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Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction
Yuwen Yang, Feifei Gao, Chengwen Xing +2
Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the…
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
Muhammad Alrabeiah, Yu Zhang, Ahmed Alkhateeb
Millimeter wave (mmWave) and massive MIMO systems are intrinsic components of 5G and beyond. These systems rely on using beamforming codebooks for both initial access and data tran…
Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements. T…
Relay Aided Intelligent Reconfigurable Surfaces: Achieving the Potential Without So Many Antennas
Xiaoyan Ying, Umut Demirhan, Ahmed Alkhateeb
This paper proposes a novel relay-aided intelligent reconfigurable surface (IRS) architecture for future wireless communication systems. The proposed architecture, which consists o…
Advanced Receiver Architectures for Millimeter Wave Communications with Low-Resolution ADCs
Jinseok Choi, Gilwon Lee, Ahmed Alkhateeb +3
Employing low-resolution analog-to-digital converters (ADCs) for millimeter wave receivers with large antenna arrays provides opportunity to efficiently reduce power consumption of…
Deep Reinforcement Learning for Intelligent Reflecting Surfaces: Towards Standalone Operation
Abdelrahman Taha, Yu Zhang, Faris B. Mismar +1
The promising coverage and spectral efficiency gains of intelligent reflecting surfaces (IRSs) are attracting increasing interest. In order to realize these surfaces in practice, h…