activity
20202025
most citedmmRAPID: Machine Learning assisted Noncoherent Compressive Millimeter-Wave Beam Alignment

19 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2025

Millimeter-Wave True-Time Delay Array Beamforming with Robustness to Mobility

Benjamin W. Domae, Ibrahim Pehlivan, Danijela Cabric

Ultra-reliable and low-latency connectivity is required for real-time and latency-sensitive applications, like wireless augmented and virtual reality streaming. Millimeter-wave (mm…

eess.SP2022

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

Benjamin W. Domae, Veljko Boljanovic, Ruifu Li +1

Future wireless networks may operate at millimeter-wave (mmW) and sub-terahertz (sub-THz) frequencies to enable high data rate requirements. While large antenna arrays are critical…

eess.SP2022

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

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. In…

eess.SP2021★ 11 cited

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

Benjamin W. Domae, Ruifu Li, Danijela Cabric

Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased a…

eess.SP2020★ 19 cited

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

Han Yan, Benjamin W. Domae, Danijela Cabric

Millimeter-wave communication has the potential to deliver orders of magnitude increases in mobile data rates. A key design challenge is to enable rapid beam alignment with phased…