{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:51Z","timestamp":1760147331160,"version":"build-2065373602"},"reference-count":16,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T00:00:00Z","timestamp":1674432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Information Technology (UIT)"},{"name":"Vietnam National University, HoChiMinh City (VNU-HCM)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless communication systems. However, because of the problem of a high peak-to-average power ratio (PAPR), OFDM symbols can be distorted at the MIMO OFDM transmitter. It degrades the signal detection and channel estimation performance at the MIMO OFDM receiver. In this paper, three deep neural network (DNN) models are proposed to solve the problem of non-linear distortions introduced by the power amplifier (PA) of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 \u00d7 2 MIMO OFDM and 4 \u00d7 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map the signals at the receiver. Proposed model type II uses the DNN model to learn and filter out the channel noises at the receiver. Proposed model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust bit error rate (BER) performances of the proposed receivers are achieved through the software and hardware implementation results. In addition, we have also implemented appropriate hardware architectures for the proposed DNN models using special techniques, such as quantization and pipeline to check the feasibility in practice, which recent studies have not done. Our hardware architectures are successfully designed and implemented on the Virtex 7 vc709 FPGA board.<\/jats:p>","DOI":"10.3390\/s23031302","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T01:29:45Z","timestamp":1674523785000},"page":"1302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hardware-Based Architecture for DNN Wireless Communication Models"],"prefix":"10.3390","volume":"23","author":[{"given":"Van Duy","family":"Tran","sequence":"first","affiliation":[{"name":"Computer Engineering Department, University of Information Technology, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2711-1408","authenticated-orcid":false,"given":"Duc Khai","family":"Lam","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, University of Information Technology, Ho Chi Minh City 700000, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City 700000, Vietnam"}]},{"given":"Thi Hong","family":"Tran","sequence":"additional","affiliation":[{"name":"Graduate School of Enginering, Osaka City University, Osaka 558-8585, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hammed, Z.S., Ameen, S.Y., and Zeebaree, S.R.M. 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Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"29922","DOI":"10.1109\/ACCESS.2020.2973000","article-title":"Generic Deep Learning-Based Linear Detectors for MIMO Systems Over Correlated Noise Environments","volume":"8","author":"He","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101181","DOI":"10.1016\/j.phycom.2020.101181","article-title":"Ultra-reliable MU-MIMO detector based on deep learning for 5G\/B5G-enabled IoT","volume":"43","author":"He","year":"2020","journal-title":"Phys. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, L., Gao, F., Zhang, W., and Ma, S. (April, January 29). Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers. 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Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1302\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:14:16Z","timestamp":1760120056000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1302"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,23]]},"references-count":16,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031302"],"URL":"https:\/\/doi.org\/10.3390\/s23031302","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,1,23]]}}}