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Phase Asynchronous Physical-Layer Network Coding: Decoder Design and Experimental Study

机译:相位异步物理层网络编码:解码器设计和实验研究

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摘要

Physical-layer network coding (PNC) channel decoding at the relay is of key importance for good performance in PNC systems. However, PNC channel decoders can have prohibitive computation complexity. Low complexity non-iterative PNC channel decoders are desired in practice. For such PNC decoders, decoding performance may degrade significantly when there is a relative phase offset between the simultaneous signals of multiple nodes received at the relay, particularly when bit-likelihood-based decoding is adopted. In this paper, we thoroughly investigate and numerically quantify the impact of relative phase offset on decoding performance. To maintain good decoding performance under relative phase offset, we introduce and experimentally evaluate symbol-likelihood-based decoding (in contrast to bit-likelihood-based decoding) for PNC systems. Our experimental results show that symbol-likelihood-based decoding improves the packet throughput over bit-likelihood-based decoding by 100% to 400% at SNR of 15 dBs. Moreover, we study the computational complexity under both these decoding methods. We find that a reduced-complexity decoder with symbol-likelihood-based decoding provides the best performance-complexity tradeoff for practical PNC systems.
机译:中继处的物理层网络编码(PNC)通道解码对于PNC系统中的良好性能至关重要。但是,PNC通道解码器的计算复杂性令人望而却步。在实践中需要低复杂度的非迭代PNC信道解码器。对于这种PNC解码器,当在中继站接收到的多个节点的同时信号之间存在相对相位偏移时,尤其是在采用基于比特似然性的解码时,解码性能可能会大大降低。在本文中,我们将彻底研究和量化量化相对相位偏移对解码性能的影响。为了在相对相位偏移下保持良好的解码性能,我们引入并实验评估了PNC系统的基于符号似然性的解码(与基于位似然性的解码相比)。我们的实验结果表明,在15 dBs的SNR下,基于符号似然的解码比基于位似然的解码将数据包吞吐量提高了100%至400%。此外,我们研究了这两种解码方法下的计算复杂度。我们发现具有基于符号似然性的解码的复杂度降低的解码器为实际PNC系统提供了最佳的性能复杂度折衷。

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