2 citations · 3 across the 10 of their papers we have counts for
13 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…
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…
First line of defense: A robust first layer mitigates adversarial attacks
Janani Suresh, Nancy Nayak, Sheetal Kalyani
Adversarial training (AT) incurs significant computational overhead, leading to growing interest in designing inherently robust architectures. We demonstrate that a carefully desig…
DRL-AdaPart: DRL-Driven Adaptive STAR-RIS Partitioning for Fair and Efficient Resource Utilization
Ashok S. Kumar, Nancy Nayak, Sheetal Kalyani +2
Efficient resource utilization is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to ensure fair and high data rates. We opti…
A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimization
Nancy Nayak, Sheetal Kalyani, Himal A. Suraweera
We propose a deep reinforcement learning (DRL) approach for a full-duplex (FD) transmission that predicts the phase shifts of the reconfigurable intelligent surface (RIS), base sta…