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Publication Detail
An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding
  • Publication Type:
    Journal article
  • Authors:
    Mohammad A, Masouros C, Andreopoulos Y
  • Publication date:
  • Keywords:
    eess.SP, eess.SP
  • Notes:
    13 pages, 8 figures, Journal
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 = number of transmit antennas = number of users. 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.
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