Centralized Dynamic Channel Reservation Mechanism via SDN for CR Networks Spectrum Allocation

Azaly, Nehal M. El; Badran, Ehab F.; Kheirallah, Hassan Nadir; Farag, Hania H.

doi:10.1109/access.2020.3032666

Cited by 10 publications

(13 citation statements)

References 30 publications

(79 reference statements)

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“…Consider the scenario in which the licensed spectrum of the CRN consists of 𝑀 channels, with M being a positive integer represented as 𝑀 ∈ 𝑍+, and 𝑍+ is a group of numbers greater than zero, excluding fractions or decimals with equal capacity as in [12], [19]. In the suggested method, the spectrum is divided non reserved channels (N-CRN) and reserved channels (R-CRN).…”

Section: A Network Scenario and Assumptionsmentioning

confidence: 99%

“…To analyze this situation, we employ a Markov chain methodology that considers discrete states and continuous time. The transition rates between these states play a role in determining the arrival and service rates for PUs and SUs across various scenarios [12]. The evaluation of channel occupancy is carried out using a continuous-time Markov chain (CTMC) approach.…”

Section: Introductionmentioning

confidence: 99%

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

Abdelgalel,

Kheirallah,

Rizk

et al. 2024

IEEE Access

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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.INDEX TERMS -licensed shared access, cognitive radio network, dynamic channel reservation, dynamic spectrum access, secondary user, primary user, quality of service, retainability, channel availability.

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Section: A Network Scenario and Assumptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Abdelgalel,

Kheirallah,

Rizk

et al. 2024

IEEE Access

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“…Another important task of mobility management is the dynamic channel reservation, which dynamically allocates spectrum. In [465], the optimal number of reserved channels is decided based on secondary user retainability and channel availability, while an application policy considers the primary user channel availability minimum by monitoring the incoming traffic requests. A Hierarchical Agglomerative Clustering (HAC) framework to control all sub-channels in the network to decide on cluster merging in ultra-dense small cell networks with an application policy to mitigate severe interference among small cell base stations has been proposed in [466].…”

Section: Network Managementmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…One algorithm was based on the primary channel availability and SU retains ability while the other algorithm checked for the channel availability of both the primary and the SU. Ahmed k. Khalifa et al [21] proposed a system for fair spectrum allocation in CRN for the devices of the internet of things (IoT). The scheme involves the following steps: firstly, during the spectrum sensing, to have an aspiration equilibrium among the SUs a direct strategy selection was used.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

Kumaran,

Roobert

et al. 2023

Preprint

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In cognitive radio networks (CRN), primary users (PU) are the licensed users and secondary users (SU) are the unlicensed users. PUs can use the spectrum in the primary network (PN) which is licensed, but the SU can only use the spectrum that is not used by the PU. Also, the SU should not affect the transmission of PU. Thus, the spectrum allocation for SU is a major problem in CRN. Therefore, in this paper, cooperative spectrum sensing (CSS) is considered with adaptive search and rescue (ASAR) optimization and auto-encoder deep learning. For the effective sharing of spectrum, a novel classification method based on deep learning is proposed. Here, for the detection of PU, multiple SUs are considered. The implementation tool used for the proposed method is NS2. Assessment metrics are performed based on delay, throughput, network lifetime, etc. Also, the balance between the transmitted power and interference is maintained to achieve high throughput.

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Centralized Dynamic Channel Reservation Mechanism via SDN for CR Networks Spectrum Allocation

Cited by 10 publications

References 30 publications

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

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

A Comprehensive Survey on Knowledge-Defined Networking

Deep Learning based Spectrum Utilization in Cognitive Radio Networks using ASAR Algorithm

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