Optimal resource allocation considering non-uniform spatial traffic distribution in ultra-dense networks: A multi-agent reinforcement learning approach
Recently, the demand for small cell base stations (SBSs) has been exploding to
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
accommodate the explosive increase in mobile data traffic. In ultra-dense small cell …
Communication-assisted multi-agent reinforcement learning improves task-offloading in UAV-aided edge-computing networks
Equipping unmanned aerial vehicles (UAVs) with computing servers allows the ground-
users to offload complex tasks to the UAVs, but the trajectory optimization of UAVs is critical …
users to offload complex tasks to the UAVs, but the trajectory optimization of UAVs is critical …
Knowledge Transfer-Based Multiagent Q-Learning for Medium Access in Dense Cellular Networks
Inter-cell interference in dense cellular networks causes severe network performance
degradation because the base stations (BSs) and devices cannot fully utilize all the network …
degradation because the base stations (BSs) and devices cannot fully utilize all the network …
A networked multi-agent reinforcement learning approach for cooperative FemtoCaching assisted wireless heterogeneous networks
To meet the explosive growth of mobile traffic requirement in the 5th generation (5 G) mobile
system, FemtoCaching at the network edge has been regarded as a promising technique for …
system, FemtoCaching at the network edge has been regarded as a promising technique for …
Request delay and survivability optimization for software defined‐wide area networking (SD‐WAN) using multi‐agent deep reinforcement learning
Data exchange between headquarters and local branches represents a major challenge
issue for business success. For this issue, traditional solutions applied to wide area …
issue for business success. For this issue, traditional solutions applied to wide area …
A deep reinforcement learning for user association and power control in heterogeneous networks
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …
demand for higher data rate in the next generation mobile networks. Different from the …
Multi-agent deep reinforcement learning for efficient passenger delivery in urban air mobility
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical
vertical takeoff and landing (eVTOL), will play a key role in future transportation. By putting …
vertical takeoff and landing (eVTOL), will play a key role in future transportation. By putting …
Deep recurrent reinforcement learning for partially observable user association in a vertical heterogenous network
H Khoshkbari, G Kaddoum - IEEE Communications Letters, 2023 - ieeexplore.ieee.org
To ensure ubiquitous connectivity and meet increasing users' demands in next-generation
wireless networks, we investigate user association in a three-layer network consisting of a …
wireless networks, we investigate user association in a three-layer network consisting of a …
Multi-Agent Reinforcement Learning for Dynamic Topology Optimization of Mesh Wireless Networks
W Sun, Q Lv, Y Xiao, Z Liu, Q Tang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In Mesh Wireless Networks (MWNs), the network coverage is extended by connecting
Access Points (APs) in a mesh topology, where transmitting frames by multi-hop routing has …
Access Points (APs) in a mesh topology, where transmitting frames by multi-hop routing has …
Distributed multi-agent deep Q-learning for load balancing user association in dense networks
Distributed learning can lead to effective user association with low overhead, but faces
significant challenges in incorporating load balancing at all base stations (BS) because of …
significant challenges in incorporating load balancing at all base stations (BS) because of …