Learning to Beamform in Heterogeneous Massive MIMO Networks
Learning to Beamform in Heterogeneous Massive MIMO Networks
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DOI：
10.1109/twc.2022.3230662
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发表时间：
2020-11
影响因子：
10.4
通讯作者：
Minghe Zhu;Tsung-Hui Chang;Mingyi Hong
中科院分区：
文献类型：
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作者：
Minghe Zhu;Tsung-Hui Chang;Mingyi Hong
由于其非凸性，在大规模多输入多输出（MIMO）网络中寻找最佳波束形成器具有挑战性，并且基于传统优化的算法计算成本很高。最近，基于深度学习的方法因其计算效率而被提出，但当部署在基站（BS）配备不同数量的天线且具有不同的BS间距离的异构场景中时，它们通常不能很好地泛化。本文提出了一种新颖的基于深度学习的波束形成算法来解决上述挑战。具体来说，我们考虑多输入单输出（MISO）干扰信道中的加权和速率（WSR）最大化问题，并通过展开并行梯度投影算法提出波束形成学习架构。通过利用最佳波束成形解决方案的低维结构，我们构建的学习网络可以独立于发射天线和基站的数量。此外，这种设计可以进一步扩展到协作多小区网络，其中用户由多个基站联合服务。基于合成和射线追踪信道模型的数值结果表明，所提出的神经网络可以在显着减少运行时间的情况下实现高WSR，同时在天线数量、基站数量和基站间距离方面表现出良好的泛化能力。
Finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks is challenging because of its non-convexity, and conventional optimization based algorithms suffer from high computational costs. Recently, deep learning based methods have been proposed because of their computational efficiency, but they typically can not generalize well when deployed in heterogeneous scenarios where the base stations (BSs) are equipped with different numbers of antennas and have different inter-BS distances. This paper proposes a novel deep learning based beamforming algorithm to address above challenges. Specifically, we consider the weighted sum rate (WSR) maximization problem in multi-input and single-output (MISO) interference channels, and propose a beamforming learning architecture by unfolding a parallel gradient projection algorithm. By leveraging the low-dimensional structures of the optimal beamforming solution, our constructed learning network can be made independent of the numbers of transmit antennas and BSs. Moreover, such a design can be further extended to a cooperative multicell network where users are jointly served by multiple BSs. Numerical results based on both synthetic and ray-tracing channel models show that the proposed neural network can achieve high WSRs with significantly reduced runtime, while exhibiting favorable generalization capability with respect to the antenna number, BS number and the inter-BS distance.