no code implementations • 12 Feb 2024 • Santosh Kumar Singh, Satyabrata Sahu, Ayushi Thawait, Prasanna Chaporkar, Gaurav S. Kasbekar
We study the problem of selecting a user equipment (UE) and a beam for each access point (AP) for concurrent transmissions in a millimeter wave (mmWave) network, such that the sum of weighted rates of UEs is maximized.
no code implementations • 21 Oct 2021 • Pranav Sankhe, Prasanna Chaporkar
Linear Non-Linear(LN) models are widely used to characterize the receptive fields of early-stage auditory processing.
no code implementations • 21 Dec 2019 • Arghyadip Roy, Vivek Borkar, Abhay Karandikar, Prasanna Chaporkar
To overcome the curses of dimensionality and modeling of Dynamic Programming (DP) methods to solve Markov Decision Process (MDP) problems, Reinforcement Learning (RL) methods are adopted in practice.
1 code implementation • 22 May 2019 • Kumar Yashashwi, Deepak Anand, Sibi Raj B Pillai, Prasanna Chaporkar, K Ganesh
The enhanced decoding speed is due to the use of convolutional neural network (CNN) as opposed to recurrent neural network (RNN) used in the best known neural net based decoders.
no code implementations • 7 Feb 2019 • Nischal Agrawal, Prasanna Chaporkar
Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action.
no code implementations • 28 Nov 2018 • Arghyadip Roy, Vivek Borkar, Abhay Karandikar, Prasanna Chaporkar
In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space.