Machine Learning Prediction for Phase-less Millimeter-Wave Beam Tracking

no code implementations • 6 Jun 2022 • Benjamin W. Domae, Veljko Boljanovic, Ruifu Li, Danijela Cabric

Future wireless networks may operate at millimeter-wave (mmW) and sub-terahertz (sub-THz) frequencies to enable high data rate requirements.

BIG-bench Machine Learning Compressive Sensing

Energy Efficiency Tradeoffs for Sub-THz Multi-User MIMO Base Station Receivers

no code implementations • 16 Feb 2022 • Benjamin W. Domae, Christopher Chen, Danijela Cabric

Sub-terahertz (sub-THz) antenna array architectures significantly impact power usage and communications capacity in multi-user multiple-input multiple-output (MU-MIMO) systems.

Machine Learning Assisted Phase-less Millimeter-Wave Beam Alignment in Multipath Channels

1 code implementation • 29 Sep 2021 • Benjamin W. Domae, Ruifu Li, Danijela Cabric

For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size.

BIG-bench Machine Learning Compressive Sensing

mmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment

1 code implementation • 23 Jul 2020 • Han Yan, Benjamin W. Domae, Danijela Cabric

A key design challenge is to enable rapid beam alignment with phased arrays.

BIG-bench Machine Learning Compressive Sensing