{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:40:59Z","timestamp":1766158859468,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T00:00:00Z","timestamp":1648080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["882","613"],"award-info":[{"award-number":["882","613"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The latest generation of communication networks, such as SDVN (Software-defined vehicular network) and VANETs (Vehicular ad-hoc networks), should evaluate their communication channels to adapt their behavior. The quality of the communication in data networks depends on the behavior of the transmission channel selected to send the information. Transmission channels can be affected by diverse problems ranging from physical phenomena (e.g., weather, cosmic rays) to interference or faults inherent to data spectra. In particular, if the channel has a good transmission quality, we might maximize the bandwidth use. Otherwise, although fault-tolerant schemes degrade the transmission speed by solving errors or failures should be included, these schemes spend more energy and are slower due to requesting lost packets (recovery). In this sense, one of the open problems in communications is how to design and implement an efficient and low-power-consumption mechanism capable of sensing the quality of the channel and automatically making the adjustments to select the channel over which transmit. In this work, we present a trade-off analysis based on hardware implementation to identify if a channel has a low or high quality, implementing four machine learning algorithms: Decision Trees, Multi-Layer Perceptron, Logistic Regression, and Support Vector Machines. We obtained the best trade-off with an accuracy of 95.01% and efficiency of 9.83 Mbps\/LUT (LookUp Table) with a hardware implementation of a Decision Tree algorithm with a depth of five.<\/jats:p>","DOI":"10.3390\/s22072497","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T23:31:43Z","timestamp":1648164703000},"page":"2497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Trade-Off Analysis of Hardware Architectures for Channel-Quality Classification Models"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6314-1084","authenticated-orcid":false,"given":"Alan","family":"Torres-Alvarado","sequence":"first","affiliation":[{"name":"Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Puebla 72840, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4753-9375","authenticated-orcid":false,"given":"Luis Alberto","family":"Morales-Rosales","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Civil, CONACYT-Universidad Michoacana de San Nicol\u00e1s de Hidalgo, Morelia 58030, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4748-3500","authenticated-orcid":false,"given":"Ignacio","family":"Algredo-Badillo","sequence":"additional","affiliation":[{"name":"Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Puebla 72840, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3332-846X","authenticated-orcid":false,"given":"Francisco","family":"L\u00f3pez-Huerta","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda de la Construcci\u00f3n y el H\u00e1bitat, Universidad Veracruzana, Boca del R\u00edo, Veracruz 94294, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2607-2032","authenticated-orcid":false,"given":"Mariana","family":"Lobato-Baez","sequence":"additional","affiliation":[{"name":"Higher Technological Institute of Libres, Libres, Puebla 73780, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3261","authenticated-orcid":false,"given":"Juan Carlos","family":"L\u00f3pez-Pimentel","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Panamericana, \u00c1lvaro del Portillo 49, Mexico City 45010, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e4005","DOI":"10.1002\/dac.4005","article-title":"Software defined vehicular networks: A comprehensive review","volume":"32","author":"Bhatia","year":"2019","journal-title":"Int. J. Commun. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stamou, A., Kakkavas, G., Tsitseklis, K., Karyotis, V., and Papavassiliou, S. (2019). Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments. Future Internet, 11.","DOI":"10.3390\/fi11020037"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khasawneh, A.M., Helou, M.A., Khatri, A., Aggarwal, G., Kaiwartya, O., Altalhi, M., Abu-ulbeh, W., and AlShboul, R. (2022). Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments. Sensors, 22.","DOI":"10.3390\/s22031247"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nkenyereye, L., Nkenyereye, L., Islam, S.M.R., Choi, Y.-H., Bilal, M., and Jang, J.-W. (2019). Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation. Sensors, 19.","DOI":"10.3390\/s19173788"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MCOM.2017.1601183","article-title":"Overcoming the Key Challenges to Establishing Vehicular Communication: Is SDN the Answer?","volume":"55","author":"Yaqoob","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101961","DOI":"10.1016\/j.sysarc.2020.101961","article-title":"Software-defined vehicular network (SDVN): A survey on architecture and routing","volume":"114","author":"Islam","year":"2021","journal-title":"J. Syst. Archit."