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Article Dans Une Revue IEEE Transactions on Wireless Communications Année : 2021

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

Résumé

We investigate the performance of multiuser multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements.

Domaines

Electronique
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Dates et versions

hal-03372930 , version 1 (11-10-2021)

Identifiants

Citer

Thang X Vu, Symeon Chatzinotas, Dinh-Van Nguyen, Dinh Thai, Ngoc-Diep Nguyen, et al.. Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance. IEEE Transactions on Wireless Communications, 2021, ⟨10.1109/twc.2021.3052973⟩. ⟨hal-03372930⟩
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