4 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…
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,…