|Title||Neural Network Aided Computation of Generalized Spatial Modulation Capacity|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Tato, A, Mosquera, C, Henarejos, P, Pérez-Neira, A|
|Conference Name||27th European Signal Processing Conference (EUSIPCO)|
|Conference Location||A Coruña (Spain)|
|Keywords||Generalized Spatial Modulation, Index Modulations, Machine Learning, Multilayer Feedforward Neural Network, Polarized Modulation|
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.