A common problem of the virtualized cloud radio access network architecture (C-RAN) is the compression of the time-domain IQ samples before transmission over the fronthaul link. Considering a multicarrier waveform such as OFDM, whose IQ samples follow a quasi-Gaussian distribution, the conventional Gaussian quantizer may be used as the optimal solution to the compression problem. However, since the high peak-to-average power ratio (PAPR) of OFDM signals remains a serious problem, various techniques may be employed to reduce the time-domain fluctuations of the IQ samples in the OFDM, resulting in a change in its distribution. The latter fact makes the Gaussian quantizer suboptimal. The literature lacks a performance analysis of the conventional OFDM-based compression techniques when the PAPR of the OFDM signal is reduced. Therefore, in this paper, we study for the first time the impact of reducing the PAPR of the OFDM signal before compression in the C-RAN architecture through rate-distortion analysis. We consider clipping and tone reservation PAPR reduction algorithms. The former is the simplest PAPR reduction approach, while the latter is one of the most effective algorithms used in broadcasting standards such as DVB-T2 and ATSC 3.0. We first derive the distribution of the PAPR-reduced OFDM IQ samples. This is used to optimize the thresholds and codebook levels of a non-uniform scalar quantizer and the number of quantization bits allocated for each quantized level in the entropy coding stage, along with the MER performance analysis. The simulation results show that the conventional Gaussian-based compression techniques applied to a PAPR-reduced signal is not very robust to the statistical changes in the signal unless the signal distribution at the input of the Gaussian quantizer is not significantly affected. However, a significant gain is obtained when the quantizer is optimized with respect to the true distribution of the PAPR-reduced IQ samples.

中文翻译：

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云无线接入网络的 PAPR 降低 OFDM IQ 样本压缩分析


虚拟化云无线接入网络架构 (C-RAN) 的一个常见问题是在通过前传链路传输之前压缩时域 IQ 样本。考虑到诸如OFDM的多载波波形，其IQ样本遵循准高斯分布，传统的高斯量化器可以用作压缩问题的最优解决方案。然而，由于OFDM信号的高峰均功率比（PAPR）仍然是一个严重的问题，因此可以采用各种技术来减少OFDM中IQ样本的时域波动，从而导致其分布发生变化。后一个事实使得高斯量化器不是最优的。文献缺乏对传统基于 OFDM 的压缩技术在 OFDM 信号的 PAPR 降低时的性能分析。因此，在本文中，我们首次通过率失真分析来研究在C-RAN架构中压缩前降低OFDM信号的PAPR的影响。我们考虑削波和音调保留 PAPR 降低算法。前者是最简单的 PAPR 降低方法，而后者是 DVB-T2 和 ATSC 3.0 等广播标准中使用的最有效的算法之一。我们首先推导出 PAPR 降低的 OFDM IQ 样本的分布。这用于优化非均匀标量量化器的阈值和码本级别以及熵编码阶段中为每个量化级别分配的量化位数，以及 MER 性能分析。 仿真结果表明，除非高斯量化器输入处的信号分布没有受到显着影响，否则应用于 PAPR 降低的信号的传统的基于高斯的压缩技术对于信号的统计变化不是很鲁棒。然而，当量化器相对于 PAPR 降低的 IQ 样本的真实分布进行优化时，可以获得显着的增益。