Neural Network Aided Computation of Generalized Spatial Modulation Capacity - Anxo Tato; Carlos Mosquera; Pol Henarejos; Ana Pérez-Neira (27th European Signal Processing Conference (EUSIPCO), 2019)
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