@article {985, title = {Link Adaptation and SINR errors in Practical Multicast Multibeam Satellite Systems with Linear Precoding}, journal = {International Journal of Satellite Communications and Networking}, year = {2020}, keywords = {link adaptation, multi-beam satellite, precoding, satellite communications}, author = {Anxo Tato and Stefano Andrenacci and Eva Lagunas and Symeon Chatzinotas and Carlos Mosquera} } @article {984, title = {Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation}, journal = { IEEE Transactions on Communications}, year = {2020}, abstract = {

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.

}, 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} } @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 = {

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.

}, keywords = {Deep Learning, link adaptation, Machine Learning, Spatial Modulation}, author = {Anxo Tato and Carlos Mosquera} } @conference {966, title = {Spatial Modulation for Beyond 5G Communications: Capacity Calculation and Link Adaptation}, booktitle = {2nd XoveTIC Conference}, year = {2019}, address = {A Coru{\~n}a (Spain)}, keywords = {5G, adaptive coding and modulation, Deep Learning, link adaptation, Machine Learning, neural networks, Spatial Modulation}, author = {Anxo Tato and Carlos Mosquera} } @conference {699, title = {Learning Based Link Adaptation in Multiuser MIMO-OFDM}, booktitle = {21st European Signal Processing Conference 2013 (EUSIPCO 2013)}, year = {2013}, address = {Marrakech, Morocco}, abstract = {

Link adaptation in multiple user multiple-input multiple-output orthogonal frequency division multiplexing communication systems is challenging because of the coupling between user selection, mode selection, precoding, and equalization. In this paper, we present a methodology to perform link adaptation under this multiuser setting, focusing on the capabilities of IEEE 802.11ac. We propose to use a machine learning classifier to solve the problem of selecting a proper modulation and coding scheme, combined with a greedy algorithm that performs user and spatial mode selection. We observe that our solution offers good performance in the case of perfect channel state information or high feedback rate, while those scenarios with less feedback suffer some degradation due to inter-user interference.

}, keywords = {dynacs, link adaptation, Machine Learning, Multiuser MIMO-OFDM}, author = {Alberto Rico-Alvari{\~n}o and Robert W. Heath Jr.} } @conference {709, title = {Link Adaptation in MIMO-OFDM with Practical Impairments}, booktitle = {Asilomar Conference on Signals Systems and Computers}, year = {2013}, keywords = {dynacs, link adaptation, MIMO-OFDM}, doi = {10.1109/ACSSC.2013.6810579}, author = {Alberto Rico-Alvari{\~n}o and Robert W. Heath Jr.} } @conference {705, title = {Open Loop Adaptive Coding and Modulation for Mobile Satellite Return Links}, booktitle = {31st AIAA International Communications Satellite Systems Conference}, year = {2013}, publisher = {American Institute of Aeronautics and Astronautics}, organization = {American Institute of Aeronautics and Astronautics}, address = {Florence, Italy}, keywords = {dynacs, link adaptation, satcom}, doi = {doi:10.2514/6.2013-5704}, author = {Arnau, Jesus and Carlos Mosquera} } @conference {698, title = {Statistical Cross Layer Adaptation in Fast Fading Mobile Satellite Channels}, booktitle = {Globecom 2013 - Symposium on Selected Areas in Communications (GC13 SAC)}, year = {2013}, address = {Atlanta, USA}, abstract = {

Link adaptation in mobile satellite channels is difficult because of the large propagation delays, the frequent signal blockage and the variation of the channel statistics. In this paper, we propose a cross-layer link adaptation strategy that exploits statistical, long-term CSI to increase the throughput when some retransmissions are available; this increase is obtained while meeting a target outage probability constraint. Our strategy takes as inputs the estimated packet error rates of the available modulation and coding schemes (MCS) and their rates; as an output, it returns the optimum sequence of MCS to be used. Results will also show that simple information acquisition strategies can still provide very good results.

}, keywords = {dynacs, fast fading channels, link adaptation, mobile satellite, satcom}, doi = {10.1109/GLOCOM.2013.6831502}, author = {Alberto Rico-Alvari{\~n}o and J. Arnau and C. Mosquera} }