Compressive sensing (CS) is a novel method for channel estimation. The recently introduced principle and the methodology of compressed sensing allow the efficient reconstruction of sparse signals of a very limited number of measurements. CS has gained a fast growing interest in applied mathematics. We consider the channel estimation in mobile environment using different methods. We identified an optimized method for compressive sensing in a mobile environment after an investigation of Orthogonal Matching Pursuit (OMP) and Delay-Doppler sparsity with reduced pilots for higher spectral efficiency. We demonstrated simulation results for 4-QAM and 16-QAM with the parameters of Least Square Estimation (LSE) and CS. Our simulation results show that the Delay-Doppler Sparsity achieved good spectral efficiency along with less probability of error.