Neuro‐fuzzy based two‐stage spectrum allocation scheme to ensure spectrum efficiency in CRN–CSS assisted by spectrum agent

Abstract

Cognitive radio network (CRN) is a promising technology to solve increasing spectrum scarcity problems in recent times. This study resolves the problem of cooperative spectrum sensing (CSS) and spectrum allocation in CRN-CSS in order to improve spectrum efficiency. A two-stage spectrum allocation scheme is initiated by grouping secondary users (SUs) with the knowledge of one-hop neighbor approach, which leads to minimised grouping time. These grouped SUs perform spectrum sensing in cooperative manner. Optimal channel assignment (OCA) process assigns channels to each SU in a group in a dynamic manner. Upon the assigned channel, SU performs spectrum sensing with the aid of the enhanced threshold energy detection (ETED) method. Finally, the available spectrum is allocated to SUs by a novel two-stage allocation approach. In this approach, SUs, which provide accurate spectrum sensing result, are given high priority for spectrum allocation. These SUs are detected through global decision making at the fusion centre (FC). A novel mining tree scheme with a spectrum agent is adopted for decision making at the FC. The neuro-fuzzy based spectrum allocation scheme in two-stage allocation supports effective spectrum allocation. An extensive experimental result exhibits promising improvements than in the previous approaches.

Fuzzy based Reliable Cooperative Spectrum Sensing for Smart Grid Environment

“…Fuzzy channel ranking estimation was presented in [28] using four descriptors of variability of the channel duration availability, duration of the channel idle time, channel condition and distance. Similarly, in [29] fuzzy based spectrum allocation was proposed to enhance spectrum efficiency based on three descriptors. IEEE 802.22 wireless regional area network (WRAN) is proposed for the smart grid to allow unlicensed broadband network to opportunistically access the unused TV white space [30].…”

Fuzzy-Based Energy Management System With Decision Tree Algorithm for Power Security System

“…The specialists suggested this methodology for huge measure of information. [11][12][13][14][15] expressed that nature of dataset once in a while makes it hard to choose fitting credits to learn, and the researchers explores different avenues regarding Naïve Bayes classifier and measure execution for each call.…”