Joint Sparse Channel and Clipping Level Estimation in OFDM-based IoT Networks: A Bayesian Learning Approach

Orthogonal frequency division multiplexing (OFDM)-based internet of things (IoT) networks undergo severe nonlinear distortion at the transmitter side owing to saturation of the high power amplifiers. Compensation of RF impairments at the receiver requires precise knowledge of the clipping amplitude level which in turn necessitates an accurate channel estimate. This paper leverages the inherent temporal sparsity associated with frequency selective wireless channels to develop a novel joint sparse channel and clipping amplitude estimation framework based on the popular sparse Bayesian learning (SBL) algorithm. The proposed scheme iteratively obtains the maximum likelihood estimate of the clipping amplitude followed by an expectation maximization (EM)-based sparse channel vector estimate. Numerical simulations are demonstrated to validate the superiority of the proposed technique over a sparsity agnostic scheme in terms of mean squared error (MSE) of channel, clipping amplitude estimates and symbol error rate (SER).