Adaptive Neural Signal Detection for Massive MIMO
Adaptive Neural Signal Detection for Massive MIMO
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DOI：
10.1109/twc.2020.2996144
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发表时间：
2020-08-01
影响因子：
10.4
通讯作者：
Fleming, Phil
中科院分区：
文献类型：
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作者：
Khani, Mehrdad;Alizadeh, Mohammad;Fleming, Phil
传统的符号检测算法在大规模多输入多输出（MIMO）系统中要么性能不佳，要么无法实现。最近，几种基于学习的方法已经在简单信道模型上取得了有希望的结果（例如，i.i.d.高斯信道系数），但正如我们所示，它们的性能在具有空间相关性的真实信道上下降。我们提出了MMNet，这是一种深度学习MIMO检测方案，在实际信道上以相同或更低的计算复杂度显著优于现有方法。MMNet的设计建立在迭代软阈值算法理论的基础上，并使用一种新型训练算法，该算法利用真实的通道中的时间和频谱相关性来加速训练。这些创新使得为每个通道的实现在线训练MMNet变得切实可行。在身份证上。高斯信道，MMNet需要比现有深度学习方案少两个数量级的操作，但实现了接近最佳的性能。在空间相关信道上，它实现了与下一个最佳学习方案（OAMPNet）相同的错误率，信噪比（SNR）低2.5dB，计算复杂度至少低10倍。MMNet总体上也比经典的线性方案（如最小均方误差（MMSE）检测器）好4-8dB。
Traditional symbol detection algorithms either perform poorly or are impractical to implement for Massive Multiple-Input Multiple-Output (MIMO) systems. Recently, several learning-based approaches have achieved promising results on simple channel models (e.g., i.i.d. Gaussian channel coefficients), but as we show, their performance degrades on real-world channels with spatial correlation. We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet 's design builds on the theory of iterative soft-thresholding algorithms, and uses a novel training algorithm that leverages temporal and spectral correlation in real channels to accelerate training. These innovations make it practical to train MMNet online for every realization of the channel. On i.i.d. Gaussian channels, MMNet requires two orders of magnitude fewer operations than existing deep learning schemes but achieves near-optimal performance. On spatially-correlated channels, it achieves the same error rate as the next-best learning scheme (OAMPNet) at 2.5dB lower signal-to-noise ratio (SNR), and with at least 10x less computational complexity. MMNet is also 4-8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.