Shamasundar, B and Chockalingam, A (2020) A dnn architecture for the detection of generalized spatial modulation signals. In: IEEE Communications Letters, 24 (12). pp. 2770-2774.
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Abstract
In this letter, we consider the problem of signal detection in generalized spatial modulation (GSM) using deep neural networks (DNN). We propose a novel modularized DNN architecture that uses small sub-DNNs to detect the active antennas and complex modulation symbols, instead of using a single large DNN to jointly detect the active antennas and modulation symbols. The main idea is that using small sub-DNNs instead of a single large DNN reduces the required size of the NN and hence requires learning lesser number of parameters. Under the assumption of i.i.d Gaussian noise, the proposed DNN detector achieves a performance very close to that of the maximum likelihood detector. We also analyze the performance of the proposed detector under two practical conditions: i ) correlated noise across receive antennas and ii ) noise distribution deviating from the standard Gaussian model. The proposed DNN-based detector learns the deviations from the standard model and achieves superior performance compared to that of the conventional maximum likelihood detector. © 1997-2012 IEEE.
| Item Type: | Journal Article |
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| Publication: | IEEE Communications Letters |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Author(s). |
| Keywords: | Deep neural networks; Gaussian noise (electronic); Global system for mobile communications; Maximum likelihood; Network architecture; Receiving antennas; Signal detection, Active antennas; Complex modulation; Correlated noise; Maximum likelihood detectors; Modulation symbols; Noise distribution; Spatial modulations; The standard model, Modulation |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 09 Jan 2023 12:08 |
| Last Modified: | 09 Jan 2023 12:08 |
| URI: | https://eprints.iisc.ac.in/id/eprint/78961 |
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