Publication

Abstract : Cognitive radio networks (CRNs) have been widely used in various applications for effective radio spectrum utilization in recent years. It is essential to fend off the growing demand for this finite natural resource for next-generation communications. In CRNs, detecting the activity of the primary user requires opportunistic spectrum sensing for efficient usage of the available radio spectrum, which is a limited and exquisite resource. Thus, CRNs are the key component in solving the spectrum scarcity issue in the presence of primary user bands through secondary users. Cognitive radio AdHoc networks (CRAHNs) are a unique kind of CRNs where infrastructures less cognitive radio (CR) nodes are furnished. In CRAHN, the CR-MAC protocol works slightly differently from the traditional wireless network MAC protocols. Particularly, the proposed method includes a high traffic scenario under contention-based IEEE 802.11 DCF MAC protocol. Accordingly, it can be observed that both, throughput and delay, increase as the CW size and packet length of the 802.11 (DCF) MAC protocol for CRAHN varies. Therefore, this paper proposed a fuzzy-based optimization framework for the 802.11 (DCF) MAC protocol in CRAHNs. Furthermore, it optimized throughput and delay by training a database of input parameters, contention window, and packet length for the Mamdani and Sugeno fuzzy inference system [FIS] models of the 802.11 (DCF) protocol simultaneously. The experimental result of the proposed framework for CRAHN with FIS shows that altering the contention window increases throughput by 25% and reduces the delay by 38% compared to the IEEE802.11 (DCF) protocol for CRAHNs without FIS. Moreover, it is also revealed that the throughput is increased by and 7% and the delay is reduced by 40% to 50% due to altering the packet length.