Deep clipping noise mitigation using ISTA with the specified observations for LED-based DCO-OFDM system

摘要： Deep clipping is beneficial for the optical orthogonal frequency division multiplexing (O-OFDM) system, since it can lower the peak-to-average power ratio, reduce the direct current requirement in light emitting diodes (LEDs), and relax the bit-resolution requirement in digital-to-analogue converters (DACs). However, it is accompanied by more signal distortions. In this study, a deep clipping noise mitigation scheme using iterative shrinkage/thresholding algorithm (ISTA) with three steps is proposed to improve bit error rate (BER) performance of the LED-based DCO-OFDM systems. In the first step, the estimated observation interference is eliminated from the received symbols to minimise the negative effect of channel noise. In the second step, two strategies are presented to generate the specified observations thus reduce the component of measurement noise in the whole observation vector. In the last step, combining the generalised cross validation and the estimation of observation interference, the appropriate regularisation parameter are calculated for ISTA to improve the robustness of the sparse recovery performance. They use simulations to show that the proposed scheme can correct the deep clipping noise with favourable reconstruction quality, which significantly improves the BER performance and therefore assist the LED non-linearity mitigation.