Abstract
In this article, doubly selective channel estimation is considered for 1amplify-and-forward-based relay networks. The complex exponential basis expansion model is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator that only targets at useful channel parameters that could guarantee the later data detection. The training sequence design that can minimize the channel estimation mean-square error is also proposed. Finally, numerical results are provided to corroborate the study.
Highlights
Wireless relay networks have been a highly active research field ever since the pioneer work [1,2,3]
A typical relay network consists of a source node, one or several relay nodes, and a destination node
We focus on complex exponential basis expansion model (BEM) (CE-BEM) [16] due to its popularity and clear physical meaning
Summary
Wireless relay networks have been a highly active research field ever since the pioneer work [1,2,3]. When the transmission data rates are high and nodes are mobile, the relay network is expected to operate over doubly selective channels. To the best of the authors’ knowledge, estimation techniques considering inter symbol interference between data and training symbols, as well as training sequence design, have not yet been developed The source node S and the relay node R, g(i; l) denote the doubly selective channel between the relay node R and the destination node D.a Without loss of generality, we assume that the channel length of both h(i; l) and g(i : l) as L + 1, and each tap is modeled as a zero mean complex Gaussian random process with power σh2,l (or σg2,l).
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