Detection and Channel Equalization with Deep Learning for Low Resolution MIMO Systems
Detection and Channel Equalization with Deep Learning for Low Resolution MIMO Systems
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DOI：
10.1109/acssc.2018.8645551
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发表时间：
2018-10
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影响因子：
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通讯作者：
A. Klautau;N. G. Prelcic;A. Mezghani;R. Heath
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文献类型：
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作者：
A. Klautau;N. G. Prelcic;A. Mezghani;R. Heath
深度学习 (DL) 提供了一个设计新通信系统的框架，以应对实际障碍。在本文中，我们对深度学习应用于设计具有低分辨率模数转换器的 MIMO 系统的物理层进行了探索。由于量化和大维 MIMO 信道引起的严重非线性失真，DL 的应用并非平凡。我们研究用于信道估计和检测的网络架构。信道估计结果表明，所采用的深度学习架构在大信噪比 (SNR) 范围内取得了良好的结果，但其性能优于最先进的迭代消息传递算法。对于解码，我们采用了具有隐式均衡和输出大小与要估计的数据符号数量线性缩放的多标签分类架构。虽然对于高 MIMO 维度是可行的，但所采用的用于解码的 DL 架构仅针对相对较小的 MIMO 维度收敛。我们论文的主要结论是，考虑到与时变通道和 1 位量化相关的收敛问题，深度学习仍然具有潜力，但需要更高效的架构。
Deep learning (DL) provides a framework for designing new communication systems that embrace practical impairments. In this paper, we present an exploration of DL as applied to design the physical layer for MIMO systems with low resolution analog-to-digital converters. The application of DL is nontrivial thanks to the severe nonlinear distortion caused by quantization and the large dimensional MIMO channel. We investigate network architectures for channel estimation and detection. The channel estimation results indicate that the adopted DL architectures lead to good results in the large signal-to-noise ratio (SNR) regime, but are outperformed by state-of-the-art iterative message passing algorithms. For decoding, we adopted a multilabel classification architecture with implicit equalization and output size scaling linearly with the number of data symbols to be estimated. While feasible for high MIMO dimensions, the adopted DL architecture for decoding converged only for relatively small MIMO dimensions. A main conclusion of our paper is that DL still has potential but more efficient architectures are required, given the convergence problems associated with time-varying channels and 1-bit quantization.