9 papers
Downlink Beamforming Design for NOMA Using Convolutional Neural Networks
Chentong Li, Saeed Mohammadzadeh, Kanapathippillai Cumanan +1
Non-orthogonal multiple access (NOMA) and beamforming are well-established techniques for enabling massive connectivity in future wireless networks. However, many optimal beamformi…
A Spatial Similarity-Guided Pilot Assignment and Access Point Selection for Cell-Free Massive MIMO Networks
Saeed Mohammadzadeh, Kanapathippillai Cumanan, Pei Liu +1
This paper investigates pilot assignment and access point (AP) selection strategies for uplink cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We propose chann…
Channel-Correlation-Based Access Point Selection and Pilot Power Allocation for Cell-Free Massive MIMO
Saeed Mohammadzadeh, Rodrigo C. De Lamare, Kanapathippillai Cumanan +1
This paper proposes a dynamic access point (AP) selection and pilot power allocation (DAPPA) framework for uplink cell-free massive multiple-input multiple-output (CFmMIMO) systems…
Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks
Saeed Mashdour, Saeed Mohammadzadeh, André R. Flores +3
In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncerta…
NOMA Assisted Downlink Power Allocation in Pinching Antenna Systems Using Convolutional Neural Network
Saeed Mohammadzadeh, Kanapathippillai Cumanan, Zhiguo Ding
In this paper, we consider a flexible-antenna architecture, referred to as a pinching-antenna (PA) system, in which multiple PAs realized by activating small dielectric particles a…
Covariance Matrix Construction with Preprocessing-Based Spatial Sampling for Robust Adaptive Beamforming
Saeed Mohammadzadeh, Rodrigo C. de Lamare, Yuriy Zakharov
This work proposes an efficient, robust adaptive beamforming technique to deal with steering vector (SV) estimation mismatches and data covariance matrix reconstruction problems. I…