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Comparing Stochastic and Ray-tracing Datasets in Machine Learning for Wireless Applications
João Morais, Akshay Malhotra, Shahab Hamidi-Rad +1
Machine learning for wireless systems is commonly studied using standardized stochastic channel models (e.g., TDL/CDL/UMa) because of their legacy in wireless communication standar…
Digital Twin Aided Millimeter Wave MIMO: Site-Specific Beam Codebook Learning
Hao Luo, Ahmed Alkhateeb
Learning site-specific beams that adapt to the deployment environment, interference sources, and hardware imperfections can lead to noticeable performance gains in coverage, data r…
Generative Decoding of Compressed CSI for MIMO Precoding Design
Hao Luo, Saeed R. Khosravirad, Ahmed Alkhateeb
Massive MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of antennas increases…
AoI-Aware Machine Learning for Constrained Multimodal Sensing-Aided Communications
Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb +1
Using environmental sensory data can enhance communications beam training and reduce its overhead compared to conventional methods. However, the availability of fresh sensory data…
RIS-Aided mmWave O-RAN: Coverage Extension and User Mobility Handling
Tawfik Osman, Aditya S. Shekhawat, Abhradeep Roy +2
Reconfigurable Intelligent Surfaces (RISs) can redirect electromagnetic waves to desired directions to enhance signal coverage and/or improve signal-to-noise ratio (SNR) at the use…
Digital Twin Aided Massive MIMO CSI Feedback: Exploring the Impact of Twinning Fidelity
Hao Luo, Shuaifeng Jiang, Saeed R. Khosravirad +1
Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIM…