{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:03:22Z","timestamp":1765357402536,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Mexican National Council of Humanities, Science, and Technology (CONAHCyT)","doi-asserted-by":"publisher","award":["490180"],"award-info":[{"award-number":["490180"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.<\/jats:p>","DOI":"10.3390\/s23115209","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:57:10Z","timestamp":1685501830000},"page":"5209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3617-7193","authenticated-orcid":false,"given":"Yanqueleth","family":"Molina-Tenorio","sequence":"first","affiliation":[{"name":"Information Science and Technology Ph.D., Metropolitan Autonomous University, Mexico City 09360, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2661-2449","authenticated-orcid":false,"given":"Alfonso","family":"Prieto-Guerrero","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Metropolitan Autonomous University, Mexico City 09360, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3551-1500","authenticated-orcid":false,"given":"Rafael","family":"Aguilar-Gonzalez","sequence":"additional","affiliation":[{"name":"Faculty of Science, Autonomous University of San Luis Potosi, San Luis Potosi 78210, Mexico"},{"name":"Engineering Department, Arkansas State University Campus Queretaro, Queretaro 76270, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0526-6687","authenticated-orcid":false,"given":"Miguel","family":"Lopez-Benitez","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"},{"name":"ARIES Research Centre, Antonio de Nebrija University, 28040 Madrid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/98.788210","article-title":"Cognitive radio: Making software radios more personal","volume":"6","author":"Mitola","year":"1999","journal-title":"IEEE Pers. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1016\/j.comnet.2006.05.001","article-title":"NeXt generation\/dynamic spectrum access\/cognitive radio wireless networks: A survey","volume":"50","author":"Akyildiz","year":"2006","journal-title":"Comput. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JPROC.2014.2303977","article-title":"Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks","volume":"102","author":"Hattab","year":"2014","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"El-Khamy, S.E., El-Mahallawy, M.S., and Youssef, E.S. (2013, January 28\u201331). Improved wideband spectrum sensing techniques using wavelet-based edge detection for cognitive radio. Proceedings of the 2013 International Conference on Computing, Networking and Communications (ICNC), San Diego, CA, USA.","DOI":"10.1109\/ICCNC.2013.6504120"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.aeue.2017.11.024","article-title":"Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs","volume":"84","author":"Kumar","year":"2018","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Diao, X., Dong, Q., Yang, Z., and Li, Y. (2017). Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension. Algorithms, 10.","DOI":"10.3390\/a10030096"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Popoola, J.J., and van Olst, R. (2011, January 13\u201315). Application of neural network for sensing primary radio signals in a cognitive radio environment. Proceedings of the IEEE Africon \u201911, Victoria Falls, Zambia.","DOI":"10.1109\/AFRCON.2011.6072009"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shamsi, N., Mousavinia, A., and Amirpour, H. (2013, January 7\u20139). A channel state prediction for multi-secondary users in a cognitive radio based on neural network. Proceedings of the 2013 International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Turkey.","DOI":"10.1109\/ICECCO.2013.6718263"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., and Aguilar-Gonzalez, R. (2019). A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension. Sensors, 19.","DOI":"10.3390\/s19061322"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zaidawi, D.J., and Sadkhan, S.B. (2021, January 24\u201325). Blind Spectrum Sensing Algorithms in CRNs: A Brief Overview. Proceedings of the 2021 7th International Engineering Conference \u201cResearch & Innovation amid Global Pandemic\u201d (IEC), Erbil, Iraq.","DOI":"10.1109\/IEC52205.2021.9476142"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., and Aguilar-Gonzalez, R. (2021). Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology. Sensors, 21.","DOI":"10.3390\/s21103506"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20807","DOI":"10.1109\/ACCESS.2018.2825885","article-title":"SDR Implementation of a Testbed for Real-Time Interference Detection with signal cancellation","volume":"6","author":"Politis","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MCOM.2015.7263347","article-title":"A low-cost desktop software defined radio design environment using MATLAB, simulink, and the RTL-SDR","volume":"53","author":"Stewart","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24214","DOI":"10.1109\/ACCESS.2017.2761859","article-title":"A Reconfigurable SDR Transmitter Platform Architecture for Space Modulation MIMO Techniques","volume":"5","author":"Hiari","year":"2017","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Santos-Luna, E., Prieto-Guerrero, A., Aguilar-Gonzalez, R., Ramos, V., Lopez-Benitez, M., and Cardenas-Juarez, M. (2019, January 17\u201319). A Spectrum Analyzer Based on a Low-Cost Hardware-Software Integration. Proceedings of the 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2019.8936239"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Aghabeiki, S., Hallet, C., Noutehou, N.E.-R., Rassem, N., Adjali, I., and Mabrouk, M.B. (2021, January 21\u201323). Machine-learning-based spectrum sensing enhancement for software-defined radio applications. Proceedings of the 2021 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW), Cleveland, OH, USA.","DOI":"10.1109\/CCAAW50069.2021.9527294"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1109\/TLA.2012.6142453","article-title":"Introduction to the Software-defined Radio Approach","volume":"10","author":"Selva","year":"2012","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_18","unstructured":"(2022, August 08). About RTL-SDR, rtl-sdr.com, 11 de Abril de 2013. Available online: https:\/\/www.rtl-sdr.com\/about-rtl-sdr\/."