Search Results for author:
Found 11 papers, 0 papers with code
User-Centric Beam Selection and Precoding Design for Coordinated Multiple-Satellite Systems
This paper introduces a joint optimization framework for user-centric beam selection and linear precoding (LP) design in a coordinated multiple-satellite (CoMSat) system, employing a Digital-Fourier-Transform-based (DFT) beamforming (BF) technique.
Doubly 1-Bit Quantized Massive MIMO
Enabling communications in the (sub-)THz band will call for massive multiple-input multiple-output (MIMO) arrays at either the transmit- or receive-side, or at both.
A Variational Bayesian Perspective on Massive MIMO Detection
In this paper, we build upon variational Bayes (VB) inference to design low-complexity multiuser detection algorithms for massive MIMO systems.
Leveraging Deep Neural Networks for Massive MIMO Data Detection
Massive multiple-input multiple-output (MIMO) is a key technology for emerging next-generation wireless systems.
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems.
Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems
Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems.
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems
We consider the problem of joint channel assignment and power allocation in underlaid cellular vehicular-to-everything (C-V2X) systems where multiple vehicle-to-network (V2N) uplinks share the time-frequency resources with multiple vehicle-to-vehicle (V2V) platoons that enable groups of connected and autonomous vehicles to travel closely together.
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a practical and promising solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems.
Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs
Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity.
Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs
This paper is concerned with channel estimation in MIMO systems with few-bit ADCs.
Linear Receivers for Massive MIMO Systems with One-Bit ADCs
In this letter, we propose three linear receivers including Bussgang-based Maximal Ratio Combining (BMRC), Bussgang-based Zero-Forcing (BZF), and Bussgang-based Minimum Mean Squared Error (BMMSE) for massive MIMO systems with one-bit analog-to-digital converters (ADCs).
