A Survey on MIMOOFDM Systems: Review of Recent Trends
Abstract
:1. Introduction
2. Overview of Recent Radio Trends
2.1. Cognitive Radio Networks
2.2. SystemDefined Radio Paradigm (SDR)
2.3. MIMO Systems
3. StateoftheArt MIMOSDR Systems
3.1. MIMO Systems
3.1.1. Educational Platforms and Testbeds
Massive MIMO Systems
 It allows the amount of data supported in both the uplink and the downlink to be 384 Gbits/s.
 The synchronization is performed with an external signal derived from 8 OctoClock devices (7 OctoClock devices commanded with a master OctoClock). However, some phase tight distortion appears during the transmission tests performed between the base station radio frequency channels; this is due to the receiver channels [24].
 The system could be extended up to 128 antennas.
 A planar Tshaped antenna array with 160 dual polarized elements was used. Moreover, five USRPRIOtype 2953Rs are deployed to emulate the receiving user equipment with a GPS reference signal connection capability.
SmallScale MIMO Systems
3.1.2. Hardware and Architecture Innovations
Distributed MIMO
 Single source transmission: a source (S), generating the signals to be transmitted, is connected via wireless or a physical interface to the noncollocated transmitting antennas. The signal is received, in the other side, by another set of antennas and transmitted to a receiving point (R) that gathers all the information. The signaling channel interfaces are necessary to set up the MIMO communications. Please refer to Figure 5a.
 Cooperative transmission: in this case, each transmitting antenna represents a signal source by itself. The transmitting and receiving antennas can cooperate, using signaling channel information, without the need of an intermediate point. Please refer to Figure 5b.
FiberBased Systems
GPU Implementations
MIMO Antennas
 Operability in multiband frequencies
 Polarization diversity
 Low size and cost with high performance.
3.2. MIMOSDR Waveforms
3.2.1. MIMO Waveforms Analysis
3.2.2. OFDM/Cyclic Prefix OFDM (CPOFDM) Theory Aspects
3.2.3. MIMOOFDM Variants
 GFDM
 UFOFDM
 FBMCOQAM
3.2.4. MIMOOFDM Variants Enhancement Studies
3.3. MIMOOFDM Block Enhancements
3.3.1. Channel Estimation
 Pilotaided channel estimation algorithms
 Blind and semiblind channel estimation algorithms
 Decisiondirected channel estimation algorithms
3.3.2. Equalization
4. Conclusions
 
 The part on implementation is more limited to research testbeds that apply traditional channel estimation algorithms.
 
 The most deployed channel estimation algorithms are complex and have a lower level of mitigation of ICI.
 
 The equalization algorithms are quite limited in terms of performance.
4.1. Challenges
4.2. Opportunities
 
 The machine learning algorithm, as well as the empirical mode decompositionbased methods, draws interesting results for the channel estimating problem.
 
 The equalization techniques based on the wavelet decomposition technique are interesting.
 
