P. T. Akhil

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4ranked-venue papers

2as first author

0since 2021 · last 2019

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks 2 papers		Internet of things and sensor networks · 41% Cellular and mobile networks · 30% Network optimization and economics · 20%
Computer architecture, parallel and distributed computing, and storage systems 1 paper		Performance modeling and evaluation · 100%

Topics — the 9 heaviest of 9, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Internet of things and sensor networks › energy management base station activation	0.6	2	2018	Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs · IEEE/ACM Trans. Netw. 2018 Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Cellular and mobile networks small cell networks	0.6	2	2018	Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs · IEEE/ACM Trans. Netw. 2018 Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Internet of things and sensor networks › energy efficiency energy-efficient scheduling	0.3	1	2018	Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs · IEEE/ACM Trans. Netw. 2018
Network optimization and economics resource allocation	0.3	1	2018	Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs · IEEE/ACM Trans. Netw. 2018
Internet of things and sensor networks › energy management energy cost minimization	0.3	1	2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Network optimization and economics › throughput-optimal scheduling max-weight scheduling	0.3	1	2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Wireless networking scheduling	0.3	1	2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Cellular and mobile networks switching cost	0.3	1	2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017
Performance modeling and evaluation › stability analysis queue stability	0.1	1	2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs · INFOCOM 2017

Methods — techniques the papers use, named apart from their topics

markov chain convergence · 0.9lyapunov optimization · 0.9reinforcement learning · 0.6maxweight · 0.3

Year	Publication	Venue	Position
2019	Network utility maximization revisited: Three issues and their resolution P. T. Akhil, Rajesh Sundaresan	Perform. Evaluation	1
2018	Augmenting Max-Weight With Explicit Learning for Wireless Scheduling With Switching Costs abstract In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to switch OFF dynamically a subset of BSs to minimize energy costs. We consider two types of energy cost: 1) the cost of maintaining a BS in the active state and 2) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach-a Max-Weight algorithm along with a Lyapunov-based stability argument-does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight-based channel scheduler that has fast temporal dynamics. We show that using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability. Subhashini Krishnasamy, P. T. Akhil, Ari Arapostathis, Rajesh Sundaresan, Sanjay Shakkottai	IEEE/ACM Trans. Netw.	2
2017	Augmenting max-weight with explicit learning for wireless scheduling with switching costs abstract In small-cell wireless networks where users are connected to multiple base stations (BSs), it is often advantageous to opportunistically switch off a subset of BSs to minimize energy costs. We consider two types of energy cost: (i) the cost of maintaining a BS in the active state, and (ii) the cost of switching a BS from the active state to inactive state. The problem is to operate the network at the lowest possible energy cost (sum of activation and switching costs) subject to queue stability. In this setting, the traditional approach - a Max-Weight algorithm along with a Lyapunov-based stability argument - does not suffice to show queue stability, essentially due to the temporal co-evolution between channel scheduling and the BS activation decisions induced by the switching cost. Instead, we develop a learning and BS activation algorithm with slow temporal dynamics, and a Max-Weight based channel scheduler that has fast temporal dynamics. We show using convergence of time-inhomogeneous Markov chains, that the co-evolving dynamics of learning, BS activation and queue lengths lead to near optimal average energy costs along with queue stability. Subhashini Krishnasamy, P. T. Akhil, Ari Arapostathis, Sanjay Shakkottai, Rajesh Sundaresan	INFOCOM	2
2008	A computationally efficient approach to warp factor estimation in VTLN using EM algorithm and sufficient statistics P. T. Akhil, Shakti Prasad Rath, Srinivasan Umesh, Rama Sanand Doddipatla	INTERSPEECH	1