Publications

R2-D2D: A Novel Deep Learning based Content-Caching Framework for D2D Networks

Published in IEEE GlobeCom, 2020

The explosive growth of wireless data and trafficaccompanied by the rapid advancements in intelligence andprocessing power of user equipments (UEs) has paved the way fordevice-to-device (D2D) communication to surface as a promisingsolution. One major benefit is that users can collaborativelycache and share content to reduce costs associated with backhaullinks. In this paper, we explore different approaches to cachecontent for users in a D2D enabled environment and propose anovel two-stacked approach to achieve a higher cache-hit ratiowhile leveraging advancements in deep learning. We propose the‘R2-D2D’framework, wherein we use long short-term memory(LSTM) networks stacked with a recently developed omni-Scaleconvolutional neural network (CNN) for making the cachingdecision. Unlike most previous works, the proposed system modelworks without any apriori knowledge like file popularity distribu-tion, or any assumptions like stationarity of the environment. Ourexperiments show that the proposed framework learns well fromhistorical information, obtaining a D2D cache hit ratio of0.418when5000timesteps of historical information was provided,outperforming a recently proposed neural network collabortivefiltering (NCF) framework by approximately10%.

Recommended citation: @misc{schakraborty2020r2d2d, title={R2-D2D: A Novel Deep Learning based Content-Caching Framework for D2D Networks}, author={Souradeep Chakraborty and Rahul Bajpai and Naveen Gupta}, year={2020}, }

Capturing Financial markets to apply Deep Reinforcement Learning

Published in 9th India Finance Conference, IIM-Ahmedabad, 2019

In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decision process (MDP) model to capture the financial trading markets. We review and propose various modifications to existing approaches and explore different techniques like the usage of technical indicators, to succinctly capture the market dynamics to model the markets. We then go on to use deep reinforcement learning to enable the agent (the algorithm) to learn how to take profitable trades in any market on its own, while suggesting various methodology changes and leveraging the unique representation of the FMDP (financial MDP) to tackle the primary challenges faced in similar works. Through our experimentation results, we go on to show that our model could be easily extended to two very different financial markets and generates a positively robust performance in all conducted experiments.

Recommended citation: @misc{chakraborty2019capturing, title={Capturing Financial markets to apply Deep Reinforcement Learning}, author={Souradeep Chakraborty}, year={2019}, eprint={1907.04373}, archivePrefix={arXiv}, primaryClass={q-fin.CP} }