Abstract
Many conventional adaptive channel estimation methods are based on minimum mean square error (MMSE) criterion, maximum correntropy criterion (MCC) or least p-norm criterion. However, these criteria are not always desirable in the presence of the various kinds of noises in different wireless channels. To further enhance the adaptability of the channel estimation to different noises, this paper introduces a new criterion named generalized maximum correntropy criterion (GMCC), and calculates the estimated channel parameters using the fixed-point iteration. The convergence analysis of the fixed-point iteration based on GMCC is discussed theoretically and is verified by experiments. More experiments are implemented to show the steady-state performance of the algorithm under different noise environments and the guide of parameter selection is provided.
Highlights
Channel estimation is an essential process in dependable wireless communications and has been researched all along [1], [2]
This paper proposes an adaptive channel estimation based on generalized maximum correntropy criterion (GMCC), which can be regarded as a weighted recursive least square (RLS) calculated by fixed-point iteration (FP-GMCC)
The convergence analysis of the fixed-point iteration is provided, and the convergence rate affected by value selection of the shape parameter α and the kenal width β of GMCC is discussed
Summary
Channel estimation is an essential process in dependable wireless communications and has been researched all along [1], [2]. P. Yue et al.: Adaptive Channel Estimation Based on Fixed-Point GMCC criterion, and optimization criteria enlightened by information-theoretic learning (ITL) are effective in dealing with the impulsive noises. With the development of the information theoretic learning research [17], [18], correntropy which measures the similarity of two random variables has appeared on the perspective of scholars [11], [18]–[22], and maximum correntropy criterion(MCC) has been adopted in robust adaptive algorithms [23]–[28].
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