Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System
复制标题
基于深度学习的超分辨率信道估计和基于 DOA 估计的大规模 MIMO 系统
DOI：
10.1109/tvt.2018.2851783
复制
发表时间：
2018-09-01
影响因子：
6.8
通讯作者：
Gui, Guan
中科院分区：
文献类型：
--
作者：
Huang, Hongji;Yang, Jie;Gui, Guan
近年来提出的大规模多输入多输出（MIMO）技术可以显著提高通信网络的容量，被认为是下一代无线通信技术的发展方向。然而，现有的大规模MIMO系统面临的根本挑战是高计算复杂度和复杂的空间结构给利用这些多天线系统的信道特性和稀疏性带来了很大的困难。为了解决这一问题，本文重点研究了信道估计和到达方向估计，并提出了一种将大规模MIMO集成到深度学习中的新框架。为了实现端到端性能，采用深度神经网络（deep neural network， DNN）进行离线学习和在线学习，可以有效地学习无线信道的统计信息和角度域的空间结构。具体来说，首先通过不同信道条件下的模拟数据，辅以离线学习对DNN进行训练，然后在在线学习过程中，根据当前输入数据得到相应的输出数据。为了实现超分辨率信道估计和DOA估计，开发了两种基于深度学习的算法，在不增加复杂度的情况下直接在角度域估计DOA。仿真结果表明，与传统方法相比，基于深度学习的方案在DOA估计和信道估计方面具有更好的性能，并在各种情况下进行了广泛的仿真研究，以检验其鲁棒性。
The recent concept of massive multiple-input multiple-output (MIMO) can significantly improve the capacity of the communication network, and it has been regarded as a promising technology for the next-generation wireless communications. However, the fundamental challenge of existing massive MIMO systems is that high computational complexity and complicated spatial structures bring great difficulties to exploit the characteristics of the channel and sparsity of these multi-antennas systems. To address this problem, in this paper, we focus on channel estimation and direction-of-arrival (DOA) estimation, and a novel framework that integrates the massive MIMO into deep learning is proposed. To realize end-to-end performance, a deep neural network (DNN) is employed to conduct offline learning and online learning procedures, which is effective to learn the statistics of the wireless channel and the spatial structures in the angle domain. Concretely, the DNN is first trained by simulated data in different channel conditions with the aids of the offline learning, and then corresponding output data can be obtained based on current input data during online learning process. In order to realize super-resolution channel estimation and DOA estimation, two algorithms based on the deep learning are developed, in which the DOA can be estimated in the angle domain without additional complexity directly. Furthermore, simulation results corroborate that the proposed deep learning based scheme can achieve better performance in terms of the DOA estimation and the channel estimation compared with conventional methods, and the proposed scheme is well investigated by extensive simulation in various cases for testing its robustness.