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：本文显式地建模了基于正交时频空（OTFS）的通信系统中为提升成本与功率效率而引入的粗量化与有噪量化过程。首先指出，在粗量化条件下，有效信道呈现非平衡特性，因此不再能沿时延-多普勒域对传输符号进行循环移位。同时，有效信道具有非各向同性特征，这给原始近似消息传递（AMP）等符号检测算法带来了显著性能损失。尽管用于信号恢复的广义期望一致算法（GEC-SR）可缓解该损失，但其计算复杂度过高，主要源于OTFS矩阵规模的急剧膨胀。为此，本文提出一种低复杂度算法，通过将准带状矩阵快速求逆方法融入GEC-SR，将复杂度从立方阶降至线性阶，且保持性能不变。