An Overloaded IoT Signal Detection Method using Non-convex Sparse Regularizers
An Overloaded IoT Signal Detection Method using Non-convex Sparse Regularizers
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发表时间：
2020-12
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通讯作者：
K. Hayashi;Ayano Nakai-Kasai;Atsuya Hirayama;Hiroki Honda;Tetsuya Sasaki;Hideki Yasukawa;Ryo Hayakawa
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作者：
K. Hayashi;Ayano Nakai-Kasai;Atsuya Hirayama;Hiroki Honda;Tetsuya Sasaki;Hideki Yasukawa;Ryo Hayakawa
本文提出了一种利用复稀疏正则化和作为传输信号离散性的正则化器，用于重载海量多用户多输入多输出（MU-MIMO）正交频分复用（OFDM）和循环前缀单载波块传输（SC-CP）系统的信号检测方法。本文的主要特点是新引入了非凸稀疏正则算子，而在以往的重载MIMO信号检测中只考虑了凸稀疏正则算子。数值结果表明，选择适当的非凸稀疏正则器可以获得比凸正则器更好的符号误码率性能，并且使用Hadamard矩阵或离散傅立叶变换矩阵（DFT）预编码对于非凸稀疏正则器的情况也有明显的好处。此外，与理想的独立同分布（i.i.d）的情况不同。在仿真条件下，基于2/3范数和1/2范数的正则化器比基于0范数的正则化器具有更好的SER性能。
This paper proposes a signal detection method for overloaded massive multi-user multi-input multi-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) and single carrier block transmission with cyclic prefix (SC-CP) systems by using sum of complex sparse regularizers (SCSR) as the regularizer of the discreteness of transmitted signal. Main feature of this work is that non-convex sparse regularizers are newly considered, while convex sparse regularizers only are considered in our previous work on the overloaded MIMO signal detection. Numerical results demonstrate that the proposed approach with the appropriate choice of the non-convex sparse regularizer can achieve better symbol error rate (SER) performance than that with the convex regularizer, and also that the precoding by Hadamard matrix or discrete Fourier transform (DFT) matrix is significantly beneficial for the case with non-convex sparse regularizers as well. Moreover, unlike the case with the ideal independent and identically distributed (i.i.d.)Gaussian measurement matrix, the regularizer based on ℓ2/3 norm or ℓ1/2 norm can achieve better SER performance than that with ℓ0 norm based regularizer under the simulation condition.