 OFDM waveform enhanced schema.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
 GarcıaNaya, J.A.; GonzálezLópez, M.; Castedo, L. An overview of MIMO testbed technology. In Proceedings of the 4th International Symposium on Image and Video Communications over Fixed and Mobile Networks (ISIVC’08), Bilbao, Spain, 9–11 July 2008. [Google Scholar]
 Delson, T.R.; Jose, I. A Survey on 5G Standards, Specifications and Massive MIMO Testbed Including Transceiver Design Models Using QAM Modulation Schemes. In Proceedings of the 2019 International Conference on Data Science and Communication (IconDSC), Bangalore, India, 1–2 March 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–7. [Google Scholar]
 Shafi, M.; Molisch, A.F.; Smith, P.J.; Haustein, T.; Zhu, P.; Silva, P.D.; Tufvesson, F.; Benjebbour, A.; Wunder, G. 5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice. IEEE J. Sel. Areas Commun. 2017, 35, 1201–1221. [Google Scholar] [CrossRef]
 Banelli, P.; Buzzi, S.; Colavolpe, G.; Modenini, A.; Rusek, F.; Ugolini, A. Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM?: An overview of alternative modulation schemes for improved spectral efficiency. IEEE Signal Process. Mag. 2014, 31, 80–93. [Google Scholar] [CrossRef]
 Wang, C.; Haider, F.; Gao, X.; You, X.; Yang, Y.; Yuan, D.; Aggoune, H.M.; Haas, H.; Fletcher, S.; Hepsaydir, E. Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun. Mag. 2014, 52, 122–130. [Google Scholar] [CrossRef] [Green Version]
 Amin, M.R.; Trapasiya, S.D. Space Time Coding Scheme for MIMO systemLiterature Survey. Procedia Eng. 2012, 38, 3509–3517. [Google Scholar] [CrossRef] [Green Version]
 Chen, S.; Zhang, J.; Zhang, J.; Björnson, E.; Ai, B. A survey on usercentric cellfree massive MIMO systems. Digit. Commun. Netw. 2021, in press. [Google Scholar] [CrossRef]
 Mokhtari, Z.; Sabbaghian, M.; Dinis, R. A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments. Sensors 2019, 19, 164. [Google Scholar] [CrossRef] [Green Version]
 Ijiga, O.E.; Ogundile, O.O.; Familua, A.D.; Versfeld, D.J. Review of channel estimation for candidate waveforms of next generation networks. Electronics 2019, 8, 956. [Google Scholar] [CrossRef] [Green Version]
 Wen, F.; Wymeersch, H.; Peng, B.; Tay, W.P.; So, H.C.; Yang, D. A survey on 5G massive MIMO localization. Digit. Signal Process. 2019, 94, 21–28. [Google Scholar] [CrossRef] [Green Version]
 Zheng, K.; Zhao, L.; Mei, J.; Shao, B.; Xiang, W.; Hanzo, L. Survey of LargeScale MIMO Systems. IEEE Commun. Surv. Tutor. 2015, 17, 1738–1760. [Google Scholar] [CrossRef] [Green Version]
 Yang, S.; Hanzo, L. Fifty Years of MIMO Detection: The Road to LargeScale MIMOs. IEEE Commun. Surv. Tutor. 2015, 17, 1941–1988. [Google Scholar] [CrossRef] [Green Version]
 Paul, B.S.; Bhattacharjee, R. MIMO channel modeling: A review. IETE Tech. Rev. 2008, 25, 315–319. [Google Scholar] [CrossRef]
 Yu, K.; Ottersten, B. Models for MIMO propagation channels: A review. Wirel. Commun. Mob. Comput. 2002, 2, 653–666. [Google Scholar] [CrossRef]
 Fatema, N.; Hua, G.; Xiang, Y.; Peng, D.; Natgunanathan, I. Massive MIMO Linear Precoding: A Survey. IEEE Syst. J. 2018, 12, 3920–3931. [Google Scholar] [CrossRef]
 Qiao, G.; Babar, Z.; Ma, L.; Ahmed, N. Channel Estimation and Equalization of Underwater Acoustic MIMOOFDM Systems: A Review Estimation du canal et l’égalisation des systèmes MEMSMROF acoustiques sousmarins: Une revue. Can. J. Electr. Comput. Eng. 2019, 42, 199–208. [Google Scholar] [CrossRef]
 Fette, B. Introducing Adaptive, Aware, and Cognitive Radios. In Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems; Arslan, H., Ed.; Springer: Dordrecht, The Netherlands, 2007; pp. 1–16. [Google Scholar]
 Luther, E. 5G massive MIMO testbed: From theory to reality. White Paper. 2014. Available online: https://www.ni.com/enrs/innovations/whitepapers/14/5gmassivemimotestbedfromtheorytoreality.html (accessed on 4 May 2022).
 Hasan, W.B.; Harris, P.; Doufexi, A.; Beach, M. RealTime Maximum Spectral Efficiency for Massive MIMO and its Limits. IEEE Access 2018, 6, 46122–46133. [Google Scholar] [CrossRef]
 Zhang, C.; Qiu, R.C. Massive MIMO testbedimplementation and initial results in system model validation. arXiv 2014, arXiv:1501.00035. [Google Scholar]
 Ryan, Ø.; Debbah, M. Random Vandermonde matricespart I: Fundamental results. IEEE Trans. Inf. Theory 2008, 1, 1–20. [Google Scholar]
 Ryan, Ø.; Debbah, M. Random vandermonde matricespart ii: Applications. IEEE Trans. Inf. Theory 2008, 1, 1–13. [Google Scholar]
 Vieira, J.; Malkowsky, S.; Nieman, K.; Miers, Z.; Kundargi, N.; Liu, L.; Wong, I.; Öwall, V.; Edfors, O.; Tufvesson, F. A flexible 100antenna testbed for Massive MIMO. In Proceedings of the 2014 IEEE Globecom Workshops (GC Wkshps), Austin, TX, USA, 8–12 December 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 287–293. [Google Scholar]
 Edfors, O. LuMaMiA Flexible Testbed for Massive MIMO. Available online: https://people.kth.se/~perz/ewtbwr/2014/abstracts/Edfors.pdf (accessed on 1 March 2022).
 Jiang, X.; Kaltenberger, F. Demo: An LTE compatible massive MIMO testbed based on OpenAirInterface. In Proceedings of the WSA 2017, 21th International ITG Workshop on Smart Antennas, Berlin, Germany, 15–17 March 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–2. [Google Scholar]
 Malkowsky, S.; Vieira, J.; Liu, L.; Harris, P.; Nieman, K.; Kundargi, N.; Wong, I.C.; Tufvesson, F.; Öwall, V.; Edfors, O. The World’s First RealTime Testbed for Massive MIMO: Design, Implementation, and Validation. IEEE Access 2017, 5, 9073–9088. [Google Scholar] [CrossRef]
 Batra, A.; Wiemeler, M.; Kreul, T.; Goehringer, D.; Kaiser, T. A Massive MIMO Signal Processing Architecture for GHz to THz Frequencies. In Proceedings of the 2018 First International Workshop on Mobile Terahertz Systems (IWMTS), Duisburg, Germany, 2–4 July 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [Google Scholar]
 Zamfirescu, C.; Vulpe, A.; Halunga, S.; Fratu, O. Spatial Multiplexing MIMO 5GSDR Open Testbed Implementation. In Proceedings of the International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, Sofia, Bulgaria, 28–29 March 2019; pp. 197–213. [Google Scholar]
 Ribeiro, C.; Gameiro, A. A softwaredefined radio FPGA implementation of OFDMbased PHY transceiver for 5G. Analog. Integr. Circuits Signal Process. 2017, 91, 343–351. [Google Scholar] [CrossRef]
 Vielva, L.; Vía, J.; Gutiérrez, J.; González, Ó.; Ibáñez, J.; Santamaría, I. Building a web platform for learning advanced digital communications using a MIMO testbed. In Proceedings of the 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA, 14–19 March 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 2942–2945. [Google Scholar]
 Naya, J. Testbed Design for Wireless Communications Systems Assessment. Ph.D. Thesis, Universidade Da Coruna, A Coruña, Spain, 2010. [Google Scholar]
 Bates, D.; Henriksen, S.; Ninness, B.; Weller, S.R. A 4× 4 FPGAbased wireless testbed for LTE applications. In Proceedings of the 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, Cannes, France, 15–18 September 2008; pp. 1–5. [Google Scholar]
