{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:46:00Z","timestamp":1780555560854,"version":"3.54.1"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comms. Net."],"abstract":"<jats:p>The integration of terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising strategy to enhance energy efficiency and reduce system complexity in future wireless networks. However, this integration faces significant challenges, such as dynamic and unpredictable channel behavior. Traditional channel estimation techniques are inadequate for handling these dynamic conditions. To address these issues, a convolutional neural network (CNN)-based hybrid precoding scheme is proposed for CFMM systems operating at THz frequencies. This method leverages CNN to predict optimal precoding weights, significantly improving the adaptability of hybrid precoding. The CNN-based model not only mitigates pilot contamination (PC) but also enhances channel estimation by capturing temporal and spatial dynamics. Simulation results indicate that the CNN-based approach achieves superior energy efficiency and lower system complexity compared to conventional techniques. At a signal-to-noise ratio (SNR) of 30\u00a0dB, it achieves 1.2 bits per joule and reduces system complexity to 1,400 FLOPs, demonstrating better scalability and resource optimization. These findings highlight the potential of CNN-based hybrid precoding to revolutionize THz communication in next-generation wireless networks by optimizing energy efficiency and system complexity.<\/jats:p>","DOI":"10.3389\/frcmn.2024.1477270","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T04:31:51Z","timestamp":1733459511000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication"],"prefix":"10.3389","volume":"5","author":[{"given":"Tadele A.","family":"Abose","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yitbarek A.","family":"Mekonen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binyam G.","family":"Assefa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naol W.","family":"Gudeta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1109\/VTC2022-Fall57202.2022.10013036","article-title":"CNN-based hybrid precoding design with geometric mean decomposition","author":"Abugubba","year":"2022"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e0289868","DOI":"10.1371\/journal.pone.0289868","article-title":"Learned-SBL-GAMP based hybrid precoders\/combiners in millimeter wave massive MIMO systems","volume":"18","author":"Ali K","year":"2023","journal-title":"PLoS One"},{"key":"B3","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1109\/comst.2021.3135119","article-title":"User-centric cell-free massive MIMO networks: a survey of opportunities, challenges and solutions","volume":"24","author":"Ammar","year":"2021","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"B4","doi-asserted-by":"publisher","first-page":"231","DOI":"10.3390\/electronics13010231","article-title":"A survey of NOMA-aided cell-free massive MIMO systems","volume":"13","author":"Apiyo","year":"2024","journal-title":"Electronics"},{"key":"B5","doi-asserted-by":"crossref","DOI":"10.1109\/WiSPNET51692.2021.9419450","article-title":"AP selection in cell-free massive MIMO system using machine learning algorithm","author":"Biswas","year":"2021"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1109\/LCOMM.2019.2915977","article-title":"CNN-based precoder and combiner design in mmWave MIMO systems","volume":"23","author":"Elbir","year":"2019","journal-title":"IEEE Commun. Lett."},{"key":"B8","first-page":"1","article-title":"Machine learning inspired energy-efficient hybrid precoding for mmWave massive MIMO systems","author":"Gao","year":"2017"},{"key":"B9","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/ACCESS.2022.3232855","article-title":"A survey on machine learning techniques for massive MIMO configurations: application areas, performance limitations and future challenges","volume":"11","author":"Gkonis","year":"2023","journal-title":"IEEE Access"},{"key":"B10","first-page":"1","article-title":"Performance analysis of deep learning based hybrid precoder\/combiner in millimeter wave massive MIMO architecture","author":"Gnanaprakash","year":"2023"},{"key":"B11","doi-asserted-by":"publisher","first-page":"7086","DOI":"10.1109\/twc.2021.3080672","article-title":"Unsupervised deep learning for massive MIMO hybrid beamforming","volume":"20","author":"Hojatian","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"B12","doi-asserted-by":"publisher","first-page":"147029","DOI":"10.1109\/access.2020.3015289","article-title":"6G wireless systems: a vision, architectural elements, and future directions","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE access"},{"key":"B13","doi-asserted-by":"publisher","first-page":"6103","DOI":"10.1109\/TVT.2022.3230931","article-title":"DL-based energy-efficient hybrid precoding for mmWave massive MIMO systems","volume":"72","author":"Liu","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1109\/JSAC.2023.3287613","article-title":"Network-assisted full-duplex cell-free massive MIMO: spectral and energy efficiencies","volume":"41","author":"Mohammadi","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.22266\/ijies2024.0229.63","article-title":"Design and optimization of hybrid precoders in massive MIMO systems: leveraging low-resolution ADCs\/DACs, reconfigurable intelligent surfaces, and deep learning algorithms","volume":"17","author":"Ng","year":"2024","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1109\/JSAC.2020.3000810","article-title":"On the spectral and energy efficiencies of full-duplex cell-free massive MIMO","volume":"38","author":"Nguyen","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2023.3319575","article-title":"Interference and beam squint aware TTD-aided beamforming for dual wideband massive MIMO","author":"Ozen","year":"2023","journal-title":"IEEE Wirel. Commun. Lett"},{"key":"B18","first-page":"1","article-title":"Akhtar saeed, \u201cspecial issue terahertz communications,\u201d","volume":"2","author":"Ozgur Gurbuz","year":"2021","journal-title":"Int. Telecommun. Union"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2102.10366","article-title":"Deep learning for cell-free massive MIMO networks","volume":"6","author":"Rajatheva","year":"2021"},{"key":"B20","doi-asserted-by":"publisher","first-page":"2786","DOI":"10.3390\/electronics10222786","article-title":"Machine learning in beyond 5G\/6G networks\u2014state-of-the-art and future trends","volume":"10","author":"Rekkas","year":"2021","journal-title":"Electronics"},{"key":"B21","first-page":"1","article-title":"Effective hybrid precoder logic with mm wave massive MIMO using novel deep learning scheme","author":"Jayarin","year":"2023"},{"key":"B22","first-page":"189","article-title":"Adaptive massive MIMO hybrid precoding based on meta learning","author":"Sun","year":"2023"},{"key":"B23","first-page":"440","article-title":"Energy efficient multi-pair massive MIMO two-way AF relaying: a deep learning approach","author":"Tentu","year":"2020"},{"key":"B25","doi-asserted-by":"publisher","first-page":"3303","DOI":"10.1109\/tvt.2021.3138802","article-title":"Energy efficiency optimization for compact massive MIMO wireless systems","volume":"71","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Wirel. Technol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"109381","DOI":"10.1016\/j.sigpro.2023.109381","article-title":"Low-complexity hybrid precoding for sub-connected millimeter wave massive MIMO systems","volume":"219","author":"Zhao","year":"2024","journal-title":"Signal Process."},{"key":"B27","doi-asserted-by":"publisher","first-page":"5162","DOI":"10.1109\/TWC.2021.31373542","article-title":"Deep learning-based channel estimation for massive MIMO with hybrid transceivers","volume":"21","author":"Zhong","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."}],"container-title":["Frontiers in Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2024.1477270\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T04:31:54Z","timestamp":1733459514000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frcmn.2024.1477270\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":25,"alternative-id":["10.3389\/frcmn.2024.1477270"],"URL":"https:\/\/doi.org\/10.3389\/frcmn.2024.1477270","relation":{},"ISSN":["2673-530X"],"issn-type":[{"value":"2673-530X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"article-number":"1477270"}}