Matthew B. Booth
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Matthew B. Booth has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- David J. Love
Purdue University, West Lafayette, USA
1 shared publication - Nicolo Michelusi
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA
1 shared publication - Vinayak Suresh
Qualcomm, Inc., San Diego, CA, USA
1 shared publication
Latest Publications
- 27 May 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Letters
- Vol. 23 (7) , 1244-1248
Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications
We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-term beamforming gain commensurate to omni-directional training.
- 27 May 2019
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Letters
- Vol. 23 (7) , 1244-1248
Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications
We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-term beamforming gain commensurate to omni-directional training.