MMU-STCNN-BDQ: a deep reinforcement learning framework for secure and energy-efficient beamforming in 6G mMIMO networks. [PDF]
Sci RepRamudu K +5 more
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Deep reinforcement learning for network resource optimization in MIMO-NOMA networks to maximize utilization with minimal overhead. [PDF]
Sci RepLahza HFM +6 more
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The Deep Learning Evolution in Wireless Physical Layer Communications: Applications, Challenges, and Evolutionary Directions. [PDF]
Sensors (Basel)Xu H, Liang Y, Xie R, Kong Y.
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Enhancing reliability and spectral efficiency in future wireless networks via intelligent omni-surface enhanced MU-MIMO cooperative hybrid NOMA. [PDF]
Sci RepKennedy HSJ +4 more
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Sustainable Development Strategies for RIS-Assisted Mobile Networks. [PDF]
Sensors (Basel)Ibrahim AH.
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Hybrid optimization-based deep learning for energy efficiency resource allocation in MIMO-enabled wireless networks. [PDF]
Sci RepKamal MM +3 more
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Weighted Sum-Rate Maximization and Task Completion Time Minimization for Multi-Tag MIMO Symbiotic Radio Networks. [PDF]
Sensors (Basel)Suo L, Wang D, Zhou W, Peng X.
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Hybrid ray-tracing-QuaDRiGa/FDTD method for realistic 28 GHz exposure with 6G CF-MaMIMO in 3D outdoor environments. [PDF]
NPJ Wirel TechnolWydaeghe R +5 more
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Robust Low-Complexity WMMSE Precoding Under Imperfect CSI with Per-Antenna Power Constraints. [PDF]
Sensors (Basel)Guo Z, Sen V, Deng H.
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Model-Data Hybrid-Driven Wideband Channel Estimation for Beamspace Massive MIMO Systems. [PDF]
Entropy (Basel)Nie Y, Ma Z, Jing L.
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