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K V Sushena S – Multi Node Communications

K V Sushena Sree received her MS in Electronics and Communication Engineering  (CSE). Her research work was supervised by Dr. Prasad Krishna. Here’s a summary of her research work on Aiding multi node communications through caches and coded delivery: Improved schemes for Interference channel and Decentralized data rebalancing:

Underscoring the importance of storage and cache memory is a steady climb in the present generation, which is highly attributable to the presence of large number of video streaming devices, big data and the profusion of IoT devices. The placement of data across the system in a structured fashion aids in bringing down the number of transmissions within the system. Coded transmissions during the demand phase enables us to use the locally stored cache data most effectively. Coded Caching and Coded Data Rebalancing are two recent techniques to reduce the communication load in scenarios which require communication intended to multiple receivers, each of which store a fraction of the total data available in the system. Building on these two ideas, we focus on two main topics in this thesis. Our first contribution is a coded caching scheme for an interference channel which achieves subexponential subpacketization. Our second contribution consists of coded data rebalancing schemes for node addition and node removal scenarios in a decentralized distributed database. Although coded caching is an effective technique in obtaining large gains in cache-aided broadcast communication networks, its practical implementation is severely hampered by its exponential subpacketization levels. It is crucial in lowering this subpacketization level for various scenarios where coded caching is applied. We specifically choose the scenario of a cache aided interference channel with KT transmitters, KR receivers and N files stored in the system library. Each transmitter and receiver can cache upto MT and MR files from the library respectively. Initial coded caching scheme for this scenario had an exponential subpacketization with respect to the number of receivers KR for large KR and achieved a sum-DoF of L + KRMR N where L , KT MT N . Recent work on this scenario improvised on the required subspacketization to approximately its L th root and thereby boosted the achievable sum-DoF by a multiplicative factor of L. In the first contribution of this thesis, we present a projective geometry based coded caching scheme that achieves a subpacketization of F = 1 m! q m(m−1) 2 mQ−1 i=0 ” k − i 1 # q , and a sum-DoF of L(m + 1) where k, m ∈ Z + with m ≤ k and q is some prime power. As the number of receivers grows large, this subpacketization is subexponential with respect to the number of receivers provided the receiver cache fraction MR N is upper bounded by a constant. We further present a comparison of this scheme with the recent work on this scenario that achieved a low subpacketization. Secondly, we focus on handling the problem of non-uniform storage across storage nodes (also known as data skew) in replication-based distributed databases. The performance of such replication based distributed databases is affected by data skew and reduction in the replication factor during operation, particularly due to node additions or removals. Data rebalancing refers to the communication involved between the nodes in correcting this data skew, while maintaining the replication factor. For carefully designed distributed databases, transmitting coded symbols during the rebalancing phase has been recently shown to reduce the communication load of rebalancing. In this thesis, we look at balanced distributed databases with random placement, in which each data segment is stored in a random subset of r nodes in the system, where r refers to the replication factor of the distributed database. We call these as decentralized databases. For a natural class of such decentralized databases, we propose rebalancing schemes for correcting data skew and the reduction in the replication factor arising due to a single node addition or removal. We give converse arguments which show that our proposed rebalancing schemes are optimal asymptotically in the size of the file.