 Nieto, X.; Ventura, L.M.; Mollfulleda, A. GEDOMIS: A broadband wireless MIMOOFDM testbed, design and implementation. In Proceedings of the 2nd International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TRIDENTCOM 2006), Barcelona, Spain, 1–3 March 2006; pp. 10–121. [Google Scholar]
 Ramirez, D.; Santamaria, I.; Pérez, J.; Via, J.; Tazón, A.; GarciaNaya, J.; FernándezCaramés, T.; López, M.G.; PerezIglesias, H.; Castedo, L. A flexible testbed for the rapid prototyping of MIMO baseband modules. In Proceedings of the 2006 3rd International Symposium on Wireless Communication Systems, Valencia, Spain, 6–8 September 2006; pp. 776–780. [Google Scholar]
 Caban, S.; Mehlführer, C.; Langwieser, R.; Scholtz, A.L.; Rupp, M. Vienna MIMO testbed. EURASIP J. Adv. Signal Process. 2006, 2006, 054868. [Google Scholar] [CrossRef] [Green Version]
 Sundance Multiprocessor Technology, SMT 365. Available online: https://www.sundance.com/productrange/sundanceproducts/archivedproducts/smt365161/ (accessed on 1 March 2022).
 Roy, S.; Bélanger, L. The design of an fpgabased mimo transceiver for wifi. DSP Mag. 2006, 1, 28–31. [Google Scholar]
 Borkowski, D.; Brühl, L.; Degen, C.; Keusgen, W.; Alirezaei, G.; Geschewski, F.; Oikonomopoulos, C.; Rembold, B. SABA: A testbed for a realtime MIMO system. EURASIP J. Appl. Signal Process. 2006, 2006, 143. [Google Scholar] [CrossRef] [Green Version]
 Dowle, J.; Kuo, S.H.; Mehrotra, K.; McLoughlin, I.V. An FPGAbased MIMO and spacetime processing platform. EURASIP J. Appl. Signal Process. 2006, 2006, 1–14. [Google Scholar] [CrossRef] [Green Version]
 Wilzeck, A.; ElHadidy, M.; Cai, Q.; Amelingmeyer, M.; Kaiser, T. MIMO prototyping testbed with offtheshelf plugin RF hardware. In Proceedings of the IEEE Workshop on Smart Antennas, Ulm, Germany, 13–14 March 2006. [Google Scholar]
 Zhu, W.; Browne, D.; Fitz, M. An open access wideband multiantenna wireless testbed with remote control capability. In Proceedings of the First International Conference on Testbeds and Research Infrastructures for the DEvelopment of NeTworks and COMmunities, Trento, Italy, 23–25 February 2005; pp. 72–81. [Google Scholar]
 Wallace, J.W.; Jeffs, B.D.; Jensen, M.A. A realtime multiple antenna element testbed for MIMO algorithm development and assessment. In Proceedings of the IEEE Antennas and Propagation Society Symposium, Monterey, CA, USA, 20–25 June 2004; pp. 1716–1719. [Google Scholar]
 Lang, S.; Rao, M.; Daneshrad, B. Design and development of a 5.25 GHz software defined wireless OFDM communication platform. IEEE Commun. Mag. 2004, 42, S6–S12. [Google Scholar] [CrossRef]
 Morawski, R.; LeNgoc, T.; Naeem, O. Wireless and wireline MIMO testbed. In Proceedings of the CCECE 2003Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No. 03CH37436), Montreal, QC, Canada, 4–7 May 2003; pp. 1913–1916. [Google Scholar]
 Murphy, P.; Lou, F.; Sabharwal, A.; Frantz, J.P. An FPGA based rapid prototyping platform for MIMO systems. In Proceedings of the The ThritySeventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 9–12 November 2003; pp. 900–904. [Google Scholar]
 Fabregas, A.G.; Guillaud, M.; Caire, G.; Gosse, K.; Rouquette, S.; Dias, A.R.; Bernardin, P.; Miet, X.; Conrat, J.M.; Toutain, Y. A MIMOOFDM testbed for wireless local area networks. In Proceedings of the Conference Record of the ThirtyNinth Asilomar Conference onSignals, Systems and Computers, Pacific Grove, CA, USA, 30 October–2 November 2005; pp. 82–86. [Google Scholar]
 Sezgin, I.C.; Dahlgren, M.; Eriksson, T.; Coldrey, M.; Larsson, C.; Gustavsson, J.; Fager, C. A LowComplexity DistributedMIMO Testbed Based on HighSpeed Sigma–DeltaOverFiber. IEEE Trans. Microw. Theory Tech. 2019, 67, 2861–2872. [Google Scholar] [CrossRef]
 Simeone, O.; Somekh, O.; Poor, H.V.; Shamai, S. Distributed MIMO Systems for Nomadic Applications Over a Symmetric Interference Channel. IEEE Trans. Inf. Theory 2009, 55, 5558–5574. [Google Scholar] [CrossRef]
 Kun, Z.; Crisp, M.J.; Sailing, H.; Penty, R.V.; White, I.H. MIMO system capacity improvements using radiooverfibre distributed antenna system technology. In Proceedings of the 2011 Optical Fiber Communication Conference and Exposition and the National Fiber Optic Engineers Conference, Los Angeles, CA, USA, 6–10 March 2011; IEEE: Piscataway, NJ, USA, 2011; pp. 1–3. [Google Scholar]
 Gordon, G.S.D.; Crisp, M.J.; Penty, R.V.; White, I.H. Experimental Evaluation of Layout Designs for 3 × 3 MIMOEnabled RadioOverFiber Distributed Antenna Systems. IEEE Trans. Veh. Technol. 2014, 63, 643–653. [Google Scholar] [CrossRef]
 Ahn, C.; Kim, J.; Ju, J.; Choi, J.; Choi, B.; Choi, S. Implementation of an SDR platform using GPU and its application to a 2 × 2 MIMO WiMAX system. Analog. Integr. Circuits Signal Process. 2011, 69, 107. [Google Scholar] [CrossRef]
 Han, S.W.; Jin, Y.; Ahn, H.S.; Choi, S.W.; Hyeon, S.H. Implementation of an MUMIMO system with GPU modem for noncodebookbased TDD LTEA. In Proceedings of the The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014), Jeju, Korea, 22–25 June 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1–2. [Google Scholar]
 Roger, S.; Ramiro, C.; Gonzalez, A.; Almenar, V.; Vidal, A.M. Fully Parallel GPU Implementation of a FixedComplexity SoftOutput MIMO Detector. IEEE Trans. Veh. Technol. 2012, 61, 3796–3800. [Google Scholar] [CrossRef]
 Wu, M.; Sun, Y.; Gupta, S.; Cavallaro, J.R. Implementation of a High Throughput Soft MIMO Detector on GPU. J. Signal Process. Syst. 2011, 64, 123–136. [Google Scholar] [CrossRef] [Green Version]
 Gokalgandhi, B.; Segerholm, C.; Paul, N.; Seskar, I. Accelerating Channel Estimation and Demodulation of Uplink OFDM symbols for Large Scale Antenna Systems using GPU. In Proceedings of the 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 18–21 February 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 955–959. [Google Scholar]
 Caire, G.; Taricco, G.; Biglieri, E. Bitinterleaved coded modulation. IEEE Trans. Inf. Theory 1998, 44, 927–946. [Google Scholar] [CrossRef] [Green Version]
 Raychaudhuri, D.; Seskar, I.; Ott, M.; Ganu, S.; Ramachandran, K.; Kremo, H.; Siracusa, R.; Liu, H.; Singh, M. Overview of the ORBIT radio grid testbed for evaluation of nextgeneration wireless network protocols. In Proceedings of the IEEE Wireless Communications and Networking Conference, Wuhan, China, 23–26 September 2005; pp. 1664–1669. [Google Scholar]
 Bhagavatula, R.; Heath, R.W., Jr.; Linehan, K. Performance evaluation of MIMO base station antenna designs. Antenna Syst. Technol. Mag. 2008, 11, 14–17. [Google Scholar]
 Li, Y.; Luo, Y.; Yang, G. HighIsolation 3.5 GHz EightAntenna MIMO Array Using Balanced OpenSlot Antenna Element for 5G Smartphones. IEEE Trans. Antennas Propag. 2019, 67, 3820–3830. [Google Scholar] [CrossRef]
 Kamran Shereen, M.; Khattak, M.I.; Witjaksono, G. A brief review of frequency, radiation pattern, polarization, and compound reconfigurable antennas for 5G applications. J. Comput. Electron. 2019, 18, 1065–1102. [Google Scholar] [CrossRef]
