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End-to-End Optimum ML Detection for DF Cooperative Diversity Networks in the Presence of Interference

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PROJECT TITLE :

End-to-End Optimum ML Detection for DF Cooperative Diversity Networks in the Presence of Interference

ABSTRACT:

Unlike the present detectors, that are developed for decode-and-forward (DF) networks in the best interference-free case, we contemplate a more practical situation where arbitrary interference exists. We tend to take into account a DF cooperative network consisting of a source, multiple relays, a destination, and multiple interferers affecting each the relays and the destination. Each relay is supplied with multiple antennas and is aware of its local instantaneous channel state info (CSI). Assuming that the destination knows the instantaneous CSI of the supply-relay, relay-destination, and source-destination channels, we have a tendency to develop, for the first time within the literature, the tip-to-finish optimum most-chance (ML) detectors in closed-type for DF systems employing either simultaneous or orthogonal transmissions in the presence of interference. Furthermore, theoretical analysis shows that the proposed detectors achieve full diversity gains within the presence of interference with finite interference-to-noise ratios. Numerical results demonstrate that the proposed optimum detectors substantially outperform the traditional schemes that simply ignore interference.


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End-to-End Optimum ML Detection for DF Cooperative Diversity Networks in the Presence of Interference - 4.8 out of 5 based on 72 votes

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