仅用下行数据训练，通过数据增强实现上下行与回传场景的统一路径损耗预测。
Reciprocity-Aware Convolutional Neural Networks for Map-Based Path Loss Prediction
- 利用少量合成上行样本进行数据增强，提升模型泛化能力。
- 在上行测试集上均方根误差降低超8分贝，显著提升预测精度。
- 适合需要跨场景无线覆盖优化的研究与工程人员使用。
路径损耗建模是通过发射端（Tx）到接收端（Rx）的通信链路估算点对点损耗的常用技术。精准的路径损耗预测可优化射频谱资源利用并减少干扰。现代路径损耗建模常采用数据驱动方法，利用机器学习在道路测试测量数据集上训练模型。道路测试主要反映下行场景（发射端位于建筑物，接收端位于移动车辆），因此训练模型通常仅适用于下行覆盖估计，缺乏上行场景表征。本文证明，通过在训练集中加入少量代表上行场景的合成样本，可仅用下行道路测试数据训练出适用于上行、下行及回传场景的通用路径损耗模型。在测试集上，上行场景的均方根误差降低了超过8分贝。
原文摘要 · Abstract (English)
Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by > 8 dB on uplink examples in the test set.
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