 Ojaroudi Parchin, N.; Jahanbakhsh Basherlou, H.; AlYasir, Y.I.; AbdAlhameed, R.A.; Abdulkhaleq, A.M.; Noras, J.M. Recent developments of reconfigurable antennas for current and future wireless communication systems. Electronics 2019, 8, 128. [Google Scholar] [CrossRef] [Green Version]
 Hussain, R.; Sharawi, M.S. A Cognitive Radio Reconfigurable MIMO and Sensing Antenna System. IEEE Antennas Wirel. Propag. Lett. 2015, 14, 257–260. [Google Scholar] [CrossRef]
 Hussain, R.; Sharawi, M.S. Integrated reconfigurable multipleinput–multipleoutput antenna system with an ultrawideband sensing antenna for cognitive radio platforms. IET Microw. Antennas Propag. 2015, 9, 940–947. [Google Scholar] [CrossRef]
 Kambali, V.; Abegaonkar, M.; Basu, A. Frequency reconfigurable compact MIMO antenna for WLAN application. In Proceedings of the 2017 International Symposium on Antennas and Propagation (ISAP), Phuket, Thailand, 30 October–2 November 2017; pp. 1–2. [Google Scholar]
 Kotwalla, A.; Choukiker, Y.K. Design and analysis of microstrip antenna with frequency reconfigurable in MIMO environment. In Proceedings of the 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 20–22 April 2017; pp. 354–358. [Google Scholar]
 Thao, H.T.P.; Luan, V.T.; Minh, N.C.; Journet, B.; Van Yem, V. A company frequency reconfigurable MIMO antenna with low mutual coupling for UMTS and LTE applications. In Proceedings of the 2017 International Conference on Advanced Technologies for Communications (ATC), Quy Nhon, Vietnam, 18–20 October 2017; pp. 174–179. [Google Scholar]
 Duyen, T.H.; Pham, A.T. Performance analysis of mimo/fso systems using scqam signaling over atmospheric turbulence channels. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 2014, 97, 49–56. [Google Scholar] [CrossRef] [Green Version]
 Cai, Q.; Wilzeck, A.; Kaiser, T. Evaluation of Synchronization andl Fractilonally Spaced Equalilzation in a MIMO SCFDE TestBed. In Proceedings of the 2007 IEEE Radio and Wireless Symposium, Long Beach, CA, USA, 9–11 January 2007; pp. 527–530. [Google Scholar]
 Wu, P.; Schober, R.; Bhargava, V.K. Optimal TxBF for MIMO SCFDE Systems. IEEE Commun. Lett. 2013, 17, 1509–1512. [Google Scholar] [CrossRef]
 Mokhtari, Z.; Sabbaghian, M.; Dinis, R. Massive MIMO downlink based on single carrier frequency domain processing. IEEE Trans. Commun. 2016, 66, 1164–1175. [Google Scholar] [CrossRef]
 Nam, Y.H.; Han, J.K.; Zhang, J. Multiplexing of control and data in UL MIMO system based on SCFDM. Patent No. CA2809325A, 1 March 2013. [Google Scholar]
 Berardinelli, G.; de Temino, L.A.M.R.; Frattasi, S.; Sørensen, T.B.; Mogensen, P.E.; Pajukoski, K. On the Feasibility of Precoded Single User MIMO for LTEA Uplink. JCM 2009, 4, 155–163. [Google Scholar] [CrossRef] [Green Version]
 Priyanto, B.E.; Codina, H.; Rene, S.; Sorensen, T.B.; Mogensen, P. Initial performance evaluation of DFTspread OFDM based SCFDMA for UTRA LTE uplink. In Proceedings of the 2007 IEEE 65th Vehicular Technology ConferenceVTC2007Spring, Dublin, Ireland, 22–25 April 2007; pp. 3175–3179. [Google Scholar]
 Torres, P.; Gusmao, A. Detection issues with many BS antennas available for bandwidthefficient uplink transmission in a MUMIMO system. In Proceedings of the 2016 IEEE Wireless Communications and Networking Conference, Doha, Qatar, 3–6 April 2016; pp. 1–6. [Google Scholar]
 Sun, Y.; Wang, J.; He, L.; Song, J. Spectral efficiency analysis for spatial modulation in massive MIMO uplink over dispersive channels. In Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France, 21–25 May 2017; pp. 1–6. [Google Scholar]
 De Temiño, L.Á.M.R.; Berardinelli, G.; Frattasi, S.; Pajukoski, K.; Mogensen, P. Singleuser MIMO for LTEA uplink: Performance evaluation of OFDMA vs. SCFDMA. In Proceedings of the 2009 IEEE Radio and Wireless Symposium, San Diego, CA, USA, 18–22 January 2009; pp. 304–307. [Google Scholar]
 Berardinelli, G.; de Temino, L.A.M.R.; Frattasi, S.; Rahman, M.I.; Mogensen, P. OFDMA vs. SCFDMA: Performance comparison in local area IMTA scenarios. IEEE Wirel. Commun. 2008, 15, 64. [Google Scholar] [CrossRef]
 Yang, H. A road to future broadband wireless access: MIMOOFDMbased air interface. IEEE Commun. Mag. 2005, 43, 53–60. [Google Scholar] [CrossRef]
 Clerckx, B.; Joudeh, H.; Hao, C.; Dai, M.; Rassouli, B. Rate splitting for MIMO wireless networks: A promising PHYlayer strategy for LTE evolution. IEEE Commun. Mag. 2016, 54, 98–105. [Google Scholar] [CrossRef] [Green Version]
 Qualcomm Technologies, I. 5G Waveform & Multiple Access Techniques. Available online: https://www.qualcomm.com/media/documents/files/5gresearchonwaveformandmultipleaccesstechniques.pdf (accessed on 1 March 2022).
 Rammyaa, B.; Vishvaksenan, K.S.; Poobal, S.; Krishnan, M.M.M. Coded downlink MIMO MCCDMA system for cognitive radio network: Performance results. Clust. Comput. 2018, 22, 8371–8378. [Google Scholar] [CrossRef]
 Han, S.; Guo, C.; Meng, W.; Li, C.; Cui, Y.; Tang, W. The uplink and downlink design of MIMOSCMA system. In Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5–9 September 2016; pp. 56–60. [Google Scholar]
 Hadjer, B.; Abdelhafid, B. Comparison & Performance Evaluation of MIMOFBMC and MIMOUFMC systems for various equalization techniques. In Proceedings of the 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, Algeria, 26–27 June 2019; pp. 1–5. [Google Scholar]
 Zayani, R.; Shaiek, H.; Cheng, X.; Fu, X.; Alexandre, C.; Roviras, D. Experimental Testbed of postOFDM Waveforms Toward Future Wireless Networks. IEEE Access 2018, 6, 67665–67680. [Google Scholar] [CrossRef]
 Mauricio, W.V.; Araujo, D.C.; Neto, F.H.C.; Lima, F.R.M.; Maciel, T.F. A Low Complexity Solution for Resource Allocation and SDMA Grouping in Massive MIMO Systems. In Proceedings of the 2018 15th International Symposium on Wireless Communication Systems (ISWCS), Lisbon, Portugal, 28–31 August 2018; pp. 1–6. [Google Scholar]
 Wu, S.; Zuo, R.; Zhang, W.; Song, Y. SuccessiveParallel Interference Cancellation Multiuser Detection Algorithm for MUSA Uplink. In Wireless and Satellite Systems, Proceedings of the 10th EAI International Conference, WiSATS 2019, Harbin, China, 12–13 January 2019; Springer: Cham, Switzerland, 2019; pp. 541–551. [Google Scholar]
 Ding, Z.; Schober, R.; Poor, H.V. A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment. IEEE Trans. Wirel. Commun. 2016, 15, 4438–4454. [Google Scholar] [CrossRef] [Green Version]
 Zayani, R.; Medjahdi, Y.; Shaiek, H.; Roviras, D. WOLAOFDM: A potential candidate for asynchronous 5G. In Proceedings of the 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, 4–8 December 2016; pp. 1–5. [Google Scholar]
 Ahmed, R.; Schaich, F.; Wild, T. OFDM Enhancements for 5G Based on Filtering and Windowing. In Multiple Access Techniques for 5G Wireless Networks and Beyond; Springer: Berlin/Heidelberg, Germany, 2019; pp. 39–61. [Google Scholar]
 Jiang, T.; Chen, D.; Ni, C.; Qu, D. (Eds.) Chapter 1—Introduction. In OQAM/FBMC for Future Wireless Communications; Academic Press: Cambridge, MA, USA, 2018; pp. 1–24. [Google Scholar]
 Goztepe, C.; Kurt, G.K. The impact of out of band emissions: A measurement based performance comparison of UFOFDM and CPOFDM. Phys. Commun. 2019, 33, 78–89. [Google Scholar] [CrossRef]
