Modulation Classification for Adaptive Orthogonal Frequency Division Multiplexing (OFDM) Systems

Orthogonal Frequency Division Multiplexing (OFDM) has been recognized as one of the effective techniques to increase the utilization of the available channel spectrum. Adaptive OFDM has been further employed to achieve reliable high-rate data transmission, by changing the modulation format on groups of subcarriers. The information on the modulation format can be transmitted with the data block, but this yields a penalty in the data throughput. An alternative is to identify the modulation format at the receive-side by applying a modulation classification algorithm. In this paper, a quasi-hybrid likelihood ratio test - based algorithm, which has been proposed in [1], is applied for modulation classification in adaptive OFDM systems. Simulations are performed to study the classification performance of this algorithm as a function of the observation interval, signal-to-noise ratio (SNR), and correlation between subcarriers. Results of the simulations show that the classification performance improves with a longer observation interval and higher SNR available at the receive-side, and this is reasonably robust to the correlation between subcarriers. Reference: [1] O. A. Dobre and F. Hameed, “Likelihood-based Algorithms for Linear Digital Modulation Classification in Fading Channels,” in IEEE CCECE, 2006, pp. 1347-1350.