@conference {965,
	title = {Neural Network Aided Computation of Generalized Spatial Modulation Capacity},
	booktitle = {27th European Signal Processing Conference (EUSIPCO)},
	year = {2019},
	address = {A Coru{\~n}a (Spain)},
	abstract = {<p>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.</p>},
	keywords = {Generalized Spatial Modulation, Index Modulations, Machine Learning, Multilayer Feedforward Neural Network, Polarized Modulation},
	author = {Anxo Tato and Carlos Mosquera and Pol Henarejos and Ana P{\'e}rez-Neira}
}
