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

Performance Analysis of Best Relaying Protocol Selection with Interferences at Relays

In this paper, we investigate the performance of selecting the best protocol between amplify and forward (AF) and decode and forward (DF) in multiple relay networks with multiple interferences at relays. In the selection scheme, the best protocol between AF and DF is selected depending on the comparisons of signal-to-interference and noise ratio (SINR) for all source-relay links. All relays measure the received SINR to decide forwarding signal or not. When SINR is above a certain threshold then DF is used otherwise AF is used. Particularly, we develop an accurate mathematical model for best relaying protocol by considering the effect of interferences to our scheme. Firstly, we derive the asymptotic closed form expression for the symbol error rate (SER) for the system under study. Also we derive an upper and lower bound of symbol error rate and show how they were tight with exact SER. Furthermore an approximate expression for the outage probability is derived. Numerical results are finally presented to validate the theoretical analysis with a different number of relays