Maximum-Likelihood-Based Performance Enhancement of Clipped and Filtered OFDM Systems With Clipping Noise Cancellation
Ying Sun, Hideki Ochiai
Abstract
Clipping and filtering (CAF) technique is known as a simple and effective approach that reduces the peak-to-average power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) signals at the cost of increasing in-band distortion. Clipping noise cancelation (CNC) can compensate for the performance degradation due to the in-band distortion caused by CAF, especially when the number of subcarriers is large, but its achievable symbol error rate (SER) performance is in general worse than that of the unclipped OFDM systems. In this letter, we propose an additional compensation scheme based on the maximum likelihood (ML) metric. We reveal that the proposed approach can even outperform the unclipped OFDM systems, only with moderate increase of complexity from the conventional CNC. Furthermore, we demonstrate that significant gain resulting from the diversity effect can be achieved in the case of frequency-selective Rayleigh fading channels.