%0 Journal Article %J IEEE Transactions on Communications %D 2020 %T Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation %A Anxo Tato %A Carlos Mosquera %A Pol Henarejos %A Ana Pérez Neira %K ACM %K Adaptive Communications %K Capacity %K Index Modulation %K link adaptation %K Machine Learning %K MFNN %K Mutual Information %K neural networks %K Polarized Modulation %K Spatial Modulation %X

Index Modulations, in the form of Spatial Modulation or Polarized Modulation, are gaining traction for both satellite and terrestrial next generation communication systems. Adaptive Spatial Modulation based links are needed to fully exploit the transmission capacity of time-variant channels. The adaptation of code and/or modulation requires a real-time evaluation of the channel achievable rates. Some existing results in the literature present a computational complexity which scales quadratically with the number of transmit antennas and the constellation order. Moreover, the accuracy of these approximations is low and it can lead to wrong Modulation and Coding Scheme selection. In this work we apply a Multilayer Feedforward Neural Network to compute the achievable rate of a generic Index Modulation link. The case of two antennas/polarizations is analyzed in depth, showing not only a one-hundred fold decrement of the Mean Square Error in the estimation of the capacity as compared with existing analytical approximations, but also a fifty times reduction of the computational complexity. Moreover, the extension to an arbitrary number of antennas is explained and supported with simulations. More generally, neural networks can be considered as promising candidates for the practical estimation of complex metrics in communication related settings.

%B IEEE Transactions on Communications %@ 1558-0857 %G eng %R https://doi.org/10.1109/TCOMM.2020.2974215 %0 Conference Paper %B 27th European Signal Processing Conference (EUSIPCO) %D 2019 %T Neural Network Aided Computation of Generalized Spatial Modulation Capacity %A Anxo Tato %A Carlos Mosquera %A Pol Henarejos %A Ana Pérez-Neira %K Generalized Spatial Modulation %K Index Modulations %K Machine Learning %K Multilayer Feedforward Neural Network %K Polarized Modulation %X

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.

%B 27th European Signal Processing Conference (EUSIPCO) %C A Coruña (Spain) %G eng