 Chen, X.; Zhang, S.; Zhang, A. On MIMOUFMC in the Presence of Phase Noise and Antenna Mutual Coupling. Radio Sci. 2017, 52, 1386–1394. [Google Scholar] [CrossRef]
 Danneberg, M.; Michailow, N.; Gaspar, I.; Matthé, M.; Dan, Z.; Mendes, L.L.; Fettweis, G. Implementation of a 2 by 2 MIMOGFDM transceiver for robust 5G networks. In Proceedings of the 2015 International Symposium on Wireless Communication Systems (ISWCS), Brussels, Belgium, 25–28 August 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 236–240. [Google Scholar]
 Zhang, W.; Zhang, Z.; Qi, L.; Dou, Z. Latticereductionaided signal detection in spatial multiplexing MIMO–GFDM systems. Phys. Commun. 2019, 33, 71–77. [Google Scholar] [CrossRef]
 Pereira de Figueiredo, F.A.; Aniceto, N.F.; Seki, J.; Moerman, I.; Fraidenraich, G. Comparing fOFDM and OFDM Performance for MIMO Systems Considering a 5G Scenario. In Proceedings of the 5GWF2019, the the 2019 IEEE 2nd 5G World Forum, Dresden, Germany, 30 September–2 October 2019; pp. 1–6. [Google Scholar]
 Caus, M.; PérezNeira, A.I. Transmitterreceiver designs for highly frequency selective channels in MIMO FBMC systems. IEEE Trans. Signal Process. 2012, 60, 6519–6532. [Google Scholar] [CrossRef]
 Delmade, A.; Browning, C.; Farhang, A.; Marchetti, N.; Doyle, L.E.; Koilpillai, R.D.; Barry, L.P.; Venkitesh, D. Performance analysis of analog IF over fiber fronthaul link with 4G and 5G coexistence. J. Opt. Commun. Netw. 2018, 10, 174–182. [Google Scholar] [CrossRef]
 Chang, Y.K.; Ueng, F.B. A novel turbo GFDMIM receiver for MIMO communications. AEU Int. J. Electron. Commun. 2018, 87, 22–32. [Google Scholar] [CrossRef]
 Sharief, A.H.; Sairam, M.S. Performance analysis of MIMORDWTOFDM system with optimal genetic algorithm. AEU Int. J. Electron. Commun. 2019, 111, 152912. [Google Scholar] [CrossRef]
 Singh, A.; Naik, K.K.; Kumar, C.R.S. NOMURA: A Spectrally Efficient Nonorthogonal 5G Multiple Access Downlink Scheme for Cognitive Radio. IETE Tech. Rev. 2018, 37, 1–10. [Google Scholar] [CrossRef]
 Zakaria, R.; Le Ruyet, D. A novel filterbank multicarrier scheme to mitigate the intrinsic interference: Application to MIMO systems. IEEE Trans. Wirel. Commun. 2012, 11, 1112–1123. [Google Scholar] [CrossRef] [Green Version]
 Zhao, Z.; Gong, X.; Schellmann, M. A Novel FBMC/OQAM Scheme Facilitating MIMO FDMA without the Need for Guard Bands. In Proceedings of the WSA 2016 20th International ITG Workshop on Smart Antennas, Munich, Germany, 9–11 March 2016; pp. 1–5. [Google Scholar]
 Yu, X.; Guanghui, Y.; Xiao, Y.; Zhen, Y.; Jun, X.; Bo, G. FBOFDM: A novel multicarrier scheme for 5G. In Proceedings of the 2016 European Conference on Networks and Communications (EuCNC), Athens, Greece, 27–30 June 2016; pp. 271–276. [Google Scholar]
 Jin, C.; Hu, S.; Huang, Y.; Li, F.; Zhang, J.; Ma, S. On design of conjugated transmission scheme for FBMC/OQAM systems with interference cancellation. China Commun. 2017, 14, 166–175. [Google Scholar] [CrossRef]
 Aminjavaheri, A.; Farhang, A.; Rezazadehreyhani, A.; Doyle, L.E.; FarhangBoroujeny, B. OFDM without CP in massive MIMO. IEEE Trans. Wirel. Commun. 2017, 16, 7619–7633. [Google Scholar] [CrossRef]
 Pereira, A.; Bento, P.; Gomes, M.; Dinis, R.; Silva, V. TIBWBOFDM: A Promising Modulation Technique for MIMO 5G Transmissions. In Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTCFall), Chicago, IL, USA, 27–30 August 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–5. [Google Scholar]
 Başar, E.; Ü, A.; Panayırcı, E.; Poor, H.V. Orthogonal Frequency Division Multiplexing With Index Modulation. IEEE Trans. Signal Process. 2013, 61, 5536–5549. [Google Scholar] [CrossRef]
 Tarrab, M.; Feuer, A. Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data. IEEE Trans. Inf. Theory 1988, 34, 680–691. [Google Scholar] [CrossRef]
 Simon, E.P.; Khalighi, M.A. Iterative softKalman channel estimation for fast timevarying MIMOOFDM channels. IEEE Wirel. Commun. Lett. 2013, 2, 599–602. [Google Scholar] [CrossRef]
 Kim, K.; Kalantarova, N.; Kozat, S.S.; Singer, A.C. Linear MMSEoptimal turbo equalization using context trees. IEEE Trans. Signal Process. 2013, 61, 3041–3055. [Google Scholar] [CrossRef] [Green Version]
 Meredith, J.M. Study on downlink multiuser superposition transmission for LTE. In Proceedings of the TSG RAN Meeting, Tokyo, Japan, 9–11 March 2015. [Google Scholar]
 Tseng, C.; Cheng, Y.; Chung, C. SubspaceBased Blind Channel Estimation for OFDM by Exploiting Cyclic Prefix. IEEE Wirel. Commun. Lett. 2013, 2, 691–694. [Google Scholar] [CrossRef]
 Yin, C.; Li, J.; Hou, X.; Yue, G. Pilot aided LS channel estimation in MIMOOFDM systems. In Proceedings of the 2006 8th International Conference on Signal Processing, Guilin, China, 16–20 November 2006. [Google Scholar]
 He, C.; Tian, C.; Li, X.; Zhang, C.; Zhang, S.; Liu, C. A channel estimation scheme for MIMOOFDM systems. J. Phys. Conf. Ser. 2017, 887, 012039. [Google Scholar] [CrossRef] [Green Version]
 Tang, R.; Zhou, X.; Wang, C. Singular Value Decomposition Channel Estimation in STBC MIMOOFDM System. Appl. Sci. 2019, 9, 3067. [Google Scholar] [CrossRef] [Green Version]
 Li, W.; Wang, X.; Gu, P.; Wang, D. Research on Channel Estimation of MIMO–OFDM System. In Informatics and Management Science III; Springer: Berlin/Heidelberg, Germany, 2013; pp. 67–73. [Google Scholar]
 Zheng, K.; Su, J.; Wang, W. Iterative DFTbased Channel Estimation for MIMOOFDM Systems. In Proceedings of the 2006 International Conference on Communications, Circuits and Systems, Hangzhou, China, 25–28 June 2006; IEEE: Piscataway, NJ, USA, 2006; pp. 1081–1085. [Google Scholar]
 Sure, P.; Bhuma, C.M. A pilot aided channel estimator using DFT based time interpolator for massive MIMOOFDM systems. AEU Int. J. Electron. Commun. 2015, 69, 321–327. [Google Scholar] [CrossRef]
 Dai, L.; Wang, Z.; Yang, Z. Spectrally efficient timefrequency training OFDM for mobile largescale MIMO systems. IEEE J. Sel. Areas Commun. 2013, 31, 251–263. [Google Scholar] [CrossRef]
 Carbonelli, C.; Franz, S. Performance analysis of MIMO OFDM ML detection in the presence of channel estimation error. In Proceedings of the 2008 IEEE 10th International Symposium on Spread Spectrum Techniques and Applications, Bologna, Italy, 25–28 August 2008; pp. 692–697. [Google Scholar]
 Hlaing, M.; AlDhahir, N.; Yinghui, L. Optimal training signals for MIMO OFDM channel estimation in the presence of frequency offset and phase noise. IEEE Trans. Commun. 2006, 54, 1754–1759. [Google Scholar] [CrossRef]
 Hardjawana, W.; Li, R.; Vucetic, B.; Li, Y.; Yang, X. A new iterative channel estimation for high mobility MIMOOFDM systems. In Proceedings of the 2010 IEEE 71st Vehicular Technology Conference, Taipei, Taiwan, 16–19 May 2010; pp. 1–5. [Google Scholar]
 Mishra, A.; Yashaswini, N.S.; Jagannatham, A.K. SBLBased Joint Sparse Channel Estimation and Maximum Likelihood Symbol Detection in OSTBC MIMOOFDM Systems. IEEE Trans. Veh. Technol. 2018, 67, 4220–4232. [Google Scholar] [CrossRef]
 Motade, S.N.; Kulkarni, A.V. Channel Estimation and Data Detection Using Machine Learning for MIMO 5G Communication Systems in Fading Channel. Technologies 2018, 6, 72. [Google Scholar] [CrossRef] [Green Version]
