Two-Stage ML Detector Using Absolute Value of IQ Components and SVM for Adaptive OFDM-IM

Abstract

Among various index modulation technology, adaptive orthogonal frequency division multiplexing with index modulation (A-OFDM-IM) improves reliability by solving the problem of deep fading. However, the maximal likelihood (ML) detector of A-OFDM-IM has high complexity by simultaneously performing active subcarrier detection and quadrature amplitude modulation (QAM) symbol demodulation. A simpler type of ML detector can be applied since the A-OFDM-IM has independent active states of each subcarrier and uses a single QAM constellation. In this paper, we propose two low-complexity detectors for the A-OFDM-IM. The proposed detectors have a two-stage receiving process, active subcarrier detection, and QAM demodulation. In active subcarrier detection, the presence or absence of the QAM symbol is more important than its information. Therefore we derive the thresholds using the absolute value of the in-phase and quadrature components of the received signal in the frequency domain. Using the absolute value, the distribution of noise added to inactive subcarrier is half normal. This is different from noise distribution of the ML detector, and the proposed detectors select the threshold considering the corresponding noise distribution. The first proposed detector uses a threshold derived by the ML estimation method. The second detector estimates the active subcarriers via a support vector machine. The proposed detectors have lower complexity than the ML detector because of the divided receiving process. Moreover, the theoretical analysis and simulation results show that the proposed detectors have better reliability than the ML detector due to different noise distribution and thresholds.

Keywords

• Index modulation
• support vector machine
• low complexity detector
• OFDM