Energy-efficient short packet communications for uplink NOMA-based massive MTC networks
The 5th-generation (5G) mobile networks and beyond need to support massive machine-
type communications (MTC) devices with limited available radio resources. In this paper, we …
type communications (MTC) devices with limited available radio resources. In this paper, we …
Decentralized power allocation for MIMO-NOMA vehicular edge computing based on deep reinforcement learning
Vehicular edge computing (VEC) is envisioned as a promising approach to process the
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates …
NOMA-based multi-user mobile edge computation offloading via cooperative multi-agent deep reinforcement learning
Mobile edge computing (MEC) is a promising solution to enable resource-limited mobile
devices to offload computation-intensive tasks to nearby edge servers. In this paper …
devices to offload computation-intensive tasks to nearby edge servers. In this paper …
Transmit power pool design for grant-free NOMA-IoT networks via deep reinforcement learning
Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access
framework for short-packet internet-of-things (IoT) networks to enhance connectivity …
framework for short-packet internet-of-things (IoT) networks to enhance connectivity …
A NOMA-Based Q-Learning Random Access Method for Machine Type Communications
Machine Type Communications (MTC) is a main use case of 5G and beyond wireless
networks. Moreover, due to the ultra-dense nature of massive MTC networks, Random …
networks. Moreover, due to the ultra-dense nature of massive MTC networks, Random …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
Hybrid NOMA offloading in multi-user MEC networks
Non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) has
recently attracted significant attention due to its superior capability to reduce the energy …
recently attracted significant attention due to its superior capability to reduce the energy …
Optimizing information freshness via multiuser scheduling with adaptive NOMA/OMA
This paper considers a wireless network with a base station (BS) conducting timely status
updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal …
updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal …
IRS assisted NOMA aided mobile edge computing with queue stability: Heterogeneous multi-agent reinforcement learning
By employing powerful edge servers for data processing, mobile edge computing (MEC) has
been recognized as a promising technology to support emerging computation-intensive …
been recognized as a promising technology to support emerging computation-intensive …
Joint radio resource allocation and cooperative caching in PD-NOMA-based HetNets
In this paper, we propose a novel joint resource allocation and cooperative caching scheme
for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous …
for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous …