Robust channel estimation based on MEP

Channel estimation (CE) is one of the crucial and fundamental elements of signal processing, especially considering the requirement of high accuracy in future wireless communication systems. Most traditional CE algorithms are explored under the assumption of Gaussian white noise, which limits the algorithms’ performance in real wireless communication situations.

Recently, a research paper entitled “Robust Channel Estimation Based on MEP” is accepted by SCIENCE CHINA Information Sciences. In this work, a novel self-adaptive CE algorithm based on the maximum entropy principle (MEP) was studied, which analyzes the statistical components of an arbitrary noise environment. In addition, an MEP channel-based signal estimation algorithm was studied. Furthermore, the statistical characteristics of channels were considered as the regularization terms in the objective function for providing prior information and further increasing the accuracy. It was found that the proposed algorithm not only provides accurate CE but also reduces pilot consumption by using estimated signal data as pseudo pilots. The superior features of the proposed method concerning CE accuracy, pilot consumption, and robustness were confirmed through Monte Carlo simulations.

EMEP (i.e., ‘EMEP-A’ and ‘EMEP-B’) outperforms the compared methods when the channel sizes are the same. Moreover, EMEP achieves the best performance among the compared methods with different environment probability density functions, which demonstrates its adaptive ability.

The table shows the parameters estimation results of the proposed method. From the table, we can see that the parameters estimation error is around 10−1 in all three cases, which also shows the strong moment information extracting ability of their algorithm. The results  verify that the proposed noise modeling method can investigate the moment information of the noise in the environment.

---

---