Compromising the performance and overhead is a crucial factor in designing cognitive\nradio networks (CRNs). One way to achieve this goal is to combine different fusion rules for a CRN\nwith multiple clusters of cognitive radios (CRs). This paper proposes a new adaptive combination\nalgorithm to balance between detection performance of a CRN and its reporting overhead through\ncombining different fusion rules over the CRN. Initially, the paper describes how to combine hard\ndecision, i.e., one-bit, and soften-hard decision, i.e., two-bit, fusion rules over a CRN with multiple\nclusters of CRs using different strategies. Simple combination and modified combination strategies,\nto consider a trade off between performance improvement and incurred reporting overhead, are\nconsidered. The paper adopts different threshold strategies to implement the proposed combinations.\nMoreover, the proposed algorithms are examined under the Rayleigh fading channel model and\nsimulated to investigate their detection performance and to compare their detection performance\nwith existing works. The simulation results show that the adaptive threshold strategy outperforms\nthe two proposed fixed threshold strategies and conventional fusion schemes.
Loading....