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[Author] Dongyan JIA(2hit)

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  • A Construction of Sparse Deterministic Measurement Matrices

    Yubo LI  Hongqian XUAN  Dongyan JIA  Shengyi LIU  

     
    LETTER-Digital Signal Processing

      Vol:
    E102-A No:11
      Page(s):
    1575-1579

    In this letter, a construction of sparse measurement matrices is presented. Based on finite fields, a base matrix is obtained. Then a Hadamard matrix or a discrete Fourier transform (DFT) matrix is nested in the base matrix, which eventually formes a new deterministic measurement matrix. The coherence of the proposed matrices is low, which meets the Welch bound asymptotically. Thus these matrices could satisfy the restricted isometry property (RIP). Simulation results demonstrate that the proposed matrices give better performance than Gaussian counterparts.

  • Constructions of Low/Zero Correlation Zone Sequence Sets and Their Application in Grant-Free Non-Orthogonal Multiple Access System

    Tao LIU  Meiyue WANG  Dongyan JIA  Yubo LI  

     
    PAPER-Information Theory

      Pubricized:
    2022/12/16
      Vol:
    E106-A No:6
      Page(s):
    907-915

    In the massive machine-type communication scenario, aiming at the problems of active user detection and channel estimation in the grant-free non-orthogonal multiple access (NOMA) system, new sets of non-orthogonal spreading sequences are proposed by using the zero/low correlation zone sequence set with low correlation among multiple sets. The simulation results show that the resulting sequence set has low coherence, which presents reliable performance for channel estimation and active user detection based on compressed sensing. Compared with the traditional Zadoff-Chu (ZC) sequences, the new non-orthogonal spreading sequences have more flexible lengths, and lower peak-to-average power ratio (PAPR) and smaller alphabet size. Consequently, these sequences will effectively solve the problem of high PAPR of time domain signals and are more suitable for low-cost devices in massive machine-type communication.