Low-Complexity Subcarrier-Wise Detection for MIMO-OFDM With Index Modulation
Author(s) -
Zeng Hu,
Shaoe Lin,
Beixiong Zheng,
Fangjiong Chen,
Qingyang Wang,
Yao Wei
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2743744
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) is a relatively new multicarrier transmission technique for the MIMO systems to achieve both energy and spectral efficiency. However, due to the dependence of subcarrier symbols introduced by the index modulation, existing MIMO detection techniques cannot be applied in MIMO-OFDM-IM as it will lead to erroneous detection on index information of the active subcarriers and further deteriorate the system performance. In this paper, we first develop an optimal detection algorithm with reduced complexity for MIMO-OFDM-IM by performing the subcarrier-wise detection under the constraint on the legal subcarrier combination within each OFDM-IM subblock. To further reduce the computational complexity, we then propose a low-complexity subcarrier-wise algorithm based on the deterministic sequential Monte Carlo technique to achieve near-optimal detection for MIMO-OFDM-IM. Specifically, the near-optimal detector applies the QR decomposition to generate an upper triangular structure and draw antenna-wise samples at the subcarrier level. Computational simulations and complexity analysis show that the proposed algorithms provide similar performance to optimal one with reduced computational complexity.
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