Dynamic spectrum access and network selection in heterogeneous cognitive wireless networks
In this paper, we propose a cross-layer architectural framework for network and channel selection in a heterogeneous cognitive wireless network (HCWN). Existing research on heterogeneous wireless networks primarily focuses on network selection among available networks, while research on cognitive networks mainly focus on improvising efficient sensing and spectrum sharing algorithms. In this paper, we introduce a novel probabilistic model for channel classification based on its adjacent channels occupancy within the spectrum of an operating network. Further, we utilize a Analytic Hierarchical Process for categorizing user applications, followed by prioritizing them based on performance metrics. Finally, a modified Hungarian algorithm is implemented for channel and network selection among secondary users. The effectiveness of our approach is evaluated for different scenarios of HCWN. Simulation results show that our approach provides a 60% and 64% improvement in blocking probability over greedy and first-come-first-serve (FCFS) algorithms, respectively. Additionally, our proposed algorithm results in 22% enhancement in spectrum utilization and 50% increase in throughput over greedy and FCFS schemes.
network selection channel selection heterogeneous wireless networks bipartite graph cognitive radio