Optimum/Sub-Optimum Detection for Multi-Branch Cooperative Diversity Networks with Limited CSI

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.