A Study of Training Sequence Design for Channel Estimation Based on Compressive Sensing

Nowadays, orthogonal frequency division multiplexing(OFDM) plays a more and more important role in mobile communications where the training sequence (TS) is usually adopted for synchronization and channel estimation. However, to achieve a channel estimation scheme which has both high accuracy and high spectral efficiency is still challenging due to the high communication capacity demand of the future 5G wireless cellular network. In this paper, by applying the compressive sensing (CS) theory into the channel estimation process with time domain TS, we turn our concentration to the maximum coherence of two different columns of the measurement matrix which determines the reconstruction accuracy. To minimize the coherence of the measurement matrix, we first propose a TS in the form of inverse discrete Fourier transform (IDFT) with cyclic structure based on CS, and then, a genetic algorithm is proposed to further lower the coherence with low complexity. Simulation results show that by using the proposed optimized TS, the system can achieve better channel estimation performance than the conventional TSs obtained by either selected PN sequence or brute force searching sequence in both recovery probability and mean square error, which might be suitable to the physical layer technology in the future 5G communications.