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Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

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Document pages: 5 pages

Abstract: Generalized Spatial Modulation (GSM) is being considered for high capacityand energy-efficient networks of the future. However, signal detection due tointer channel interference among the active antennas is a challenge in GSMsystems and is the focus of this letter. Specifically, we explore thefeasibility of using deep neural networks (DNN) for signal detection in GSM. Inparticular, we propose a block DNN (B-DNN) based architecture, where the activeantennas and their transmitted constellation symbols are detected by smallersub-DNNs. After $N$-ordinary DNN detection, the Euclidean distance-based softconstellation algorithm is implemented. The proposed B-DNN detector achieves aBER performance that is superior to traditional block zero-forcing (B-ZF) andblock minimum mean-squared error (B-MMSE) detection schemes and similar to thatof classical maximum likelihood (ML) detector. Further, the proposed methodrequires less computation time and is more accurate than alternativeconventional numerical methods.

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