Generalized orthogonal matching pursuit and improved algorithms for compressive sensing based sparse channel estimation in OFDM systems

The sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems was studied to solve the channel sparsity problem in many communication systems. The sparse channel estimation problem was formulated as the reconstruction problem of sparse signals. Based on compressive sensing theory, generalized orthogonal matching pursuit (GOMP) was applied to the channel estimation, and an improved GOMP algorithm was proposed. Simulation results demonstrate that, compared with OMP, though GOMP brings in some sort mean square error (MSE), but it shows lesser running time and lower computational complexity. And the effect of improved GOMP algorithm is better for the performance improvement of GOMP.