da Conceição Paulo Francisco, Rocha Flávio Geraldo Coelho
Department of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil.
Sensors (Basel). 2023 Nov 9;23(22):9073. doi: 10.3390/s23229073.
In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing-Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.
在这项工作中，我们使用毫米波（mmWave）和大规模多输入多输出（mMIMO）技术对5G下行链路信道进行建模，考虑以下定位参数：到达时间（TOA）、二维出发角（2D-AoD）和二维到达角（2D-AoA），这两者都包含方位角和仰角。我们的研究重点是在三维（3D）环境中对这些参数进行精确估计，这在诸如智能仓储等工业4.0应用中至关重要。在这种场景下，确定设备的定位至关重要，因为产品必须高精度地处理。为了实现这些精确估计，我们采用了一种基于分布式压缩感知 - 子空间正交匹配追踪（DCS-SOMP）算法的自适应方法。我们通过一种在每次迭代中动态调整感知矩阵的自适应方法获得了更好的估计结果，有效地限制了搜索空间。结果表明，我们的方法在准确性、收敛速度和内存使用方面优于传统方法。
