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Publication Detail
An Unsupervised Learning-Based Approach for Symbol-Level-Precoding
  • Publication Type:
  • Authors:
    Mohammad A, Masouros C, Andreopoulos Y
  • Publisher:
  • Publication date:
  • Published proceedings:
    Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM)
  • Name of conference:
    2021 IEEE Global Communications Conference (GLOBECOM)
  • Conference place:
    Madrid, Spain
  • Conference start date:
  • Conference finish date:
  • Keywords:
    eess.SP, eess.SP
  • Notes:
    6 pages, 2 figures, GLOBECOM 2021 Conference
This paper proposes an unsupervised learning-based precoding framework that trains deep neural networks (DNNs) with no target labels by unfolding an interior point method (IPM) proximal `log' barrier function. The proximal `log' barrier function is derived from the strict power minimization formulation subject to signal-to-interference-plus-noise ratio (SINR) constraint. The proposed scheme exploits the known interference via symbol-level precoding (SLP) to minimize the transmit power and is named strict Symbol-Level-Precoding deep network (SLP-SDNet). The results show that SLP-SDNet outperforms the conventional block-level-precoding (Conventional BLP) scheme while achieving near-optimal performance faster than the SLP optimization-based approach
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