@article {984,
	title = {Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation},
	journal = { IEEE Transactions on Communications},
	year = {2020},
	abstract = {<p>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.</p>},
	keywords = {ACM, Adaptive Communications, Capacity, Index Modulation, link adaptation, Machine Learning, MFNN, Mutual Information, neural networks, Polarized Modulation, Spatial Modulation},
	isbn = {1558-0857 },
	issn = {0090-6778 },
	doi = {https://doi.org/10.1109/TCOMM.2020.2974215},
	author = {Anxo Tato and Carlos Mosquera and Pol Henarejos and Ana P{\'e}rez Neira}
}
