@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 = {

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.

}, 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} }