LIDAR and Position-Aided mmWave Beam Selection With Non-Local CNNs and Curriculum Training
LIDAR and Position-Aided mmWave Beam Selection With Non-Local CNNs and Curriculum Training
复制标题
使用非局部 CNN 和课程培训的激光雷达和位置辅助毫米波波束选择
DOI：
10.1109/tvt.2022.3142513
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发表时间：
2021
影响因子：
6.8
通讯作者：
D. Gesbert
中科院分区：
文献类型：
--
作者：
Matteo Zecchin;Mahdi Boloursaz Mashhadi;Mikolaj Jankowski;Deniz Gündüz;M. Kountouris;D. Gesbert
由于毫米波波束宽度窄且用户移动性高，车辆到基础设施 (V2I) 通信中的高效毫米波 (mmWave) 波束选择是一项至关重要但具有挑战性的任务。为了减少迭代光束发现过程的搜索开销，数据驱动方法利用安装在车辆上的光探测和测距 (LIDAR) 传感器的上下文信息来生成有用的辅助信息。在本文中，我们提出了一种轻量级神经网络（NN）架构以及相应的激光雷达预处理，其性能显着优于以前的工作。我们的解决方案包含多项新颖之处，可以提高模型的收敛速度和最终精度。特别是，我们受知识蒸馏思想的启发，定义了一种新颖的损失函数，引入了一种利用视距（LOS）/非视距（NLOS）信息的课程训练方法，并提出了一种非局部注意力模块来提高更具挑战性的 NLOS 情况的性能。基准数据集的仿真结果表明，仅利用 LIDAR 数据和接收器位置，我们基于神经网络的波束选择方案可以在没有任何波束搜索开销的情况下实现详尽波束扫描方法的 79.9% 吞吐量，并且通过在少至 6 个波束中进行搜索即可实现 95% 的吞吐量。在典型的毫米波 V2I 场景中，与逆指纹识别和分层波束选择方案相比，我们提出的方法大大减少了实现所需吞吐量所需的波束搜索时间。
Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility. To reduce the search overhead of iterative beam discovery procedures, contextual information from light detection and ranging (LIDAR) sensors mounted on vehicles has been leveraged by data-driven methods to produce useful side information. In this paper, we propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing, which significantly outperforms previous works. Our solution comprises multiple novelties that improve both the convergence speed and the final accuracy of the model. In particular, we define a novel loss function inspired by the knowledge distillation idea, introduce a curriculum training approach exploiting line-of-sight (LOS)/non-line-of-sight (NLOS) information, and we propose a non-local attention module to improve the performance for the more challenging NLOS cases. Simulation results on benchmark datasets show that, utilizing solely LIDAR data and the receiver position, our NN-based beam selection scheme can achieve 79.9% throughput of an exhaustive beam sweeping approach without any beam search overhead and 95% by searching among as few as 6 beams. In a typical mmWave V2I scenario, our proposed method considerably reduces the beam search time required to achieve a desired throughput, in comparison with the inverse fingerprinting and hierarchical beam selection schemes.