%0 Conference Paper %B 27th European Signal Processing Conference (EUSIPCO) %D 2019 %T Neural Network Aided Computation of Generalized Spatial Modulation Capacity %A Anxo Tato %A Carlos Mosquera %A Pol Henarejos %A Ana Pérez-Neira %K Generalized Spatial Modulation %K Index Modulations %K Machine Learning %K Multilayer Feedforward Neural Network %K Polarized Modulation %X

Generalized Spatial Modulation (GSM) is being considered for future high-capacity and energy efficient terrestrial networks. A variant such as Polarized Modulation (PMod) has also a role in Dual Polarization Mobile Satellite Systems. The implementation of adaptive GSM systems requires fast methods to evaluate the channel dependent GSM capacity, which amounts to solve multi-dimensional integrals without closed-form solutions. For this purpose, we propose the use of a Multilayer Feedforward Neural Network and an associated feature selection algorithm. The resulting method is highly accurate and with much lower complexity than alternative numerical methods.

%B 27th European Signal Processing Conference (EUSIPCO) %C A Coruña (Spain) %G eng