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…
Rotate the ReLU to implicitly sparsify deep networks
Nancy Nayak, Sheetal Kalyani
In the era of Deep Neural Network based solutions for a variety of real-life tasks, having a compact and energy-efficient deployable model has become fairly important. Most of the…
BayesAoA: A Bayesian method for Computation Efficient Angle of Arrival Estimation
Akshay Sharma, Nancy Nayak, Sheetal Kalyani
The angle of Arrival (AoA) estimation is of great interest in modern communication systems. Traditional maximum likelihood-based iterative algorithms are sensitive to initializatio…
Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge
Deepsayan Sadhukhan, Nitin Priyadarshini Shankar, Nancy Nayak +2
Recently, deep neural networks (DNNs) have been used extensively for automatic modulation classification (AMC), and the results have been quite promising. However, DNNs have high m…
Realizing Neural Decoder at the Edge with Ensembled BNN
Devannagari Vikas, Nancy Nayak, Sheetal Kalyani
In this work, we propose extreme compression techniques like binarization, ternarization for Neural Decoders such as TurboAE. These methods reduce memory and computation by a facto…
Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck
Vishnu Raj, Nancy Nayak, Sheetal Kalyani
Compact neural networks are essential for affordable and power efficient deep learning solutions. Binary Neural Networks (BNNs) take compactification to the extreme by constraining…
Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training
Nancy Nayak, Thulasi Tholeti, Muralikrishnan Srinivasan +1
This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms…
Leveraging online learning for CSS in frugal IoT network
Nancy Nayak, Vishnu Raj, Sheetal Kalyani
We present a novel method for centralized collaborative spectrum sensing for IoT network leveraging cognitive radio network. Based on an online learning framework, we propose an al…