Recently, EM lens-embedded massive array antennas have been proposed for next 5G mobile wireless communications, as the adoption of a lens allows to discriminate the AOA of signals in the analog domain, with the possibility to preserve the processing complexity lower with respect to traditional massive arrays. In fact, in such a way, complex ADC chains can be avoided and the number of required antennas can be decreased. By exploiting these advantages, in this paper we study the possibility to use a single EM lens massive array at mm-wave for the AOA estimation of the received signal. In this perspective, ML estimator and practical approaches, tailored for the considered scenario, are derived. Results, obtained for different number of antennas, confirm the possibility to achieve interesting AOA estimation performance with an extremely compact architecture.
AOA estimation with EM lens-embedded massive arrays / Guidi, Francesco*. - ELETTRONICO. - 2018:(2018), pp. 8417728.1-8417728.5. (Intervento presentato al convegno The 87th IEEE Vehicular Technology Conference VTC2018-Spring tenutosi a Porto, Portugal nel 3–6 June 2018) [10.1109/VTCSpring.2018.8417728].
AOA estimation with EM lens-embedded massive arrays
Guidi, Francesco
2018
Abstract
Recently, EM lens-embedded massive array antennas have been proposed for next 5G mobile wireless communications, as the adoption of a lens allows to discriminate the AOA of signals in the analog domain, with the possibility to preserve the processing complexity lower with respect to traditional massive arrays. In fact, in such a way, complex ADC chains can be avoided and the number of required antennas can be decreased. By exploiting these advantages, in this paper we study the possibility to use a single EM lens massive array at mm-wave for the AOA estimation of the received signal. In this perspective, ML estimator and practical approaches, tailored for the considered scenario, are derived. Results, obtained for different number of antennas, confirm the possibility to achieve interesting AOA estimation performance with an extremely compact architecture.File | Dimensione | Formato | |
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