Learning to Modulate for Non-coherent MIMO
This work addresses communication system design for non-coherent MIMO, offering a domain-knowledge-integrated alternative to black-box neural networks, though it is incremental in its approach.
The paper tackles modulation and signal detection design for non-coherent MIMO channels by using simulation-driven optimization without neural networks, achieving performance comparable to neural network-based methods and enabling MIMO communications with as few as two time slots in short coherence windows.
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/simulation-driven optimization, but are often employed as black boxes detached from direct application of domain knowledge. Our work considers learning-based approaches addressing modulation and signal detection design for the non-coherent MIMO channel. We demonstrate that simulation-driven optimization can be performed while entirely avoiding neural networks, yet still perform comparably. Additionally, we show the feasibility of MIMO communications over extremely short coherence windows (i.e., channel coefficient stability period), with as few as two time slots.