Channel estimation algorithm based on compressed sensing for OFDM systems

Channel estimation for orthogonal frequency division multiplexing (OFDM) is a crucial technology.The quality of channel estimation has vital impact on the overall system performance.The traditional channel based on linear minimum mean-squared error suffers low accuracy.Exploiting the sparsity of channel impulse response,a channel estimation algorithm based on approximately l₀ norm is proposed.We use three functions to approximate l₀ norm,and get the optimal solution of the cost function by applying gradient descend and gradient projection algorithm,thus obtains the sparsest channel response.Simulated experiment results show that under the same conditions,compared with the channel estimation algorithm based on l₁ norm,the proposed algorithm’s convergence speed is faster,and the signal-to-noise ratio is higher.