5 citations · 8 across the 4 of their papers we have counts for
6 papers
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning
Aldebaro Klautau, Pedro Batista, Nuria Gonzalez-Prelcic +2
The increasing complexity of configuring cellular networks suggests that machine learning (ML) can effectively improve 5G technologies. Deep learning has proven successful in ML ta…
Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems
Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic +1
Millimeter wave vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements compared to exhaustive beam search. With fixed layouts…
Site-specific online compressive beam codebook learning in mmWave vehicular communication
Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic +1
Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility…
Deep learning-based beam alignment in mmWave vehicular networks
Nitin Jonathan Myers, Yuyang Wang, Nuria González-Prelcic +1
Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the r…
MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach
Yuyang Wang, Murali Narasimha, Robert W. Heath
Millimeter-wave communication is a challenge in the highly mobile vehicular context. Traditional beam training is inadequate in satisfying low overheads and latency. In this paper,…
MmWave vehicle-to-infrastructure communication: Analysis of urban microcellular networks
Yuyang Wang, Kiran Venugopal, Andreas F. Molisch +1
Vehicle-to-infrastructure (V2I) communication may provide high data rates to vehicles via millimeter-wave (mmWave) microcellular networks. This paper uses stochastic geometry to an…