Massive multiple-input multiple-output (mMIMO) is a critical component in upcoming 5G wireless deployment as an enabler for high data rate communications. mMIMO is effective when each corresponding antenna pair of the respective transmitter-receiver arrays experiences an independent channel. While increasing the number of antenna elements increases the achievable data rate, at the same time computing the channel state information (CSI) becomes prohibitively expensive. In this article, we propose to use deep learning via a multi-layer perceptron architecture that exceeds the performance of traditional CSI processing methods like least square (LS) and linear minimum mean square error (LMMSE) estimation, thus leading to a beyond fifth generation (B5G) networking paradigm wherein machine learning fully drives networking optimization. By computing the CSI of all pairwise channels simultaneously via our deep learning approach, our method scales with large antenna arrays as opposed to traditional estimation methods. The key insight here is to design the learning architecture such that it is implementable on massively parallel architectures, such as GPU or FPGA. We validate our approach by simulating a 32-element array base station and a user equipment with a 4-element array operating on millimeter-wave frequency band. Results reveal an improvement up to five and two orders of magnitude in BER with respect to fastest LS estimation and optimal LMMSE, respectively, substantially improving the end-to-end system performance and providing higher spatial diversity for lower SNR regions, achieving up to 4 dB gain in received power signal compared to performance obtained through LMMSE estimation.

中文翻译：

---

超 5G 大规模 MIMO 中信道估计的边缘深度学习


大规模多输入多输出 (mMIMO) 是即将到来的 5G 无线部署的关键组件，是高数据速率通信的推动者。当各个发射器-接收器阵列的每个对应天线对经历独立信道时，mMIMO 是有效的。虽然增加天线元件的数量可以提高可实现的数据速率，但同时计算信道状态信息 (CSI) 的成本却变得异常昂贵。在本文中，我们建议通过多层感知器架构使用深度学习，该架构超越了最小二乘 (LS) 和线性最小均方误差 (LMMSE) 估计等传统 CSI 处理方法的性能，从而导致超越第五代(B5G) 网络范例，其中机器学习完全驱动网络优化。通过我们的深度学习方法同时计算所有成对通道的 CSI，我们的方法可以使用大型天线阵列进行扩展，而不是传统的估计方法。这里的关键见解是设计学习架构，使其可以在大规模并行架构（例如 GPU 或 FPGA）上实现。我们通过模拟在毫米波频段上运行的 32 元件阵列基站和具有 4 元件阵列的用户设备来验证我们的方法。结果表明，最快 LS 估计和最佳 LMMSE 的 BER 分别提高了 5 个和 2 个数量级，显着提高了端到端系统性能，并为较低 SNR 区域提供了更高的空间分集，实现了高达 4与通过 LMMSE 估计获得的性能相比，接收功率信号的 dB 增益。