{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:47:27Z","timestamp":1770821247424,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T00:00:00Z","timestamp":1517270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Uplink and Downlink channel estimation in massive Multiple Input Multiple Output (MIMO) systems is an intricate issue because of the increasing channel matrix dimensions. The channel feedback overhead using traditional codebook schemes is very large, which consumes more bandwidth and decreases the overall system efficiency. The purpose of this paper is to decrease the channel estimation overhead by taking the advantage of sparse attributes and also to optimize the Energy Efficiency (EE) of the system. To cope with this issue, we propose a novel approach by using Compressed-Sensing (CS), Block Iterative-Support-Detection (Block-ISD), Angle-of-Departure (AoD) and Structured Compressive Sampling Matching Pursuit (S-CoSaMP) algorithms to reduce the channel estimation overhead and compare them with the traditional algorithms. The CS uses temporal-correlation of time-varying channels to produce Differential-Channel Impulse Response (DCIR) among two CIRs that are adjacent in time-slots. DCIR has greater sparsity than the conventional CIRs as it can be easily compressed. The Block-ISD uses spatial-correlation of the channels to obtain the block-sparsity which results in lower pilot-overhead. AoD quantizes the channels whose path-AoDs variation is slower than path-gains and such information is utilized for reducing the overhead. S-CoSaMP deploys structured-sparsity to obtain reliable Channel-State-Information (CSI). MATLAB simulation results show that the proposed CS based algorithms reduce the feedback and pilot-overhead by a significant percentage and also improve the system capacity as compared with the traditional algorithms. Moreover, the EE level increases with increasing Base Station (BS) density, UE density and lowering hardware impairments level.<\/jats:p>","DOI":"10.3390\/e20020092","type":"journal-article","created":{"date-parts":[[2018,1,30]],"date-time":"2018-01-30T05:02:34Z","timestamp":1517288554000},"page":"92","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8900-0888","authenticated-orcid":false,"given":"Imran","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Engineering and Technology, Peshawar 814, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Jan","sequence":"additional","affiliation":[{"name":"Department of Physics, Kohat University of Science and Technology (KUST), Kohat 26000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0862-0533","authenticated-orcid":false,"given":"Jaime","family":"Lloret","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n para la Gesti\u00f3n Integrada de Zonas Costeras, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Camino de Vera, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Basheri","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3822-9348","authenticated-orcid":false,"given":"Dhananjay","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Hankuk University of Foreign Studies, Yongin 449-791, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,30]]},"reference":[{"key":"ref_1","unstructured":"Krishna, M.B., and Lloret, J. (2016). Evolution toward 5G Networks. Advances in Mobile Computing and Communications: Perspectives and Emerging Trends in 5G Networks, CRC Press. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lloret, J., Parra, L., Taha, M., and Tom\u00e1s, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Comput. Netw.","DOI":"10.1016\/j.comnet.2017.05.018"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mansoor, B., Nawaz, S.J., and Gulfam, S.M. (2017). Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing. Appl. Sci., 7.","DOI":"10.3390\/app7010063"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jnca.2015.09.003","article-title":"Improvement of SCTP congestion control in the LTE-A network","volume":"58","author":"Najm","year":"2015","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Taha, M., Parra, L., Garcia, L., and Lloret, J. (2017, January 21\u201325). An Intelligent handover process algorithm in 5G networks: The use case of mobile cameras for environmental surveillance. Proceedings of the IEEE International Conference Communications Workshops (ICC Workshops 2017), Paris, France.","DOI":"10.1109\/ICCW.2017.7962763"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/MCOM.2014.6736761","article-title":"Massive MIMO for next-generation wireless systems","volume":"52","author":"Larsson","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MWC.2015.7306533","article-title":"MmWave massive-MIMO-based wireless backhaul for the 5G ultra-dense network","volume":"22","author":"Gao","year":"2015","journal-title":"IEEE Wirel. Commun."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MSP.2011.2178495","article-title":"Scaling up MIMO: Opportunities and challenges with very large arrays","volume":"30","author":"Rusek","year":"2013","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_9","first-page":"1","article-title":"Efficient Bayesian compressed sensing-based channel estimation techniques for massive MIMO-OFDM systems","volume":"38","author":"Nakhai","year":"2017","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MCOM.2014.6815892","article-title":"The role of small cells, coordinated multipoint, and massive MIMO in 5G","volume":"52","author":"Jungnickel","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1109\/TVT.2015.2414651","article-title":"On superimposed pilot for channel estimation in multi-cell multiuser MIMO uplink: Large system analysis","volume":"65","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/TWC.2016.2639485","article-title":"Training Signal Design for correlated Massive MIMO Channel Estimation","volume":"16","author":"Soltanalian","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/JSTSP.2014.2327572","article-title":"Pilot beam pattern design for channel estimation in massive MIMO systems","volume":"8","author":"Noh","year":"2014","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s10470-017-0943-1","article-title":"Blind estimation for massive MIMO","volume":"91","author":"Peken","year":"2017","journal-title":"Analog IC Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Mawatwal, K., Sen, D., and Roy, R. (2016, January 4\u20138). An iterative SAGE-based semi-blind channel estimation for massive MIMO. Proceedings of the IEEE Conference on GLOBECOM, Washington, DC, USA.","DOI":"10.1109\/GLOCOM.2016.7841595"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fan, D., Gao, F., Wang, G., Zhong, Z., and Nallanathan, A. (2017, January 21\u201325). A practical channel estimation scheme for indoor 60GHz massive MIMO systems via array signal processing. