Deep Learning-Based Pilotless Spatial Multiplexing
This addresses the need for more efficient communication systems by reducing pilot overhead, though it is incremental as it builds on existing ML and MIMO concepts.
The paper tackles the problem of pilotless spatial multiplexing in MIMO communication systems by training the transmitter and receiver jointly, resulting in a learned scheme that outperforms conventional pilot-based systems by 15-20% in spectral efficiency.
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.