ProtoBeam：利用 Prototypical Networks 将深度波束预测泛化到未见过的天线
ProtoBeam: Generalizing Deep Beam Prediction to Unseen Antennas using Prototypical Networks
AI总结：
本文提出基于 Prototypical Networks 的 ProtoBeam，通过数据与原型归一化实现跨未见过天线的深度 mmWave 波束预测，并在 DeepBeam 上取得 74.11% 准确率。
摘要：
深度学习技术最近被用于高效管理毫米波（mmWave）波束传输，而无需耗时的波束扫描策略。这些方法的一个根本挑战是其依赖于特定硬件的训练数据，且泛化能力有限。文献报告称，当在一种天线环境中训练的深度学习（DL）模型应用于另一种环境时，性能会大幅下降。本文提出应用 Prototypical Networks（原型网络）来应对这一挑战，并利用 DeepBeam 真实世界数据集验证所开发的解决方案。Prototypical Networks 擅长在训练期间提取特征以建立特定类别的原型，从而生成封装数据定义特征的精确嵌入。我们证明了 PN 在使深度波束预测器能够跨未见过的天线进行泛化方面的有效性。我们的方法将数据归一化和原型归一化与 PN 相结合，在不同天线数据集上进行训练和测试时，实现了 74.11% 的平均波束分类准确率。与文献中报告的未考虑此类域偏移的基线性能相比，这提升了 398%。据我们所知，本工作首次展示了 Prototypical Networks 在无线网络域适应中的价值，为该领域未来研究奠定了基础。
英文摘要：
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.
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