EM based channel estimation in an Amplify-and-Forward relaying network

Abstract

Abstract-Cooperative communication offers a way to obtain spatial diversity in a wireless network without increasing hardware demands. The different cooperation protocols proposed in the literature [1] are often studied under the assumption that all channel state information is available at the destination. In a practical scenario, channel estimates need to be derived from the broadcasted signals. In this paper, we study the Amplify-andForward protocol and use the expectation-maximization (EM) algorithm to obtain the channel estimates in an iterative way. Our results show that the performance of the system that knows the channels can be approached at the cost of an increased computational complexity. In case a small constellation is used, a low complexity approximation is proposed with a similar performance.

Exploiting a priori information for iterative channel estimation in block-fading amplify-and-forward cooperative networks

“…that, when properly initialized, converges to the MAP estimate (8); the conditional expectation in (10) is with respect to the nuisance parameter c. We will use the SAGE algorithm [10] instead of the EM algorithm in order to avoid the complexity of the multidimensional maximization associated with (10). The SAGE algorithm replaces the multidimensional maximization in (10) by several lower dimensional maximizations over mutually exclusive subsets of ν.…”

“…Taking (7) into account, obtaining the pilotbased MAP estimate of (h SRD , N SRD ) that maximizes p(r RD,p , h SRD , N SRD ) is, as shown in Appendix 2, intractable. To circumvent this problem, we firstly compute from r RD,p the ML estimate of h SRD , which is given by [8] …”

“…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 these channels at the destination. In [8], ML estimation of the overall channel gain and noise variance is performed at the destination; these results are extended in [9] to the case of a multi-antenna destination.…”

“…In contrast with [6][7][8][9], the proposed algorithms also take a priori channel knowledge into account. We restrict our attention to a low-complexity amplify-and-forward relay, which does not add pilot symbols of its own nor performs http://jwcn.eurasipjournals.com/content/2014/1/222 channel estimation.…”

“…We derive in closed form the joint a priori distribution of the parameters to be estimated and use this distribution to derive an approximately maximum a posteriori (MAP) estimate and an approximation of the LMMSE estimate. 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].…”