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Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems.
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2015
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R. Méndez-Rial, C. Rusu, A. Alkhateeb, N. González-Prelcic, and R. W. Heath, “Channel estimation and hybrid combining for mmWave: Phase shifters or switches?” in IEEE Inf.ormation Th.eory and Appl.ications Workshop , 2015, pp. 90–97
2015
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2016
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2016
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2016
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R. Méndez-Rial, C. Rusu, N. González-Prelcic, A. Alkhateeb, and R. W. Heath, “Hybrid MIMO architectures for millimeter wave communications: Phase shifters or switches?” IEEE Access , vol. 4, pp. 247–267, 2016
2016
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2016
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F. Sohrabi and W. Yu, “Hybrid analog and digital beamforming for mmWave OFDM large-scale antenna arrays,” IEEE Journal on Selected Areas in Communications , vol. 35, no. 7, pp. 1432–1443, 2017
2017
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A. Ayyar and K. V. Mishra, “Robust communications-centric coexistence for turbo-coded OFDM with non-traditional radar interference models,” in IEEE Radar Conf. , 2019, 1-5
2019
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E. Björnson, L. Van der Perre, S. Buzzi, and E. G. Larsson, “Massive MIMO in sub-6 GHz and mmWave: Physical, practical, and use-case differences,” IEEE Wireless Commun. , vol. 26, no. 2, pp. 100–108, 2019
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T. Mir, M. Zain Siddiqi, U. Mir, R. Mackenzie, and M. Hao, “Machine learning inspired hybrid precoding for wideband millimeter-wave massive MIMO systems,” IEEE Access , vol. 7, pp. 62 852–62 864, 2019
2019
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K. Venugopal, A. Alkhateeb, N. González Prelcic, and R. W. Heath, “Channel estimation for hybrid architecture-based wideband millimeter wave systems,” IEEE J. Sel. Areas Commun. , vol. 35, no. 9, pp. 1996–2009, 2017
2017
Cited alongside, same era.
C. G. Tsinos, S. Maleki, S. Chatzinotas, and B. Ottersten, “On the Energy-Efficiency of Hybrid Analog–Digital Transceivers for Single- and Multi-Carrier Large Antenna Array Systems,” IEEE J. Sel. Areas Commun. , vol. 35, no. 9, pp. 1980–1995, 2017
2017
Cited alongside, same era.
Z. Wang, M. Li, Q. Liu, and A. L. Swindlehurst, “Hybrid Precoder and Combiner Design With Low-Resolution Phase Shifters in mmWave MIMO Systems,” IEEE J. Sel. Topics Signal Process. , vol. 12, no. 2, pp. 256–269, May 2018
2018
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J. Rodríguez-Fernández, N. González-Prelcic, K. Venugopal, and R. W. Heath, “Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems,” IEEE Trans. Wireless Commun. , vol. 17, no. 5, pp. 2946–2960, Mar 2018
2018
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J. P. González-Coma, J. Rodríguez-Fernández, N. González-Prelcic, L. Castedo, and R. W. Heath, “Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems,” IEEE J. Sel. Top. Signal Process. , vol. 12, no. 2, pp. 353–367, Mar 2018
2018
Cited alongside, same era.
Y. Lin, “Hybrid MIMO-OFDM beamforming for wideband mmWave channels without instantaneous feedback,” IEEE Trans.actions on Signal Process.ing , vol. 66, no. 19, pp. 5142–5151, 2018
2018
Cited alongside, same era.
Y. Long, Z. Chen, J. Fang, and C. Tellambura, “Data-driven-based analog beam selection for hybrid beamforming under mm-Wave channels,” IEEE J. Sel. Topics Signal Process. , vol. 12, no. 2, pp. 340–352, 2018
2018
Cited alongside, same era.
S. Dörner, S. Cammerer, J. Hoydis, and S. t. Brink, “Deep learning based communication over the air,” IEEE J. Sel. Topics Signal Process. , vol. 12, no. 1, pp. 132–143, Feb 2018
2018
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P. Dong, H. Zhang, G. Y. Li, I. S. Gaspar, and N. NaderiAlizadeh, “Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems,” IEEE J. Sel. Topics Signal Process. , vol. 13, no. 5, pp. 989–1000, Sep. 2019
2019
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C. Chun, J. Kang, and I. Kim, “Deep Learning-Based Channel Estimation for Massive MIMO Systems,” IEEE Trans. Wireless Commun. , vol. 8, no. 4, pp. 1228–1231, 2019
2019
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J. A. Hodge, K. V. Mishra, and A. I. Zaghloul, “Multi-discriminator distributed generative model for multi-layer RF metasurface discovery,” in IEEE Global Conf. Signal Inf. Process. , 2019, in press
2019
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H. Huang, Y. Song, J. Yang, G. Gui, and F. Adachi, “Deep-learning-based millimeter-wave massive MIMO for hybrid precoding,” IEEE Trans. Veh. Technol. , vol. 68, no. 3, pp. 3027–3032, 2019
2019
Closest in time.
A. M. Elbir and A. Papazafeiropoulos, “Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach,” IEEE Trans. Veh. Technol. , pp. 1–1, 2019
2019
Closest in time.
A. M. Elbir, K. V. Mishra, and Y. C. Eldar, “Cognitive radar antenna selection via deep learning,” IET Radar, Sonar & Navigation , vol. 13, pp. 871–880, 2019
2019
Closest in time.
X. Li and A. Alkhateeb, “Deep learning for direct hybrid precoding in millimeter wave massive mimo systems,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers , 2019, pp. 800–805
2019
Closest in time.
2020
Closest in time.
A. M. Elbir and K. V. Mishra, “Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks,” IEEE Trans. Wireless Commun. , vol. 19, no. 3, pp. 1677–1688, March 2020
2020
Closest in time.
A. M. Elbir, “A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback,” IEEE Trans. Veh. Technol. , vol. 69, no. 10, pp. 11 743–11 755, Aug 2020
2020
Closest in time.
A. M. Elbir and K. V. Mishra, “Low-complexity limited-feedback deep hybrid beamforming for broadband massive MIMO communications,” in IEEE International Workshop on Signal Processing Advances in Wireless Communications , 2020, pp. 1–5
2020
Closest in time.
A. M. Elbir and K. V. Mishra, “Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning,” IEEE Trans. on Cogn. Commun. Netw. , pp. 1–1, 2020
2020
Closest in time.
J. Tan and L. Dai, “Wideband Beam Tracking in THz Massive MIMO Systems,” IEEE J. Sel. Areas Commun. , vol. 39, no. 6, pp. 1693–1710, Apr 2021
2021
Closest in time.