Performance comparison of different OFDM transmission schemes: Exploiting compressed sensing for channel estimation

Abstract

Compressed Sensing (CS) enables representation of sparse signals using fewer coefficients as compared to its original size. In this paper, the application of CS technique in channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) based communication system is done effectively. Here, channel estimation is made possible from the Inter Block Interference (IBI) free region of the received signal of Time Domain Synchronous (TDS) OFDM with the help of CS reconstruction algorithms. The different reconstruction algorithms used for channel estimation in this paper are Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), Subspace Pursuit (SP), Priority Aided CoSaMP (PA-CoSaMP), and Fusion based algorithms. The simulation results show that OMP gives good performance for higher sparsity levels and PA-CoSaMP works well for lower sparsity levels. Though the Bit Error Rate (BER) performance of the CS based systems is decreased, there is gain in overall complexity of the system due to the less number of samples used for estimation.