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Alexandros G. Dimakis
Researcher at University of Texas at Austin
Publications - 321
Citations - 24601
Alexandros G. Dimakis is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Erasure code & Compressed sensing. The author has an hindex of 67, co-authored 307 publications receiving 22242 citations. Previous affiliations of Alexandros G. Dimakis include University of Southern California & Symantec.
Papers
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Journal ArticleDOI
Network Coding for Distributed Storage Systems
TL;DR: It is shown that there is a fundamental tradeoff between storage and repair bandwidth which is theoretically characterize using flow arguments on an appropriately constructed graph and regenerating codes are introduced that can achieve any point in this optimal tradeoff.
Posted Content
Network Coding for Distributed Storage Systems
TL;DR: In this paper, the authors introduce a general technique to analyze storage architectures that combine any form of coding and replication, as well as presenting two new schemes for maintaining redundancy using erasure codes.
Journal ArticleDOI
FemtoCaching: Wireless Content Delivery Through Distributed Caching Helpers
Karthikeyan Shanmugam,Negin Golrezaei,Alexandros G. Dimakis,Andreas F. Molisch,Giuseppe Caire +4 more
TL;DR: This work shows that the uncoded optimum file assignment is NP-hard, and develops a greedy strategy that is provably within a factor 2 of the optimum, and provides an efficient algorithm achieving a provably better approximation ratio of 1-1/d d, where d is the maximum number of helpers a user can be connected to.
Proceedings ArticleDOI
FemtoCaching: Wireless video content delivery through distributed caching helpers
Negin Golrezaei,Karthikeyan Shanmugam,Alexandros G. Dimakis,Andreas F. Molisch,Giuseppe Caire +4 more
TL;DR: The theoretical contribution of this paper lies in formalizing the distributed caching problem, showing that this problem is NP-hard, and presenting approximation algorithms that lie within a constant factor of the theoretical optimum.
Journal ArticleDOI
Gossip Algorithms for Distributed Signal Processing
TL;DR: An overview of recent gossip algorithms work, including convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping, and the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.