Deep learning meets swarm intelligence for UAV-assisted IoT coverage in massive MIMO
This study considers an unmanned aerial vehicle (UAV)-assisted multiuser massive multiple-
input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the …
input multiple-output (MU-mMIMO) systems, where a decode-and-forward (DF) relay in the …
Intelligent non-orthogonal beamforming with large self-interference cancellation capability for full-duplex multiuser massive MIMO systems
A Koc, T Le-Ngoc - IEEE Access, 2022 - ieeexplore.ieee.org
This work introduces a novel full-duplex hybrid beamforming (FD-HBF) technique for the
millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) …
millimeter-wave (mmWave) multi-user massive multiple-input multiple-output (MU-mMIMO) …
Defending adversarial attacks on deep learning-based power allocation in massive MIMO using denoising autoencoders
Recent work has advocated for the use of deep learning to perform power allocation in the
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …
Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System
S Ramanathan, AB Maria - IETE Journal of Research, 2024 - Taylor & Francis
Precoding is a critical signal processing technique used in wireless communication systems
to enhance transmission performance. This paper initially provides a brief overview of …
to enhance transmission performance. This paper initially provides a brief overview of …
Dynamic Energy Efficient Resource Allocation for Massive MIMO Networks Using Randomized Ensembled Double Q-learning Algorithm
This paper tackles the challenge of power consumption in the massive multiple-input
multiple-output (mMIMO) base station (BS), where continuous operation of all antennas …
multiple-output (mMIMO) base station (BS), where continuous operation of all antennas …
Resource allocation in heterogeneous network with node and edge enhanced graph attention network
Q Sun, Y He, O Petrosian - Applied Intelligence, 2024 - Springer
In wireless networks, the effectiveness of beamforming and power allocation strategies is
crucial in meeting the increasing data demands of users and ensuring rapid data …
crucial in meeting the increasing data demands of users and ensuring rapid data …
Resource Allocation in Heterogeneous Network with Supervised GNNs
Abstract Device-to-device (D2D) transmission has become an essential form of wireless
communication due to the rise of 5G and Internet of Things (IoT). Unfortunately, most …
communication due to the rise of 5G and Internet of Things (IoT). Unfortunately, most …
DPC-CNN Algorithm for Multiuser Hybrid Precoding With Dynamic Structure
F Liu, Z Duan, L Zhang, B Shi, Y Liu… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
This paper presents a dynamic partially connected (DPC) structure-based convolutional
neural network (CNN) hybrid precoding with multi-user optimization algorithm. In the …
neural network (CNN) hybrid precoding with multi-user optimization algorithm. In the …
Deep Reinforcement Learning-based Sum-Rate Maximization in Hybrid Beamforming Multi-User Massive MIMO Systems
This work develops deep reinforcement learning (RL)-based techniques to perform power
allocation in hybrid beamforming (HB) multi-user massive multiple-input multiple-output (MU …
allocation in hybrid beamforming (HB) multi-user massive multiple-input multiple-output (MU …
Intelligent Subcarrier Allocation in Hybrid Beamforming Multi-User mMIMO-OFDM Systems
This paper proposes a genetic-algorithm (GA)-based subcarrier allocation in orthogonal
frequency division multiplexing (OFDM)-based hybrid beamforming multi-user massive …
frequency division multiplexing (OFDM)-based hybrid beamforming multi-user massive …