5 papers
Single or Multiple Policies for Phase-Structured Reinforcement Learning?
Guilhem Loussouarn, Nancy Nayak, Kin K. Leung
Many reinforcement-learning (RL) problems are non-stationary yet structured and can be decomposed into phases, each with its own transition probabilities and reward functions. When…
RL-based Joint Coverage and Beam Optimization of High Altitude Platform Systems
Guilhem Loussouarn, Nancy Nayak, Kin K. Leung +1
High Altitude Platform Systems (HAPS) are a promising component of 6G network architectures, offering a unique "freedom of movement" that distinguishes them from static terrestrial…
Decentralized AI Service Placement, Selection and Routing in Mobile Networks
Jinkun Zhang, Stefan Vlaski, Kin Leung
The rapid development and usage of large-scale AI models by mobile users will dominate the traffic load in future communication networks. The advent of AI technology also facilitat…
Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Molly Wang, Kin. K Leung
Reinforcement Learning (RL) has been widely used for packet routing in communication networks, but traditional RL methods rely on the Markov assumption that the current state conta…
DRL-based Dolph-Tschebyscheff Beamforming in Downlink Transmission for Mobile Users
Nancy Nayak, Kin K. Leung, Lajos Hanzo
With the emergence of AI technologies in next-generation communication systems, machine learning plays a pivotal role due to its ability to address high-dimensional, non-stationary…