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The development of intelligent radios in wireless applications is mainly driven by the growing need for higher data rates, along with constrained spectrum resources. An intelligent radio is one that can autonomously assess the communication environment and automatically update the communication parameters to achieve optimal performance. The problem of determining the type of space-frequency block coding (SFBC) for orthogonal frequency division multiplexing (OFDM) transmissions is one of the main tasks of an intelligent receiver. Previous approaches to this problem are restricted to uncoded communications; nevertheless, existing systems typically utilize error-correcting codes. This study develops a maximum-likelihood (ML) classifier that discriminates among SFBC-OFDM signals using the soft outputs of a channel decoder. The mathematical analysis shows that the maximization of the likelihood function can be carried out by employing an iterative expectation-maximization (EM) procedure. A channel estimator is also included in the proposed classifier as a vital step. The findings show that the classification performance of the proposed algorithm is considerably better than the classical classifiers reported in the literature, at the cost of an acceptable increase in computing complexity.

Power of Error Correcting Codes for SFBC-OFDM Classification Over Unknown Channels