Exploiting a priori information for iterative channel estimation in block-fading amplify-and-forward cooperative network
- PDF / 505,843 Bytes
- 9 Pages / 595 x 794 pts Page_size
- 50 Downloads / 194 Views
R ESEA R CH
Open Access
Exploiting a priori information for iterative channel estimation in block-fading amplify-and-forward cooperative networks Nico Aerts* , Iancu Avram and Marc Moeneclaey
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. Keywords: Cooperative communication; MAP estimation; Amplify-and-forward; SAGE algorithm
1 Introduction As wireless channels suffer from multipath propagation, several methods to combat the detrimental effect of fading have been proposed [1]. Cooperative communication [2] is a relatively new method where spatial diversity is achieved by exploiting the presence of other terminals in the network. The source time-shares its allocated time frame with other terminals that acts as relays. During the first slot of the frame, the source broadcasts information to the destination and the relays; the remaining slots are used by the relays to transmit to the destination information that is related to the message sent by the source. In this paper, we consider the amplify-and-forward protocol [3], and hence, each relay simply amplifies and retransmits the signal received from the source. In real-world situations, the channels between the different terminals are unknown and must be estimated, before detection at the destination can start. 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. It has been proposed (e.g., the linear minimum mean-square *Correspondence: [email protected] TELIN, UGent, Gent 9000, Belgium
error (LMMSE) cascaded channel estimation from [4,5]) that the destination estimates the overall channel gain but takes the overall noise variance equal to the variance obtained by averaging over the statistic of the relaydestination channel, whereas in [6] the relay-destination channel gain is estimated separately (and the noise variance computed accordingly) at the expense of a more sophisticated relay (that adds pilot symbols of its own). The LMMSE disintegrated estimation from [5] involves the estimation of the source-relay channel at the relay (which significantly increases relay complexity) and the relay-destination channel at the destination, whereas [7] considers maximum-likelihood (ML) estimation of both thes
Data Loading...
