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

Thang X Vu (1) , Symeon Chatzinotas (1) , Dinh-Van Nguyen (1) , Dinh Thai (2) , Ngoc-Diep Nguyen (2) , Marco Di Renzo (3, 4, 5) , Bjorn Ottersten (1)

1 University of Luxembourg [Luxembourg]
2 UTS - University of Technology Sydney
3 L2S - Laboratoire des signaux et systèmes
4 CNRS - Centre National de la Recherche Scientifique
5 Université Paris-Saclay

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

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⟩