Sparse Channel Estimation Using Overcomplete Dictionaries in OFDM Systems
With the in-depth study of the wireless channel, more and more experimental evidence show that many wireless channels are sparse in the conditions of large bandwidth and long signaling durations. Thus, Compressed Sensing theory applied for sparse channel estimation can reduce the number of pilots, so as to increase spectral efficiency. However, the non-integer times of sampling period about the time-delay or Doppler frequency shift will lead to the energy leakage, and reduce the time delay-Doppler sparsity of the equivalent channel, thus affect the accuracy of channel estimation. In this paper, we utilize over-complete dictionaries based on super resolution to enhance the sparsity of the equivalent channel. Simulation results demonstrate that the overcomplete dictionary representation of the double-selective channel is much sparser than the classical delay-Doppler representation. The method proposed in this paper can effectively improve the performance of sparse reconstruction algorithms, and then obtain the better precision of channel estimation.
KeywordsChannel estimation Compressed sensing OFDM Over-complete dictionaries
This work was supported by the special fund of Chongqing key laboratory (CSTC) and by the project of Chongqing Municipal Education Commission (Kjzh11206) and National Science and Technology Major Program (2011ZX03006-003 (7)) and by Fundamental and Frontier Research Project of Chongqing (cstc2013jcyjA40034).
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