@article {1069, title = {Information Forensics and Security: A quarter-century-long journey}, journal = {IEEE Signal Processing Magazine}, volume = {40}, year = {2023}, pages = {67-79}, keywords = {forensics, History, Information processing, Information security, Intellectual property, Machine Learning, privacy, Signal processing, Surveillance}, doi = {10.1109/MSP.2023.3275319}, url = {https://ieeexplore.ieee.org/document/10188887}, author = {Barni, Mauro and Campisi, Patrizio and Delp, Edward J. and Do{\"e}rr, Gwena{\"e}l and Fridrich, Jessica and Memon, Nasir and Fernando P{\'e}rez-Gonz{\'a}lez and Rocha, Anderson and Verdoliva, Luisa and Wu, Min} } @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 {965, title = {Neural Network Aided Computation of Generalized Spatial Modulation Capacity}, booktitle = {27th European Signal Processing Conference (EUSIPCO)}, year = {2019}, address = {A Coru{\~n}a (Spain)}, abstract = {

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}, author = {Anxo Tato and Carlos Mosquera and Pol Henarejos and Ana P{\'e}rez-Neira} } @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.} }