FD-MIMO via pilot-data superposition: Tensor-based DOA estimation and system performance
Z Zhou, L Liu, J Zhang - IEEE Journal of Selected Topics in …, 2019 - ieeexplore.ieee.org
Increased pilot overhead is one of the major issues for 5G full-dimension MIMO (FD-MIMO)
systems. In this paper, we introduce the FD-MIMO system using pilot-data superposition to …
systems. In this paper, we introduce the FD-MIMO system using pilot-data superposition to …
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by
adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with …
adjusting the phase of the incident signal. However, optimizing the phase shifts jointly with …
A deep learning framework for optimization of MISO downlink beamforming
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 …
multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming …
Deep reinforcement learning for distributed coordinated beamforming in massive MIMO
In this paper, we investigate a dynamic coordinated beamforming (CBF) problem to enhance
the sum rate of a massive multiple-input multiple-output (MIMO) cellular network. Although …
the sum rate of a massive multiple-input multiple-output (MIMO) cellular network. Although …
Low complexity delay-constrained beamforming for multi-user MIMO systems with imperfect CSIT
We consider the delay-constrained beamforming control for downlink multi-user MIMO (MU-
MIMO) systems with imperfect channel state information at the transmitter (CSIT). The delay …
MIMO) systems with imperfect channel state information at the transmitter (CSIT). The delay …
Data-driven deep learning based hybrid beamforming for aerial massive MIMO-OFDM systems with implicit CSI
In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency
division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi …
division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi …
Non-iterative downlink training sequence design based on sum rate maximization in FDD massive MIMO systems
This paper considers the problem of downlink (DL) training sequence design with limited
coherence time for frequency division duplex (FDD) massive MIMO systems in a general …
coherence time for frequency division duplex (FDD) massive MIMO systems in a general …
Reward-maximization-based passive beamforming for multi-RIS-aided multi-user MISO systems
Recently, reconfigurable intelligent surfaces (RISs) have emerged as a potential technique
for future 6G communications. Considering the practical hardware constraints of RISs, eg …
for future 6G communications. Considering the practical hardware constraints of RISs, eg …
Deep learning methods for universal MISO beamforming
This letter studies deep learning (DL) approaches to optimize beamforming vectors in
downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given …
downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given …
Statistical beamforming for FDD massive MIMO downlink systems
In this paper, we first present a statistical beam-forming (SBF) design for FDD massive MIMO
systems, which is realized only based on statistical channel state information (CSI) and thus …
systems, which is realized only based on statistical channel state information (CSI) and thus …