Optimal decoding of convolutional-coded physical-layer network coding

This paper investigates the decoding process of convolutional-coded physical-layer network coding (PNC) systems. Specifically, we put forth a joint channel-decoding network coding (Jt-CNC) algorithm, based on belief propagation (BP), for convolutional-coded PNC. Previously proposed XOR and channel decoding (XOR-CD) algorithm and reduced-state Viterbi algorithm are not optimal. Our Jt-CNC decoder is BER-optimal with feasible computational complexity. Simulations show that Jt-CNC outperforms XOR-CD and reduced-state Viterbi by 2dB. Furthermore, Jt-CNC is more resilient to phase offset.