PROJECT TITLE :
Asynchronous Convolutional-Coded Physical-Layer Network Coding
This paper investigates the decoding method of asynchronous convolutional-coded physical-layer network coding (PNC) systems. Specifically, we put forth a layered decoding framework for convolutional-coded PNC consisting of 3 layers: symbol realignment layer, codeword realignment layer, and joint channel-decoding network coding (Jt-CNC) decoding layer. Our framework can house section asynchrony (phase offset) and symbol arrival-time asynchrony (image misalignment) between the signals simultaneously transmitted by multiple sources. A salient feature of this framework is that it will handle each fractional and integral image misalignments. For the decoding layer, instead of Jt-CNC, previously proposed PNC decoding algorithms (e.g., XOR-CD and reduced-state Viterbi algorithms) will also be used with our framework to deal with general symbol misalignments. Our Jt-CNC algorithm, based mostly on belief propagation, is BER-optimal for synchronous PNC and close to optimal for asynchronous PNC. Extending beyond convolutional codes, we have a tendency to further generalize the Jt-CNC decoding algorithm for all cyclic codes. Our simulation shows that Jt-CNC outperforms the previously proposed XOR-CD algorithm and reduced-state Viterbi algorithm by a pair of dB for synchronous PNC. For each section-asynchronous and symbol-asynchronous PNC, Jt-CNC performs better than the other two algorithms. Importantly, for real wireless network experimentation, we tend to implemented our decoding algorithm during a PNC prototype built on the USRP software radio platform. Our experiment shows that the proposed Jt-CNC decoder works well in observe.
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