TítuloNeural Network Aided Computation of Generalized Spatial Modulation Capacity
Tipo de publicaciónConference Paper
Year of Publication2019
AutoresTato, A, Mosquera, C, Henarejos, P, Pérez-Neira, A
Conference Name27th European Signal Processing Conference (EUSIPCO)
Conference LocationA Coruña (Spain)
Palabras claveGeneralized 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.