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：本文针对正交时频空间（OTFS）通信系统中为实现成本与能效优化而采用的粗量化与噪声量化过程进行精确建模。首先指出，在粗量化条件下，有效信道呈现非均衡特性，不再能够沿延迟-多普勒域对传输符号进行循环移位。同时，该有效信道具有非各向同性特征，这对原始近似消息传递（AMP）等符号检测算法造成显著性能损失。尽管用于信号恢复的广义期望一致性算法（GEC-SR）能够缓解这种损失，但其计算复杂度极高，主要源于OTFS矩阵维度的急剧膨胀。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技术融入GEC-SR框架，将复杂度从立方阶降至线性阶，同时保持同等性能水平。