Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
Deep Learning for Estimation and Pilot Signal Design in Few-Bit Massive MIMO Systems
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DOI：
10.1109/twc.2022.3193885
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发表时间：
2021-07
影响因子：
10.4
通讯作者：
Ly V. Nguyen;D. Nguyen;A. L. Swindlehurst
中科院分区：
文献类型：
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作者：
Ly V. Nguyen;D. Nguyen;A. L. Swindlehurst
由于接收信号被低分辨率ADC非线性失真，因此在几位MIMO系统中的估计是具有挑战性的。在本文中，我们提出了一个用于信道估计、数据检测和导频信号设计的深度学习框架，以解决此类系统中的非线性问题。所提出的信道估计和数据检测网络是模型驱动的，并具有特殊的结构，在几位量化过程中利用领域知识。虽然第一数据检测网络B-DetNet基于从Bussgang分解获得的线性化模型，但是信道估计网络和第二数据检测网络FBM-CENet和FBM-DetNet分别依赖于原始量化系统模型。为了开发FBM-CENet和FBM-DetNet，最大似然信道估计和数据检测问题被重新表述以克服不确定梯度问题。所提出的FBM-CENet结构的一个重要特征是导频矩阵被集成到其信道估计器的权重矩阵中。因此，训练所提出的FBM-CENet使得能够联合优化基站处的信道估计器和从用户发送的导频信号。仿真结果表明，所提出的深度学习框架在估计精度方面有显着的性能提升。
Estimation in few-bit MIMO systems is challenging, since the received signals are nonlinearly distorted by the low-resolution ADCs. In this paper, we propose a deep learning framework for channel estimation, data detection, and pilot signal design to address the nonlinearity in such systems. The proposed channel estimation and data detection networks are model-driven and have special structures that take advantage of domain knowledge in the few-bit quantization process. While the first data detection network, B-DetNet, is based on a linearized model obtained from the Bussgang decomposition, the channel estimation network and the second data detection network, FBM-CENet and FBM-DetNet respectively, rely on the original quantized system model. To develop FBM-CENet and FBM-DetNet, the maximum-likelihood channel estimation and data detection problems are reformulated to overcome the indeterminant gradient issue. An important feature of the proposed FBM-CENet structure is that the pilot matrix is integrated into the weight matrices of its channel estimator. Thus, training the proposed FBM-CENet enables a joint optimization of both the channel estimator at the base station and the pilot signal transmitted from the users. Simulation results show significant performance gains in estimation accuracy by the proposed deep learning framework.