Abstract
While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas. Devices with non-isotropic antenna elements need multiple panels to provide good spherical coverage, and exhaustive search over all beams of all the panels leads to unacceptable overhead. This paper proposes a location- and orientation-aware solution that manages the initial BA for multi-panel devices. We present three different neural network structures that provide efficient BA with a wide range of training dataset sizes, complexity, and feedback message sizes. Our proposed methods outperform the generalized inverse fingerprinting and hierarchical panel-beam selection methods for two considered edge and edge-face antenna placement designs.
Original language | English (US) |
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Pages (from-to) | 597-602 |
Number of pages | 6 |
Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
DOIs | |
State | Published - 2022 |
Event | 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil Duration: Dec 4 2022 → Dec 8 2022 |
Keywords
- beam alignment
- location-aware
- millimeter wave
- multi-panel
- orientation-aware
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing