期刊
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
卷 19, 期 8, 页码 5635-5648出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.2996144
关键词
Massive MIMO; signal detection; deep learning; online adaptation; spatial channel correlation
资金
- National Science Foundation [CNS-1563826, CNS-1751009]
- Alfred P. Sloan Research Fellowship
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.
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