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Koutlia, K., Bojovi\u0107, B., Lag\u00e9n, S., and Giupponi, L. (2021, January 23\u201324). Novel radio environment map for the ns-3 NR simulator. Proceedings of the Workshop on ns-3, Virtual Event USA: ACM, New York, NY, USA.","DOI":"10.1145\/3460797.3460803"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/978-3-319-73564-1_25","article-title":"Recent Advances in Radio Environment Map: A Survey","volume":"Volume 226","author":"Gu","year":"2018","journal-title":"Machine Learning and Intelligent Communications"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Spooner, C.M., and Khambekar, N.V. (February, January 30). Spectrum sensing for cognitive radio: A signal-processing perspective on signal-statistics exploitation. Proceedings of the 2012 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA.","DOI":"10.1109\/ICCNC.2012.6167485"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jia, M., Guo, Q., and Meng, W. (2019). Wireless and Satellite Systems, Springer International Publishing. En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.","DOI":"10.1007\/978-3-030-19156-6"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Santana, Y.H., Plets, D., Alonso, R.M., Nieto, G.G., Martens, L., and Joseph, W. (2022, January 15\u201317). Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels. Proceedings of the 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Bilbao, Spain.","DOI":"10.1109\/BMSB55706.2022.9828582"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Borisov, V., Leemann, T., Sessler, K., Haug, J., Pawelczyk, M., and Kasneci, G. (2022). Deep Neural Networks and Tabular Data: A Survey. IEEE Trans. Neural Netw. Learn. Syst., early access.","DOI":"10.1109\/TNNLS.2022.3229161"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xu, M., Yin, Z., Zhao, Y., and Wu, Z. (2022). Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network. Entropy, 24.","DOI":"10.3390\/e24010129"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1559\/152304083783914958","article-title":"Spatial Interpolation Methods: A Review","volume":"10","author":"Lam","year":"1983","journal-title":"Am. Cartogr."},{"key":"ref_27","unstructured":"Burrough, P.A., McDonnell, R., and Burrough, P.A. (1998). Principles of Geographical Information System, Oxford University Press. en Spatial Information Systems."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.apacoust.2016.05.024","article-title":"Performance evaluation of IDW, Kriging and multiquadric interpolation methods in producing noise mapping: A case study at the city of Isparta, Turkey","volume":"112","author":"Harman","year":"2016","journal-title":"Appl. Acoust."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Arseni, M., Voiculescu, M., Georgescu, L.P., Iticescu, C., and Rosu, A. (2019). Testing Different Interpolation Methods Based on Single Beam Echosounder River Surveying. Case Study: Siret River. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110507"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.2113\/gsecongeo.58.8.1246","article-title":"Principles of geostatistics","volume":"58","author":"Matheron","year":"1963","journal-title":"Econ. Geol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/02693799008941549","article-title":"Kriging: A method of interpolation for geographical information systems","volume":"4","author":"Oliver","year":"1990","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s12040-007-0006-6","article-title":"Spatial analyses of groundwater levels using universal kriging","volume":"116","author":"Gundogdu","year":"2007","journal-title":"J. Earth Syst. Sci."},{"key":"ref_33","unstructured":"Cressie, N.A.C. (2015). Statistics for Spatial Data, Revised ed., John Wiley & Sons, Inc."},{"key":"ref_34","unstructured":"Isaaks, E.H., and Srivastava, R.M. (1989). Applied Geostatistics, Oxford University Press."},{"key":"ref_35","unstructured":"Han, J., and Kamber, M. (2012). Data Mining: Concepts and Techniques, Elsevier. [3rd ed.]."},{"key":"ref_36","first-page":"1","article-title":"Development of convolutional neural network and its application in image classification: A survey","volume":"58","author":"Wang","year":"2019","journal-title":"Opt. Eng."},{"key":"ref_37","unstructured":"Haykin, S.S., and Haykin, S.S. (2009). Neural Networks and Learning Machines, Prentice Hall. [3rd ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., and Aguilar-Gonzalez, R. (2022). Multiband Spectrum Sensing Based on the Sample Entropy. Entropy, 24.","DOI":"10.3390\/e24030411"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Molina-Tenorio, Y., Prieto-Guerrero, A., Aguilar-Gonzalez, R., and Ruiz-Boqu\u00e9, S. (2019). Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios. Sensors, 19.","DOI":"10.3390\/s19214715"},{"key":"ref_42","unstructured":"(2021, March 08). Nooelec-Nooelec NESDR SMArt v4 SDR-Premium RTL-SDR w\/Aluminum Enclosure, 0.5PPM TCXO, SMA Input. RTL2832U & R820T2-Based-Software Defined Radio. Available online: https:\/\/www.nooelec.com\/store\/sdr\/nesdr-smart-sdr.html."},{"key":"ref_43","unstructured":"(2021, March 08). HackRF One-Great Scott Gadgets, 8 de Marzo de 2021. Available online: https:\/\/greatscottgadgets.com\/hackrf\/one\/."},{"key":"ref_44","unstructured":"(2022, March 13). LimeSDR Mini is a $135 Open Source Hardware, Full Duplex USB SDR Board (Crowdfunding)-CNX Software, CNX Software -Embedded Systems News, 18 de Septiembre de 2017. Available online: https:\/\/www.cnx-software.com\/2017\/09\/18\/limesdr-mini-is-a-135-open-source-hardware-full-duplex-usb-sdr-board-crowdfunding\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","article-title":"The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms","volume":"15","author":"Welch","year":"1967","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering: 50 years beyond K-means","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_48","unstructured":"Sasaki, Y. (2007, April 07). The Truth of the F-Measure, oct. 2007 [En L\u00ednea]. Available online: https:\/\/www.cs.odu.edu\/~mukka\/cs795sum11dm\/Lecturenotes\/Day3\/F-measure-YS-26Oct07.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:27Z","timestamp":1760125527000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115209"],"URL":"https:\/\/doi.org\/10.3390\/s23115209","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,5,30]]}}}