AI-Enhanced Cloud-Edge-Terminal Collaborative Network: Survey, Applications, and Future Directions
The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm
for emerging applications owing to its huge potential in providing low-latency and ultra …
for emerging applications owing to its huge potential in providing low-latency and ultra …
Intra-domain knowledge reuse assisted reinforcement learning for fast anti-jamming communication
Z Quan, N Yingtao, X Peng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The novel technology based on reinforcement learning (RL) is considered as a promising
direction to achieve cognitive and even intelligent anti-jamming communication. However …
direction to achieve cognitive and even intelligent anti-jamming communication. However …
Evolution of RAN Architectures Towards 6G: Motivation, Development, and Enabling Technologies
J Chen, X Liang, J Xue, Y Sun, H Zhou… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
In this survey paper, we first provide insights on the evolution of radio access networks
(RANs) through investigating the existing paradigms and future trends towards 6G. We then …
(RANs) through investigating the existing paradigms and future trends towards 6G. We then …
Joint design of access and backhaul in densely deployed mmWave small cells
With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has
become increasingly prominent. Millimeter wave (mmWave) small cells can be densely …
become increasingly prominent. Millimeter wave (mmWave) small cells can be densely …
Joint device scheduling and bandwidth allocation for federated learning over wireless networks
Federated Learning (FL) has been widely used to train shared machine learning models
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …
while addressing the privacy concerns. When deployed in wireless networks, bandwidth …
A deep-Q learning scheme for secure spectrum allocation and resource management in 6G environment
P Bhattacharya, F Patel, A Alabdulatif… - … on Network and …, 2022 - ieeexplore.ieee.org
In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the
backdrop of sixth-generation (6G) communication networks that address the challenges of …
backdrop of sixth-generation (6G) communication networks that address the challenges of …
[HTML][HTML] Energy-efficient joint resource allocation in 5G HetNet using multi-agent parameterized deep reinforcement learning
Small cells are a promising technique to improve the capacity and throughput of future
wireless networks. However, user association and power allocation in heterogeneous …
wireless networks. However, user association and power allocation in heterogeneous …
Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems
Sensor-cloud systems (SCSs) aim to provide flexible configurable platforms for monitoring
and controlling the IoT-enabled applications. By integrating sensors, wireless networks and …
and controlling the IoT-enabled applications. By integrating sensors, wireless networks and …
Multi-agent learning and bargaining scheme for cooperative spectrum sharing process
S Kim - IEEE Access, 2023 - ieeexplore.ieee.org
Recently, the lack of spectrum resources has become a key technical bottleneck to develop
the Industrial Internet of Things (IIoT). Based on cognitive radio technology, the cognitive IIoT …
the Industrial Internet of Things (IIoT). Based on cognitive radio technology, the cognitive IIoT …
Distributed entropy-regularized multi-agent reinforcement learning with policy consensus
Sample efficiency is a limiting factor for existing distributed multi-agent reinforcement
learning (MARL) algorithms over networked multi-agent systems. In this paper, the sample …
learning (MARL) algorithms over networked multi-agent systems. In this paper, the sample …