We consider the problem of channel estimation in low-resolution multiple-input multiple-output (MIMO) systems operating at millimeter wave (mmWave) and present a deep transfer learning (DTL) approach that exploits previously trained models to speed up site adaptation. The proposed model is composed of a feature extractor and a regressor, with only the regressor requiring training for the new environment. The DTL approach is evaluated using two 3D scenarios where ray-tracing is performed to generate the mmWave MIMO channels used in the simulations. Under the defined testing setup, the proposed DTL approach can reduce the computational cost of the training stage without decreasing the estimation accuracy.

中文翻译：

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用于低分辨率毫米波 MIMO 中特定站点信道估计的深度迁移学习

我们考虑了在毫米波 (mmWave) 下运行的低分辨率多输入多输出 (MIMO) 系统中的信道估计问题，并提出了一种深度转移学习 (DTL) 方法，该方法利用先前训练的模型来加速站点适应。所提出的模型由特征提取器和回归器组成，只有回归器需要针对新环境进行训练。DTL 方法使用两个 3D 场景进行评估，其中执行光线跟踪以生成模拟中使用的毫米波 MIMO 信道。在定义的测试设置下，所提出的 DTL 方法可以在不降低估计精度的情况下降低训练阶段的计算成本。