Pilot pattern optimization for compressed sensing based sparse channel estimation in OFDM systems

Abstract

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Optimal Pilot Pattern Design for Compressed Sensing-Based Sparse Channel Estimation in OFDM Systems

“…Conventional methods in general need even pilot patterns, while CS-based channel estimation may yield a better performance by using an irregular pilot pattern, i.e., a pattern where a smaller number of pilot symbols are randomly and dispersedly located within N subcarriers. The simulation results in [13] show that an even pilot pattern is not the optimal pattern in CS-based channel estimation. According to CS theory, the pilot subcarriers in CS-based channel estimation should be selected uniformly at random within N subcarriers.…”

“…Conventional channel estimation methods such as minimal mean-squared error (MMSE) and least squares (LS) do not take into account this inherent sparsity of the channel and thus cannot obtain good channel estimation performance. Therefore, sparse channel estimation is of crucial importance in OFDM systems [1,3,4,13,[15][16][17][18]21].…”

“…The number of pilot symbols used in conventional pilot-assisted channel estimation techniques is large since it is determined by the Nyquist sampling theorem. By employing the CS theory and exploiting the sparsity of the channel, it is expected that the number of pilot symbols can be reduced [1,3,4,13,15,17,18]. In [1,3,4,13,17,18], the CS theory has been employed for sparse channel estimation in multicarrier systems.…”

“…By employing the CS theory and exploiting the sparsity of the channel, it is expected that the number of pilot symbols can be reduced [1,3,4,13,15,17,18]. In [1,3,4,13,17,18], the CS theory has been employed for sparse channel estimation in multicarrier systems. In particular, the pilot symbols are placed randomly [1,17,18] or evenly [3,4].…”

A novel threshold based compressed channel sensing in OFDM system

“…Additionally, the pilot pattern arrangement is obtained by the suboptimal method proposed in [21]. Moreover, we consider S max = 20 and 6000 randomly generated subsets of indices are used for searching a suboptimal subset of indices selection of m selected columns.…”