 Chen, Y.S.; Wu, J.Y. Statistical covariancematching based blind channel estimation for zeropadding MIMO–OFDM systems. EURASIP J. Adv. Signal Process. 2012, 2012, 139. [Google Scholar] [CrossRef] [Green Version]
 Chen, Y.S.; Song, J.H. Semiblind channel estimation for MIMO–OFDM systems. EURASIP J. Adv. Signal Process. 2012, 2012, 212. [Google Scholar] [CrossRef] [Green Version]
 Wan, F.; Zhu, W.P.; Swamy, M. An enhanced scheme for secondorderstatistics estimation in MIMOOFDM systems. In Proceedings of the 2009 IEEE International Symposium on Circuits and Systems, Taipei, Taiwan, 24–27 May 2009; pp. 701–704. [Google Scholar]
 Bhandari, R.; Jadhav, S. Novel Spectral Efficient Technique for MIMOOFDM Channel Estimation with Reference to PAPR and BER Analysis. Wirel. Pers. Commun. 2019, 104, 1227–1242. [Google Scholar] [CrossRef]
 Peken, T.; Vanhoy, G.; Bose, T. Blind channel estimation for massive MIMO. Analog. Integr. Circuits Signal Process. 2017, 91, 257–266. [Google Scholar] [CrossRef]
 Wang, K.; Gan, Z.; Liu, J.; He, W.; Xu, S. Deterministic compressed sensing based channel estimation for MIMO OFDM systems. Clust. Comput. 2019, 22, 2971–2980. [Google Scholar] [CrossRef]
 Hedayati, M.K.; Bakhshi, H.; Cheraghi, M. SAGE algorithm for semiblind channel estimation and symbol detection for STBC MIMO OFDM systems. Wirel. Pers. Commun. 2013, 71, 1541–1555. [Google Scholar] [CrossRef]
 Mawatwal, K.; Sen, D.; Roy, R. A SemiBlind Channel Estimation Algorithm for Massive MIMO Systems. IEEE Wirel. Commun. Lett. 2017, 6, 70–73. [Google Scholar] [CrossRef]
 Jeya, R.; Amutha, B. Optimized semiblind sparse channel estimation algorithm for MUMIMO OFDM system. Comput. Commun. 2019, 146, 103–109. [Google Scholar] [CrossRef]
 Tang, L.; AbuRgheff, M.A. Joint PilotAided and Blind DecisionDirected Channel Estimation for MIMOOFDM System. In Proceedings of the 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece, 3–7 September 2007; pp. 1–5. [Google Scholar]
 Park, S.; Choi, J.W.; Lee, K.; Shim, B. Soft decisiondirected channel estimation for multiuser MIMO systems. In Proceedings of the 2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, China, 30 August–2 September 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 95–99. [Google Scholar]
 Park, S.; Shim, B.; Choi, J.W. Iterative channel estimation using virtual pilot signals for MIMOOFDM systems. IEEE Trans. Signal Process. 2015, 63, 3032–3045. [Google Scholar] [CrossRef]
 Yoon, D.; Moon, J. Softdecisiondirected MIMO channel estimation geared to pipelined turbo receiver Architecture. In Proceedings of the 2010 IEEE International Conference on Communications, Cape Town, South Africa, 23–27 May 2010; pp. 1–6. [Google Scholar]
 Mehrabi, M.; Mohammadkarimi, M.; Ardakani, M.; Jing, Y. Decision Directed Channel Estimation Based on Deep Neural Network kstep Predictor for MIMO Communications in 5G. arXiv 2019, arXiv:1901.03435. [Google Scholar] [CrossRef] [Green Version]
 Ketonen, J.; Juntti, M.; Ylioinas, J.; Cavallaro, J.R. Implementation of LS, MMSE and SAGE channel estimators for mobile MIMOOFDM. In Proceedings of the 2012 Conference Record of the Forty 6th Asilomar Conference on Signals, Systems and Computers (ASILOMAR), Pacific Grove, CA, USA, 4–7 November 2012; pp. 1092–1096. [Google Scholar]
 Coleri, S.; Ergen, M.; Puri, A.; Bahai, A. Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans. Broadcast. 2002, 48, 223–229. [Google Scholar] [CrossRef] [Green Version]
 Xie, H.; Andrieux, G.; Wang, Y.; Diouris, J.F.; Feng, S. Efficient time domain threshold for sparse channel estimation in OFDM system. AEU Int. J. Electron. Commun. 2014, 68, 277–281. [Google Scholar] [CrossRef] [Green Version]
 Belgiovine, M.; Sankhe, K.; Bocanegra, C.; Roy, D.; Chowdhury, K.R. Deep learning at the edge for channel estimation in beyond5G massive MIMO. IEEE Wirel. Commun. 2021, 28, 19–25. [Google Scholar] [CrossRef]
 Kirik, M.; HAMAMREH, J.M. Interference Signal Superpositionaided MIMO with Antenna Number Modulation and Adaptive Antenna Selection for Achieving Perfect Secrecy. RS Open J. Innov. Commun. Technol. 2021, 2, 1–11. [Google Scholar] [CrossRef]
 Yang, S.; Kobayashi, M.; Piantanida, P.; Shamai, S. Secrecy degrees of freedom of MIMO broadcast channels with delayed CSIT. IEEE Trans. Inf. Theory 2013, 59, 5244–5256. [Google Scholar] [CrossRef] [Green Version]
 Hyvarinen, A. Fast and robust fixedpoint algorithms for independent component analysis. IEEE Trans. Neural Netw. 1999, 10, 626–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
 Immanuvel, A.; Suganthi, M. Performance Analysis of Low Power Channel Estimator for Multi User MIMOOFDM System. Wirel. Pers. Commun. 2019, 107, 341–350. [Google Scholar] [CrossRef]
 Gao, J.; Zhu, X.; Nandi, A.K. Nonredundant precoding and PAPR reduction in MIMO OFDM systems with ICA based blind equalization. IEEE Trans. Wirel. Commun. 2009, 8, 3038–3049. [Google Scholar]
 Chen, B.S.; Yang, C.Y.; Liao, W.J. Robust fast timevarying multipath fading channel estimation and equalization for MIMOOFDM systems via a fuzzy method. IEEE Trans. Veh. Technol. 2012, 61, 1599–1609. [Google Scholar] [CrossRef]
 Chang, Y.K.; Ueng, F.B.; Shen, Y.S.; Liao, C.H. Joint channel estimation and turbo equalisation for MIMOOFDMIM systems. Int. J. Electron. 2019, 106, 721–740. [Google Scholar] [CrossRef]
 ChenHu, K.; Armada, A.G. LowComplexity Computation of ZeroForcing Equalizers for Massive MIMOOFDM. In Proceedings of the 2019 IEEE 89th Vehicular Technology Conference (VTC2019Spring), Kuala Lumpur, Malaysia, 28 April–1 May 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1–5. [Google Scholar]
 Chu, L.; Li, H.; Qiu, R.C. LEMO: Learn to Equalize for MIMOOFDM Systems with LowResolution ADCs. arXiv 2019, arXiv:1905.06329. [Google Scholar]
 Pereira, A.; Bento, P.; Gomes, M.; Dinis, R.; Silva, V. Iterative MRC and EGC Receivers for MIMOOFDM Systems. In Proceedings of the 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, Portugal, 3–6 June 2018; pp. 1–4. [Google Scholar]
 Chern, S.; Chen, J.; Wu, C. Novel frequencydomain DFE equalizer with oblique projection for CPfree spacetime block coded MIMOOFDM systems. In Proceedings of the 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Kanazawa, Japan, 7–9 January 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 541–545. [Google Scholar]
 Ma, S.; Ng, T.S. Semiblind timedomain equalization for MIMOOFDM systems. IEEE Trans. Veh. Technol. 2008, 57, 2219–2227. [Google Scholar]
 Noori, K.; Haider, S.A. Channel Equalization of MIMO OFDM system using RLS Algorithm. In Proceedings of the 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China, 21–25 September 2007; pp. 160–163. [Google Scholar]
 Boher, L.; Rabineau, R.; Hélard, M. An Efficient MMSE Equalizer Implementation for 4 × 4 MIMOOFDM Systems in Frequency Selective Fast Varying Channels. In Proceedings of the 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece, 3–7 September 2007; pp. 1–5. [Google Scholar]
Year  Details Paper Reference  Special Focus/General Vision of the Paper  MIMOSDR System Research Axes  

