TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Bailon, Daniel Nicolas
A1  - Kühn, Volker
A1  - Shavgulidze, Sergo
A1  - Freudenberger, Jürgen
T1  - Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation Systems with Neural Networks
T2  - 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
N2  - 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.
KW  - Low-complexity detection
KW  - Generalized multistream spatial modulation
KW  - Multistage detection
Y1  - 2023
UR  - https://ieeexplore.ieee.org/document/10104548
SN  - 978-3-8007-6050-3
SB  - 978-3-8007-6050-3
N1  - Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich
SP  - 203
EP  - 207
PB  - VDE Verlag
CY  - Berlin; Offenbach
ER  -