This paper explicitly models a coarse and noisy quantization in a communication system empowered by orthogonal time frequency space (OTFS) for cost and power efficiency. We first point out, with coarse quantization, the effective channel is imbalanced and thus no longer able to circularly shift the transmitted symbols along the delay-Doppler domain. Meanwhile, the effective channel is non-isotropic, which imposes a significant loss to symbol detection algorithms like the original approximate message passing (AMP). Although the algorithm of generalized expectation consistent for signal recovery (GEC-SR) can mitigate this loss, the complexity in computation is prohibitively high, mainly due to an dramatic increase in the matrix size of OTFS. In this context, we propose a low-complexity algorithm that incorporates into the GEC-SR a quick inversion of quasi-banded matrices, reducing the complexity from a cubic order to a linear order while keeping the performance at the same level.

翻译：本文对基于正交时频空（OTFS）的通信系统中的粗量化与噪声过程进行了显式建模，以提升系统成本与能效。我们首先指出，在粗量化条件下，等效信道呈现非平衡特性，不再具备沿时延-多普勒域对发射符号进行循环移位的能力。同时，等效信道的各向异性特征会导致原始近似消息传递（AMP）等符号检测算法出现显著性能损失。尽管面向信号恢复的广义期望一致算法（GEC-SR）可缓解该损失，但其计算复杂度极高——这主要源于OTFS矩阵规模的急剧膨胀。针对此问题，我们提出一种低复杂度算法，将准带状矩阵快速求逆方法融入GEC-SR框架，将复杂度从立方阶降至线性阶，同时保持与原始算法相当的检测性能。