Scalable Phase-Coherent Beam-Training for Dense Millimeter-wave Networks
| Autor: | Rafael Ruiz, Jesus Omar Lacruz, Dolores Garcia Marti, Joan Palacios, Joerg Widmer, Pablo Jimenez Mateo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science Acoustics Phase (waves) Mobile computing IEEE 802.11ad Signal-to-noise ratio Compressed sensing Beamtraining Compressive Sensing Extremely high frequency Scalability Computer Science::Networking and Internet Architecture Phase-Coherent Electrical and Electronic Engineering Phased Antenna Array Field-programmable gate array Software Beam (structure) FPGA |
| Zdroj: | IMDEA Networks Institute Digital Repository Banco de España IEEE Transactions on Mobile Computing |
| Popis: | Millimeter-wave communications (mm-wave) use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constant training are supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance,full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training. TRUE pub |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|