@conference {964,
	title = {Deep Learning Assisted Rate Adaptation in Spatial Modulation Links},
	booktitle = {16th International Symposium on Wireless Communication Systems (ISWCS)},
	year = {2019},
	address = {Oulu (Finland)},
	abstract = {<p>The adaptation of Spatial Modulation based links to the channel conditions is challenged by the complicated dependence between performance (either error rate metrics or theoretically achievable rates) and the multiple antenna channel description. In this paper a coding rate selection mechanism is presented based on a carefully selected set of channel features and the proper training of a deep neural network, which all together can satisfy a given error rate bound.</p>},
	keywords = {Deep Learning, link adaptation, Machine Learning, Spatial Modulation},
	author = {Anxo Tato and Carlos Mosquera}
}
