We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel measurements, and predicts the wireless channel accurately at any location in the site. We introduce a series of techniques that integrate wireless propagation properties into the NeRF framework to account for the fundamental differences between the behavior of light and wireless signals. We conduct extensive evaluations of our framework and show that our approach can accurately predict channels at unvisited locations with significantly lower measurement density than prior state-of-the-art

翻译：我们提出了NeWRF——一种用于预测无线信道的深度学习框架。无线信道预测是无线通信领域长期存在的难题，也是提升无线网络部署覆盖质量的关键技术。当前，无线网络部署需通过现场勘测进行评估，这一过程极其繁琐，需要经验丰富的工程师执行大量信道测量工作。为降低现场勘测成本，我们基于神经辐射场（NeRF）领域的最新进展开发了NeWRF。该框架通过稀疏信道测量数据训练神经网络模型，能够精确预测站点内任意位置的无线信道。我们引入了一系列将无线传播特性融入NeRF框架的技术，以充分考虑光信号与无线信号行为之间的根本差异。通过大量实验评估，我们证明该方法能够在显著低于现有最优技术的测量密度条件下，准确预测未测量位置的信道参数。