Channel Equalization and Detection With ELM-Based Regressors for OFDM Systems

Extreme learning machine (ELM) is commonly adopted and best known for its extremely fast learning capability and notable performance. In this paper, a multiple split-complex ELM (Multi-SCELM) regressor based equalization and detection method is proposed for OFDM systems. This method combines ELM regressors for equalization and minimum-distance based symbol slicers for symbol detection. Furthermore, the proposed Multi-SCELM is extended to fully complex ELM (CELM) for channel equalization and detection. Simulations demonstrate that compared to existing ELM based methods, the proposed one owns the advantages of lower computational complexity, higher detection accuracy, stronger activation function adaptability, shorter training length and better subchannel number adaptability especially in strong frequency selective channels. Compared to the benchmark MMSE method, the proposed method has minor performance degradation but significant reduction in computational complexity.