Channel (broadcasting)

Expectation–maximization algorithm

Maximization

Mathematics

Maximum likelihood

Wireless

Algorithm

Modulation (music)

Statistics

Aesthetics

Mathematical optimization

Channel state information

Philosophy

Signal-to-noise ratio (imaging)

Demodulation

Computer science

Fading

Likelihood-ratio test

Orthogonal frequency-division multiplexing

Telecommunications

Detector

IEEE Transactions on Vehicular Technology

In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified. In this paper, to solve this challenging problem, the likelihood-based automatic modulation classification (AMC) is studied. First, in the scenario with known channel state information (CSI), two classifiers based on average likelihood ratio test (ALRT) and hybrid likelihood ratio test (HLRT), respectively, are derived. Concretely, in HLRT-based classifier, energy-based detector, and log-likelihood ratio-based detector are employed to identify the active subcarriers. Second, a HLRT-based blind AMC is proposed in the scenario with unknown CSI, where the efficient implementation of expectation maximization algorithm is presented to estimate channel fading coefficients and noise variance. Finally, the effectiveness of the proposed AMC algorithms is confirmed by computer simulations.

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation