论文标题
深度展开的多播横梁形成
Deep Unfolded Multicast Beamforming
论文作者
论文摘要
多播布置是多播通信的一种有前途的技术。提供高效且强大的波束形成设计算法是一个至关重要的问题，因为多播横梁成形问题（例如Max-Min-Fair问题）通常是NP-HARD。最近，已经提出了基于深度学习的方法，以进行波束形成设计。尽管与常规基于优化的算法相比，使用深神网络的这些方法表现出合理的性能增长，但它们的可伸缩性是大型系统的新兴问题，在该算法中，波束形成设计成为一项更苛刻的任务。在本文中，我们提出了一种具有高可扩展性和效率的新型深层展开的可训练的波束形成设计。该算法的设计是通过根据投影将现有算法的递归结构扩展到凸集集合，并将恒定数量的可训练参数嵌入到扩展的网络中，从而导致可扩展且稳定的训练过程。数值结果表明，所提出的算法可以通过使用无监督的学习来加速其收敛速度，这是一个充满挑战的深层发展训练过程。
Multicast beamforming is a promising technique for multicast communication. Providing an efficient and powerful beamforming design algorithm is a crucial issue because multicast beamforming problems such as a max-min-fair problem are NP-hard in general. Recently, deep learning-based approaches have been proposed for beamforming design. Although these approaches using deep neural networks exhibit reasonable performance gain compared with conventional optimization-based algorithms, their scalability is an emerging problem for large systems in which beamforming design becomes a more demanding task. In this paper, we propose a novel deep unfolded trainable beamforming design with high scalability and efficiency. The algorithm is designed by expanding the recursive structure of an existing algorithm based on projections onto convex sets and embedding a constant number of trainable parameters to the expanded network, which leads to a scalable and stable training process. Numerical results show that the proposed algorithm can accelerate its convergence speed by using unsupervised learning, which is a challenging training process for deep unfolding.
