用深度网络加速大规模MIMO信道估计，训练快、运行快、精度高。
ISDNN: A Deep Neural Network for Channel Estimation in Massive MIMO systems
- 将梯度下降迭代过程转化为深度网络结构，一步完成信道估计。
- 相比同类方法，训练提速13%，推理快4.6%，精度提升0.43dB。
- 可融合信号方向等先验信息，适合有阵列结构的通信系统应用。
大规模多输入多输出（massive MIMO）技术是5G及以后的关键支撑。尽管该技术进步显著，但天线数量庞大带来了信道估计（CE）的巨大挑战。本文提出一种单步深度神经网络（DNN）用于信道估计，命名为迭代顺序神经网络（ISDNN），其灵感来源于数据检测算法的最新进展。ISDNN基于投影梯度下降算法，通过深度展开方法将迭代过程转化为DNN结构。此外，我们还提出结构化信道ISDNN（S-ISDNN），在ISDNN基础上引入信号方向和天线阵列配置等辅助信息以提升估计性能。仿真结果表明，ISDNN在训练时间（减少13%）、运行时间（减少4.6%）和精度（提升0.43 dB）上均显著优于另一基于DNN的信道估计方法（DetNet）。S-ISDNN在训练时间上甚至更快，但整体性能仍有提升空间。
原文摘要 · Abstract (English)
Massive Multiple-Input Multiple-Output (massive MIMO) technology stands as a cornerstone in 5G and beyonds. Despite the remarkable advancements offered by massive MIMO technology, the extreme number of antennas introduces challenges during the channel estimation (CE) phase. In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iterative Sequential DNN (ISDNN), inspired by recent developments in data detection algorithms. ISDNN is a DNN based on the projected gradient descent algorithm for CE problems, with the iterative iterations transforming into a DNN using the deep unfolding method. Furthermore, we introduce the structured channel ISDNN (S-ISDNN), extending ISDNN to incorporate side information such as directions of signals and antenna array configurations for enhanced CE. Simulation results highlight that ISDNN significantly outperforms another DNN-based CE (DetNet), in terms of training time (13%), running time (4.6%), and accuracy (0.43 dB). Furthermore, the S-ISDNN demonstrates even faster than ISDNN in terms of training time, though its overall performance still requires further improvement.
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