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.
翻译:由于未来的无线系统趋向于更高的载体频率和大型天线阵列,正在探索使用一比位模拟数字转换器(ADCs)的接收器,因为其耗电量减少,但是,大型天线阵列和一比位ADCs的结合使得对频道的估计具有挑战性。在本文件中,我们从有限数量的一比位四分化试验测量中将频道估算作为一个反向问题,并通过优化预先培训的深层染色模型的输入矢量来重建频道,目的是最大限度地发挥新的基于相关损失的功能。我们注意到,与基础信道模型相适应的深层基因前科大大超出Bernoulli-Gausian Appear Messation(BG-GAMP),而一个单一的基因化模型则使用有条件投入来区分视线(LOS)和非视线(NLOS)频道的实现情况,超越了海洋线-G-GAMP,从而实现海洋线-GAMP在海洋通道上的新的相关损失功能。我们发现,在标准化的频道重建错误方面,在NLOS各频道上取得了类似的业绩。