The performance of OFDM systems can be sharply deteriorated with effect of impulsive noise. It’s proposed in this paper about the combined station impulse reaction approximation& impulsive noise mitigation-process on the basis of compressed-sensing concept. From these algorithms, we can treat channel impulsive responses & impulsive noise as joint sparse-vector. Thereafter, framework of sparse-Bayesian learning is utilised for helping estimating the station-impulse responses, impulsive noise & data signs. Here, data sign can be known as the unknown parameter. The Cramer Rao Lower Bound’s elaborated as the bench-mark. From all used impulse noise- mitigation techniques, proposed procedure in the paper can be used with the sub-pilot by not using past info of the station& impulsive-noise. The model outcomes conclude at the process projected in the paper improves the efficiency in the station approximation& enhances performance of bit error rate.