Adaptive Neural Signal Detection for Massive MIMO

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),...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2020-08, Vol.19 (8), p.5635-5648
Main Authors: Khani, Mehrdad, Alizadeh, Mohammad, Hoydis, Jakob, Fleming, Phil
Format: Article
Language:English
Subjects:
Algorithms
Channels
Codes
Complexity
Complexity theory
Deep learning
Detectors
Error detection
Iterative methods
Machine learning
Massive MIMO
MIMO (control systems)
MIMO communication
Neural networks
Noise reduction
online adaptation
Performance degradation
Signal detection
Signal to noise ratio
spatial channel correlation
Spectral correlation
Training
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Summary: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 10\times less computational complexity. MMNet is also 4-8dB better overall than a classic linear scheme like the minimum mean square error (MMSE) detector.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2020.2996144