Quantized Network Coding for Correlated Sources

TL;DR

This paper introduces quantized network coding, combining network coding and distributed source coding, to efficiently transmit correlated sources with reduced delay and good decoding quality, leveraging compressed sensing principles.

Contribution

It proposes a novel quantized network coding method that integrates random linear coding and quantization, with theoretical analysis and practical design guidelines based on compressed sensing.

Findings

Good quality decoding with fewer packets at the sink

Reduced delivery delay compared to traditional routing

Feasible design of network coding coefficients using restricted isometry property

Abstract

Non-adaptive joint source network coding of correlated sources is discussed in this paper. By studying the information flow in the network, we propose quantized network coding as an alternative for packet forwarding. This technique has both network coding and distributed source coding advantages, simultaneously. Quantized network coding is a combination of random linear network coding in the (infinite) field of real numbers and quantization to cope with the limited capacity of links. With the aid of the results in the literature of compressed sensing, we discuss theoretical and practical feasibility of quantized network coding in lossless networks. We show that, due to the nature of the field it operates on, quantized network coding can provide good quality decoding at a sink node with the reception of a reduced number of packets. Specifically, we discuss the required conditions on local network coding coefficients, by using restricted isometry property and suggest a design, which yields in appropriate linear measurements. Finally, our simulation results show the achieved gain in terms of delivery delay, compared to conventional routing based packet forwarding.