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mansour, A.M., Nada, A.E.R., and Mehana, A.H. (2015, January 14\u201316). Effect of noise variance estimation on channel quality indicator in LTE systems. Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, USA.","DOI":"10.1109\/GlobalSIP.2015.7418176"},{"key":"ref_8","unstructured":"Eriksson, E. (2007). Channel Quality Information Reporting and Channel Quality dependent Scheduling in LTE. [Master\u2019s Thesis, Department of Electrical Engineering, Link\u00f6pings University]. Available online: https:\/\/www.diva-portal.org\/smash\/record.jsf?pid=diva2%3A17485&dswid=177."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1049\/iet-com.2016.0919","article-title":"Channel quality estimation metrics in cognitive radio networks: A survey","volume":"11","author":"Elderini","year":"2017","journal-title":"IET Commun."},{"key":"ref_10","first-page":"45","article-title":"Authentication and privacy schemes for vehicular ad hoc networks (VANETs): A survey","volume":"16","author":"Ali","year":"2019","journal-title":"Veh. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.future.2019.07.006","article-title":"Integration of VANET and 5G Security: A review of design and implementation issues","volume":"101","author":"Hussain","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"02034","DOI":"10.1051\/matecconf\/201710002034","article-title":"Internet of Things: Application and prospect","volume":"Volume 100","author":"Liu","year":"2017","journal-title":"MATEC Web of Conferences"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"El-hajj, M., Fadlallah, A., Chamoun, M., and Serhrouchni, A. (2019). A Survey of Internet of Things (IoT) Authentication Schemes. Sensors, 19.","DOI":"10.3390\/s19051141"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2409","DOI":"10.1109\/JPROC.2013.2271951","article-title":"Opportunities and challenges of vehicle-to-home, vehicle-to-vehicle, and vehicle-to-grid technologies","volume":"101","author":"Liu","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"136","DOI":"10.3141\/2129-16","article-title":"Driver Behavior and User Acceptance of Cooperative Systems Based on Infrastructure-to-Vehicle Communication","volume":"2129","author":"Fuchs","year":"2009","journal-title":"Transp. Res. Rec."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MCOM.2002.1006968","article-title":"A brief overview of ad hoc networks: Challenges and directions","volume":"40","author":"Ramanathan","year":"2002","journal-title":"IEEE Commun. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s11554-020-01035-1","article-title":"FPGA-based architecture for bi-cubic interpolation: The best trade-off between precision and hardware resource consumption","volume":"18","author":"Boukhtache","year":"2021","journal-title":"J. Real Time Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vaeztourshizi, M., Kamal, M., and Pedram, M. (2020, January 25\u201326). EGAN: A Framework for Exploring the Accuracy vs. Energy Efficiency Trade-off in Hardware Implementation of Error Resilient Applications. Proceedings of the 2020 21st International Symposium on Quality Electronic Design (ISQED), Santa Clara, CA, USA.","DOI":"10.1109\/ISQED48828.2020.9137041"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tann, H., Hashemi, S., Bahar, R., and Reda, S. (2016). Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off. arXiv.","DOI":"10.1145\/2968456.2968458"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.adcom.2020.07.001","article-title":"Introduction to hardware accelerator systems for artificial intelligence and machine learning","volume":"122","author":"Gupta","year":"2021","journal-title":"Adv. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101977","DOI":"10.1016\/j.telpol.2020.101977","article-title":"Artificial Intelligence Applications in Telecommunications and other network industries","volume":"44","author":"Balmer","year":"2020","journal-title":"Telecommun. Policy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3193827","article-title":"FPGA dynamic and partial reconfiguration: A survey of architectures, methods, and applications","volume":"51","author":"Vipin","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_24","unstructured":"K\u00f6ksal, B., Schmidt, R., Vasilakos, X., and Nikaien, N. (2019, August 03). CRAWDAD Dataset Eurecom\/elasticmon5G2019 (v. 2019-08-29). Available online: https:\/\/crawdad.org\/eurecom\/elasticmon5G2019\/20190829."