Research graph
References from A Coupled Multi-Stage Hybrid Framework for BER Prediction and Beam Angle Optimization in Massive MIMO Systems: Combining Classical Regression with Coupled Deep Learning Approaches. Local targets link to admitted publications; unresolved targets remain external evidence.
Massive MIMO networks: Spectral, energy, and hardware efficiency
10.1561/2000000093 · 2017 · External reference
An overview of massive MIMO: Benefits and challenges
10.1109/jstsp.2014.2317671 · 2014 · External reference
Noncooperative cellular wireless with unlimited numbers of base station antennas
10.1109/twc.2010.092810.091092 · 2010 · External reference
Scaling up MIMO: Opportunities and challenges with very large arrays
10.1109/msp.2011.2178495 · 2013 · External reference
An overview of signal processing techniques for millimeter wave MIMO systems
10.1109/jstsp.2016.2523924 · 2016 · External reference
Channel estimation and hybrid precoding for millimeter wave cellular systems
10.1109/jstsp.2014.2334278 · 2014 · External reference
Deep-learning-based millimeter-wave massive MIMO for hybrid precoding
10.1109/tvt.2019.2893928 · 2019 · External reference
Hybrid precoding for multiuser millimeter wave massive MIMO systems: A deep learning approach
10.1109/tvt.2019.2951501 · 2020 · External reference
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Learning to optimize: Training deep neural networks for interference management
10.1109/tsp.2018.2866382 · 2018 · External reference
Power of deep learning for channel estimation and signal detection in OFDM systems
10.1109/lwc.2017.2757490 · 2018 · External reference
Deep-learning-based wireless resource allocation with application to vehicular networks
10.1109/jproc.2019.2957798 · 2020 · External reference
Deep learning for channel sensing and hybrid precoding in TDD massive MIMO OFDM systems
2021 · External reference
Joint deep reinforcement learning and unfolding: Beam selection and precoding for mmWave multiuser MIMO with lens arrays
10.1109/jsac.2021.3087233 · 2021 · External reference
Massive MIMO NOMA with wavelet pulse shaping to minimize undesired channel interference
10.1016/j.icte.2022.06.005 · 2023 · External reference
Analog Computing for Signal Processing and Communications—Part II: Toward Gigantic MIMO Beamforming
10.1109/tsp.2025.3641053 · 2025 · External reference
An introduction to deep learning for the physical layer
10.1109/tccn.2017.2758370 · 2017 · External reference
High Dimensional Channel Estimation Using Deep Generative Networks
10.1109/jsac.2020.3036947 · 2021 · External reference
Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels
10.1109/twc.2020.2970707 · 2020 · External reference
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
10.1016/j.jcp.2018.10.045 · 2019 · External reference
Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks
10.1038/s41598-025-16571-8 · 2025 · External reference
Deep learning for distributed channel feedback and multiuser precoding in FDD massive MIMO
10.1109/twc.2021.3055202 · 2021 · External reference
10.1007/978-0-387-84858-7
10.1007/978-0-387-84858-7 · External reference
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10.1017/cbo9780511841224
10.1017/cbo9780511841224 · External reference
Ridge regression: Biased estimation for nonorthogonal problems
10.1080/00401706.1970.10488634 · 1970 · External reference
Orthogonal AMP
10.1109/access.2017.2653119 · 2017 · External reference
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AMP-inspired deep networks for sparse recovery
10.1109/tsp.2017.2708040 · 2017 · External reference
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A tutorial on conformal prediction
2008 · External reference
Bayesian model averaging: A tutorial
1999 · External reference
Bayesian model averaging for linear regression models
10.1080/01621459.1997.10473615 · 1997 · External reference
Random forests
10.1023/a:1010933404324 · 2001 · External reference
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Dropout: A simple way to prevent neural networks from overfitting
2014 · External reference
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