An Adaptive Sparse Channel Estimation Algorithm for OFDM Systems Based on Distributed Compressive Sensing

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.