Optimum ML Detection for DF Cooperative Diversity Networks in the Presence of Interference
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Unlike the existing detectors for decode-and-forward (DF) relaying in the ideal case without interference, this thesis considers a more practical scenario where arbitrary interference is present. We consider a DF cooperative diversity network consisting of one source, multiple relays, one destination and multiple interferers affecting the relays as well as the destination. Under this scenario, for the first time in the literature, we develop the exact closed-form rules for optimum maximum-likelihood (ML) detectors for DF systems employing any one- or two-dimensional modulations in the presence of interference. In particular, we derive the optimum ML detection rules for two transmission schemes: simultaneous transmission and orthogonal transmission. In each transmission scheme, we derive the detection rules by considering two different cases of the instantaneous channel state information (CSI): the CSI of the second-hop is known or unknown at the relays. Numerical results demonstrate that the developed optimum detectors significantly outperform the conventional detector which simply ignores the existence of interference.