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7996972"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gonzalez-Prelcic, N., Truong, K.T., Rusu, C., and Heath, R.W. (2016, January 4\u20138). Compressive Channel Estimation in FDD Multi-Cell Massive MIMO Systems with Arbitrary Arrays. Proceedings of the Globecom Workshops (GC Wkshps), Washington, DC, USA.","DOI":"10.1109\/GLOCOMW.2016.7848864"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, H., and Cabria, D. (2016, January 7\u20139). Compressive sensing based initial beamforming training for massive MIMO millimeter-wave systems. Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, USA.","DOI":"10.1109\/GlobalSIP.2016.7905916"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/TCOMM.2016.2557791","article-title":"Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave communications","volume":"64","author":"Lee","year":"2014","journal-title":"IEEE Trans. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"173","DOI":"10.2478\/eletel-2014-0021","article-title":"On the Comparison of Various Overhead Arrangements for Massive MIMO-OFDM Channel Estimation","volume":"60","author":"Sure","year":"2014","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1049\/el.2014.3576","article-title":"Spatially correlated channel estimation based on block iterative support detection for large-scale MIMO","volume":"51","author":"Shen","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4770","DOI":"10.1109\/TWC.2017.2702617","article-title":"Auxilliary Beam Pair Enabled AoD and AoA Estimation in Closed-Loop Large-Scale Millimeter-Wave MIMO Systems","volume":"16","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shen, W., Wang, B., Feng, J., Gao, C., and Ma, J. (2015, January 1\u201314). Differential CSIT Acquisition Based on Compressive Sensing for FDD Massive MIMO Systems. Proceedings of the 81st Vehicular Technology Conference (VTC Spring), Glasgow, UK.","DOI":"10.1109\/VTCSpring.2015.7145776"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5270","DOI":"10.1109\/TSP.2013.2273196","article-title":"Dynamic compressive sensing of time-varying signals via approximate message passing","volume":"61","author":"Ziniel","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TVT.2013.2280655","article-title":"Pilot design for sparse channel estimation in OFDM-based cognitive radio systems","volume":"63","author":"Qi","year":"2014","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1109\/TIT.2009.2016006","article-title":"Subspace pursuit for compressive sensing signal reconstruction","volume":"55","author":"Dai","year":"2009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_27","first-page":"460","article-title":"Compressive sensing-based time domain synchronous OFDM transmission for vehicular communications","volume":"32","author":"Dai","year":"2013","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1049\/iet-com.2013.0665","article-title":"Stable adaptive sparse filtering algorithms for estimating multiple-input-multiple-output channels","volume":"8","author":"Gui","year":"2014","journal-title":"IET Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3705","DOI":"10.1109\/TCOMM.2012.091112.110439","article-title":"Estimation of sparse MIMO channels with common support","volume":"60","author":"Barbotin","year":"2012","journal-title":"IEEE Trans. Commun."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1002\/wcm.78","article-title":"Models for MIMO propagation channels: A review","volume":"2","author":"Yu","year":"2002","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tsai, Y., Zheng, L., and Wang, X. (arXiv, 2017). Millimeter-Wave Beamformed Full-dimension MIMO Channel Estimation Based on Automatic Norm Minimization, arXiv.","DOI":"10.1109\/TCOMM.2018.2864737"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tse, D., and Viswanath, P. (2005). The capacity of wireless channels. Fundamentals of Wireless Communication, Cambridge University Press.","DOI":"10.1017\/CBO9780511807213"},{"key":"ref_33","first-page":"5266","article-title":"Analysis of precoding algorithms and pilot contamination problem for massive MIMO System-Review","volume":"5","author":"Abdelrahman","year":"2017","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4053","DOI":"10.1109\/TSP.2011.2161982","article-title":"Structured compressed sensing: From theory to applications","volume":"59","author":"Duarte","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/COMST.2007.382406","article-title":"Channel estimation for wireless OFDM systems","volume":"9","author":"Ozdemir","year":"2007","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bjornson, E., Sanguinetti, L., and Kountouris, M. (2015, January 8\u201312). Designing wireless broadband access for energy efficiency: Are small cells the only answer?. Proceedings of the IEEE International Conference on Communications (ICC), London, UK.","DOI":"10.1109\/ICCW.2015.7247168"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/2\/92\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:53:00Z","timestamp":1760194380000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/2\/92"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,30]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["e20020092"],"URL":"https:\/\/doi.org\/10.3390\/e20020092","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,30]]}}}