Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Asynchronous Convolutional-Coded Physical-Layer Network Coding (1312.1447v1)

Published 5 Dec 2013 in cs.IT and math.IT

Abstract: This paper investigates the decoding process of asynchronous convolutional-coded physical-layer network coding (PNC) systems. Specifically, we put forth a layered decoding framework for convolutional-coded PNC consisting of three layers: symbol realignment layer, codeword realignment layer, and joint channel-decoding network coding (Jt-CNC) decoding layer. Our framework can deal with phase asynchrony and symbol arrival-time asynchrony between the signals simultaneously transmitted by multiple sources. A salient feature of this framework is that it can handle both fractional and integral symbol offsets; previously proposed PNC decoding algorithms (e.g., XOR-CD and reduced-state Viterbi algorithms) can only deal with fractional symbol offset. Moreover, the Jt-CNC algorithm, based on belief propagation (BP), is BER-optimal for synchronous PNC and near optimal for asynchronous PNC. Extending beyond convolutional codes, we 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 2dB for synchronous PNC. For phase-asynchronous PNC, Jt-CNC is 4dB better than the other two algorithms. Importantly, for real wireless environment testing, we have also implemented our decoding algorithm in a PNC system built on the USRP software radio platform. Our experiment shows that the proposed Jt-CNC decoder works well in practice.

Summary

We haven't generated a summary for this paper yet.