Abstract:
We study the optimum maximum-likelihood (ML) detection and sub-optimum detection with limited channel state information (CSI) for a multi-branch dual-hop cooperative dive...Show MoreMetadata
Abstract:
We study the optimum maximum-likelihood (ML) detection and sub-optimum detection with limited channel state information (CSI) for a multi-branch dual-hop cooperative diversity network which consists of a source, multiple relays, and a destination without a direct source-destination path. With the limited CSI, the signalling overhead at each relay is reduced by 50%. We first derive the optimum ML detection with the limited CSI, which involves numerical integral evaluations. To reduce the computational complexity, we then propose a closed-form suboptimum detection rule. It is demonstrated that the proposed sub-optimum detection rule performs almost identically to the optimum ML detection when the non-Gaussianity in the added noise component dominates.
Date of Conference: 30 November 2009 - 04 December 2009
Date Added to IEEE Xplore: 04 March 2010
Print ISBN:978-1-4244-4148-8
Print ISSN: 1930-529X