Abstract
In an amplify-and-forward cooperative network, a closed-form expression of the a priori distribution of the complex-valued gain of the global relay channel is intractable, so that a priori information is often not exploited for estimating this gain. Here, we present two iterative channel gain and noise variance estimation algorithms that make use of a priori channel information and exploit the presence of not only pilot symbols but also unknown data symbols. These algorithms are approximations of maximum a posteriori estimation and linear minimum mean-square error estimation, respectively. A substantially reduced frame error rate is achieved as compared to the case where only pilot symbols are used in the estimation.
Highlights
As wireless channels suffer from multipath propagation, several methods to combat the detrimental effect of fading have been proposed [1]
We present two pilot-based and two space-alternating generalized expectation-maximization (SAGE) [10] algorithms for estimating at the destination both the overall channel gain and the overall noise variance
We investigate the mean-square estimation error (MSEE) and frame error rate (FER) performance resulting from these estimates and make the comparison with the performance that results from the joint ML channel gain and the noise variance estimates from [8]
Summary
As wireless channels suffer from multipath propagation, several methods to combat the detrimental effect of fading have been proposed [1]. The overall noise in the signal received from the relay has a variance depending on the realization of the relay-destination channel, and the overall channel gain is the product of the source-relay and relay-destination channel gains. In this contribution, we present two pilot-based and two space-alternating generalized expectation-maximization (SAGE) [10] algorithms for estimating at the destination both the overall channel gain and (unlike the cascaded channel estimation from [5]) the overall noise variance. All vectors are row vectors and boldface; the Hermitian transpose, statistical expectation, and estimate of the row vector x are denoted by xH , E[x], and x, respectively
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