Maximum likelihood detection for decode and forward cooperation with interference

In this paper, we obtain the maximum likelihood (ML) decision for a decode and forward (DF) cooperative system in Nakagami-m fading in the presence of co-channel interference at the relay as well as the destination. Through simulation results, we first show that conventional ML designed for interference free systems fails to combat the deleterious effect of interference. An optimum ML decision for combating interference is then derived for integer m. This receiver is shown to be superior to conventional ML through bit error rate (BER) performance simulations. Further, our results also indicate that optimum ML preserves relay diversity in the presence of interference

Outage Probability Analysis of Dual Hop Relay Networks in Presence of Interference

Cooperative relaying improves the performance of wireless networks by forming a network of multiple independent virtual sources transmitting the same information as the source node. However, interference induced in the network reduces the performance of cooperative communications. In this work the statistical properties, the cumulative distribution function (CDF) and the probability density function (PDF) for a basic dual hop cooperative relay network with an arbitrary number of interferers over Rayleigh fading channels are derived. Two system models are considered: in the first system model, the interferers are only at the relay node; and in the second system model, interferers are both at the relay and the destination. This work is further extended to Nakagami-m faded interfering channels. Simulation results are presented on outage probability performance to verify the theoretical analysis