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Creators/Authors contains: "Zheng, Lizhong"

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  1. ; ; ; (, IEEE Transactions on Wireless Communications)
    Free, publicly-accessible full text available January 1, 2026
  2. ; ; ; (, IEEE Transactions on Wireless Communications)
    Free, publicly-accessible full text available December 1, 2025
  3. ; ; ; ; ; ; ; ; ; (, ACM)
    Full Text Available 
  4. ; ; ; (, IEEE Journal of Selected Topics in Signal Processing)
    Full Text Available 
  5. ; ; ; (, IEEE Transactions on Communications)
    Full Text Available 
  6. ; ; ; ; ; (, Proc. NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences (ML4PS))
    This paper proposes an optimization-based method to learn the singular value decomposition (SVD) of a compact operator with ordered singular functions. The proposed objective function is based on Schmidt’s low-rank approximation theorem (1907) that characterizes a truncated SVD as a solution minimizing the mean squared error, accompanied with a technique called nesting to learn the ordered structure. When the optimization space is parameterized by neural networks, we refer to the proposed method as NeuralSVD. The implementation does not require sophisticated optimization tricks unlike existing approaches. 
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    Accepted Manuscript Full Text Available
  7. ; (, ISIT 2023)
    Full Text Available 
  8. ; ; (, ISIT 2023)
    Full Text Available 
  9. ; ; ; (, IEEE Wireless Communications Letters)
    Full Text Available 
  10. ; ; ; (, IEEE Transactions on Wireless Communications)
    Full Text Available