Genetic algorithm optimized fuzzy decision system for efficient data transmission with deafness avoidance in multihop cognitive radio networks

Cognitive radio (CR) is an emergent communication platform that offers solutions for spectrum scarcity issues. Cognitive radio networks (CRNs) will offer increased bandwidth to mobile consumers through wireless heterogeneous architectures and dynamic spectrum acquisition mechanisms. However, CRNs enforce challenges because of the fluctuating behaviour of the spectrum available and the diverse requirements for a varied range of applications. The functions of spectrum management can resolve those challenges to realize a new paradigm of the network. Secondary users (SUs) can opportunistically explore and employ the blank spaces present in licensed channels. This makes the SU evacuate the licensed channel and then switch to a vacant channel, when an incumbent primary user (PU) interferes with the channel, it causes degradation of SUs because of the frequent switching of channels. Also, the deafness problem is commonly seen in a CRN, where the QoS is critically affected due to the hidden interferences. This research proposes a Genetic Algorithm Optimized Fuzzy decision system for performing channel selection, channel switching, and spectrum allocation in a multi-channel multi-hop CRN. The proposed scheme acts as a decision support system (DSS), focusing on reducing the channel switching rate, hidden node interferences, and efficient spectrum allocation. Meta-heuristic genetic algorithm (GA) optimizes the parameters of the fuzzy decision system (FDS), for obtaining optimized decisions. The proposed DSS in the CR environment is simulated in the MATLAB platform and the results show improved performance concerning throughput and channel utilization.