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Enabling NLoS LEO Satellite Communications with Reconfigurable Intelligent Surfaces
In this paper, we propose to enable LEO SatCom in non-line-of-sight (NLoS) channels, as those corresponding to links to users in urban canyons, with the aid of reconfigurable intelligent surfaces (RISs).
Optimizing the Deployment of Reconfigurable Intelligent Surfaces in MmWave Vehicular Systems
Millimeter wave (MmWave) systems are vulnerable to blockages, which cause signal drop and link outage.
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning
The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies.
A hybrid beamforming design for massive MIMO LEO satellite communications
5G and future cellular networks intend to incorporate low earth orbit (LEO) satellite communication systems (SatCom) to solve the coverage and availability problems that cannot be addressed by satellite-based or ground-based infrastructure alone.
Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach
To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation.
Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors
Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data.
