标题： ProtoBeam：使用原型网络将深度波束预测推广到未见过的天线 显示英文标题

标题： ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks

摘要： 深度学习技术最近出现，以高效管理毫米波波束传输，而无需耗时的波束扫描策略。 这些方法的一个基本挑战是它们对特定硬件的训练数据的依赖性以及其有限的泛化能力。 文献中报告了当在一种天线环境中训练的深度学习模型应用于另一种环境时，性能会出现大幅下降。 本文提出了应用原型网络来解决这一挑战，并利用DeepBeam真实世界数据集来验证所开发的解决方案。 原型网络在训练过程中擅长提取特征以建立类特定的原型，从而产生精确的嵌入，这些嵌入包含了数据的定义特征。 我们展示了原型网络（PN）的有效性，以实现深度波束预测器在未见过的天线上的泛化能力。 我们的方法结合了数据归一化和原型归一化与PN，在不同天线数据集上进行训练和测试时，实现了平均74.11%的波束分类准确率。 这相比文献中未考虑此类领域变化的基线性能提高了398%。 据我们所知，这项工作代表了原型网络在无线网络领域适应中的价值的首次展示，为该领域的未来研究提供了基础。 显示英文摘要

摘要： Deep learning techniques have recently emerged to efficiently manage mmWave beam transmissions without requiring time consuming beam sweeping strategies. A fundamental challenge in these methods is their dependency on hardware-specific training data and their limited ability to generalize. Large drops in performance are reported in literature when DL models trained in one antenna environment are applied in another. This paper proposes the application of Prototypical Networks to address this challenge and utilizes the DeepBeam real-world dataset to validate the developed solutions. Prototypical Networks excel in extracting features to establish class-specific prototypes during the training, resulting in precise embeddings that encapsulate the defining features of the data. We demonstrate the effectiveness of PN to enable generalization of deep beam predictors across unseen antennas. Our approach, which integrates data normalization and prototype normalization with the PN, achieves an average beam classification accuracy of 74.11 percent when trained and tested on different antenna datasets. This is an improvement of 398 percent compared to baseline performances reported in literature that do not account for such domain shifts. To the best of our knowledge, this work represents the first demonstration of the value of Prototypical Networks for domain adaptation in wireless networks, providing a foundation for future research in this area.