Beamforming is an effective way to improve spectrum efficiency in multi-antenna systems.
However, conventional iterative algorithms suffering from high computational delay renders …

This article studies fast adaptive beamforming optimization for the signal-to-interference-plus-
noise ratio balancing problem in a multiuser multiple-input single-output downlink system …

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-
output downlink. Existing deep learning-based approaches assume that training and testing …

This work proposes a novel deep learning approach to tackle multitask optimization
problems in multi-user multi-antenna downlink systems. In practice, there is a tradeoff …

Beamforming techniques play an important role in multi-antenna communication systems
and this work focuses on the downlink power minimization problem under a set of quality of …

Downlink beamforming is an essential technology for wireless cellular networks; however,
the design of beamforming vectors that maximize the weighted sum rate (WSR) is an NP …

The advancement of fifth generation (5G) wireless communication networks has created a
greater demand for wireless resource management solutions that offer high data rates …

This letter investigates a learning solution for robust beamforming optimization in downlink
multi-user systems. A base station (BS) identifies efficient multi-antenna transmission …

Beamforming is an effective means to improve the quality of the received signals in multiuser
multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming …

Accurate downlink channel information is crucial to the beamforming design, but it is difficult
to obtain in practice. This paper investigates a deep learning-based optimization approach …