Learning to Modulate for Non-coherent MIMO
Ye Wang, Toshiaki Koike–Akino · 2020
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs. Neural networks flexibly allow for data-driven optimization, but are often employed as black boxes detached from direct application of domain knowledge. Our work considers learning-based approaches to end-to-end design of modulation and signal detection for the non-coherent multi-input multi-output (MIMO) channels. We demonstrate that simulation-driven optimization can outperform traditional Grassmann designs. Additionally, we show the feasibility of non-coherent MIMO communications over extremely short channel coherence time, with as few as two time slots, which have never been explored in existing literature due to design hardness.