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Millimeter-Wave Beam Training Acceleration through Low-Complexity Hybrid Transceivers

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TWC - Final.pdf (5.118Mb)
Identifiers
URI: http://hdl.handle.net/20.500.12761/347
ISSN: 1536-1276
DOI: https://doi.org/10.1109/TWC.2017.2686402
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Author(s)
De Donno, Danilo; Palacios, Joan; Widmer, Joerg
Date
2017-06
Abstract
Millimeter-wave (mm-wave) communication systems can provide much higher data rates than systems operating at lower frequencies, but achieving such rates over sufficiently large distances requires highly directional beamforming at both the transmitter and receiver. These antenna beams have to be aligned very precisely in order to obtain sufficient link margin. In this paper, we first propose a parallel-adaptive beam training protocol which significantly accelerates the link establishment between mm-wave devices by exploiting the ability of hybrid analog-digital beamforming antennas to scan multiple spatial sectors simultaneously. Second, we deal with practical constraints of mm-wave transceivers and design a novel greedy geometric algorithm to synthesize sector beam patterns featuring configurable beamwidth and multi-beam radiation as required by the proposed beam training protocol. These multi-beam patterns are then also used for concurrent data communication over multiple paths, in case several suitable directions are found during the beam training. Simulation results show that our algorithm is able to shape antenna patterns very close to those attained by a fully-digital beamforming architecture, yet requires lower complexity hardware compared with state-of-the-art solutions. Exploiting such multi-beam antenna patterns, our parallel beam training protocol can provide up to 82% effective rate increase and 70% search time decrease compared to existing sequential protocols. The acceleration of the beam training phase shifts the optimum balance between the search overhead and the achieved directivity gain so that the best performance is reached with a training load 30% to 60% lower than that of sequential beam training.
Share
Files
TWC - Final.pdf (5.118Mb)
Identifiers
URI: http://hdl.handle.net/20.500.12761/347
ISSN: 1536-1276
DOI: https://doi.org/10.1109/TWC.2017.2686402
Metadata
Show full item record

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