Detecting Detection Symbols in OTFS Systems with UAMP-MFIC Iterative Detector

In this article, we present a novel approach to improve the performance of multidimensional fusion of inference (MFI) detectors in large-scale occupancy grid mapping (OGM) systems. MFI detectors are widely used in various applications such as autonomous driving and robotics, but they suffer from high computational complexity and slow convergence. To address these issues, we propose an iterative detector based on the approximate message passing (AMP) framework with a fast recursive scheme. This approach allows for faster convergence and better performance compared to traditional MFI detectors.
We begin by explaining the basic concept of MFI detectors and how they are used in OGM systems. We then delve into the limitations of traditional MFI detectors, particularly their computational complexity and slow convergence. To address these limitations, we propose an iterative detector based on AMP with a fast recursive scheme, which allows for faster convergence and better performance.
To understand how our proposed detector works, let’s consider a simple example. Imagine you are trying to find the location of a specific object in a large room using various sensors. Each sensor provides an estimate of the object’s location, but these estimates may be inaccurate or incomplete. By combining the estimates from multiple sensors using MFI, we can improve the accuracy of the final location estimate. However, this process can be computationally expensive and slow, especially when dealing with large datasets.
Our proposed detector addresses these limitations by using an iterative approach based on AMP. In each iteration, we pass messages between connected factor nodes to update the posteriori mean of detection symbols. This allows us to improve the accuracy of the final location estimate while reducing computational complexity. We also introduce a fast recursive scheme that enables faster convergence and better performance compared to traditional MFI detectors.
We evaluate the performance of our proposed detector using various simulations and compare it with traditional MFI detectors. The results show that our proposed detector outperforms traditional MFI detectors in terms of computational complexity and accuracy. This makes it more suitable for large-scale OGM systems where computational efficiency is crucial.
In conclusion, our proposed detector provides a significant improvement over traditional MFI detectors by reducing computational complexity and improving performance. By leveraging the AMP framework with a fast recursive scheme, we can create an iterative detector that enables faster convergence and better accuracy in large-scale OGM systems. This work has important implications for various applications such as autonomous driving, robotics, and surveillance, where high-quality mapping is crucial for decision-making.