Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

Basak, Surajit; Acharya, Tamaghna

doi:10.1016/j.phycom.2015.12.003

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…The author [57] adopted a cross-layer approach to link the control of power, the assignment of the channel, and route selection in a bounded optimisation framework. The goal of this way is to reduce interference between SU and PU, and for proving a routing session in CR based on the constraint an average data rate of the end-to-end connection for SU.…”

Section: Network and Link (Mac) Cross-layersmentioning

confidence: 99%

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

et al. 2020

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The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning the routing protocol operations. Applying a cross-layer design is considered a sufficient remedy to overcome routing protocol challenges such (e.g. channel diversity, integration route discovery with spectrum decision, mobility, etc.). Consequently, the cross-layer design has a magic solution to overwhelm routing challenges in MCRNs due to the ability to be free from the strict boundary and share the information and services with other layers in a manner that contributes to enhancing routing performance. Thus, the scope of this survey is to review and taxonomy numerous routing protocols in MCRNs according to methods of design to highlight the strength and weakness points. Also, machine learning has acquired much interest in this literature. A cross-layer framework for smart routing protocol in MCRNs has been proposed by exploiting machine learning mechanisms. Finally, the open research issues of routing protocol in MCRNs are summed up.INDEX TERMS Mobile cognitive radio network, cross-layer design, non-cross-layer design, machine learning, smart routing protocol.

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Section: Network and Link (Mac) Cross-layersmentioning

confidence: 99%

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

et al. 2020

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“…The environment of spectrum sharing between these two layers of users is becoming a comprehensive target. For example, according to [31], [47], [73], [99]- [100], [101], novel schemes propose the operations on the upper layer to solve the multiple-PUs-multiple-SUs spectrum sensing issue which is more complicated than single-PU case.…”

Section: Current Challenge and Future Workmentioning

confidence: 99%

A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

Tian¹,

Esseghir²,

Khoukhi³

2019

NPA

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A large scale of spectrum sensing techniques are proposed to improve the use of spectrum resources. However, the EE (energy efficiency) should be guaranteed for both primary and secondary users, especially under various detection performance constraints. In this regard, the linking between activities of PU (primary user) and dynamic access behaviors of SUs (secondary users) should be considered in an integrated way. This survey has compared different existing scenarios and frameworks on EE optimizations. The principal objective is to enhance the system throughput and to coordinate on the physical layer of both PU and SUs, in order to enable a high-quality spectrum detection and a more efficient spectrum access. In the technical part, several optimization methods are introduced under PU’s constraints, and different methods based on game theory are applied to suitable cooperative sensing scenarios for SUs’ optimal access. Finally, the complexity of algorithms is compared, to further reduce the execution time and deploy real-time adaptation for users with lower delay.

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“…In this study, the mode of SUs for spectrum access is not considered opportunistic. In [33], this group added to the study in [32] the opportunistic mode of spectrum access for SUs.…”

Section: Multimetric-based Routing Criteria (Combined Routing Critementioning

confidence: 99%

Design of optimum criterion for opportunistic multi-hop routing in cognitive radio networks

2018

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The instability of operational channels on cognitive radio networks (CRNs), which is due to the stochastic behavior of primary users (PUs), has increased the complexity of the design of the optimal routing criterion (ORC) in CRNs. The exploitation of available opportunities in CRNs, such as the channel diversity, as well as alternative routes provided by the intermediate nodes belonging to routes (internal backup routes) in the route‐cost (or weight) determination, complicate the ORC design. In this paper, to cover the channel diversity, the CRN is modeled as a multigraph in which the weight of each edge is determined according to the behavior of PU senders and the protection of PU receivers. Then, an ORC for CRNs, which is referred to as the stability probability of communication between the source node and the destination node (SPC_SD), is proposed. SPC_SD, which is based on the obtained model, internal backup routes, and probability theory, calculates the precise probability of communication stability between the source and destination. The performance evaluation is conducted using simulations, and the results show that the end‐to‐end performance improved significantly.

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Route selection for interference minimization to primary users in cognitive radio ad hoc networks: A cross layer approach

Cited by 15 publications

References 36 publications

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

Design of optimum criterion for opportunistic multi-hop routing in cognitive radio networks

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