Convolutional Network-Coded Cooperation in Multi-Source Networks with a Multi-Antenna Relay

TL;DR

This paper introduces a new cooperative transmission scheme called CNCC that uses convolutional network coding in multi-source, multi-antenna relay networks, improving diversity and throughput.

Contribution

It proposes a novel CNCC scheme utilizing convolutional codes for network coding, enhancing diversity order and throughput in multi-source relay networks.

Findings

CNCC outperforms existing schemes in diversity and throughput.

Theoretical analysis matches simulation results.

CNCC achieves higher diversity order with convolutional coding.

Abstract

We propose a novel cooperative transmission scheme called "Convolutional Network-Coded Cooperation" (CNCC) for a network including N sources, one M-antenna relay, and one common destination. The source-relay (S-R) channels are assumed to be Nakagami-m fading, while the source-destination (S-D) and the relay-destination (R-D) channels are considered Rayleigh fading. The CNCC scheme exploits the generator matrix of a good (N+M', N, v) systematic convolutional code, with the free distance of d_free designed over GF(2), as the network coding matrix which is run by the network's nodes, such that the systematic symbols are directly transmitted from the sources, and the parity symbols are sent by the best antenna of the relay. An upper bound on the BER of the sources, and consequently, the achieved diversity orders are obtained. The numerical results indicate that the CNCC scheme outperforms the other cooperative schemes considered, in terms of the diversity order and the network throughput. The simulation results confirm the accuracy of the theoretical analysis.