A Machine Learning Approach to Link Adaptation for SC-FDE System

Single carrier frequency domain equalization (SC-FDE) uses cyclically prefixed quadrature amplitude modulation to permit simple frequency domain equalization at the receiver. Link adaptation for SC-FDE systems, where the modulation and coding rate are adapted based on the current channel state, is straightforward with perfect channel state information due to the simple analytical form of the post-processing signal-to-noise ratio (SNR). Imperfect channel state information, however, introduces adaptation errors. This paper proposes a machine learning-based approach for link adaptation in bit interleaved convolutionally encoded SC-FDE systems. To improve performance in the presence of channel uncertainty, principal component analysis is used to reduce the feature space dimensionality consisting of the channel coefficients, noise variance, and post-processing SNR. The reduced dimension feature set improves performance of the link adaptation classifier and leads to higher performance versus just the post-processing SNR estimate. Simulation results indicate that the proposed algorithm increases the goodput while maintaining the target packet error rate, achieving optimal adaptation in 95% of the tested cases.