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Generalized quadrature spatial modulation (GQSM) schemes are known to achievehigh energy- and spectral- efficiencies by modulating information both intransmitted symbols and in coded combinatorial activations of subsets ofmultiple transmit antennas. A challenge of the approach is, however, thedecoding complexity which scales with the efficiency of the scheme. In order tocircumvent this bottleneck and enable high-performance and feasible GQSM inmassive multiple-input multiple-output (mMIMO) scenarios, we propose a noveldecoding algorithm which enjoys a complexity order that is independent of thecombinatorial factor. This remarkable feature of the proposed decoder is aconsequence of a novel vectorized Gaussian belief propagation (GaBP) algorithm,here contributed, whose message passing (MP) rules leverage both pilot symbolsand the unit vector decomposition (UVD) of the GQSM signal structure. Theeffectiveness of the proposed UVD-GaBP method is illustrated via computersimulations including numerical results for systems of a size never beforereported in related literature (up to 32 transmit antennas), which demonstratesthe potential of the approach in paving the way towards high energy andspectral efficiency for wireless systems in a truly mMIMO setting.

Enabling Energy-Efficiency in Massive-MIMO: A Scalable Low-Complexity Decoder for Generalized Quadrature Spatial Modulation