Deep Learning-Aided Spatial Multiplexing with Index Modulation
This work addresses improved error performance in MIMO communication systems, but it appears incremental as it builds on existing methods like ZF and IM.
The paper tackles the problem of data detection in spatial multiplexing MIMO with index modulation by proposing Deep-SMX-IM, which combines a zero-forcing detector with deep learning to learn transmission characteristics, resulting in significant error performance gains without increasing computational complexity.
In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblockbased detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.