Simple overview  2019  Delson and Jose [2]  5G standards, specifications, and massive MIMO testbed, including transceiver design models using QAM modulation scheme 

2017  Shafi, et al. [3]  5G standards, trials, challenges, deployment, and practice 
 
2014  Banelli, et al. [4]  Modulation formats and waveforms for 5G networks 
 
2014  Wang, et al. [5]  Key technologies for 5G wireless communications 
 
2012  Amin and Trapasiya [6]  Space–time coding scheme for MIMO system 
 
Deep review  2022  Our paper  MIMOSDR OFDM systems 

2021  Chen, et al. [7]  Massive MIMO systems 
 
2019  Mokhtari, et al. [8]  MIMO systems in presence of channel and hardware impairments 
 
2019  Ijiga, et al. [9]  Channel estimation algorithms for 5G candidate waveforms 
 
2019  Wen, et al. [10]  5G massive MIMO localization 
 
2015  Zheng, et al. [11]  Largescale MIMO Systems 
 
2015  Yang and Hanzo [12]  MIMO Detection 
 
2008  Paul and Bhattacharjee [13]  MIMO channel modelling  
2002  Yu and Ottersten [14]  Models for MIMO propagation channels  
2018  Fatema, et al. [15]  Massive MIMO linear precoding techniques for single and multicell systems  
2008  GarcıaNaya, GonzálezLópez and Castedo [1]  Overview of MIMO testbed technology 

Testbed ^{1}  Year  Tx × Rx ^{2}  Hardware Implementation  Software ^{3}  BW ^{4}  Operating Frequency  Waveform  

DSP  FPGA  
Zamfirescu, et al. [28]  2019  2 × 2/3 × 3  ___ 
 GNU radio  ___  2 GHz  OFDM 
Ribeiro and Gameiro [29]  2017  2 × 2  DSP48 
 MATLAB  Max61.44 MHz  400 MHz–4 GHz 

Vielva, et al. [30]  2010  4 × 4  MAX2829 single chip RF transceiver  MATLAB  Up to 40 MHz  2.412–2.472 GHz and 5.15–5.35 GHz  OFDM 802.11 WLAN  
GTEC [31]  2010  2 × 2/4 × 4  Texas Instruments TMS320C6416 DSP running at 600 MHz  Xilinx Virtex II XC2V1000–6  3L Diamond software  20 MHz  2.4 GHz  16 QAM modulation 
2 × 3  5.2 GHz  
Bates, et al. [32]  2008  4 × 4  Texas Instruments DSP development kit: 440 Logic Elements 9bit DSPs  Altera Stratix II EP2560 2.5 Mb Memory 60  MATLAB  Up to 40 MHz  The 2.4 GHz to 2.5 GHz  OFDM with 64QAM modulation 
GEDOMIS [33]  2006  4 × 4  MultiDSP processing board, Pentek, model 4292, provides four fixedpoint DSPs, operating, Texas Instruments model TMS320C6203, at 300 MHz in a singleslot VME motherboard.  8 FPGAs: six SpartanII and two VirtexII  ___  ___  2.412–2.472 GHz and 5.15–5.35 GHz 

