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

Abstract

The frequency selective channel estimation problem in orthogonal frequency division multiplexing (OFDM) systems is investigated from the perspective of compressed sensing (CS). By minimizing the mutual coherence or the modified mutual coherence of the measurement matrix in CS theory, two criteria for optimizing the pilot pattern for CS-based channel estimation are proposed. Simulation results show that using the pilot pattern designed by either of the two criteria gives a much better performance than using other pilot patterns in terms of the mean-squared error of the channel estimate as well as the bit error rate of the system. Moreover, the optimal pilot pattern designed by minimizing the modified mutual coherence offers a larger performance gain than that obtained by minimizing the mutual coherence.

Sparse Channel Estimation of Single Carrier Frequency Division Multiple Access Based on Compressive Sensing

“…In practice, the receiver needs to store the random pilot pattern, which increases the system storage capacity. In [14] the optimal pilot pattern is designed by minimizing the mutual coherence of the measurement matrix. This idea can be borrowed to further improve the performance of estimation.…”

“…Hence, it is natural to consider using the CS theory in pilot-assisted sparse channel estimation to reduce the number of pilot symbols. In [11][12][13][14], the CS theory has been employed for sparse channel estimation in OFDM systems. However, litter work has been found in the SC-FDMA system.…”

Sparse channel estimation of MIMO-OFDM systems with unconstrained smoothed l0-norm-regularized least squares compressed sensing

“…In [11], a CCS approach for doubly selective channels and a sparsity-enhancing basis expansion with a method for optimizing it were proposed. In [12], two criteria as guiding principles to optimize the pilot pattern for CCS in OFDM systems were proposed. Methods of this type utilize the prior sparse information of the unknown channel and the advantage of CS and thus can improve the spectral efficiency by reducing the number of pilot symbols to be transmitted.…”

“…Different from literatures [9][10][11][12] that used the existing sparse reconstruction algorithms for CCS in OFDM or single carrier systems, we aim to exploit a novel reconstruction algorithm for CCS in MIMO-OFDM systems. The proposed smoothed l 0 -norm-regularized least squares reconstruction algorithm is named l 2 -Sl 0 in this paper, which differs from the smoothed l 0 -norm reconstruction algorithm (Sl 0 [13]) in two aspects.…”

Single-Pass K-SVD for Efficient Dictionary Learning

“…K-SVD alternates between encoding the data sparsely with the presently calculated dictionary and then updating the dictionary atoms from the sparse representation of the data. This method is effective in finding the codewords and the associated sparse representation of a dataset, which has received much attention for applications such as de-noising [20,21], source separation [2], restoration [20,25], channel estimation [13], face and object-category recognition [16,24], and so on. Details of K-SVD will be introduced in Sect.…”