Optimal Training in Large TDD Multi-user Downlink Systems under Zero-forcing and Regularized Zero-forcing PrecodingReportar como inadecuado




Optimal Training in Large TDD Multi-user Downlink Systems under Zero-forcing and Regularized Zero-forcing Precoding - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

1 ST-Ericsson Cornadero 2 Chaire Sciences des Systèmes et Défis Énergétiques 3 E3S - Supélec Sciences des Systèmes Gif-sur-Yvette 4 Eurecom Sophia Antipolis

Abstract : This paper considers a large multi-user time-division duplex TDD system, where the base station BS acquires channel state information via pilot signaling from the users. In the downlink the BS employs zero-forcing ZF and regularized zero-forcing RZF precoding. We derive the optimal sum rate maximizing amount of channel training using sum rate approximations from the large system analysis of MISO downlink channels under RZF precoding. Moreover, in the regime of high signal-to-noise ratio SNR, we derive approximate solutions of the optimal amount of training for both schemes that are of closed-form. By comparing the two schemes, we find that RZF requires less training than ZF, but the training interval of both schemes is equal for asymptotically high SNR. Furthermore, simulations are carried out which demonstrate the accuracy of our approximate solutions.





Autor: Sebastian Wagner - Romain Couillet - Merouane Debbah - Dirk Slock -

Fuente: https://hal.archives-ouvertes.fr/



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