Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks

We consider scenarios such as IoT-based 5G or IoT-based machine type communication, where a low-cost low-power transmitter communicates with a high-quality receiver. Then, digital predistortion of the nonlinear power amplifier may be too expensive. In order to investigate the feasibility of receiver-side compensation of the transmitter RF impairments, we study joint maximum-likelihood estimation of channel and clipping level in multipath fading OFDM systems. In particular, we propose an alternative optimization algorithm, which uses frequency-domain block-type training symbols, and prove that this algorithm always converges, at least to a local optimum point. Then, we calculate the Cramér-Rao lower bound, and show that the proposed estimator attains it for high signal-to-noise ratios. Finally, we perform numerical evaluations to illustrate the performance of the estimator, and show that iterative decoding can be done using the estimated channel and clipping level with almost the same performance as a genie-aided scenario, where the channel and clipping level are perfectly known.