Intelligent dynamic spectrum allocation with bandwidth flexibility in cognitive radio network

Abstract

Cognitive radio (CR) is a popular wireless technology for efficient utilization of spectrum. CR network senses the unused frequency bands of the primary users (PUs) and assign them to the unlicensed secondary users (SUs). One of the major challenges in cognitive network is the allocation of available spectrum which indirectly contributes to the efficient spectrum utilization. A novel intelligent dynamic spectrum allocation with bandwidth flexibility is presented in this paper. Fuzzy inference system has been used to evaluate and rate the channel (CH) quality and the SU. The quality based spectrum allocation has been carried out. As the primary CH bandwidth differ from the SU and in the case, the SU bandwidth is lower to PU, accommodation of more than one secondary CH into primary is possible. To utilize this facility, bandwidth flexibility is incorporated in the intelligent spectrum allocation. The intelligent spectrum allocation techniques are compared with the sequence based method of spectrum allocation and priority based allocation. The quality measures utilized for comparison are service rate, average packet loss and average delay. On these measures intelligent dynamic bandwidth flexible spectrum allocation method is found to be better, compared to other three methods.

Spectrum Estimation and Optimal Secondary User Selection in Cognitive Radio Networks

“…In [16], a predictive double spectrum auction was proposed that initially obtained the range concerning bidding via statistical analysis, and then employed Markovian prediction based algorithm to produce requirements for bidding primary and secondary users, respectively, therefore improving utilization ratio. Yet another novel intelligent dynamic spectrum allocation model with bandwidth flexibility was proposed in [17], therefore concentrating on average packet loss and average delay. Quality of Service (QoS) via pricing and distributed power control utilizing Nash Equilibrium, therefore contributing to significant minimization in power consumption and high improvement in speed of convergence was proposed in [18].…”

Cognitive Radio Technology Based Spectrum Allocation for D2d-U Communication

“…Now a day's huge amount of unlicensed accessing devices is introduced. So, spectrum scarcity problem is becoming a major issue in this wireless network [8].…”

ILFCS: an intelligent learning fuzzy-based channel selection framework for cognitive radio networks

“…Veeramakali et al recommended an intelligent fuzzybased dynamic spectrum allocation (IDSA) with bandwidth flexibility for CR. The suggested strategy achieved an appreciable performance enhancement taking into consideration three measures: service rate, average packet loss, and average delay [24]. López et al solve the problem of spectrum allocation regarding the criteria of equity by enabling all the cognitive radio nodes to access the available network resources in a reasonable way without monopolizing the current capacity.…”