IEEE Open Journal of the Communications Society | Mohammad A, Masouros C, Andreopoulos Y | Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit...
An Unsupervised Deep Unfolding Framework for Robust Symbol Level Precoding
Abstract
Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission. This paper proposes an unsupervised deep-neural network (DNN) based SLP framework. Instead of naively training a DNN architecture for SLP without considering the specifics of the optimization objective of the SLP domain, our proposal unfolds a power minimization SLP formulation based on the interior point method (IPM) proximal ‘log’ barrier function. Furthermore, we extend our proposal to a robust precoding design under channel state information (CSI) uncertainty. The results show that our proposed learning framework provides near-optimal performance while reducing the computational cost from O(n7.5) to O(n3) for the symmetrical system case where n=numberoftransmitantennas=numberofusers. This significant complexity reduction is also reflected in a proportional decrease in the proposed approach’s execution time compared to the SLP optimization-based solution.
Publication Type: | Journal Article |
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Publication Sub Type: | Article |
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Authors: | Mohammad A, Masouros C, Andreopoulos Y |
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Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
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Publication date: | 28/04/2023 |
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Pagination: | 1075 - 1090 |
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Journal: | IEEE Open Journal of the Communications Society |
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Volume: | 4 |
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Status: | Published |
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Print ISSN: | 2644-125X |
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DOI: | https://doi.org/10.1109/ojcoms.2023.3270455 |
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