Reinforcement learning based RSS-threshold optimization for D2D-aided HTC/MTC in dense NOMA systems
To fulfill the stringent requirements brought by human-type communication (HTC) along with
massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal …
massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal …
Deep reinforcement learning based throughput maximization scheme for d2d users underlaying noma-enabled cellular network
Abstract Device-to-Device (D2D) communication is a potential technology that efficiently
reuses spectrum resources with CMUs in a fifth-generation (5G) underlay and even beyond …
reuses spectrum resources with CMUs in a fifth-generation (5G) underlay and even beyond …
Sum throughput maximization scheme for NOMA-Enabled D2D groups using deep reinforcement learning in 5G and beyond networks
Device-to-device (D2D) communication underlaying cellular network is a capable system for
advancing the spectrum's efficiency. However, in this condition, D2D generates cross …
advancing the spectrum's efficiency. However, in this condition, D2D generates cross …
Deep reinforcement learning for throughput improvement of the uplink grant-free NOMA system
Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-
free nonorthogonal multiple access (NOMA) emerges as an enabling technology for …
free nonorthogonal multiple access (NOMA) emerges as an enabling technology for …
Outage constrained design in NOMA-Based D2D offloading systems
Non-orthogonal multiple access (NOMA) is a new multiple access method that has been
considered in 5G cellular communications in recent years, and can provide better …
considered in 5G cellular communications in recent years, and can provide better …
A deep reinforcement learning scheme for sum rate and fairness maximization among d2d pairs underlaying cellular network with noma
Device-to-device (D2D) communication is an emerging technology in 5G and the upcoming
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
A Deep Reinforcement Learning based Approach for NOMA-based Random Access Network with Truncated Channel Inversion Power Control
As a main use case of 5G and Beyond wireless network, the ever-increasing machine type
communications (MTC) devices pose critical challenges over MTC network in recent years. It …
communications (MTC) devices pose critical challenges over MTC network in recent years. It …
Priority-based joint resource allocation with deep Q-learning for heterogeneous NOMA systems
For heterogeneous demands in fifth-generation (5G) new radio (NR), a massive machine
type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable and …
type communication (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable and …
DRL-Based Resource Allocation for NOMA-Enabled D2D Communications Underlay Cellular Networks
YJ Jeong, S Yu, JW Lee - IEEE Access, 2023 - ieeexplore.ieee.org
Since the emergence of device-to-device (D2D) communications, an efficient resource
allocation (RA) scheme with low-complexity suited for high variability of network …
allocation (RA) scheme with low-complexity suited for high variability of network …
Multi-agent deep reinforcement learning for massive access in 5G and beyond ultra-dense NOMA system
With the rapid development of machine-type communications (MTC), the future
communication architecture needs to provide services for both human-type communications …
communication architecture needs to provide services for both human-type communications …