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, Z., Sinha, S., and Zhang, W. (May, January 28). Towards efficient and scalable acceleration of online decision tree learning on FPGA. Proceedings of the 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), San Diego, CA, USA.","DOI":"10.1109\/FCCM.2019.00032"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42979-021-00748-9","article-title":"Efficient Hardware Implementation of Decision Tree Training Accelerator","volume":"2","author":"Choudhury","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Novickis, R., Justs, D.J., Ozols, K., and Greit\u0101ns, M. (2020). An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA. Electronics, 9.","DOI":"10.3390\/electronics9122193"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kachris, C., Koromilas, E., Stamelos, I., and Soudris, D. (2017, January 16\u201320). SPynq: Acceleration of machine learning applications over Spark on Pynq. Proceedings of the 2017 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), Samos, Greece.","DOI":"10.1109\/SAMOS.2017.8344613"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104907","DOI":"10.1016\/j.mejo.2020.104907","article-title":"A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system","volume":"105","author":"Batista","year":"2020","journal-title":"Microelectron. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, R., Liu, B., Fu, P., Li, J., and Feng, S. (2019). An Accelerator Architecture of Changeable-Dimension Matrix Computing Method for SVM. Electronics, 8.","DOI":"10.3390\/electronics8020143"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ye, M., and Guan, L. (2021). QoS-aware Link Scheduling Strategy for Data Transmission in SDVN. arXiv.","DOI":"10.1109\/ISNCC52172.2021.9615863"},{"key":"ref_32","unstructured":"Russell, S., and Norvig, P. (2002). Artificial Intelligence: A Modern Approach, Pearson. ISBN-10: 0134610997."},{"key":"ref_33","unstructured":"Copeland, B.J. (2021, July 03). Artificial Intelligence, Retrieved. Available online: https:\/\/www.britannica.com\/technology\/artificial-intelligence."},{"key":"ref_34","unstructured":"Mitchell, T.M. (1997). Machine Learning, McGraw-Hill. ISBN-10: 0070428077."},{"key":"ref_35","first-page":"3","article-title":"Advantages and Disadvantages of Artificial Intelligence and Machine Learning: A Literature Review","volume":"9","author":"Khanzode","year":"2020","journal-title":"Int. J. Libr. Inf. Sci."},{"key":"ref_36","first-page":"17","article-title":"Big Data: Preprocesamiento y calidad de datos","volume":"237","author":"Herrera","year":"2016","journal-title":"Nov\u00e1tica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","article-title":"A framework for sensitivity analysis of decision trees","volume":"26","author":"Jakubczyk","year":"2018","journal-title":"Cent. Eur. J. Oper. Res."},{"key":"ref_38","unstructured":"Rudolf, K. (2016). Computational Intelligence: A Methodological Introduction, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3399","DOI":"10.1007\/s12652-020-02560-4","article-title":"Secure prediction and assessment of sports injuries using deep learning based convolutional neural network","volume":"12","author":"Song","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_40","first-page":"98","article-title":"Artificial intelligence in business and economics research: Trends and future","volume":"22","author":"Torres","year":"2021","journal-title":"J. Bus. Econ. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"128","DOI":"10.14445\/22312803\/IJCTT-V48P126","article-title":"Supervised machine learning algorithms: Classification and comparison","volume":"48","author":"Osisanwo","year":"2017","journal-title":"Int. J. Comput. Trends Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_43","unstructured":"ATMEL (2022, January 14). Rad-Hard 32 Bit SPARC V8 Reconfigurable Processor: ATF697FF. Available online: https:\/\/www.microchip.com\/content\/dam\/mchp\/documents\/OTH\/ProductDocuments\/DataSheets\/ATF697FF.pdf."},{"key":"ref_44","unstructured":"Davidson, A. (2015). A new FPGA Architecture and Leading-Edge FinFET Process Technology Promise to Meet Next Generation System Requirements, High-End FPGA Products. Available online: https:\/\/www.intel.com\/content\/dam\/www\/programmable\/us\/en\/pdfs\/literature\/wp\/wp-01220-hyperflex-architecture-fpga-socs.pdf."},{"key":"ref_45","unstructured":"(2022, January 14). Intel Arria 10 FPGAs & SoCs. Available online: https:\/\/www.intel.com\/content\/www\/us\/en\/products\/details\/fpga\/arria\/10.html."},{"key":"ref_46","unstructured":"(2022, January 14). Intel MAX 10 FPGA. Available online: https:\/\/www.intel.com\/content\/www\/us\/en\/products\/details\/fpga\/max\/10.html."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sasidharan, A., and Nagarajan, P. (2014, January 27\u201328). VHDL Implementation of IEEE 754 floating point unit. Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES2014), Chennai, India.","DOI":"10.1109\/ICICES.2014.7033999"},{"key":"ref_48","unstructured":"ETSI (2017). Evolved Universal Terrestrial Radio Access (E-UTRA), Multiplexing and Channel Coding, ETSI."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Hyperparameter Optimization. Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.procs.2020.08.043","article-title":"A Resampling Method for Imbalanced Datasets Considering Noise and Overlap","volume":"176","author":"Sasada","year":"2020","journal-title":"Procedia Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:42:37Z","timestamp":1760136157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,24]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072497"],"URL":"https:\/\/doi.org\/10.3390\/s22072497","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,24]]}}}