论文标题
使用深层生成网络的一位MMWAVE MIMO通道估计
One-bit mmWave MIMO Channel Estimation using Deep Generative Networks
论文作者
论文摘要
随着未来的无线系统趋向较高的载波频率和较大的天线阵列，由于其功耗降低，因此探索了具有一位模数转换器（ADC）的接收器（ADC）。但是，大型天线阵列和一位ADC的组合使通道估计具有挑战性。在本文中，我们从有限数量的一位量化试验测量值作为反问题中制定通道估计，并通过优化预训练的深入生成模型的输入向量，目的是最大化基于新型相关性损失函数的目的。 We observe that deep generative priors adapted to the underlying channel model significantly outperform Bernoulli-Gaussian Approximate Message Passing (BG-GAMP), while a single generative model that uses a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations outperforms BG-GAMP on LOS channels and achieves comparable performance on NLOS channels in terms of the normalized通道重建错误。
As future wireless systems trend towards higher carrier frequencies and large antenna arrays, receivers with one-bit analog-to-digital converters (ADCs) are being explored owing to their reduced power consumption. However, the combination of large antenna arrays and one-bit ADCs makes channel estimation challenging. In this paper, we formulate channel estimation from a limited number of one-bit quantized pilot measurements as an inverse problem and reconstruct the channel by optimizing the input vector of a pre-trained deep generative model with the objective of maximizing a novel correlation-based loss function. We observe that deep generative priors adapted to the underlying channel model significantly outperform Bernoulli-Gaussian Approximate Message Passing (BG-GAMP), while a single generative model that uses a conditional input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) channel realizations outperforms BG-GAMP on LOS channels and achieves comparable performance on NLOS channels in terms of the normalized channel reconstruction error.
