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21.06.2019 | Ausgabe 3/2020

Wireless Networks 3/2020

Effective capacity optimization for cognitive radio networks under primary QoS provisioning

Zeitschrift:
Wireless Networks > Ausgabe 3/2020
Autoren:
Mai A. Abdel-Malek, Karim G. Seddik, Tamer ElBatt, Yahya Mohasseb
Wichtige Hinweise
This publication was made possible by NPRP 4-1034-2-385, NPRP 09-1168-2-455 and NPRP 5-782-2-322 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Part of this work was presented at the 2015 Cognitive Radio Oriented Wireless Networks (CROWNCOM), Qatar [50]
This work was done when Mai A. Abdel-Malek and Tamer ElBatt were with WINC, Nile University, Egypt.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Cognitive radios have emerged as a key enabler for opportunistic spectrum access, in order to tackle the wireless spectrum scarcity and under utilization problems over the past two decades. In this paper, we aim to enhance the secondary user (SU) performance while maintaining the desired average packet delay for the primary user (PU). In particular, we investigate the trade-off between delay-constrained primary and secondary users in cog- nitive radio systems. In the first part of this work, we use the hard-sensing scheme to make a decision on the PU activity and maximize the SU effective capacity subject to an average PU delay constraint. Second, we propose a soft-sensing scheme by dividing the PU energy interval where the PU is decided to be idle into multiple decision. We also maximize the SU effective capacity subject to an average primary user delay constraint; then, we present three modifications for the proposed soft-sensing scheme to allow for low complexity implementation that is comparable to the complexity of the hard-sensing scheme, but with better performance. The numerical results reveal interesting insights comparing our soft sensing to the hard-sensing models in terms of the optimal performance obtained from our optimization solution compared to the unconstrained PU delay baseline system studied earlier in the literature. For instance, the hard sensing system in Akin and Gursoy (IEEE Trans Wirel Commun 9(11):3354–3364, 2010) and Abdel-Malek et al. (CrownCom 156:30–42, 2015) yields a SU effective capacity of only 50 % of the ideal, perfect sensing system. On the other hand, we show that the soft sensing system yields almost 87 % of the perfect sensing performance (at a primary user arrival rate of \(\lambda _p = 0.1\)), which further increases for a larger number of decision sub-intervals.

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