GTAS by Ramirez, et al. [34]  2006  2 × 2  An SMT365 module contains a DSP at 600 MHz with 1 MB of internal memory  Xilinx VirtexII Pro X2VP7  MATLAB  Up to 20 MHz  The band around 2.4 GHz  Quadrature Phase Shift Keying (QPSK) modulation and Alamouti space–time coding 
Vienna [35]  2006  4 × 4  FPGA boards from Sundance [36]: equipped with a fixedpoint DSP (600 MHz, 4800 MIPS peak performance, Texas Instruments TMS320C6416), a Xilinx FPGA (Virtex II XC2V10004FF896), and 8 Mbytes of RAM  MATLAB  20 MHz  2.45 GHz  4QAM/16QAM constellation  
Roy and Bélanger [37]  2006  4 × 4  C6701 [1]  Virtex II [1]  ___  40 MHz [1]  ___  ___ 
SABA by Borkowski, et al. [38]  2006  4 × 4  ___  The BenBLUE II (BigBlue) module is equipped with two XC2V3000 Virtex II FPGAs  ___  30 MHz  10.525 GHz, following the IEEE 802.16 standard  OFDM with 16QAM modulation 
STAR [39]  2006  TRSTBC: 2 × 1 
 MATLAB or octave  2 MHz  2.0–2.7 GHzcentred on 2.45 GHz  BPSK  
DFEMIMO: 4 × 4  1 MHz  π/4 DQPSK  
OFDMMIMO: 4 × 4  15 MHz  64carrier QPSK  
STARS [40]  2005  2 × 4  Sundance’s signal processing modules are based on XILINX Virtex II/Virtex IIpro FPGAs and Texas Instruments’ TMS320C6416 DSPs  MATLAB  Up to 30 MHz  2.4 GHz band  ___  
UCLA2 [41]  2005  4 × 4  Pentek 4291 Quad DSP [(TMS320C6701)]/Pentek 4292 Quad DSP [(TMS320C6203)] processing boards  Xilinx Vertex II X3000 FPGA  MATLAB  Up to 20 MHz  OFDM with 64QAM modulation  
Wallace, et al. [42]  2004  4 × 4  Base on Pentek DSP platform: four separate TI TMS320C6203 fixedpoint DSPs  ___  MATLAB  2.45 GHz  4QAM constellation  
UCLA [43]  2004  2 × 2/4 × 4  ___  ___  MATLAB  25 MHz  5.25 GHz  OFDM with 4/16/32QAM constellation 
Morawski, et al. [44]  2003  4 × 4  ___  ___  ___  Up to 3.5 MHz  1.88756 Hz  OFDM with 64QAM modulation 
Rice Murphy, et al. [45]  2003  2 × 2  XtremeDSP Kit FPGA board (XC2V2000 Xilinx Virtex II FPGA)  ___  Up to 20 MHz  From 900 MHz to 2.6 GHz  802.11b wireless LAN standard  
Fabregas, et al. [46]  2003  2 × 2  ___  A 1.5M gates ALTERA EP20K1500EBC6521X  MATLAB  20 MHz  5.15 GHz and 5.35 GHz  OFDM with 16QAM modulation 
Waveform  Reference  Advantages/Disadvantages/Summaries  

Name  Acronym  
SingleCarrier  Singlecarrier QAM  SCQAM  [67] 
 
Singlecarrier transmission with frequency domain equalization  SCFDE  [68,69,70]  Considered as a direct alternative to OFDM as it overcomes the drawbacks presented by this technique. However, it does not offer the same flexibility given by OFDM concerning the management of the bandwidth and the energy resources.  
Singlecarrier frequency division multiplexing  SCFDM  [71,72]  SCFDM has a low PeaktoAverage Power Ratio (PAPR) compared to OFDM. However, it suffers from noise enhancement [72,73] phenomena.  
Singlecarrier FDP  SCFDP  [70,74,75]  SCFDP has lower PAPR in comparison with OFDM for a low number of end users. However, with a higher number of endusers SCFPD performs the same as OFDM [70].  
Singlecarrier frequency division multiple access  SCFDMA  [76,77]  Presents some disadvantages compared to OFDM:
 
MultiCarrier  Orthogonal  Orthogonal frequency division multiplexing  OFDM  [78]  This waveform has some drawbacks:

orthogonal frequency division multiple access  OFDMA  [76,77] 
 
Ratesplitting multiple access  RSMA  [79]  RSMA is a robust technique that allows the deploying of more powerful coding approaches [80].  
Multicarrier code division multiple access  MCCDMA or OFDMCDM  [81] 
 
NonOrthogonal  Sparse code multiple access  SCMA  [82] 
 
Filter Bank MultiCarrier  FBMC  [83] 
 
Space Division Multiple Access  SDMA  [85] 
 
MultiUser Shared Access  MUSA  [86] 
 
Nonorthogonal multiple access  NOMA  [87] 

OFDM Variants  

CPOFDM  Cyclic prefix OFDM  [91] 
UFOFDM or UFMC  Universal filtered OFDM or universal filtered multicarrier  [83,91,92] 
CPOFDMWOLA  Weighted overlap and add CPOFDM  [88,89] ^{1} 
GFDM  Generalized OFDM  [93,94] 
FOFDM  Filtered OFDM  [95] 
FBMC  Filterbank multicarrier  [83,96] 
Enhancement of Study  OFDM  CPOFDM  UFMC  GFDM  FOFDM  FBMC 

Chang and Ueng [98]  X  
Sharief and Sairam [99]  X  
Singh, et al. [100]  X  
Zakaria and Le Ruyet [101]  X  
Zhao, et al. [102]  X  
Yu, et al. [103]  X ^{1}  
Jin, et al. [104]  X  
Aminjavaheri, et al. [105]  X  
Pereira, et al. [106]  X 
Classification  PilotAided (or TrainingBased)  Blind [112] and SemiBlind  DecisionDirected  

Statistical Methods  Deterministic Methods  Hard  Soft  
2nd Order  High Order  
References  [113,114,115,116,117,118,119,120,121,122,123,124]  [125,126,127]  [128,129]  [130,131,132,133]  [134]  [135,136,137,138,139] 
Type  References  Year  Main Idea  Key Algorithms 

Type 1  [150]  2019  A symmetric successive overrelaxation (SSOR) method to reduce the complexity of the classical ZF precoding which uses the channel property of asymptotical orthogonality to compute the optimal relaxation parameters.  SSOR technique for ZF 
Type 3  [151]  2019 
 Deep neural network with gradient descent algorithm 
Type 2  [149]  2019 
 Turbo MMSE equalizer with MAP decoder 
Type 1  [148]  2018  Testing MMSE equalizer with a decisiondirected channel estimator in a multipath fading channel  MMSE algorithm 
Type 2  [152]  2012  Iterative receiver based on equal gain combining (EGC) and maximum ratio combining (MRC)  RGC, MRC and LLR 
Type 3  [147]  2009  An independent component analysisbased equalizer: First the received signal is whitened by principal component analysis, using JADE to gather uncorrelated signals. A phase shifting is performed as well as a reordering technique.  JADE batch algorithm 
Type 1  [147]  2009  Exchanging the order of the processing block: interpolation and the ZF equalizer stage. This operation is performed at each pilot position, then the ZF equalizer is interpolated over the whole grid.  ZF equalization 
Type 2  [153]  2009  The oblique projection (OB) with QRbased factorization is used to separate the noise from the data. Afterwards, the resulting matrix is forwarded to the DFE equalizer.  DFE equalizer, associated with the OB 
Type 3  [154]  2008  A semiblind time domain equalization, using secondorder statistics and a onetape equalizer.  
Type 2  [155]  2007  a DFE equalizer combined with Recursive Least Squares (RLS) to compute the coefficient of the adaptive filter.  RLS algorithm 
Type 1  [156]  2007  An MMSE equalizer based on QR factorization implemented on FPGA to compute the inverse of the filter matrix.  MMSE and QR factorization 
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Harkat, H.; Monteiro, P.; Gameiro, A.; Guiomar, F.; Farhana Thariq Ahmed, H. A Survey on MIMOOFDM Systems: Review of Recent Trends. Signals 2022, 3, 359395. https://doi.org/10.3390/signals3020023
Harkat H, Monteiro P, Gameiro A, Guiomar F, Farhana Thariq Ahmed H. A Survey on MIMOOFDM Systems: Review of Recent Trends. Signals. 2022; 3(2):359395. https://doi.org/10.3390/signals3020023
Chicago/Turabian StyleHarkat, Houda, Paulo Monteiro, Atilio Gameiro, Fernando Guiomar, and Hasmath Farhana Thariq Ahmed. 2022. "A Survey on MIMOOFDM Systems: Review of Recent Trends" Signals 3, no. 2: 359395. https://doi.org/10.3390/signals3020023