An Adaptive Combiner Model for Multi-Rate Relaying Transmissions in Cognitive Systems

This paper proposes an adaptive combiner model that maximizes the performance of multi-rate cooperative relaying transmission in resources aware systems. Users’ cooperation is applied to smartly profit from the network resources to reach for efficient multi-rate transmissions. The low rate terminal uses a part of his bandwidth and power to relay the high rate user data side by side with his own data. This results in small performance degradation to the low rate user performance. The combiner model at the high rate terminal is carefully investigated for an optimized system performance and an adaptive combiner is proposed. The model calculates the weights adaptively for each bit based on a dynamic-blind method. The adaptive combiner is evaluated against the maximal-ratio combiner MRC and the equal gain combiner EGC to reach for the best model for the multi-rate relaying framework. Based on half-duplex mode, the probability of error performance is studied and compared for each combiner model under different channel conditions. The results show that the proposed adaptive combiner, characterized by its reliability and simplicity, leads to the best performance compared to the other models.