Exploiting Channel Locality for Adaptive Massive MIMO Signal Detection

Abstract

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 spatially-correlated channels, MMNet 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× less computational complexity. MMNet is also 4–8dB better overall than the linear minimum mean square error (MMSE) detector.