A New Approach to Analyse Qosparameters in Cognitive Radio
Ankit Awasthi1, Vipul Awasthi2

1Ankita Awasthi, Research Scholar, Department of Electronics and Communication Engineering, Gyan Ganga Institute of Technology & Sciences, Jabalpur (M.P), India.
2Vipul Awasthi, Asst. Prof. Department of Electronics and Communication Engineering, Gyan Ganga Institute of Technology & Sciences, Jabalpur (M.P), India.
Manuscript received on 21 July 2013 | Revised Manuscript received on 28 July 2013 | Manuscript published on 30 July 2013 | PP: 62-65 | Volume-2 Issue-3, July 2013 | Retrieval Number: C0710072313/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Cognitive radio is a technology for wireless communication in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. The spectrum sharing network consists of a pair of primary users (PUs) and a pair of cognitive users (CRs).The pair of PUs establishes a wireless link as the PU link. The PU link and CR link utilize spectrum simultaneously with different priorities. The PU link has a higher priority to utilize spectrum with respect to the CR link. The proposed work focusses on different spectrum allocation techniques for the secondary users, based on Genetic Algorithms and an analysis of the performance of these techniques with an assumption that the radio environment has already been sensed and the QoS requirements for the application have been specified either by the sensed radio environment or by the secondary user itself. The proposed work focusses towards the technique that not only work on the QoS of cognitive radio but also covers all the parameters for efficient communication like power and bandwidth. When the PU link utilizes spectrum, a desired quality of service (QoS) is to be assured and the CR utilizes spectrum with an opportunistic power scale under this constraint, assuring the desired QoS on the PU link. To compute an optimal opportunistic power scale for the CR link, a fuzzy-based opportunistic power control strategy is proposed based on the Mamdani fuzzy control model using four input variables: QoS, RSSI, bandwidth as well as noise delay with the opportunistic power management along with bandwidth allocation.
Keywords: Cognitive Radio Networks, Fuzzy Control, Power Control, Bandwidth Allocation

Scope of the Article: Predictive Analysis