论文标题
学会从过去的经验和少数飞行员中适应新的环境
Learn to Adapt to New Environment from Past Experience and Few Pilot
论文作者
论文摘要
近年来，深度学习已被广泛应用于沟通，并取得了显着的绩效提高。大多数现有作品都是基于数据驱动的深度学习，这需要大量的培训数据来适应新的环境，并为收集数据和重新培训模型提供庞大的计算资源。在本文中，我们将通过利用已知环境的学习经验来大大减少新环境所需的培训数据。因此，我们介绍了很少的学习学习，以使通信模型推广到新环境，这是通过基于注意的方法实现的。随着注意网络嵌入了基于深度学习的沟通模型中，具有不同功率延迟概况的环境可以在培训过程中一起学习，这称为学习经验。通过利用学习经验，沟通模型只需要很少的飞行员块即可在新环境中表现良好。通过基于深度学习的渠道估计的示例，我们证明，这种新颖的设计方法比为少量学习的现有数据驱动方法实现的性能更好。
In recent years, deep learning has been widely applied in communications and achieved remarkable performance improvement. Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model. In this paper, we will significantly reduce the required amount of training data for new environments by leveraging the learning experience from the known environments. Therefore, we introduce few-shot learning to enable the communication model to generalize to new environments, which is realized by an attention-based method. With the attention network embedded into the deep learning-based communication model, environments with different power delay profiles can be learnt together in the training process, which is called the learning experience. By exploiting the learning experience, the communication model only requires few pilot blocks to perform well in the new environment. Through an example of deep-learning-based channel estimation, we demonstrate that this novel design method achieves better performance than the existing data-driven approach designed for few-shot learning.
