{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:35:51Z","timestamp":1783510551762,"version":"3.55.0"},"reference-count":86,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T00:00:00Z","timestamp":1641168000000},"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>This paper brings forward a Deep Learning (DL)-based Chaos Shift Keying (DLCSK) demodulation scheme to promote the capabilities of existing chaos-based wireless communication systems. In coherent Chaos Shift Keying (CSK) schemes, we need synchronization of chaotic sequences, which is still practically impossible in a disturbing environment. Moreover, the conventional Differential Chaos Shift Keying (DCSK) scheme has a drawback, that for each bit, half of the bit duration is spent sending non-information bearing reference samples. To deal with this drawback, a Long Short-Term Memory (LSTM)-based receiver is trained offline, using chaotic maps through a finite number of channel realizations, and then used for classifying online modulated signals. We presented that the proposed receiver can learn different chaotic maps and estimate channels implicitly, and then retrieves the transmitted messages without any need for chaos synchronization or reference signal transmissions. Simulation results for both the AWGN and Rayleigh fading channels show a remarkable BER performance improvement compared to the conventional DCSK scheme. The proposed DLCSK system will provide opportunities for a new class of receivers by leveraging the advantages of DL, such as effective serial and parallel connectivity. A Single Input Multiple Output (SIMO) architecture of the DLCSK receiver with excellent reliability is introduced to show its capabilities. The SIMO DLCSK benefits from a DL-based channel estimation approach, which makes this architecture simpler and more efficient for applications where channel estimation is problematic, such as massive MIMO, mmWave, and cloud-based communication systems.<\/jats:p>","DOI":"10.3390\/s22010333","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Design of a SIMO Deep Learning-Based Chaos Shift Keying (DLCSK) Communication System"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2514-7490","authenticated-orcid":false,"given":"Majid","family":"Mobini","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol 47148-71167, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-6624","authenticated-orcid":false,"given":"Georges","family":"Kaddoum","sequence":"additional","affiliation":[{"name":"D\u00e9partement de G\u00e9nie \u00c9lectrique, University of Qu\u00e9bec, \u00c9cole de Technologie Sup\u00e9rieure, Montr\u00e9al, QC H3C1K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4812-0493","authenticated-orcid":false,"given":"Marijan","family":"Herceg","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.neunet.2021.06.025","article-title":"When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning","volume":"143","author":"Harikrishnan","year":"2021","journal-title":"Neural Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"25037","DOI":"10.1109\/ACCESS.2019.2900259","article-title":"Correlation receiver with nonlinearity blanking for DCSK systems under pulse jamming attack","volume":"7","author":"Vuong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/LCOMM.2020.2981060","article-title":"Closed-form BER expressions of M-ary DCSK systems over multipath Rayleigh fading channels","volume":"24","author":"Cai","year":"2020","journal-title":"IEEE Commun. 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