Multi-cell Multi-beam Prediction using Auto-encoder LSTM for mmWave systems
Multi-cell Multi-beam Prediction using Auto-encoder LSTM for mmWave systems
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DOI：
10.1109/twc.2022.3183632
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发表时间：
2021-12
影响因子：
10.4
通讯作者：
Syed Hashim Ali Shah;S. Rangan
中科院分区：
文献类型：
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作者：
Syed Hashim Ali Shah;S. Rangan
毫米波（mmWave）系统依赖于窄波束中的通信以获得定向和空间复用增益。实现这些系统的一个关键挑战是波束跟踪，特别是在具有高移动性和阻塞的环境中。此外，在广域毫米波蜂窝系统中，用户设备（UE）装置必须经常同时跟踪来自多个小区的信号，因为到各个小区的链路可能不可靠。跨多个小区和多个波束的信道动态模型难以从第一原理导出。在这项工作中，我们提出了一种完全数据驱动的方法，该方法基于一种新型的自动编码器集成的长短期记忆（LSTM）网络，该网络预测来自多个细胞的多个波束，未来的一个时间步长。关键的创新是使用自动编码器预处理步骤，这降低了输入的维度-多细胞，多波束跟踪的主要挑战。所提出的网络的预测能力进行了验证，并比较常见的基线预测，以及流行的机器学习（ML）为基础的神经网络预测在现实的系统级模拟使用商业射线跟踪器。我们观察到，利用自动编码器进行降维的拟议网络的预测，提供了显着更好的最佳波束精度和更低的波束失准损失比常见的基线方法。我们还讨论了中断预测和主动波束切换作为多小区多波束预测的应用。
Millimeter wave (mmWave) systems rely on communication in narrow beams for directional and spatial multiplexing gains. A key challenge in realizing these systems is beam tracking, particularly in environments with high mobility and blockage. Additionally, in wide-area mmWave cellular systems, user equipment (UE) devices must often simultaneously track signals from multiple cells, since links to individual cells can be unreliable. Models of the channel dynamics across multiple cells and multiple beams are difficult to derive from first principles. In this work, we propose a fully data-driven approach based on a novel auto-encoder integrated long short term memory (LSTM) network, which predicts multiple beams from multiple cells, one time step in the future. The key innovation is to use an auto-encoder pre-processing step, which reduces the dimensionality of the input – the main challenge in multi-cell, multi-beam tracking. The prediction capability of the proposed network is verified and compared to common baseline predictors as well as popular machine learning (ML) based neural network predictors in realistic system-level simulations using a commercial ray-tracer. We observe that predictions from the proposed network, which utilizes auto-encoders for dimensionality reduction, offers significantly better best beam accuracy and lower beam misalignment loss than common baseline approaches. We also discuss outage prediction and proactive beam switching as applications of the multi-cell multi-beam prediction.