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Performance Analysis in the Presence of Channel Failure in Cognitive Radio Networks With Dynamic Spectrum Reservation

Authors :
Mai M. Abdelgalel
Hassan Nadir Kheirallah
Mohamed R. M. Rizk
Nehal M. El Azaly
Source :
IEEE Access, Vol 12, Pp 37483-37492 (2024)
Publication Year :
2024
Publisher :
IEEE, 2024.

Abstract

In wireless networks, there are two prominent challenges. The first challenge is ensuring that users have opportunities to access channels and request new services. The second challenge is maintaining connections for data flows. These challenges are compounded by the occurrence of channel failures, which often occur due to characteristics of radio transmission such as signal attenuation, signal blockage or device and power outages. Channel failures can significantly impact the effectiveness of both the primary and secondary networks. Therefore, it becomes crucial to prioritize retainability which denotes the need to maintain uninterrupted user connections even during network disruptions. This paper proposes an analytical model that evaluates performance of cognitive radio networks in the context of random channel failure rates. Additionally, the dynamic channel reservation (DCR) scheme is introduced. It can be integrated into dynamic spectrum access (DSA) strategies. This integration aims to give priority to existing services over requests from users to provide cognitive networks with more opportunities to allocate idle channels or maintain their current services. Moreover, the cost functions for both the primary user (PU) and the secondary user (SU) are calculated. This calculation considers the failure rate specifically in either reserved channels (RCN) or non-reserved channels (N-RCN) to meet different performance requirements. The results show a decrease in the SUs cost function, which guarantees that the quality of service (QoS) requirements for the PU are fulfilled. Importantly, this reduction in SU cost leads to an enhancement in SU channel availability or throughput when compared to previous models.

Details

Language :
English
ISSN :
21693536
Volume :
12
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.01dc8c42b6648dbbda2687fda36a171
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2024.3371884