Likelihood-Based Modulation Classification for Full-Duplex SC-FDMA Relaying Communications

This study investigates the problem of automatic modulation classification (AMC) for uplink single-carrier frequency-division multiple-access (SC-FDMA) transmissions. The analysis is conducted in the context of full-duplex decode-and-forward (FD-DF) relaying systems. The interaction among modulation formats, multiple-access interference (MAI), multipath channels, and relative delays yields a tightly coupled maximum-likelihood (ML) problem that is computationally prohibitive for practical implementation. To overcome this difficulty, we propose a novel low-complexity space-alternating generalized expectation–maximization (SAGE) solution that enables joint parameter estimation. By exploiting the inherent characteristics of FD-DF signals, the proposed approach achieves a substantial reduction in computational burden while preserving high modulation classification accuracy. Simulation findings demonstrate that the proposed algorithm delivers robust and reliable performance compared to existing techniques under realistic operating conditions.