{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:40:26Z","timestamp":1783701626675,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"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>The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem.<\/jats:p>","DOI":"10.3390\/s20226475","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T20:17:52Z","timestamp":1605212272000},"page":"6475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0009-8154","authenticated-orcid":false,"given":"Tommaso","family":"Pecorella","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5934-3321","authenticated-orcid":false,"given":"Romano","family":"Fantacci","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedetta","family":"Picano","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50139 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51634","DOI":"10.1109\/ACCESS.2020.2980285","article-title":"Deep Learning-Based mmWave Beam Selection for 5G NR\/6G with Sub-6 GHz Channel Information: Algorithms and Prototype Validation","volume":"8","author":"Sim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TNSE.2018.2848960","article-title":"Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach","volume":"7","author":"Luo","year":"2018","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1109\/TIT.2005.850094","article-title":"Impact of antenna correlation on the capacity of multiantenna channels","volume":"51","author":"Tulino","year":"2005","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, M., Meng, Y., Liu, J., Zhu, H., Liang, X., Liu, Y., and Ruan, N. (2016, January 24\u201328). When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS \u201916), Vienna, Austria.","DOI":"10.1145\/2976749.2978397"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3482","DOI":"10.1109\/TWC.2011.072511.110020","article-title":"Downlink Radio Resource Allocation in OFDMA Spectrum Sharing Environment with Partial Channel State Information","volume":"10","author":"Mokari","year":"2011","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6561","DOI":"10.1109\/TIT.2017.2736560","article-title":"Generalized Degrees of Freedom of the Symmetric K User Interference Channel Under Finite Precision CSIT","volume":"63","author":"Jafar","year":"2017","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/TWC.2010.110910.100135","article-title":"Maximum Likelihood Based Channel Estimation for Macrocellular OFDM Uplinks in Dispersive Time-Varying Channels","volume":"10","author":"Du","year":"2011","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TVT.2017.2746562","article-title":"Iterative LMMSE Individual Channel Estimation Over Relay Networks With Multiple Antennas","volume":"67","author":"Ma","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1109\/TCOMM.2007.908549","article-title":"Tracking Performance of Least Squares MIMO Channel Estimation Algorithm","volume":"55","author":"Karami","year":"2007","journal-title":"IEEE Trans. Commun."},{"key":"ref_10","unstructured":"Wei, X.H., Hu, C., and Dai, L. (2019). Knowledge-Aided Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Samuel, N., Diskin, T., and Wiesel, A. (2017, January 3\u20136). Deep MIMO detection. Proceedings of the 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Sapporo, Japan.","DOI":"10.1109\/SPAWC.2017.8227772"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An Introduction to Deep Learning for the Physical Layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_13","unstructured":"Montgomery, D.C., Jennings, C.L., and Kulahci, M. (2015). Time Series Analysis and Forecasting, Wiley. [2nd ed.]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1016\/j.ijforecast.2014.08.008","article-title":"Electricity price forecasting: A review of the state-of-the-art with a look into the future","volume":"30","author":"Weron","year":"2014","journal-title":"Int. J. Forecast."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10922-010-9188-3","article-title":"A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM","volume":"19","author":"Liu","year":"2011","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/OJCOMS.2020.2982513","article-title":"Deep Learning for Fading Channel Prediction","volume":"1","author":"Jiang","year":"2020","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"49029","DOI":"10.1109\/ACCESS.2018.2868480","article-title":"Jointly Optimized Extreme Learning Machine for Short-Term Prediction of Fading Channel","volume":"6","author":"Sui","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, H., and Wu, Q. (2019, January 15\u201318). Optimizing Adaptive Coding and Modulation for Satellite Network with ML-based CSI Prediction. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco.","DOI":"10.1109\/WCNC.2019.8885616"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Konstantinov, A.S., and Pestryakov, A.V. (2019, January 1\u20133). Fading Channel Prediction for 5G. Proceedings of the 2019 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), Yaroslavl, Russia.","DOI":"10.1109\/SYNCHROINFO.2019.8813950"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sapavath, N.N., Rawat, D.B., and Song, M. (2020). Machine Learning for RF Slicing using CSI Prediction in Software Defined Large-scale MIMO Wireless Networks. IEEE Trans. Netw. Sci. Eng.","DOI":"10.1109\/TNSE.2020.2993984"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dong, P., Zhang, H., and Li, G.Y. (2018, January 9\u201313). Machine Learning Prediction Based CSI Acquisition for FDD Massive MIMO Downlink. Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE.","DOI":"10.1109\/GLOCOM.2018.8647328"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khan, H., Butt, M.M., Samarakoon, S., Sehier, P., and Bennis, M. (2020, January 7\u201311). Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation. Proceedings of the 2020 IEEE International Conference on Communications Workshops (ICC Workshops), Dublin, Ireland.","DOI":"10.1109\/ICCWorkshops49005.2020.9145297"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mishra, P., Dixit, A., and Jain, V.K. (2019, January 16\u201319). Machine Learning Techniques for Channel Estimation in Free Space Optical Communication Systems. In Proceedings of the 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Goa, India.","DOI":"10.1109\/ANTS47819.2019.9117976"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TCOMM.2003.822171","article-title":"On the design of MIMO block-fading channels with feedback-link capacity constraint","volume":"52","author":"Lau","year":"2004","journal-title":"IEEE Trans. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xie, Y., Li, Z., and Li, M. (2015, January 7\u201311). Precise Power Delay Profiling with Commodity WiFi. Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom \u201915), Paris, France.","DOI":"10.1145\/2789168.2790124"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schulz, M., Link, J., Gringoli, F., and Hollick, M. (2018, January 10\u201315). Shadow Wi-Fi: Teaching Smartphones to Transmit Raw Signals and to Extract Channel State Information to Implement Practical Covert Channels over Wi-Fi. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys \u201918), Munich, Germany.","DOI":"10.1145\/3210240.3210333"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.neunet.2007.04.003","article-title":"An experimental unification of reservoir computing methods","volume":"20","author":"Verstraeten","year":"2007","journal-title":"Neural Netw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","article-title":"Reservoir computing approaches to recurrent neural network training","volume":"3","author":"Jaeger","year":"2009","journal-title":"Comput. Sci. Rev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1162\/089976602760407955","article-title":"Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations","volume":"14","author":"Maass","year":"2002","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.1091277","article-title":"Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication","volume":"304","author":"Jaeger","year":"2004","journal-title":"Science"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/j.neunet.2011.02.002","article-title":"Architectural and Markovian factors of echo state networks","volume":"24","author":"Gallicchio","year":"2011","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2012.07.005","article-title":"Re-visiting the echo state property","volume":"35","author":"Yildiz","year":"2012","journal-title":"Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Manjunath, G., and Jaeger, H. (2012). Echo State Property Linked to an Input: Exploring a Fundamental Characteristic of Recurrent Neural Networks. Neural Comput., 25.","DOI":"10.1162\/NECO_a_00411"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"042809","DOI":"10.1103\/PhysRevE.87.042809","article-title":"Mean-field theory of echo state networks","volume":"87","author":"Massar","year":"2013","journal-title":"Phys. Rev. E"},{"key":"ref_35","unstructured":"Ti\u00f1o, P. (2017, January 26\u201328). Fisher memory of linear Wigner echo state networks. Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Bruges, Belgium."},{"key":"ref_36","unstructured":"Gallicchio, C., Micheli, A., and Ti\u00f1o, P. (2018, January 25\u201327). Randomized Recurrent Neural Networks. Proceedings of the 26th European Symposium on Artificial Neural Networks (ESANN 2018), Bruges, Belgium."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gallicchio, C., and Micheli, A. (2017). Deep Echo State Network (DeepESN): A Brief Survey. arXiv.","DOI":"10.1109\/IJCNN.2018.8489464"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2016.12.089","article-title":"Deep reservoir computing: A critical experimental analysis","volume":"268","author":"Gallicchio","year":"2017","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6475\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:32:45Z","timestamp":1760178765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6475"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":38,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20226475"],"URL":"https:\/\/doi.org\/10.3390\/s20226475","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,12]]}}}