| Paper ID | C-1-2.3 |
| Paper Title |
AN OVERLOADED IOT SIGNAL DETECTION METHOD USING NON-CONVEX SPARSE REGULARIZERS |
| Authors |
Kazunori Hayashi, Ayano Nakai-Kasai, Kyoto University, Japan; Atsuya Hirayama, Hiroki Honda, Tetsuya Sasaki, Hideki Yasukawa, Osaka City University, Japan; Ryo Hayakawa, Osaka University, Japan |
| Session |
C-1-2: Advanced Signal Processing and Data Analysis for Environmental Recognition in Wireless Communication |
| Time | Tuesday, 08 December, 15:30 - 17:00 |
| Presentation Time: | Tuesday, 08 December, 16:00 - 16:15 Check your Time Zone |
|
All times are in New Zealand Time (UTC +13) |
| Topic |
Wireless Communications and Networking (WCN): Special Session: Advanced Signal Processing and Data Analysis for Environmental Recognition in Wireless Communication |
| Abstract |
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 $¥ell_{2/3}$ norm or $¥ell_{1/2}$ norm can achieve better SER performance than that with $¥ell_{0}$ norm based regularizer under the simulation condition. |