A novel method for sparse channel estimation using super-resolution dictionary

Due to the sparse distribution of reflectors in space, wireless channels are commonly sparse. Thus, utilizing the sparsity of channels in the delay-Doppler domain, a channel estimation method based on compressed sensing (CS) theory can reduce the number of pilots. However, because of discrete truncation in the time domain and limited bandwidth, the time delay and frequency shift of non-integer multiple samples can cause energy leakage in the delay and Doppler domain, which seriously reduce the delay-Doppler sparsity of the equivalent channel, thus affecting the accuracy of channel estimation. In this paper, we use an over-complete dictionary based on super-resolution to enhance the sparsity of the equivalent channel and reconstruct a doubly selective channel with greater accuracy. Simulation results demonstrate that the equivalent channel frequency response in the dictionary is sparser than that in the delay-Doppler domain. Compared with the traditional algorithm, the method proposed in this paper can effectively improve the performance of channel estimation.