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Energy Efficiency Optimization of Secondary Network Considering Primary User Return With Alternating-Phase-Type Traffic | IEEE Journals & Magazine | IEEE Xplore

Energy Efficiency Optimization of Secondary Network Considering Primary User Return With Alternating-Phase-Type Traffic

Publisher: IEEE

Abstract:

Much work has been done in the area of cognitive radio networks to formulate the inherent tradeoff among different metrics, such as sensing and throughput. Primary user (...View more

Abstract:

Much work has been done in the area of cognitive radio networks to formulate the inherent tradeoff among different metrics, such as sensing and throughput. Primary user (PU) traffic behavior can affect the mentioned tradeoff and must be considered in analyses, especially when cognitive users coexist with the dynamic-traffic PU. In this case, PU-returns to the spectrum during secondary users' (SUs) transmission are more likely and, hence, such kinds of interference are inevitable. Furthermore, how to model the PU traffic is important. Note that there are two kinds of interference between SU and PU in cognitive radio networks, one due to sensing error and the other due to primary user reoccupancy. In this paper, a new metric called energy per successful transmission time is defined to address both kinds of inference by formulating SUs' successful transmission time as well as energy consumption. Furthermore, collision probability and average packet delay are formulated considering these interference metrics and the primary user traffic is modeled as an alternating phase type renewal process by which many traffic behaviors, such as long range dependency and self-similarity can be modeled. Finally, some numerical examples are given and the corresponding curves for different metrics are discussed.
Published in: IEEE Transactions on Communications ( Volume: 65, Issue: 7, July 2017)
Page(s): 3095 - 3109
Date of Publication: 04 April 2017

ISSN Information:

Publisher: IEEE

I. Introduction

Cognitive radio (CR) concept has been proposed [1], [2] and investigated widely as a promising approach to reconstruct the traditional spectrum allocation policy and realize the spectrum underutilization. It suggests Dynamic Spectrum Access (DSA) instead of Fixed Spectrum Access (FSA) and offers a secondary network to co-exist with the primary users which are incumbent on the secondary ones to use the spectrum. Two main strategies exist in this co-existence, Listen Before Talk (LBT) and Listen And Talk (LAT) [3], [4]. In the former, in order for the cognitive users to access the channel, they have to sense and wait till the channel becomes idle. Due to this policy, there exists an innate trade-off between sensing and transmission time of secondary users on accessing the spectrum which has been considered widely in the literature [5]–[8]. Some have used network throughput as the main metric of their optimization problem [8], while some others have exploited Energy Efficiency to make a contribution on Green Communication, which has been the center of attention recently [9]–[14]. It is worth mentioning that in networks with dynamic spectrum access, collision with primary users is so important that collision probability (or equivalently, interference level) is the absolute constraint on the secondary network before accessing any channel. There are two types of PU-SU interference: 1-interference due to the miss-detection of primary users or sensing-error interference, and 2- interference due to primary user re-occupancy or PU-Returns. The former is a result of sensing error and can be reduced by selecting the optimum operating point of SU sensor on its ROC curve. However, the latter depends on the primary user traffic behavior. The sensing errors were widely addressed and formulated in the network throughput and energy efficiency; nevertheless, to date a few studies have paid attention to the interference due to PU-Returns. Note that when the primary network traffic is dynamic or highly dynamic, PU-Returns contribute a vast amount of interference, causing a major reduction in access efficiency. Not to mention that PU-Returns make a huge increase in packet transmission delay (due to packet loss and packet retransmissions) and collision probability and a huge decrease in network throughput. For such reasons, PU-Returns have to be considered in the network metrics formulation.

References

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