depthwise and pointwise operations, the model achieves a substantial reduction in computational complexity. To validate its effectiveness, simulations are conducted under a standard OTFS configuration with 64-QAM modulation, comparing the proposed SeCNN-OTFS with conventional CNN-based models and classical linear estimators, such as least squares (LS) and minimum mean square error (MMSE). The results show that SeCNN-OTFS consistently outperforms LS and MMSE, and when the signal-to-noise ratio (SNR) exceeds 12.5 dB, its bit error rate (BER) performance becomes nearly identical to that of 2D-CNN. Notably, SeCNN-OTFS requires only 19% of the parameters compared to 2D-CNN, making it highly suitable for resource-constrained environments such as satellite and IoT communication systems. For scenarios where higher accuracy is required and computational resources are sufficient, the CNN-OTFS model\u2014with conventional convolutional layers replacing the separable convolutional layers\u2014can be adopted as a more precise alternative.<\/jats:p>","DOI":"10.3390\/e27080839","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T08:33:06Z","timestamp":1754555586000},"page":"839","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Signal Detection Based on Separable CNN for OTFS Communication Systems"],"prefix":"10.3390","volume":"27","author":[{"given":"Ying","family":"Wang","sequence":"first","affiliation":[{"name":"The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Global Big Data Technologies Centre, University of Technology, Sydney 2007, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9996-9695","authenticated-orcid":false,"given":"Hang","family":"Li","sequence":"additional","affiliation":[{"name":"The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8558-7610","authenticated-orcid":false,"given":"Tao","family":"Zhou","sequence":"additional","affiliation":[{"name":"The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqun","family":"Cheng","sequence":"additional","affiliation":[{"name":"The School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hadani, R., Rakib, S., Tsatsanis, M., Monk, A., Goldsmith, A.J., Molisch, A.F., and Calderbank, R. 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