On practical design for joint distributed source and network coding

This paper considers the problem of communicating correlated information from multiple source nodes over a network of noiseless channels to multiple destination nodes, where each destination node wants to recover all sources. The problem involves a joint consideration of distributed compression and network information relaying. Although the optimal rate region has been theoretically characterized, it was not clear how to design practical communication schemes with low complexity. This work provides a partial solution to this problem by proposing a low-complexity scheme for the special case with two sources whose correlation is characterized by a binary symmetric channel. Our scheme is based on a careful combination of linear syndrome-based Slepian-Wolf coding and random linear mixing (network coding). It is in general suboptimal; however, its low complexity and robustness to network dynamics make it suitable for practical implementation

Approximate Decoding Approaches for Network Coded Correlated Data

This paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources

A Novel Network Coded Parallel Transmission Framework for High-Speed Ethernet

Parallel transmission, as defined in high-speed Ethernet standards, enables to use less expensive optoelectronics and offers backwards compatibility with legacy Optical Transport Network (OTN) infrastructure. However, optimal parallel transmission does not scale to large networks, as it requires computationally expensive multipath routing algorithms to minimize differential delay, and thus the required buffer size, optimize traffic splitting ratio, and ensure frame synchronization. In this paper, we propose a novel framework for high-speed Ethernet, which we refer to as network coded parallel transmission, capable of effective buffer management and frame synchronization without the need for complex multipath algorithms in the OTN layer. We show that using network coding can reduce the delay caused by packet reordering at the receiver, thus requiring a smaller overall buffer size, while improving the network throughput. We design the framework in full compliance with high-speed Ethernet standards specified in IEEE802.3ba and present solutions for network encoding, data structure of coded parallel transmission, buffer management and decoding at the receiver side. The proposed network coded parallel transmission framework is simple to implement and represents a potential major breakthrough in the system design of future high-speed Ethernet.Comment: 6 pages, 8 figures, Submitted to Globecom201