Consider a multi-source network coding problem with correlated sources. While the fundamental limits are known, achieving them, in general, involves a computational burden due to the complex decoding process. Efficient solutions, on the other hand, are by large based on source and network coding separation, thus imposing strict topological constraints on the networks which can be solved. In thi...

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine (ML) paradigm. One of the main challenges in FL is huge communication cost for aggregation. Many compression/quantization schemes have been proposed to reduce However, following question remains unanswered: What fundamental trade-off between convergence per...

In this work, lossy distributed compression of a pair correlated sources is considered. Conventionally, Shannon's random coding arguments - using randomly generated unstructured codebooks whose blocklength taken to be asymptotically large are used derive achievability results. However, in some multi-terminal communications scenarios, codes with constant finite certain architectures leads improv...

Recent works have proposed the combination of Multiple Description Coding (MDC) schemes with the novel Distributed Video Coding (DVC) paradigm in order to enable robust video transmission over lossy channels. The paper presents a Multiple Description Distributed Video Coder (MDDVC) that combines a polyphase-subsampling MDC approach with a novel hybrid Distributed Source Coding technique that pr...

In this paper, mathematical bases for non-adaptive joint source network coding of correlated messages in a Bayesian scenario are studied. Specifically, we introduce one-step Quantized Network Coding (QNC), which is a hybrid combination of network coding and packet forwarding for transmission. Motivated by the work on Bayesian compressed sensing, we derive theoretical guarantees on robust recove...

We consider a sensor network where distributed source coding and network coding are used. We show how to design distributed source coding and network coding jointly to optimize the compression efficiency and network robustness tradeoff.

This thesis treats the problem of source coding in situations where the encoded data is subject to errors. The typical scenario is a communication system, where source data such as speech or images should be transmitted from one point to another. A problem is that most communication systems introduce some sort of error in the transmission. A wireless communication link is prone to introduce ind...

This paper addresses the problem of coding a continuous random source correlated with another source which is only available at the decoder. The proposed approach is based on the extension of the channel coding concept of syndrome from the discrete into the continuous domain. If the correlation between the sources can be described by an additive Gaussian backward channel and capacity-achieving ...

Hierarchical source coding, also known as successive refinement of information, was introduced by Koshelev [1,2] and Equitz and Cover [3]. This problem is concerned with the construction of a source code for a discrete memoryless source X with respect to a given distortion measure d1 that can be further refined to represent the same source within another distortion measure d2 so that both repre...