Maximum likelihood channel estimation in decode-and-forward relay networks

Abstract

In this paper, we provide a complete study on the training based channel estimation for relay networks that employ the decode-and-forward (DF) scheme. Since multiple relay nodes are geographically distributed over the service region, channel estimation is different from the traditional way in that each relay has its own individual power constraint. We consider the maximum likelihood (ML) channel estimation and derive closed form solutions for the optimal training as well as for the optimal power allocation. It is seen that the optimal power allocation follows a multi-level waterfilling structure.

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