@inproceedings{Nicolas BailonK{\"u}hnShavgulidzeetal.2023,
  author    = {Nicolas Bailon, Daniel and K{\"u}hn, Volker and Shavgulidze, Sergo and Freudenberger, J{\"u}rgen},
  title     = {Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation Systems with Neural Networks},
  booktitle = {WSA \& SCC 2023 : 26th International ITG Workshop on Smart Antennas and 13th Conference on Systems, Communications, and Coding, 27.2.-3.3.2023, Braunschweig, Germany},
  isbn      = {978-3-8007-6050-3},
  url       = {https://ieeexplore.ieee.org/document/10104548},
  institution = {Institut f{\"u}r Systemdynamik - ISD},
  pages     = {203 -- 207},
  year      = {2023},
  abstract  = {Spatial modulation (SM) is a low-complexity multiple-input/multiple-output transmission technique that combines index modulation and quadrature amplitude modulation for wireless communications. In this work, we consider the problem of link adaption for generalized spatial modulation (GSM) systems that use multiple active transmit antennas simultaneously. Link adaption algorithms require a real-time estimation of the link quality of the time-variant communication channels, e.g., by means of estimating the mutual information. However, determining the mutual information of SM is challenging because no closed-form expressions have been found so far. Recently, multilayer feedforward neural networks were applied to compute the achievable rate of an index modulation link. However, only a small SM system with two transmit and two receive antennas was considered. In this work, we consider a similar approach but investigate larger GSM systems with multiple active antennas. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes, which depend on the GSM variant and the signal constellation.},
  language  = {en}
}