论文标题
输入噪声的系统中的最佳数据检测和信号估计
Optimal Data Detection and Signal Estimation in Systems with Input Noise
论文作者
论文摘要
实用系统通常会遭受信号生成期间已经出现的硬件障碍。尽管这种输入噪声障碍对信号处理系统的限制效果，但文献中通常会忽略它们。在本文中，我们提出了一种用于数据检测和信号估计的算法，称为输入噪声（AMPI）的近似消息，该算法考虑了输入噪声损害。为了证明AMPI的疗效，我们研究了两个应用：大型多输入多重输出（MIMO）无线系统中的数据检测以及压缩传感中的稀疏信号恢复。对于这两种应用，我们提供了AMPI实现最佳性能的大型系统限制的精确条件。此外，我们还使用模拟来证明AMPI在现实，有限维系统中在低复杂性下实现了近乎最佳的性能。
Practical systems often suffer from hardware impairments that already appear during signal generation. Despite the limiting effect of such input-noise impairments on signal processing systems, they are routinely ignored in the literature. In this paper, we propose an algorithm for data detection and signal estimation, referred to as Approximate Message Passing with Input noise (AMPI), which takes into account input-noise impairments. To demonstrate the efficacy of AMPI, we investigate two applications: Data detection in large multiple-input multiple output (MIMO) wireless systems and sparse signal recovery in compressive sensing. For both applications, we provide precise conditions in the large-system limit for which AMPI achieves optimal performance. We furthermore use simulations to demonstrate that AMPI achieves near-optimal performance at low complexity in realistic, finite-dimensional systems.
