基于混合分辨RF链的两步机器学习信道估计方法
Two-step Machine Learning Approach for Channel Estimation with Mixed Resolution RF Chains
摘要：
大规模多输入多输出是5G移动无线电系统的主要特征之一。然而，它通常导致高成本、尺寸和功耗。为了克服这些问题，已经提出了使用约束射频（RF）前端以及新颖的预编码器，例如，多天线、贪婪、迭代和量化预编码算法（MAGIQ）。然而，MAGIQ的最佳性能假设每个天线元件例如来自上行链路探测参考信号的准确信道知识。在这种情况下，我们提出了一个有效的上行链路信道估计器，通过应用机器学习（ML）算法。在第一步骤中，条件生成对抗网络（cGAN）预测从全分辨率RF链的有限集合到低分辨率RF链天线元件的其余部分的无线电信道。长短期记忆（LSTM）神经网络从低分辨率RF链天线元件提取进一步的相位信息。我们的研究结果表明，我们提出的方法是有竞争力的，与传统的酉张量ESPRIT的各种紧密间隔的多径组件（MPC）的情况下。
英文摘要：
Massive MIMO is one of the main features of 5G mobile radio systems. However, it often leads to high cost, size and power consumption. To overcome these issues, the use of constrained radio frequency (RF) frontends has been proposed, as well as novel precoders, e.g., a multi-antenna, greedy, iterative and quantized precoding algorithm (MAGIQ). Nevertheless, the best performance of MAGIQ assumes accurate channel knowledge per antenna element, for example, from uplink sounding reference signals. In this context, we propose an efficient uplink channel estimator by applying machine learning (ML) algorithms. In a first step a conditional generative adversarial network (cGAN) predicts the radio channels from a limited set of full resolution RF chains to the rest of the low resolution RF chain antenna elements. A long-short term memory (LSTM) neural network extracts further phase information from the low resolution RF chain antenna elements. Our results indicate that our proposed approach is competitive with traditional Unitary tensor-ESPRIT in scenarios with various closely spaced multipath components (MPCs).
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