Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges
Sixth generation (6G) internet of things (IoT) networks will modernize the applications and
satisfy user demands through implementing smart and automated systems. Intelligence …
satisfy user demands through implementing smart and automated systems. Intelligence …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Joint resource management for MC-NOMA: A deep reinforcement learning approach
This paper presents a novel and effective deep reinforcement learning (DRL)-based
approach to addressing joint resource management (JRM) in a practical multi-carrier non …
approach to addressing joint resource management (JRM) in a practical multi-carrier non …
Computation offloading and resource allocation in NOMA-MEC: A deep reinforcement learning approach
C Shang, Y Sun, H Luo… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Multiaccess edge computing has emerged as a powerful paradigm for increasing the
computation performance of mobile devices (MDs). Applying nonorthogonal multiple access …
computation performance of mobile devices (MDs). Applying nonorthogonal multiple access …
Intelligent computation offloading for MEC-based cooperative vehicle infrastructure system: A deep reinforcement learning approach
In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a
mobile edge computing (MEC) server and sensors could provide vehicle infrastructure …
mobile edge computing (MEC) server and sensors could provide vehicle infrastructure …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
The role of deep learning in NOMA for 5G and beyond communications
In the coming future, it is obvious that the wireless networks will be congested with massive
amounts of data traffic with the increasing number of users. Current multiple access …
amounts of data traffic with the increasing number of users. Current multiple access …
Quantum neural networks for resource allocation in wireless communications
B Narottama, SY Shin - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
This study exploits a quantum neural network (QNN) for resource allocation in wireless
communications. A QNN is presented to reduce time complexity while still maintaining …
communications. A QNN is presented to reduce time complexity while still maintaining …
Double deep q-network method for energy efficiency and throughput in a uav-assisted terrestrial network
Increasing the coverage and capacity of cellular networks by deploying additional base
stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it …
stations is one of the fundamental objectives of fifth-generation (5G) networks. However, it …
Deep learning-based NOMA system for enhancement of 5G networks: A review
RK Senapati, PJ Tanna - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
The fresh and rising demands for high-reliability and ultrahigh-capacity wireless
communication have led to extensive research into 5G communications. The wide progress …
communication have led to extensive research into 5G communications. The wide progress …