Deep Learning-Based Pilotless Spatial Multiplexing
Dani Korpi Mikko Honkala Janne M. J. Huttunen
This paper investigates the feasibility of machine learning (ML)-based
pilotless spatial multiplexing in multiple-input and multiple-output (MIMO)
communication systems. Especially, it is shown that by training the transmitter
and receiver jointly, the transmitter can learn such constellation shapes for
the spatial streams which facilitate completely blind separation and detection
by the simultaneously learned receiver. To the best of our knowledge, this is
the first time ML-based spatial multiplexing without channel estimation pilots
is demonstrated. The results show that the learned pilotless scheme can
outperform a conventional pilot-based system by as much as 15-20% in terms of
spectral efficiency, depending on the modulation order and signal-to-noise
ratio.